Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (714 journals)
    - MATHEMATICS (GENERAL) (45 journals)
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    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (GENERAL) (45 journals)

Showing 1 - 35 of 35 Journals sorted alphabetically
Acta Universitatis Sapientiae, Mathematica     Open Access  
Algebra Letters     Open Access   (Followers: 1)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 2)
Archiv der Mathematik     Hybrid Journal  
Beiträge zur Algebra und Geometrie / Contributions to Algebra and Geometry     Partially Free   (Followers: 2)
Bulletin of the American Mathematical Society     Open Access   (Followers: 5)
Communications in Mathematics     Open Access  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 3)
Conformal Geometry and Dynamics     Full-text available via subscription  
Difficoltà in Matematica     Full-text available via subscription  
Ergodic Theory and Dynamical Systems     Hybrid Journal   (Followers: 3)
International Journal of Applied Metaheuristic Computing     Full-text available via subscription   (Followers: 2)
International Journal of Computing Science and Mathematics     Hybrid Journal   (Followers: 1)
International Journal of Mathematics and Statistics     Full-text available via subscription   (Followers: 2)
Journal of Elliptic and Parabolic Equations     Hybrid Journal  
Journal of Mathematical Physics     Hybrid Journal   (Followers: 26)
Journal of Physics A : Mathematical and Theoretical     Hybrid Journal   (Followers: 23)
Journal of the American Mathematical Society AMS     Full-text available via subscription   (Followers: 6)
Jurnal Fourier     Open Access   (Followers: 1)
Mathematical Journal of Interdisciplinary Sciences     Open Access   (Followers: 1)
Mathematical Programming     Hybrid Journal   (Followers: 15)
Mathematics     Open Access   (Followers: 3)
Mathematics of Computation     Full-text available via subscription   (Followers: 6)
Mathematika     Full-text available via subscription  
Memoirs of the American Mathematical Society AMS     Full-text available via subscription   (Followers: 2)
Optimization: A Journal of Mathematical Programming and Operations Research     Hybrid Journal   (Followers: 6)
Pesquimat     Open Access  
Pro Mathematica     Open Access  
Proceedings of the American Mathematical Society AMS     Full-text available via subscription   (Followers: 4)
Representation Theory     Full-text available via subscription   (Followers: 1)
St. Petersburg Mathematical Journal     Full-text available via subscription   (Followers: 1)
Theoretical Mathematics & Applications     Open Access  
Transactions of the Moscow Mathematical Society     Full-text available via subscription   (Followers: 1)
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Mathematics
Number of Followers: 3  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2227-7390
Published by MDPI Homepage  [258 journals]
  • Mathematics, Vol. 12, Pages 620: Integrating Business Analytics in
           Educational Decision-Making: A Multifaceted Approach to Enhance Learning
           Outcomes in EFL Contexts

    • Authors: Minsu Cho, Jiyeon Kim, Juhyeon Kim, Kyudong Park
      First page: 620
      Abstract: This study introduces a framework that integrates business analytics into educational decision-making to improve learner engagement and performance in Massive Open Online Courses (MOOCs), focusing on learning environments in English as a Foreign Language (EFL). By examining three specific research questions, this paper delineates patterns in learner engagement, evaluates factors that affect these patterns, and examines the relationship between these factors and educational outcomes. The study provides an empirical analysis that elucidates the connection between learner behaviors and learning outcomes by employing machine learning, process mining, and statistical methods such as hierarchical clustering, process discovery, and the Mann–Kendall test. The analysis determines that learning patterns, characterized as single-phase or multi-phase, repetitive or non-repetitive, and sequential or self-regulated, are more closely associated with the nature of the educational content—such as books, series, or reading levels—than learner characteristics. Furthermore, it has been observed that learners exhibiting self-regulated learning patterns tend to achieve superior academic outcomes. The findings advocate for integrating analytics in educational practices, offer strategic insights for educational enhancements, and propose a new perspective on the connection between learner behavior and educational success.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050620
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 621: Optimization Method of Fog Computing High
           Offloading Service Based on Frame of Reference

    • Authors: Deng Li , Chengqin Yu, Ying Tan, Jiaqi Liu
      First page: 621
      Abstract: The cost of offloading tasks is a crucial parameter that influences the task selection of fog nodes. Low-cost tasks can be completed quickly, while high-cost tasks are rarely chosen. Therefore, it is essential to design an effective incentive mechanism to encourage fog nodes to actively participate in high-cost offloading tasks. Current incentive mechanisms generally increase remuneration to enhance the probability of participants selecting high-cost tasks, which inevitably leads to increased platform costs. To improve the likelihood of choosing high-cost tasks, we introduce a frame of reference into fog computing offloading and design a Reference Incentive Mechanism (RIM) by incorporating reference objects. Leveraging the characteristics of the frame of reference, we set an appropriate reference task as the reference point that influences the attraction of offloading tasks to fog nodes and motivates them towards choosing high-cost tasks. Finally, simulation results demonstrate that our proposed mechanism outperforms existing algorithms in enhancing the selection probability of high-cost tasks and improving platform utility.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050621
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 622: Composite Backbone Small Object Detection
           Based on Context and Multi-Scale Information with Attention Mechanism

    • Authors: Xinhan Jing, Xuesong Liu, Baolin Liu
      First page: 622
      Abstract: Object detection has gained widespread application across various domains; nevertheless, small object detection still presents numerous challenges due to the inherent limitations of small objects, such as their limited resolution and susceptibility to interference from neighboring elements. To improve detection accuracy of small objects, this study presents a novel method that integrates context information, attention mechanism, and multi-scale information. First, to realize feature augmentation, a composite backbone network is employed which can jointly extract object features. On this basis, to efficiently incorporate context information and focus on key features, the composite dilated convolution and attention module (CDAM) is designed, consisting of a composite dilated convolution module (CDM) and convolutional block attention module (CBAM). Then, a feature elimination module (FEM) is introduced to reduce the feature proportion of medium and large objects on feature layers; the impact of neighboring objects on small object detection can thereby be mitigated. Experiments conducted on MS COCO validate the superior performance of the method compared with baseline detectors, while it yields an average enhancement of 0.8% in overall detection accuracy, with a notable enhancement of 2.7% in small object detection.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050622
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 623: Fuzzy Testing Method of Process
           Incapability Index

    • Authors: Kuen-Suan Chen, Tsun-Hung Huang, Jin-Shyong Lin, Wen-Yang Kao, Wei Lo
      First page: 623
      Abstract: The process capability index is a tool for quality measurement and analysis widely used in the industry. It is also a good tool for the sales department to communicate with customers. Although the value of the process capability index can be affected by the accuracy and precision of the process, the index itself cannot be differentiated. Therefore, the process incapability index is directly divided into two items, accuracy and precision, based on the expected value of the Taguchi process loss function. In fact, accuracy and precision are two important reference items for improving the manufacturing process. Thus, the process incapability index is good for evaluating process quality. The process incapability index contains two unknown parameters, so it needs to be estimated with sample data. Since point estimates are subject to misjudgment incurred by the inaccuracy of sampling, and since modern businesses are in the era of rapid response, the size of sampling usually tends to be small. A number of studies have suggested that a fuzzy testing method built on the confidence interval be adopted at this time because it integrates experts and the experience accumulated in the past. In addition to a decrease in the possibility of misjudgment resulting from sampling error, this method can improve the test accuracy. Therefore, based on the confidence interval of the process incapability index, we proposed the fuzzy testing method to assess whether the process capability can attain a necessary level of quality. If the quality level fails to meet the requirement, then an improvement must be made. If the quality level exceeds the requirement, then it is equivalent to excess quality, and a resource transfer must be considered to reduce costs.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050623
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 624: Designing Ecotourism Routes with
           Time-Dependent Benefits along Arcs and Waiting Times at Nodes

    • Authors: Ramón Piedra-de-la-Cuadra, Francisco A. Ortega
      First page: 624
      Abstract: Ecotourism routes serve as powerful tools for fostering environmental awareness. To achieve this, it is crucial to design itineraries within natural parks that strike a balance between visitor experience and ecological preservation. Limiting the duration of visits prevents undue strain on both visitors and ecosystems. Effective routes should showcase high biodiversity, traversing diverse sites to enhance knowledge acquisition. Considering natural factors such as light conditions and climate, it is prudent to tailor visiting times to optimize the experience. Therefore, it makes sense to incorporate time-dependent benefits at arcs and the possibility of introducing waiting times at nodes in the design models. These two characteristics have enriched the optimization models developed to solve the tourist trip design problem based on maximizing benefit only when points of interest are visited. However, the specific application of these aforementioned characteristics and enriched optimization models to the arc orientation problem remains yet to be reported on and published in the literature. Our contribution addresses this gap, proposing a route design model with scenic value in the arches of the graph where the benefits perceived by travelers are maximized, taking into account a diversity of evaluations depending on the time of starting the trip through each arc.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050624
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 625: Optimizing PV Sources and Shunt
           Capacitors for Energy Efficiency Improvement in Distribution Systems Using
           Subtraction-Average Algorithm

    • Authors: Idris H. Smaili, Dhaifallah R. Almalawi, Abdullah M. Shaheen, Hany S. E. Mansour
      First page: 625
      Abstract: This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050625
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 626: KDTM: Multi-Stage Knowledge Distillation
           Transfer Model for Long-Tailed DGA Detection

    • Authors: Baoyu Fan, Han Ma, Yue Liu, Xiaochen Yuan, Wei Ke
      First page: 626
      Abstract: As the most commonly used attack strategy by Botnets, the Domain Generation Algorithm (DGA) has strong invisibility and variability. Using deep learning models to detect different families of DGA domain names can improve the network defense ability against hackers. However, this task faces an extremely imbalanced sample size among different DGA categories, which leads to low classification accuracy for small sample categories and even classification failure for some categories. To address this issue, we introduce the long-tailed concept and augment the data of small sample categories by transferring pre-trained knowledge. Firstly, we propose the Data Balanced Review Method (DBRM) to reduce the sample size difference between the categories, thus a relatively balanced dataset for transfer learning is generated. Secondly, we propose the Knowledge Transfer Model (KTM) to enhance the knowledge of the small sample categories. KTM uses a multi-stage transfer to transfer weights from the big sample categories to the small sample categories. Furthermore, we propose the Knowledge Distillation Transfer Model (KDTM) to relieve the catastrophic forgetting problem caused by transfer learning, which adds knowledge distillation loss based on the KTM. The experimental results show that KDTM can significantly improve the classification performance of all categories, especially the small sample categories. It can achieve a state-of-the-art macro average F1 score of 84.5%. The robustness of the KDTM model is verified using three DGA datasets that follow the Pareto distributions.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050626
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 627: Prediction Model of Ammonia Nitrogen
           Concentration in Aquaculture Based on Improved AdaBoost and LSTM

    • Authors: Yiyang Wang, Dehao Xu, Xianpeng Li, Wei Wang
      First page: 627
      Abstract: The concentration of ammonia nitrogen is significant for intensive aquaculture, and if the concentration of ammonia nitrogen is too high, it will seriously affect the survival state of aquaculture. Therefore, prediction and control of the ammonia nitrogen concentration in advance is essential. This paper proposed a combined model based on X Adaptive Boosting (XAdaBoost) and the Long Short-Term Memory neural network (LSTM) to predict ammonia nitrogen concentration in mariculture. Firstly, the weight assignment strategy was improved, and the number of correction iterations was introduced to retard the shortcomings of data error accumulation caused by the AdaBoost basic algorithm. Then, the XAdaBoost algorithm generated and combined several LSTM su-models to predict the ammonia nitrogen concentration. Finally, there were two experiments conducted to verify the effectiveness of the proposed prediction model. In the ammonia nitrogen concentration prediction experiment, compared with the LSTM and other comparison models, the RMSE of the XAdaBoost–LSTM model was reduced by about 0.89–2.82%, the MAE was reduced by about 0.72–2.47%, and the MAPE was reduced by about 8.69–18.39%. In the model stability experiment, the RMSE, MAE, and MAPE of the XAdaBoost–LSTM model decreased by about 1–1.5%, 0.7–1.7%, and 7–14%. From these two experiments, the evaluation indexes of the XAdaBoost–LSTM model were superior to the comparison models, which proves that the model has good prediction accuracy and stability and lays a foundation for monitoring and regulating the change of ammonia nitrogen concentration in the future.
      Citation: Mathematics
      PubDate: 2024-02-20
      DOI: 10.3390/math12050627
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 628: Performance of a Synchronisation Station
           with Abandonment

    • Authors: Dieter Fiems
      First page: 628
      Abstract: The paper presents a Markovian queueing model for assessing the performance of synchronisation between stations in a production system. The system at hand consists of K distinct buffers, each buffer storing an item that is needed for the next production stage. Departures are immediate when all items are present. Due to the presence of multiple buffers, there is no reasonably fast way to calculate the stationary distribution of the Markov chain. Therefore, we focused on the series expansion of the stationary distribution in terms of the arrival rate. We provide a fast algorithm for calculating these terms. Comparing our results with stochastic simulation, we show that the expansion approach converges to the simulated values for a wide range of arrival rates.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050628
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 629: Computer Science Education in ChatGPT
           Era: Experiences from an Experiment in a Programming Course for Novice
           Programmers

    • Authors: Tomaž Kosar, Dragana Ostojić, Yu David Liu, Marjan Mernik
      First page: 629
      Abstract: The use of large language models with chatbots like ChatGPT has become increasingly popular among students, especially in Computer Science education. However, significant debates exist in the education community on the role of ChatGPT in learning. Therefore, it is critical to understand the potential impact of ChatGPT on the learning, engagement, and overall success of students in classrooms. In this empirical study, we report on a controlled experiment with 182 participants in a first-year undergraduate course on object-oriented programming. Our differential study divided students into two groups, one using ChatGPT and the other not using it for practical programming assignments. The study results showed that the students’ performance is not influenced by ChatGPT usage (no statistical significance between groups with a p-value of 0.730), nor are the grading results of practical assignments (p-value 0.760) and midterm exams (p-value 0.856). Our findings from the controlled experiment suggest that it is safe for novice programmers to use ChatGPT if specific measures and adjustments are adopted in the education process.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050629
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 630: Randomly Shifted Lattice Rules with
           Importance Sampling and Applications

    • Authors: Hejin Wang, Zhan Zheng
      First page: 630
      Abstract: In financial and statistical computations, calculating expectations often requires evaluating integrals with respect to a Gaussian measure. Monte Carlo methods are widely used for this purpose due to their dimension-independent convergence rate. Quasi-Monte Carlo is the deterministic analogue of Monte Carlo and has the potential to substantially enhance the convergence rate. Importance sampling is a widely used variance reduction technique. However, research into the specific impact of importance sampling on the integrand, as well as the conditions for convergence, is relatively scarce. In this study, we combine the randomly shifted lattice rule with importance sampling. We prove that, for unbounded functions, randomly shifted lattice rules combined with a suitably chosen importance density can achieve convergence as quickly as O(N−1+ϵ), given N samples for arbitrary ϵ values under certain conditions. We also prove that the conditions of convergence for Laplace importance sampling are stricter than those for optimal drift importance sampling. Furthermore, using a generalized linear mixed model and Randleman–Bartter model, we provide the conditions under which functions utilizing Laplace importance sampling achieve convergence rates of nearly O(N−1+ϵ) for arbitrary ϵ values.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050630
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 631: Novel Design and Analysis for Rare
           Disease Drug Development

    • Authors: Shein Chung Chow, Annpey Pong, Susan S. Chow
      First page: 631
      Abstract: For rare disease drug development, the United States (US) Food and Drug Administration (FDA) has indicated that the same standards as those for drug products for common conditions will be applied. To assist the sponsors in rare disease drug development, the FDA has initiated several incentive programs to encourage the sponsors in rare disease drug development. In practice, these incentive programs may not help in achieving the study objectives due to the limited small patient population. To overcome this problem, some out-of-the-box innovative thinking and/or approaches, without jeopardizing the integrity, quality, and scientific validity of rare disease drug development, are necessarily considered. These innovative thinking and/or approaches include but are not limited to (i) sample size justification based on probability statements rather than conventional power analysis; (ii) demonstrating not-ineffectiveness and not-unsafeness rather than demonstrating effectiveness and safety with the small patient population (i.e., limited sample size) available; (iii) the use of complex innovative designs such as a two-stage seamless adaptive trial design and/or an n-of-1 trial design for flexibility and the efficient assessment of the test treatment under study; (iv) using real-world data (RWD) and real-world evidence (RWE) to support regulatory submission; and (v) conducting an individual benefit–risk assessment for a complete picture of the clinical performance of the test treatment under investigation. In this article, we provide a comprehensive summarization of this innovative thinking and these approaches for an efficient, accurate and reliable assessment of a test treatment used for treating patients with rare diseases under study. Statistical considerations including challenges and justifications are provided whenever possible. In addition, an innovative approach that combines innovative thinking and these approaches is proposed for regulatory consideration in rare disease drug development.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050631
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 632: Hybrid Modified Accelerated Gradient
           Method for Optimization Processes

    • Authors: Milena J. Petrović, Ana Vučetić, Tanja Jovanović Spasojević
      First page: 632
      Abstract: This research reveals a hybrid variant of the modified accelerated gradient method. We prove that derived iteration is linearly convergent on the set of uniformly convex functions. Performance profiles of the introduced hybrid method were numerically compared with its non-hybrid version. The analyzed characteristics were CPU time, the number of iterations and the number of function evaluations. The results of the numerical experiments show a better performance in favor of the derived hybrid accelerated model compared with its forerunner.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050632
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 633: Medical Image-Based Diagnosis Using a
           Hybrid Adaptive Neuro-Fuzzy Inferences System (ANFIS) Optimized by GA with
           a Deep Network Model for Features Extraction

    • Authors: Baidaa Mutasher Rashed, Nirvana Popescu
      First page: 633
      Abstract: Predicting diseases in the early stages is extremely important. By taking advantage of advances in deep learning and fuzzy logic techniques, a new model is proposed in this paper for disease evaluation depending on the adaptive neuro-fuzzy inference system (ANFIS) with a genetic algorithm (GA) for classification, and the pre-trained DenseNet-201 model for feature extraction, in addition to the whale optimization algorithm (WOA) for feature selection. Two medical databases (chest X-ray and MRI brain tumor) for the diagnosis of two disease types were used as input in the suggested model. The optimization of ANFIS parameters was performed by GA to achieve the optimum prediction capability. DenseNet-201 for feature extraction was employed to obtain better classification accuracy. Having more features sometimes leads to lower accuracy, and this issue can be rectified using a feature selection strategy WOA which gave good results. The proposed model was evaluated utilizing statistical metrics root mean square error (RMSE), mean square error (MSE), standard deviation (STD), and coefficient of determination (R2), and it was compared with the conventional ANFIS model, with the proposed model (ANFIS-GA) showing a superior prediction capability over the ANFIS model. As a result, it can be concluded that the proposed ANFIS-GA model is efficient and has the potential for a robust diseases evaluation with good accuracy. Also, we conclude from this work that integrating optimization algorithms with ANFIS boosts its performance, resulting in a more accurate and reliable model.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050633
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 634: Further Results on the Input-to-State
           Stability of a Linear Disturbed System with Control Delay

    • Authors: Daniela Enciu, Adrian Toader, Ioan Ursu
      First page: 634
      Abstract: In this paper, a theorem is obtained that gives sufficient input-to-state stability conditions for linear systems with control delay and additive disturbances. Stabilizing feedback is considered available in the absence of delay and disturbances. The mathematical tools are the Lyapunov–Krasovskii functional, the Jensen inequality and the double Hadamard inequality. The critical delay is highlighted.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050634
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 635: Potential Applications of Explainable
           Artificial Intelligence to Actuarial Problems

    • Authors: Catalina Lozano-Murcia, Francisco P. Romero, Jesus Serrano-Guerrero, Arturo Peralta, Jose A. Olivas
      First page: 635
      Abstract: Explainable artificial intelligence (XAI) is a group of techniques and evaluations that allows users to understand artificial intelligence knowledge and increase the reliability of the results produced using artificial intelligence. XAI can assist actuaries in achieving better estimations and decisions. This study reviews the current literature to summarize XAI in common actuarial problems. We proposed a research process based on understanding the type of AI used in actuarial practice in the financial industry and insurance pricing and then researched XAI implementation. This study systematically reviews the literature on the need for implementation options and the current use of explanatory artificial intelligence (XAI) techniques for actuarial problems. The study begins with a contextual introduction outlining the use of artificial intelligence techniques and their potential limitations, followed by the definition of the search equations used in the research process, the analysis of the results, and the identification of the main potential fields for exploitation in actuarial problems, as well as pointers for potential future work in this area.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050635
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 636: A Novel Improved Whale Optimization
           Algorithm for Global Optimization and Engineering Applications

    • Authors: Ziying Liang, Ting Shu, Zuohua Ding
      First page: 636
      Abstract: The Whale Optimization Algorithm (WOA) is a swarm intelligence algorithm based on natural heuristics, which has gained considerable attention from researchers and engineers. However, WOA still has some limitations, including limited global search efficiency and a slow convergence rate. To address these issues, this paper presents an improved whale optimization algorithm with multiple strategies, called Dynamic Gain-Sharing Whale Optimization Algorithm (DGSWOA). Specifically, a Sine–Tent–Cosine map is first adopted to more effectively initialize the population, ensuring a more uniform distribution of individuals across the search space. Then, a gaining–sharing knowledge based algorithm is used to enhance global search capability and avoid falling into a local optimum. Finally, to increase the diversity of solutions, Dynamic Opposition-Based Learning is incorporated for population updating. The effectiveness of our approach is evaluated through comparative experiments on blackbox optimization benchmarking and two engineering application problems. The experimental results suggest that the proposed method is competitive in terms of solution quality and convergence speed in most cases.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050636
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 637: Homogeneously Weighted Moving Average
           Control Charts: Overview, Controversies, and New Directions

    • Authors: Jean-Claude Malela-Majika, Schalk William Human, Kashinath Chatterjee
      First page: 637
      Abstract: The homogeneously weighted moving average (HWMA) chart is a recent control chart that has attracted the attention of many researchers in statistical process control (SPC). The HWMA statistic assigns a higher weight to the most recent sample, and the rest is divided equally between the previous samples. This weight structure makes the HWMA chart more sensitive to small shifts in the process parameters when running in zero-state mode. Many scholars have reported its superiority over the existing charts when the process runs in zero-state mode. However, several authors have criticized the HWMA chart by pointing out its poor performance in steady-state mode due to its weighting structure, which does not reportedly comply with the SPC standards. This paper reviews and discusses all research works on HWMA-related charts (i.e., 55 publications) and provides future research ideas and new directions.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050637
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 638: A New Chaos-Based Image Encryption
           Algorithm Based on Discrete Fourier Transform and Improved Joseph
           Traversal

    • Authors: Mingxu Wang, Xianping Fu, Xiaopeng Yan, Lin Teng
      First page: 638
      Abstract: To further enhance the security of image encryption, a new chaos-based image encryption algorithm (IEA) based on discrete Fourier transform and Joseph traversal is proposed to encrypt the plain image in both the frequency domain and space domain simultaneously. In the proposed IEA, the logistic map is used to generate the appropriate chaotic sequence, and the improved Joseph traversal is used to scramble the image in different starting positions and variable step sizes. Then, block diffusion is performed at the end. The main finding concerning the proposed IEA is that the combination of discrete Fourier transform and Joseph traversal can enhance the security of the image information, which has been validated by measuring the performance in resisting the common types of attacks.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050638
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 639: The Strictly Dissipative Condition of
           Continuous-Time Markovian Jump Systems with Uncertain Transition Rates

    • Authors: WonIl Lee, JaeWook Shin, BumYong Park
      First page: 639
      Abstract: This study addresses the problem of strictly dissipative stabilization for continuous-time Markovian jump systems (MJSs) with external disturbances and generally uncertain transition rates that contain completely unknown transition rates and their bound values. A stabilization condition is obtained to guarantee strict dissipativity for the MJSs with partial knowledge in terms of the transition rates. To reduce the conservativity of the proposed condition, we used a boundary condition related to the bounds of the transition rate with slack variables. Finally, two simulation results are presented to describe the feasibility of the proposed controller.
      Citation: Mathematics
      PubDate: 2024-02-21
      DOI: 10.3390/math12050639
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 640: AGCN-Domain: Detecting Malicious Domains
           with Graph Convolutional Network and Attention Mechanism

    • Authors: Xi Luo, Yixin Li, Hongyuan Cheng, Lihua Yin
      First page: 640
      Abstract: Domain Name System (DNS) plays an infrastructure role in providing the directory service for mapping domains to IPs on the Internet. Considering the foundation and openness of DNS, it is not surprising that adversaries register massive domains to enable multiple malicious activities, such as spam, command and control (C&C), malware distribution, click fraud, etc. Therefore, detecting malicious domains is a significant topic in security research. Although a substantial quantity of research has been conducted, previous work has failed to fuse multiple relationship features to uncover the deep underlying relationships between domains, thus largely limiting their level of performance. In this paper, we proposed AGCN-Domain to detect malicious domains by combining various relations. The core concept behind our work is to analyze relations between domains according to their behaviors in multiple perspectives and fuse them intelligently. The AGCN-Domain model utilizes three relationships (client relation, resolution relation, and cname relation) to construct three relationship feature graphs to extract features and intelligently fuse the features extracted from the graphs through an attention mechanism. After the relationship features are extracted from the domain names, they are put into the trained classifier to be processed. Through our experiments, we have demonstrated the performance of our proposed AGCN-Domain model. With 10% initialized labels in the dataset, our AGCN-Domain model achieved an accuracy of 94.27% and the F1 score of 87.93%, significantly outperforming other methods in the comparative experiments.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050640
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 641: Model Averaging for Accelerated Failure
           Time Models with Missing Censoring Indicators

    • Authors: Longbiao Liao, Jinghao Liu
      First page: 641
      Abstract: Model averaging has become a crucial statistical methodology, especially in situations where numerous models vie to elucidate a phenomenon. Over the past two decades, there has been substantial advancement in the theory of model averaging. However, a gap remains in the field regarding model averaging in the presence of missing censoring indicators. Therefore, in this paper, we present a new model-averaging method for accelerated failure time models with right censored data when censoring indicators are missing. The model-averaging weights are determined by minimizing the Mallows criterion. Under mild conditions, the calculated weights exhibit asymptotic optimality, leading to the model-averaging estimator achieving the lowest squared error asymptotically. Monte Carlo simulations demonstrate that the method proposed in this paper has lower mean squared errors compared to other model-selection and model-averaging methods. Finally, we conducted an empirical analysis using the real-world Acute Myeloid Leukemia (AML) dataset. The results of the empirical analysis demonstrate that the method proposed in this paper outperforms existing approaches in terms of predictive performance.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050641
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 642: Modeling Robotic Thinking and Creativity:
           A Classic–Quantum Dialogue

    • Authors: Maria Mannone, Antonio Chella, Giovanni Pilato, Valeria Seidita, Filippo Vella, Salvatore Gaglio
      First page: 642
      Abstract: The human mind can be thought of as a black box, where the external inputs are elaborated in an unknown way and lead to external outputs. D’Ariano and Faggin schematized thinking and consciousness through quantum state dynamics. The complexity of mental states can be formalized through the entanglement of the so-called qualia states. Thus, the interaction between the mind and the external world can be formalized as an interplay between classical and quantum-state dynamics. Since quantum computing is more and more often being applied to robots, and robots constitute a benchmark to test schematic models of behavior, we propose a case study with a robotic dance, where the thinking and moving mechanisms are modeled according to quantum–classic decision making. In our research, to model the elaboration of multi-sensory stimuli and the following decision making in terms of movement response, we adopt the D’Ariano–Faggin formalism and propose a case study with improvised dance based on a collection of poses, whose combination is presented in response to external and periodic multi-sensory stimuli. We model the dancer’s inner state and reaction to classic stimuli through a quantum circuit. We present our preliminary results, discussing further lines of development.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050642
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 643: Network Evolution Model with Preferential
           Attachment at Triadic Formation Step

    • Authors: Sergei Sidorov, Timofei Emelianov, Sergei Mironov, Elena Sidorova, Yuri Kostyukhin, Alexandr Volkov, Anna Ostrovskaya, Lyudmila Polezharova
      First page: 643
      Abstract: It is recognized that most real systems and networks exhibit a much higher clustering with comparison to a random null model, which can be explained by a higher probability of the triad formation—a pair of nodes with a mutual neighbor have a greater possibility of having a link between them. To catch the more substantial clustering of real-world networks, the model based on the triadic closure mechanism was introduced by P. Holme and B. J. Kim in 2002. It includes a “triad formation step” in which a newly added node links both to a preferentially chosen node and to its randomly chosen neighbor, therefore forming a triad. In this study, we propose a new model of network evolution in which the triad formation mechanism is essentially changed in comparison to the model of P. Holme and B. J. Kim. In our proposed model, the second node is also chosen preferentially, i.e., the probability of its selection is proportional to its degree with respect to the sum of the degrees of the neighbors of the first selected node. The main goal of this paper is to study the properties of networks generated by this model. Using both analytical and empirical methods, we show that the networks are scale-free with power-law degree distributions, but their exponent γ is tunable which is distinguishable from the networks generated by the model of P. Holme and B. J. Kim. Moreover, we show that the degree dynamics of individual nodes are described by a power law.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050643
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 644: Tropical Modeling of Battery Swapping and
           Charging Station

    • Authors: Nikolai Krivulin, Akhil Garg
      First page: 644
      Abstract: We propose and investigate a queueing model of a battery swapping and charging station (BSCS) for electric vehicles (EVs). A new approach to the analysis of the queueing model is developed, which combines the representation of the model as a stochastic dynamic system with the use of the methods and results of tropical algebra, which deals with the theory and applications of algebraic systems with idempotent operations. We describe the dynamics of the queueing model by a system of recurrence equations that involve random variables (RVs) to represent the interarrival time of incoming EVs. A performance measure for the model is defined as the mean operation cycle time of the station. Furthermore, the system of equations is represented in terms of the tropical algebra in vector form as an implicit linear state dynamic equation. The performance measure takes on the meaning of the mean growth rate of the state vector (the Lyapunov exponent) of the dynamic system. By applying a solution technique of vector equations in tropical algebra, the implicit equation is transformed into an explicit one with a state transition matrix with random entries. The evaluation of the Lyapunov exponent reduces to finding the limit of the expected value of norms of tropical matrix products. This limit is then obtained using results from the tropical spectral theory of deterministic and random matrices. With this approach, we derive a new exact formula for the mean cycle time of the BSCS, which is given in terms of the expected value of the RVs involved. We present the results of the Monte Carlo simulation of the BSCS’s operation, which show a good agreement with the exact solution. The application of the obtained solution to evaluate the performance of one BSCS and to find the optimal distribution of battery packs between stations in a network of BSCSs is discussed. The solution may be of interest in the case when the details of the underlying probability distributions are difficult to determine and, thus, serves to complement and supplement other modeling techniques with the need to fix a distribution.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050644
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 645: Discounting the Distant Future: What Do
           Historical Bond Prices Imply about the Long-Term Discount Rate'

    • Authors: J. Doyne Farmer, John Geanakoplos, Matteo G. Richiardi, Miquel Montero, Josep Perelló, Jaume Masoliver
      First page: 645
      Abstract: We present a thorough empirical study on real interest rates by also including risk aversion through the introduction of the market price of risk. From the viewpoint of complex systems science and its multidisciplinary approach, we use the theory of bond pricing to study the long-term discount rate to estimate the rate when taking historical US and UK data, and to further contribute to the discussion about the urgency of climate action in the context of environmental economics and stochastic methods. Century-long historical records of 3-month bonds, 10-year bonds, and inflation allow us to estimate real interest rates for the UK and the US. Real interest rates are negative about a third of the time and the real yield curves are inverted more than a third of the time, sometimes by substantial amounts. This rules out most of the standard bond-pricing models, which are designed for nominal rates that are assumed to be positive. We, therefore, use the Ornstein–Uhlenbeck model, which allows negative rates and gives a good match to inversions of the yield curve. We derive the discount function using the method of Fourier transforms and fit it to the historical data. The estimated long-term discount rate is 1.7% for the UK and 2.2% for the US. The value of 1.4% used by Stern is less than a standard deviation from our estimated long-run return rate for the UK, and less than two standard deviations of the estimated value for the US. All of this once more reinforces the need for immediate and substantial spending to combat climate change.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050645
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 646: Distributed Sparse Precision Matrix
           Estimation via Alternating Block-Based Gradient Descent

    • Authors: Wei Dong, Hongzhen Liu
      First page: 646
      Abstract: Precision matrices can efficiently exhibit the correlation between variables and they have received much attention in recent years. When one encounters large datasets stored in different locations and when data sharing is not allowed, the implementation of high-dimensional precision matrix estimation can be numerically challenging or even infeasible. In this work, we studied distributed sparse precision matrix estimation via an alternating block-based gradient descent method. We obtained a global model by aggregating each machine’s information via a communication-efficient surrogate penalized likelihood. The procedure chooses the block coordinates using the local gradient, to guide the global gradient updates, which can efficiently accelerate precision estimation and lessen communication loads on sensors. The proposed method can efficiently achieve the correct selection of non-zero elements of a sparse precision matrix. Under mild conditions, we show that the proposed estimator achieved a near-oracle convergence rate, as if the estimation had been conducted with a consolidated dataset on a single computer. The promising performance of the method was supported by both simulated and real data examples.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050646
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 647: Nonlinear Optimal Control for Stochastic
           Dynamical Systems

    • Authors: Manuel Lanchares, Wassim M. Haddad
      First page: 647
      Abstract: This paper presents a comprehensive framework addressing optimal nonlinear analysis and feedback control synthesis for nonlinear stochastic dynamical systems. The focus lies on establishing connections between stochastic Lyapunov theory and stochastic Hamilton–Jacobi–Bellman theory within a unified perspective. We demonstrate that the closed-loop nonlinear system’s asymptotic stability in probability is ensured through a Lyapunov function, identified as the solution to the steady-state form of the stochastic Hamilton–Jacobi–Bellman equation. This dual assurance guarantees both stochastic stability and optimality. Additionally, optimal feedback controllers for affine nonlinear systems are developed using an inverse optimality framework tailored to the stochastic stabilization problem. Furthermore, the paper derives stability margins for optimal and inverse optimal stochastic feedback regulators. Gain, sector, and disk margin guarantees are established for nonlinear stochastic dynamical systems controlled by nonlinear optimal and inverse optimal Hamilton–Jacobi–Bellman controllers.
      Citation: Mathematics
      PubDate: 2024-02-22
      DOI: 10.3390/math12050647
      Issue No: Vol. 12, No. 5 (2024)
       
  • Mathematics, Vol. 12, Pages 548: Generalized Almost Periodicity in Measure

    • Authors: Marko Kostić, Wei-Shih Du, Halis Can Koyuncuoğlu, Daniel Velinov
      First page: 548
      Abstract: This paper investigates diverse classes of multidimensional Weyl and Doss ρ-almost periodic functions in a general measure setting. This study establishes the fundamental structural properties of these generalized ρ-almost periodic functions, extending previous classes such as m-almost periodic and (equi-)Weyl-p-almost periodic functions. Notably, a new class of (equi-)Weyl-p-almost periodic functions is introduced, where the exponent p>0 is general. This paper delves into the abstract Volterra integro-differential inclusions, showcasing the practical implications of the derived results. This work builds upon the extensions made in the realm of Levitan N-almost periodic functions, contributing to the broader understanding of mathematical functions in diverse measure spaces.
      Citation: Mathematics
      PubDate: 2024-02-10
      DOI: 10.3390/math12040548
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 549: Finite-Time Robust Path-Following Control
           of Perturbed Autonomous Ground Vehicles Using a Novel Self-Tuning
           Nonsingular Fast Termina Sliding Manifold

    • Authors: Cong Phat Vo, Quoc Hung Hoang, Tae-Hyun Kim, and Jeong hwan Jeon
      First page: 549
      Abstract: This work presents a finite-time robust path-following control scheme for perturbed autonomous ground vehicles. Specifically, a novel self-tuning nonsingular fast-terminal sliding manifold that further enhances the convergence rate and tracking accuracy is proposed. Then, uncertain dynamics and external disturbances are estimated by a high-gain disturbance observer to compensate for the designed control input. Successively, a super-twisting algorithm is incorporated into the final control law, significantly mitigating the chattering phenomenon of both the input control signal and the output trajectory. Furthermore, the global finite-time convergence and stability of the whole proposed control algorithm are proven by the Lyapunov theory. Finally, the efficacy of the proposed method is validated with comparisons in a numerical example. It obtains high control performance, reduced chattering, fast convergence rate, singularity avoidance, and robustness against uncertainties.
      Citation: Mathematics
      PubDate: 2024-02-10
      DOI: 10.3390/math12040549
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 550: Branching Random Walks in a Random
           Killing Environment with a Single Reproduction Source

    • Authors: Vladimir Kutsenko, Stanislav Molchanov, Elena Yarovaya
      First page: 550
      Abstract: We consider a continuous-time branching random walk on Z in a random non-homogeneous environment. The process starts with a single particle at initial time t=0. This particle can walk on the lattice points or disappear with a random intensity until it reaches the certain point, which we call the reproduction source. At the source, the particle can split into two offspring or jump out of the source. The offspring of the initial particle evolves according to the same law, independently of each other and the entire prehistory. The aim of this paper is to study the conditions for the presence of exponential growth of the average number of particles at every lattice point. For this purpose, we investigate the spectrum of the random evolution operator of the average particle numbers. We derive the condition under which there is exponential growth with probability one. We also study the process under the violation of this condition and present the lower and upper estimates for the probability of exponential growth.
      Citation: Mathematics
      PubDate: 2024-02-11
      DOI: 10.3390/math12040550
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 551: Laplace Transformation of the Ruin Time
           for a Risk Model with a Parisian Implementation Delay

    • Authors: Tao Sun, Xinqiu Zhang
      First page: 551
      Abstract: In this paper, we apply the concept of the Parisian implementation delay to dividend payments and assume that the claim amount paid Zi by the insurance company for the ith time follows an exponential distribution. We give the Laplace transformation of the ruin time for a risk model with a Parisian implementation delay and give display expressions using the scale function.
      Citation: Mathematics
      PubDate: 2024-02-11
      DOI: 10.3390/math12040551
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 552: Pooled Steganalysis via Model Discrepancy

    • Authors: Jiang Yu, Jing Zhang, Fengyong Li
      First page: 552
      Abstract: Pooled steganalysis aims to discover the guilty actor(s) among multiple normal actor(s). Existing techniques mainly rely on the high-dimension and time-consuming features. Moreover, the minor feature distance between cover and stego is detrimental to pooled steganalysis. To overcome these issues, this paper focuses on the discrepancy of the statistical characteristics of transmitted multiple images and designs a model-based effective pooled steganalysis strategy. Facing the public and monitored channel, without using the feature extractions, pooled steganalysis collects a set of images transmitted by a suspicious actor and use the corresponding distortion values as the statistic representation of the selected image set. Specifically, the normalized distortion of the suspicious image set generated via normal/guilty actor(s) is modelled as a normal distribution, and we apply maximum likelihood estimation (MLE) to estimate the parameter (cluster center) of the distribution by which we can represent the defined model. Considering the tremendous distortion difference between normal and stego image sets, we can deduce that the constructed model can effectively discover and reveal the existence of abnormal behavior of guilty actors. To show the discrepancy of different models, employing the logistic function and likelihood ratio test (LRT), we construct a new detector by which the ratio of cluster centers is turned into a probability. Depending on the generated probability and an optimal threshold, we make a judgment on whether the dubious actor is normal or guilty. Extensive experiments demonstrate that, compared to existing pooled steganalysis techniques, the proposed scheme exhibits great detection performance on the guilty actor(s) with lower complexity.
      Citation: Mathematics
      PubDate: 2024-02-11
      DOI: 10.3390/math12040552
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 553: Nonlinear Phenomena of Fluid Flow in a
           Bioinspired Two-Dimensional Geometric Symmetric Channel with Sudden
           Expansion and Contraction

    • Authors: Liquan Yang, Mo Yang, Weijia Huang
      First page: 553
      Abstract: Inspired by the airway for phonation, fluid flow in an idealized model within a sudden expansion and contraction channel with a geometrically symmetric structure is investigated, and the nonlinear behaviors of the flow therein are explored via numerical simulations. Numerical simulation results show that, as the Reynolds number (Re = U0H/ν) increases, the numerical solution undergoes a pitchfork bifurcation, an inverse pitchfork bifurcation and a Hopf bifurcation. There are symmetric solutions, asymmetric solutions and oscillatory solutions for flows. When the sudden expansion ratio (Er) = 6.00, aspect ratio (Ar) = 1.78 and Re ≤ Rec1 (≈185), the numerical solution is unique, symmetric and stable. When Rec1 < Re ≤ Rec2 (≈213), two stable asymmetric solutions and one symmetric unstable solution are reached. When Rec2 < Re ≤ Rec3 (≈355), the number of numerical solution returns one, which is stable and symmetric. When Re > Rec3, the numerical solution is oscillatory. With increasing Re, the numerical solution develops from periodic and multiple periodic solutions to chaos. The critical Reynolds numbers (Rec1, Rec2 and Rec3) and the maximum return velocity, at which reflux occurs in the channel, change significantly under conditions with different geometry. In this paper, the variation rules of Rec1, Rec2 and Rec3 are investigated, as well as the maximum return velocity with the sudden expansion ratio Er and the aspect ratio Ar.
      Citation: Mathematics
      PubDate: 2024-02-12
      DOI: 10.3390/math12040553
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 554: A Hybrid Initialization and Effective
           Reproduction-Based Evolutionary Algorithm for Tackling Bi-Objective
           Large-Scale Feature Selection in Classification

    • Authors: Hang Xu, Chaohui Huang, Hui Wen, Tao Yan, Yuanmo Lin, Ying Xie
      First page: 554
      Abstract: Evolutionary algorithms have been widely used for tackling multi-objective optimization problems, while feature selection in classification can also be seen as a discrete bi-objective optimization problem that pursues minimizing both the classification error and the number of selected features. However, traditional multi-objective evolutionary algorithms (MOEAs) can encounter setbacks when the dimensionality of features explodes to a large scale, i.e., the curse of dimensionality. Thus, in this paper, we focus on designing an adaptive MOEA framework for solving bi-objective feature selection, especially on large-scale datasets, by adopting hybrid initialization and effective reproduction (called HIER). The former attempts to improve the starting state of evolution by composing a hybrid initial population, while the latter tries to generate more effective offspring by modifying the whole reproduction process. Moreover, the statistical experiment results suggest that HIER generally performs the best on most of the 20 test datasets, compared with six state-of-the-art MOEAs, in terms of multiple metrics covering both optimization and classification performances. Then, the component contribution of HIER is also studied, suggesting that each of its essential components has a positive effect. Finally, the computational time complexity of HIER is also analyzed, suggesting that HIER is not time-consuming at all and shows promising computational efficiency.
      Citation: Mathematics
      PubDate: 2024-02-12
      DOI: 10.3390/math12040554
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 555: Multicriteria Assessment Method for
           Network Structure Congestion Based on Traffic Data Using Advanced Computer
           Vision

    • Authors: Roman Ekhlakov, Nikita Andriyanov
      First page: 555
      Abstract: Overloading of network structures is a problem that we encounter every day in many areas of life. The most associative structure is the transport graph. In many megacities around the world, the so-called intelligent transport system (ITS) is successfully operating, allowing real-time monitoring and making changes to traffic management while choosing the most effective solutions. Thanks to the emergence of more powerful computing resources, it has become possible to build more complex and realistic mathematical models of traffic flows, which take into account the interactions of drivers with road signs, markings, and traffic lights, as well as with each other. Simulations using high-performance systems can cover road networks at the scale of an entire city or even a country. It is important to note that the tool being developed is applicable to most network structures described by such mathematical apparatuses as graph theory and the applied theory of network planning and management that are widely used for representing the processes of organizing production and enterprise management. The result of this work is a developed model that implements methods for modeling the behavior of traffic flows based on physical modeling and machine learning algorithms. Moreover, a computer vision system is proposed for analyzing traffic on the roads, which, based on vision transformer technologies, provides high accuracy in detecting cars, and using optical flow, allows for significantly faster processing. The accuracy is above 90% with a processing speed of more than ten frames per second on a single video card.
      Citation: Mathematics
      PubDate: 2024-02-12
      DOI: 10.3390/math12040555
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 556: Domain Adaptation for Sensor-Based Human
           Activity Recognition with a Graph Convolutional Network

    • Authors: Jing Yang, Tianzheng Liao, Jingjing Zhao, Yan Yan, Yichun Huang, Zhijia Zhao, Jing Xiong, Changhong Liu
      First page: 556
      Abstract: Sensor-based human activity recognition (HAR) plays a fundamental role in various mobile application scenarios, but the model performance of HAR heavily relies on the richness of the dataset and the completeness of data annotation. To address the shortage of comprehensive activity types in collected datasets, we adopt the domain adaptation technique with a graph neural network-based approach by incorporating an adaptive learning mechanism to enhance the action recognition model’s generalization ability, especially when faced with limited sample sizes. To evaluate the effectiveness of our proposed approach, we conducted experiments using three well-known datasets: MHealth, PAMAP2, and TNDA. The experimental results demonstrate the efficacy of our approach in sensor-based HAR tasks, achieving impressive average accuracies of 98.88%, 98.58%, and 97.78% based on the respective datasets. Furthermore, we conducted transfer learning experiments to address the domain adaptation problem. These experiments revealed that our proposed model exhibits exceptional transferability and distinguishing ability, even in scenarios with limited available samples. Thus, our approach offers a practical and viable solution for sensor-based HAR tasks.
      Citation: Mathematics
      PubDate: 2024-02-12
      DOI: 10.3390/math12040556
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 557: Geometry of Enumerable Class of Surfaces
           Associated with Mylar Balloons

    • Authors: Vladimir I. Pulov, Vasyl Kovalchuk, Ivaïlo M. Mladenov
      First page: 557
      Abstract: In this paper, the very fundamental geometrical characteristics of the Mylar balloon like the profile curve, height, volume, arclength, surface area, crimping factor, etc. are recognized as geometrical moments In(x) and In and this observation has been used to introduce an infinite family of surfaces Sn specified by the natural numbers n=0,1,2,…. These surfaces are presented via explicit formulas (through the incomplete Euler’s beta function) and can be identified as an interesting family of balloons. Their parameterizations is achieved relying on the well-known relationships among elliptic integrals, beta and gamma functions. The final results are expressed via the fundamental mathematical constants, such as π and the lemniscate constant ϖ. Quite interesting formulas for recursive calculations of various quantities related to associated figures modulo four are derived. The most principal results are summarized in a table, illustrated via a few graphics, and some direct relationships with other fundamental areas in mathematics, physics, and geometry are pointed out.
      Citation: Mathematics
      PubDate: 2024-02-12
      DOI: 10.3390/math12040557
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 558: Optimization of User Service Rate with
           Image Compression in Edge Computing-Based Vehicular Networks

    • Authors: Liujing Zhang, Jin Li, Wenyang Guan, Xiaoqin Lian
      First page: 558
      Abstract: The prevalence of intelligent transportation systems in alleviating traffic congestion and reducing the number of traffic accidents has risen in recent years owing to the rapid advancement of information and communication technology (ICT). Nevertheless, the increase in Internet of Vehicles (IoV) users has led to massive data transmission, resulting in significant delays and network instability during vehicle operation due to limited bandwidth resources. This poses serious security risks to the traffic system and endangers the safety of IoV users. To alleviate the computational load on the core network and provide more timely, effective, and secure data services to proximate users, this paper proposes the deployment of edge servers utilizing edge computing technologies. The massive image data of users are processed using an image compression algorithm, revealing a positive correlation between the compression quality factor and the image’s spatial occupancy. A performance analysis model for the ADHOC MAC (ADHOC Medium Access Control) protocol is established, elucidating a positive correlation between the frame length and the number of service users, and a negative correlation between the service user rate and the compression quality factor. The optimal service user rate, within the constraints of compression that does not compromise detection accuracy, is determined by using the target detection result as a criterion for effective compression. The simulation results demonstrate that the proposed scheme satisfies the object detection accuracy requirements in the IoV context. It enables the number of successfully connected users to approach the total user count, and increases the service rate by up to 34%, thereby enhancing driving safety, stability, and efficiency.
      Citation: Mathematics
      PubDate: 2024-02-12
      DOI: 10.3390/math12040558
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 559: A Low-Inertia and High-Stiffness
           Cable-Driven Biped Robot: Design, Modeling, and Control

    • Authors: Jun Tang, Haiming Mou, Yunfeng Hou, Yudi Zhu, Jian Liu, Jianwei Zhang
      First page: 559
      Abstract: In this paper, a biped robot system for dynamic walking is presented. It has two 2-degree-of-freedom (DOF) lightweight legs and a 6-DOF hip. All the joint pulleys of the legs are driven by motors that are placed at the hip using steel cables. Since all the heavy motors are mounted at the hip, the biped robot has remarkably low-mass legs beyond the hip, which guarantees low inertia during walking at high speeds. Utilizing cable-amplification mechanisms, high stiffness and strength are achieved, resulting in better control performance compared to conventional direct-driven methods. Techniques are developed to estimate joint-angle errors caused by the elastic deformation of the cables. To achieve smooth control, we introduce the concept of a virtual leg, which is an imaginary leg connecting the hip joint and the ankle joint. A robust control approach based on the “virtual leg” is presented, which considers the variances of the virtual leg length during walking. Experiments are conducted to validate the effectiveness of the mechanical design and the proposed control approach.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040559
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 560: Influence of the Schottky Junction on the
           Propagation Characteristics of Shear Horizontal Waves in a Piezoelectric
           Semiconductor Semi-Infinite Medium

    • Authors: Xiao Guo, Yilin Wang, Chunyu Xu, Zibo Wei, Chenxi Ding
      First page: 560
      Abstract: In this paper, a theoretical model of the propagation of a shear horizontal wave in a piezoelectric semiconductor semi-infinite medium is established using the optimized spectral method. First, the basic equations of the piezoelectric semiconductor semi-infinite medium are derived with the consideration of biased electric fields. Then, considering the propagation of a shear horizontal wave in the piezoelectric semiconductor semi-infinite medium, two equivalent mathematical models are established. In the first mathematical model, the Schottky junction is theoretically treated as an electrically imperfect interface, and an interface characteristic length is utilized to describe the interface effect of the Schottky junction. To legitimately confirm the interface characteristic length, a second mathematical model is established, in which the Schottky junction is theoretically treated as an electrical gradient layer. Finally, the dispersion and attenuation curves of shear horizontal waves are numerically calculated using these two mathematical models to discuss the influence of the Schottky junction on the dispersion and attenuation characteristics of shear horizontal waves. Utilizing the equivalence of these two mathematical models and the above numerical results, the numerical value of the interface characteristic length is reliably legitimately confirmed; this value is independent of the thickness of the upper metal layer, the doping concentration of the lower n-type piezoelectric semiconductor substrate, and biasing electric fields. Only the biasing electric field parallel to the Schottky junction can provide an evident influence on the attenuation characteristics of shear horizontal waves and enhance the interface effect of the Schottky junction. Since the second mathematical model is also a validation of our previous mathematical model established through the state transfer equation method, some numerical results calculated using these two mathematical models are compared with those obtained using the previous method to verify the correctness and superiority of the research work presented in this paper. Since these two mathematical models can better calculate the dispersion and attenuation curves of high-frequency waves in micro- and nano-scale piezoelectric semiconductor materials, the establishment of mathematical models and the revelation of physical mechanisms are fundamental to the analysis and optimization of micro-scale resonators, energy harvesters, and amplifications.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040560
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 561: On K-Banhatti, Revan Indices and Entropy
           Measures of MgO(111) Nanosheets via Linear Regression

    • Authors: Norah Almalki, Hafsah Tabassum
      First page: 561
      Abstract: The structure and topology of chemical compounds can be determined using chemical graph theory. Using topological indices, we may uncover much about connectivity, complexity, and other important aspects of molecules. Numerous research investigations have been conducted on the K-Banhatti indices and entropy measurements in various fields, including the study of natural polymers, nanotubes, and catalysts. At the same time, the Shannon entropy of a graph is widely used in network science. It is employed in evaluating several networks, including social networks, neural networks, and transportation systems. The Shannon entropy enables the analysis of a network’s topology and structure, facilitating the identification of significant nodes or structures that substantially impact network operation and stability. In the past decade, there has been a considerable focus on investigating a range of nanostructures, such as nanosheets and nanoparticles, in both experimental and theoretical domains. As a very effective catalyst and inert substrate, the MgO nanostructure has received a lot of interest. The primary objective of this research is to study different indices and employ them to look at entropy measures of magnesium oxide(111) nanosheets over a wide range of p values, including p=1,2,3,⋯,j. Additionally, we conducted a linear regression analysis to establish the correlation between indices and entropies.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040561
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 562: The Beddington–DeAngelis
           Competitive Response: Intra-Species Interference Enhances Coexistence in
           Species Competition

    • Authors: María Carmen Vera, Marcos Marvá, Víctor José García-Garrido, René Escalante
      First page: 562
      Abstract: Species coexistence is a major issue in ecology. We disentangled the role of individual interference when competing in the classical interference competition model. For the first time, we considered simultaneously intra- and inter-species interference by introducing the Beddington–DeAngelis competitive response into the classical competition model. We found a trade-off between intra- and inter-species interference that refines in a sense the well-known balance of intra- and inter-species competition coefficients. As a result, we found that (i) global coexistence is possible for a larger range of values of the inter-/intra-species competition coefficients and contributes to explaining the high prevalence of species coexistence in nature. This feature is exclusively due to intra-species interference. (ii) We found multi-stability scenarios previously described in the literature that can be reinterpreted in terms of individuals interference.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040562
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 563: Moving Discretized Control Set Model
           Predictive Control with Dominant Parameter Identification Strategy for
           Dual Active Bridge Converters

    • Authors: Tan-Quoc Duong, Sung-Jin Choi
      First page: 563
      Abstract: The dual active bridge (DAB) converter has grown significantly as one of the most important units for energy distribution, connecting various types of renewable energy sources with the DC microgrid. For controlling the DAB converter, moving discretized control set model predictive control (MDCS-MPC) is considered one of the most effective methods because of its advantages, such as high dynamic performance and multiobjective control. However, MDCS-MPC strongly depends on the accuracy of system parameters. Meanwhile, the system parameters can be changed due to temperature drift, manufacturing tolerance, age, and operating circumstances. As a result, the steady-state performance of the output voltage of MDCS-MPC is affected. Motivated by this, this paper proposes MDCS-MPC combined with the parameter identification technique to improve the steady-state performance of the output voltage of the DAB converter. Then, analysis of the percentage of the steady-state error of the output voltage is defined on six model parameters, and sensitivity analysis of two dominant parameters is chosen. After that, a straightforward least-squares analysis (LSA) technique is used to identify the two parameters online. The proposed method is verified through simulation in several different operating scenarios to verify its effectiveness.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040563
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 564: Solitary Wave Solutions of a Hyperelastic
           Dispersive Equation

    • Authors: Yuheng Jiang, Yu Tian, Yao Qi
      First page: 564
      Abstract: This paper explores solitary wave solutions arising in the deformations of a hyperelastic compressible plate. Explicit traveling wave solution expressions with various parameters for the hyperelastic compressible plate are obtained and visualized. To analyze the perturbed equation, we employ geometric singular perturbation theory, Melnikov methods, and invariant manifold theory. The solitary wave solutions of the hyperelastic compressible plate do not persist under small perturbations for wave speed c>−βk2. Further exploration of nonlinear models that accurately depict the persistence of solitary wave solution on the significant physical processes under the K-S perturbation is recommended.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040564
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 565: Toeplitz Operators on Harmonic Fock
           Spaces with Radial Symbols

    • Authors: Zhi-Ling Sun, Wei-Shih Du, Feng Qi
      First page: 565
      Abstract: The main aim of this paper is to study new features and specific properties of the Toeplitz operator with radial symbols in harmonic Fock spaces. A new spectral decomposition of a Toeplitz operator with Wick symbols is also established.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040565
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 566: Advancing Semantic Classification: A
           Comprehensive Examination of Machine Learning Techniques in Analyzing
           Russian-Language Patient Reviews

    • Authors: Irina Kalabikhina, Vadim Moshkin, Anton Kolotusha, Maksim Kashin, German Klimenko, Zarina Kazbekova
      First page: 566
      Abstract: Currently, direct surveys are used less and less to assess satisfaction with the quality of user services. One of the most effective methods to solve this problem is to extract user attitudes from social media texts using natural language text mining. This approach helps to obtain more objective results by increasing the representativeness and independence of the sample of service consumers being studied. The purpose of this article is to improve existing methods and test a method for classifying Russian-language text reviews of patients about the work of medical institutions and doctors, extracted from social media resources. The authors developed a hybrid method for classifying text reviews about the work of medical institutions and tested machine learning methods using various neural network architectures (GRU, LSTM, CNN) to achieve this goal. More than 60,000 reviews posted by patients on the two most popular doctor review sites in Russia were analysed. Main results: (1) the developed classification algorithm is highly efficient—the best result was shown by the GRU-based architecture (val_accuracy = 0.9271); (2) the application of the method of searching for named entities to text messages after their division made it possible to increase the classification efficiency for each of the classifiers based on the use of artificial neural networks. This study has scientific novelty and practical significance in the field of social and demographic research. To improve the quality of classification, in the future, it is planned to expand the semantic division of the review by object of appeal and sentiment and take into account the resulting fragments separately from each other.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040566
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 567: Multivalued Contraction Fixed-Point
           Theorem in b-Metric Spaces

    • Authors: Bachir Slimani, John R. Graef, Abdelghani Ouahab
      First page: 567
      Abstract: The authors explore fixed-point theory in b-metric spaces and strong b-metric spaces. They wish to prove some new extensions of the Covitz and Nadler fixed-point theorem in b-metric spaces. In so doing, they wish to answer a question proposed by Kirk and Shahzad about Nadler’s theorem holding in strong b-metric spaces. In addition, they offer an improvement to the fixed-point theorem proven by Dontchev and Hager.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040567
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 568: Leveraging Chain-of-Thought to Enhance
           Stance Detection with Prompt-Tuning

    • Authors: Daijun Ding, Xianghua Fu, Xiaojiang Peng, Xiaomao Fan, Hu Huang, Bowen Zhang
      First page: 568
      Abstract: Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental task in opinion mining. Conventional approaches mainly focus on sentence-level classification techniques. Recent research has shown that the integration of background knowledge can significantly improve stance detection performance. Despite the significant improvement achieved by knowledge-enhanced methods, applying these techniques in real-world scenarios remains challenging for several reasons. Firstly, existing methods often require the use of complex attention mechanisms to filter out noise and extract relevant background knowledge, which involves significant annotation efforts. Secondly, knowledge fusion mechanisms typically rely on fine-tuning, which can introduce a gap between the pre-training phase of pre-trained language models (PLMs) and the downstream stance detection tasks, leading to the poor prediction accuracy of the PLMs. To address these limitations, we propose a novel prompt-based stance detection method that leverages the knowledge acquired using the chain-of-thought method, which we refer to as PSDCOT. The proposed approach consists of two stages. The first stage is knowledge extraction, where instruction questions are constructed to elicit background knowledge from a VLPLM. The second stage is the multi-prompt learning network (M-PLN) for knowledge fusion, which learns model performance based on the background knowledge and the prompt learning framework. We evaluated the performance of PSDCOT on publicly available benchmark datasets to assess its effectiveness in improving stance detection performance. The results demonstrate that the proposed method achieves state-of-the-art results in in-domain, cross-target, and zero-shot learning settings.
      Citation: Mathematics
      PubDate: 2024-02-13
      DOI: 10.3390/math12040568
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 569: Charged Cavitation Multibubbles Dynamics
           Model: Growth Process

    • Authors: Ahmed K. Abu-Nab, Amerah M. Hakami, Ali F. Abu-Bakr
      First page: 569
      Abstract: The nonlinear dynamics of charged cavitation bubbles are investigated theoretically and analytically in this study through the Rayleigh–Plesset model in dielectric liquids. The physical and mathematical situations consist of two models: the first one is noninteracting charged cavitation bubbles (like single cavitation bubble) and the second one is interacting charged cavitation bubbles. The proposed models are formulated and solved analytically based on the Plesset–Zwick technique. The study examines the behaviour of charged cavitation bubble growth processes under the influence of the polytropic exponent, the number of bubbles N, and the distance between the bubbles. From our analysis, it is observed that the radius of charged cavitation bubbles increases with increases in the distance between the bubbles, dimensionless phase transition criteria, and thermal diffusivity, and is inversely proportional to the polytropic exponent and the number of bubbles N. Additionally, it is evident that the growth process of charged cavitation bubbles is enhanced significantly when the number of bubbles is reduced. The electric charges and polytropic exponent weakens the growth process of charged bubbles in dielectric liquids. The obtained results are compared with experimental and theoretical previous works to validate the given solutions of the presented models of noninteraction and interparticle interaction of charged cavitation bubbles.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040569
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 570: Variable Selection in Data Analysis: A
           Synthetic Data Toolkit

    • Authors: Rohan Mitra, Eyad Ali, Dara Varam, Hana Sulieman, Firuz Kamalov
      First page: 570
      Abstract: Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs’ resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040570
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 571: Next–Generation Intrusion Detection
           for IoT EVCS: Integrating CNN, LSTM, and GRU Models

    • Authors: Dusmurod Kilichev, Dilmurod Turimov, Wooseong Kim
      First page: 571
      Abstract: In the evolving landscape of Internet of Things (IoT) and Industrial IoT (IIoT) security, novel and efficient intrusion detection systems (IDSs) are paramount. In this article, we present a groundbreaking approach to intrusion detection for IoT-based electric vehicle charging stations (EVCS), integrating the robust capabilities of convolutional neural network (CNN), long short-term memory (LSTM), and gated recurrent unit (GRU) models. The proposed framework leverages a comprehensive real-world cybersecurity dataset, specifically tailored for IoT and IIoT applications, to address the intricate challenges faced by IoT-based EVCS. We conducted extensive testing in both binary and multiclass scenarios. The results are remarkable, demonstrating a perfect 100% accuracy in binary classification, an impressive 97.44% accuracy in six-class classification, and 96.90% accuracy in fifteen-class classification, setting new benchmarks in the field. These achievements underscore the efficacy of the CNN-LSTM-GRU ensemble architecture in creating a resilient and adaptive IDS for IoT infrastructures. The ensemble algorithm, accessible via GitHub, represents a significant stride in fortifying IoT-based EVCS against a diverse array of cybersecurity threats.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040571
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 572: Evolution Equations with Liouville
           Derivative on R without Initial Conditions

    • Authors: Vladimir E. Fedorov, Nadezhda M. Skripka
      First page: 572
      Abstract: New classes of evolution differential equations with the Liouville derivative in Banach spaces are studied. Equations are considered on the whole real line and are not endowed by the initial conditions. Using the methods of the Fourier transform theory, we prove the unique solvability in the sense of classical solutions for the equation solved with respect to the Liouville fractional derivative with a bounded operator at the unknown function. This allows us to obtain the analogous result for the equation with a linear degenerate operator at the fractional derivative and with a spectrally bounded pair of operators. Abstract results are applied to obtain new results on the unique solvability of systems of ordinary differential equations, boundary problems to partial differential equations, and systems of equations.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040572
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 573: Supply Chain Inventory Management from
           the Perspective of “Cloud Supply Chain”—A Data Driven
           Approach

    • Authors: Yue Tan, Liyi Gu, Senyu Xu, Mingchao Li
      First page: 573
      Abstract: This study systematically investigates the pivotal role of inventory management within the framework of “cloud supply chain” operations, emphasizing the efficacy of leveraging machine learning methodologies for inventory allocation with the dual objectives of cost reduction and heightened customer satisfaction. Employing a rigorous data-driven approach, the research endeavors to address inventory allocation challenges inherent in the complex dynamics of a “cloud supply chain” through the implementation of a two-stage model. Initially, machine learning is harnessed for demand forecasting, subsequently refined through the empirical distribution of forecast errors, culminating in the optimization of inventory allocation across various service levels.The empirical evaluation draws upon data derived from a reputable home appliance logistics company in China, revealing that, under conditions of ample data, the application of data-driven methods for inventory allocation surpasses the performance of traditional methods across diverse supply chain structures. Specifically, there is an improvement in accuracy by approximately 13% in an independent structure and about 16% in a dependent structure. This study transcends the constraints associated with examining a singular node, adopting an innovative research perspective that intricately explores the interplay among multiple nodes while elucidating the nuanced considerations germane to supply chain structure. Furthermore, it underscores the methodological significance of relying on extensive, large-scale data. The investigation brings to light the substantial impact of supply chain structure on safety stock allocation. In the context of a market characterized by highly uncertain demand, the strategic adaptation of the supply chain structure emerges as a proactive measure to avert potential disruptions in the supply chain.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040573
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 574: An Assembly Sequence Planning Method
           Based on Multiple Optimal Solutions Genetic Algorithm

    • Authors: Xin Wan, Kun Liu, Weijian Qiu, Zhenhang Kang
      First page: 574
      Abstract: Assembly sequence planning (ASP) is an indispensable and important step in the intelligent assembly process, and aims to solve the optimal assembly sequence with the shortest assembly time as its optimization goal. This paper focuses on modular cabin construction for large cruise ships, tackling the complexities and challenges of part assembly during the process, based on real engineering problems. It introduces the multiple optimal solutions genetic algorithm (MOSGA). The MOSGA analyzes product constraints and establishes a mathematical model. Firstly, the traditional genetic algorithm (GA) is improved in the case of falling into the local optimum when facing complex problems, so that it can jump out of the local optimum under the condition of satisfying the processing constraints and achieve the global search effect. Secondly, the problem whereby the traditional search algorithm converges to the unique optimal solution is solved, and multiple unique optimal solutions that are more suitable for the actual assembly problem are solved. Thirdly, for a variety of restrictions and emergencies that may occur during the assembly process, the assembly sequence flexible planning (ASFP) method is introduced so that each assembly can be flexibly adjusted. Finally, an example is used to verify the feasibility and effectiveness of the method. This method improves the assembly efficiency and the diversity of assembly sequence selection, and can flexibly adjust the assembly sequence, which has important guiding significance for the ASP problem.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040574
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 575: A Comparative Study of Vehicle Velocity
           Prediction for Hybrid Electric Vehicles Based on a Neural Network

    • Authors: Pei Zhang, Wangda Lu, Changqing Du, Jie Hu, Fuwu Yan
      First page: 575
      Abstract: Vehicle velocity prediction (VVP) plays a pivotal role in determining the power demand of hybrid electric vehicles, which is crucial for establishing effective energy management strategies and, subsequently, improving the fuel economy. Neural networks (NNs) have emerged as a powerful tool for VVP, due to their robustness and non-linear mapping capabilities. This paper describes a comprehensive exploration of NN-based VVP methods employing both qualitative theory analysis and quantitative numerical simulations. The used methodology involved the extraction of key feature parameters for model inputs through the utilization of Pearson correlation coefficients and the random forest (RF) method. Subsequently, three distinct NN-based VVP models were constructed comprising the following: a backpropagation neural network (BPNN) model, a long short-term memory (LSTM) model, and a generative pre-training (GPT) model. Simulation experiments were conducted to investigate various factors, such as the feature parameters, sliding window length, and prediction horizon, and the prediction accuracy and computation time were identified as key performance metrics for VVP. Finally, the relationship between the model inputs and velocity prediction performance was revealed through various comparative analyses. This study not only facilitated the identification of an optimal NN model configuration to balance prediction accuracy and computation time, but also serves as a foundational step toward enhancing the energy efficiency of hybrid electric vehicles.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040575
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 576: Modeling Asymmetric Dependence Structure
           of Air Pollution Characteristics: A Vine Copula Approach

    • Authors: Mohd Sabri Ismail, Nurulkamal Masseran, Mohd Almie Alias, Sakhinah Abu Bakar
      First page: 576
      Abstract: Contaminated air is unhealthy for people to breathe and live in. To maintain the sustainability of clean air, air pollution must be analyzed and controlled, especially after unhealthy events. To do so, the characteristics of unhealthy events, namely intensity, duration, and severity are studied using multivariate modeling. In this study, the vine copula approach is selected to study the characteristics data. Vine copula is chosen here because it is more potent than the standard multivariate distributions, and multivariate copulas, especially in modeling the tails related to extreme events. Here, all nine different vine copulas are analyzed and compared based on model fitting and the comparison of models. In model fitting, the best model obtained is Rv123-Joint-MLE, a model with a root nodes sequence of 123, and optimized using the joint maximum likelihood. The components for the best model are the Tawn type 1 and Rotated Tawn type 1 180 degrees representing the pair copulas of (intensity, duration), and (intensity, severity), respectively, with the Survival Gumbel for the conditional pair copula of (duration, severity; intensity). Based on the best model, the tri-variate dependence structure of the intensity, duration, and severity relationship is positively correlated, skewed, and follows an asymmetric distribution. This indicates that the characteristic’s, including intensity, duration, and severity, tend to increase together. Using comparison tests, the best model is significantly different from others, whereas only two models are quite similar. This shows that the best model is well-fitted, compared to most models. Overall, this paper highlights the capability of vine copula in modeling the asymmetric dependence structure of air pollution characteristics, where the obtained model has a better potential to become a tool to assess the risks of extreme events in future work.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040576
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 577: Mathematical Modelling of Traction
           Equipment Parameters of Electric Cargo Trucks

    • Authors: Boris V. Malozyomov, Nikita V. Martyushev, Svetlana N. Sorokova, Egor A. Efremenkov, Denis V. Valuev, Mengxu Qi
      First page: 577
      Abstract: Electric vehicles are one of the most innovative and promising areas of the automotive industry. The efficiency of traction equipment is an important factor in the operation of an electric vehicle. In electric vehicles, the energy stored in the battery is converted into mechanical energy to drive the vehicle. The higher the efficiency of the battery, the less energy is lost in the conversion process, which improves the overall energy efficiency of the electric vehicle. Determining the performance characteristics of the traction battery of an electric vehicle plays an important role in the selection of the vehicle and its future operation. Using mathematical modelling, it is shown that battery capacity, charging rate, durability and efficiency are essential to ensure the comfortable and efficient operation of an electric vehicle throughout its lifetime. A mathematical model of an electric truck including a traction battery has been developed. It is shown that, with the help of the developed mathematical model, it is possible to calculate the load parameters of the battery in standardised driving cycles. The data verification is carried out by comparing the data obtained during standardised driving with the results of mathematical modelling.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040577
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 578: Two-Stage Estimation of Partially Linear
           Varying Coefficient Quantile Regression Model with Missing Data

    • Authors: Shuanghua Luo, Yuxin Yan, Cheng-yi Zhang
      First page: 578
      Abstract: In this paper, the statistical inference of the partially linear varying coefficient quantile regression model is studied under random missing responses. A two-stage estimation procedure is developed to estimate the parametric and nonparametric components involved in the model. Furthermore, the asymptotic properties of the estimators obtained are established under some mild regularity conditions. In addition, the empirical log-likelihood ratio statistic based on imputation is proposed, and it is proven that this statistic obeys the standard Chi-square distribution; thus, the empirical likelihood confidence interval of the parameter component of the model is constructed. Finally, simulation results show that the proposed estimation method is feasible and effective.
      Citation: Mathematics
      PubDate: 2024-02-14
      DOI: 10.3390/math12040578
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 579: Apriorics: Information and Graphs in the
           Description of the Fundamental Particles—A Mathematical Proof

    • Authors: Yakir Shoshani, Asher Yahalom
      First page: 579
      Abstract: In our earlier work, we suggested an axiomatic framework for deducing the fundamental entities which constitute the building block of the elementary particles in physics. The basic concept of this theory, named apriorics, is the ontological structure (OS)—an undirected simple graph satisfying specified conditions. The vertices of this graph represent the fundamental entities (FEs), its edges are binary compounds of the FEs (which are the fundamental bosons and fermions), and the structures constituting more than two connected vertices are composite particles. The objective of this paper is to focus the attention on several mathematical theorems and ideas associated with such graphs of order n, including their enumeration, showing what is the information content of apriorics.
      Citation: Mathematics
      PubDate: 2024-02-15
      DOI: 10.3390/math12040579
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 580: A Semiclassical Approach to the Nonlocal
           Nonlinear Schrödinger Equation with a Non-Hermitian Term

    • Authors: Anton E. Kulagin, Alexander V. Shapovalov
      First page: 580
      Abstract: The nonlinear Schrödinger equation (NLSE) with a non-Hermitian term is the model for various phenomena in nonlinear open quantum systems. We deal with the Cauchy problem for the nonlocal generalization of multidimensional NLSE with a non-Hermitian term. Using the ideas of the Maslov method, we propose the method of constructing asymptotic solutions to this equation within the framework of semiclassically concentrated states. The semiclassical nonlinear evolution operator and symmetry operators for the leading term of asymptotics are derived. Our approach is based on the solutions of the auxiliary dynamical system that effectively linearizes the problem under certain algebraic conditions. The formalism proposed is illustrated with the specific example of the NLSE with a non-Hermitian term that is the model of an atom laser. The analytical asymptotic solution to the Cauchy problem is obtained explicitly for this example.
      Citation: Mathematics
      PubDate: 2024-02-15
      DOI: 10.3390/math12040580
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 581: New Memory-Updating Methods in Two-Step
           Newton’s Variants for Solving Nonlinear Equations with High
           Efficiency Index

    • Authors: Chein-Shan Liu, Chih-Wen Chang
      First page: 581
      Abstract: In the paper, we iteratively solve a scalar nonlinear equation f(x)=0, where f∈C(I,R),x∈I⊂R, and I includes at least one real root r. Three novel two-step iterative schemes equipped with memory updating methods are developed; they are variants of the fixed-point Newton method. A triple data interpolation is carried out by the two-degree Newton polynomial, which is used to update the values of f′(r) and f′′(r). The relaxation factor in the supplementary variable is accelerated by imposing an extra condition on the interpolant. The new memory method (NMM) can raise the efficiency index (E.I.) significantly. We apply the NMM to five existing fourth-order iterative methods, and the computed order of convergence (COC) and E.I. are evaluated by numerical tests. When the relaxation factor acceleration technique is combined with the modified Dzˇunic´’s memory method, the value of E.I. is much larger than that predicted by the paper [Kung, H.T.; Traub, J.F. J. Assoc. Comput. Machinery 1974, 21]. for the iterative method without memory.
      Citation: Mathematics
      PubDate: 2024-02-15
      DOI: 10.3390/math12040581
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 582: Improving Risk Assessment Model for Cyber
           Security Using Robust Aggregation Operators for Bipolar Complex Fuzzy Soft
           Inference Systems

    • Authors: Zeeshan Ali, Miin-Shen Yang
      First page: 582
      Abstract: Improving a risk assessment technique for the problem of cyber security is required to modify the technique’s capability to identify, evaluate, assess, and mitigate potential cyber threats and ambiguities. The major theme of this paper is to find the best strategy to improve and refine the cyber security risk assessment model. For this, we compute some operational laws for bipolar complex fuzzy soft (BCFS) sets and then propose the BCFS weighted averaging (BCFSWA) operator, BCFS ordered weighted averaging (BCFSOWA) operator, BCFS weighted geometric (BCFSWG) operator, and BCFS ordered weighted geometric (BCFSOWG) operator. Furthermore, we give their properties, such as idempotency, monotonicity, and boundedness. Additionally, we improve the risk assessment technique for the cyber security model based on the proposed operators. We illustrate the technique of multi-attribute decision-making (MADM) problems for the derived operators based on BCFS information. Finally, we compare our ranking results with those of some existing operators for evaluating and addressing the supremacy, validity, and efficiency of these operators under BCFS information.
      Citation: Mathematics
      PubDate: 2024-02-15
      DOI: 10.3390/math12040582
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 583: Novel Robust Stability Criteria for
           Lur’e Systems with Time-Varying Delay

    • Authors: Wei Wang, Jinming Liang, Mihan Liu, Liming Ding, Hongbing Zeng
      First page: 583
      Abstract: This paper focuses on addressing the issue of absolute stability for uncertain Lur’e systems with time-varying delay using a delay-segmentation approach. The approach involves decomposing the delay interval into two distinct subintervals of unequal lengths. This allows for the introduction of a delay-segmentation-based augmented Lyapunov–Krasovskii functional that ensures piecewise continuity at the partition points. By selecting two sets of Lyapunov matrices for the time-varying delay in each interval, the obtained results are less conservative, providing a more accurate assessment of absolute stability. Finally, a numerical example is given to demonstrate the superiority of the delay-segmentation approach.
      Citation: Mathematics
      PubDate: 2024-02-15
      DOI: 10.3390/math12040583
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 584: The Computational Testing Procedure for
           the Comprehensive Lifetime Performance Index of Burr XII Products in
           Multiple Production Lines

    • Authors: Shu-Fei Wu, Pei-Hsuan Kuo, Wen-Shuenn Deng
      First page: 584
      Abstract: Extending from a single production line to multiple production lines, a comprehensive life performance index is proposed for evaluating the quality of lifetime products. The connection between the comprehensive lifetime performance index and the individual lifetime performance index is explored. For products with a lifetime following Burr XII distribution for the ith production line, the maximum likelihood estimation method and the corresponding asymptotic distribution for all lifetime performance indices are derived. Checking whether the comprehensive lifetime performance index has achieved the target value is essentially the same as testing whether each individual lifetime performance index has reached its corresponding target value. A testing procedure is proposed for a given significance level using the maximum likelihood estimator as the test statistic, and the power analysis is presented through graphical representations. For the power analysis, the impacts of sample size, the number of inspection intervals, the removal probability, the level of significance, and the number of production lines on the test power are analyzed, and the results show that there is a monotonic relationship between the test power and the above five impact factors. To illustrate how to apply the proposed testing procedure, we give one practical example with two production lines to test whether the comprehensive production process is capable.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040584
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 585: Transient Electromagnetic Monitoring of
           Permafrost: Mathematical Modeling Based on Sumudu Integral Transform and
           Artificial Neural Networks

    • Authors: Viacheslav Glinskikh, Oleg Nechaev, Igor Mikhaylov, Marina Nikitenko, Kirill Danilovskiy
      First page: 585
      Abstract: Due to the ongoing global warming on the Earth, permafrost degradation has been extensively taking place, which poses a substantial threat to civil and industrial facilities and infrastructure elements, as well as to the utilization of natural resources in the Arctic and high-latitude regions. In order to prevent the negative consequences of permafrost thawing under the foundations of constructions, various geophysical techniques for monitoring permafrost have been proposed and applied so far: temperature, electrical, seismic and many others. We propose a cross-borehole exploration system for a high localization of target objects in the cryolithozone. A novel mathematical apparatus for three-dimensional modeling of transient electromagnetic signals by the vector finite element method has been developed. The original combination of the latter, the Sumudu integral transform and artificial neural networks makes it possible to examine spatially heterogeneous objects of the cryolithozone with a high contrast of geoelectric parameters, significantly reducing computational costs. We consider numerical simulation results of the transient electromagnetic monitoring of industrial facilities located on permafrost. The formation of a talik has been shown to significantly manifest itself in the measured electromagnetic responses, which enables timely prevention of industrial disasters and environmental catastrophes.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040585
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 586: Conditional Optimization of Algorithms
           for Estimating Distributions of Solutions to Stochastic Differential
           Equations

    • Authors: Tatyana Averina
      First page: 586
      Abstract: This article discusses an alternative method for estimating marginal probability densities of the solution to stochastic differential equations (SDEs). Two algorithms for calculating the numerical–statistical projection estimate for distributions of solutions to SDEs using Legendre polynomials are proposed. The root-mean-square error of this estimate is studied as a function of the projection expansion length, while the step of a numerical method for solving SDE and the sample size for expansion coefficients are fixed. The proposed technique is successfully verified on three one-dimensional SDEs that have stationary solutions with given one-dimensional distributions and exponential correlation functions. A comparative analysis of the proposed method for calculating the numerical–statistical projection estimate and the method for constructing the histogram is carried out.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040586
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 587: Dynamic S-Box Construction Using Mordell
           Elliptic Curves over Galois Field and Its Applications in Image Encryption
           

    • Authors: Amal S. Alali, Rashad Ali, Muhammad Kamran Jamil, Javed Ali, Gulraiz
      First page: 587
      Abstract: Elliptic curve cryptography has gained attention due to its strong resilience against current cryptanalysis methods. Inspired by the increasing demand for reliable and secure cryptographic methods, our research investigates the relationship between complex mathematical structures and image encryption. A substitution box (S-box) is the single non-linear component of several well-known security systems. Mordell elliptic curves are used because of their special characteristics and the immense computational capacity of Galois fields. These S-boxes are dynamic, which adds a layer of complexity that raises the encryption process’s security considerably. We suggest an effective technique for creating S-boxes based on a class of elliptic curves over GF(2n),n≥8. We demonstrate our approach’s robustness against a range of cryptographic threats through thorough examination, highlighting its practical applicability. The assessment of resistance of the newly generated S-box to common attack methods including linear, differential, and algebraic attacks involves a thorough analysis. This analysis is conducted by quantifying various metrics such as non-linearity, linear approximation, strict avalanche, bit independence, and differential approximation to gauge the S-box’s robustness against these attacks. A recommended method for image encryption involves the use of built-in S-boxes to quickly perform pixel replacement and shuffling. To evaluate the efficiency of the proposed strategy, we employed various tests. The research holds relevance as it can provide alternative guidelines for image encryption, which could have wider consequences for the area of cryptography as a whole. We believe that our findings will contribute to the development of secure communication and data protection, as digital security is becoming increasingly important.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040587
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 588: Respiratory Motion Prediction with
           Empirical Mode Decomposition-Based Random Vector Functional Link

    • Authors: Asad Rasheed, Kalyana C. Veluvolu
      First page: 588
      Abstract: The precise prediction of tumor motion for radiotherapy has proven challenging due to the non-stationary nature of respiration-induced motion, frequently accompanied by unpredictable irregularities. Despite the availability of numerous prediction methods for respiratory motion prediction, the prediction errors they generate often suffer from large prediction horizons, intra-trace variabilities, and irregularities. To overcome these challenges, we have employed a hybrid method, which combines empirical mode decomposition (EMD) and random vector functional link (RVFL), referred to as EMD-RVFL. In the initial stage, EMD is used to decompose respiratory motion into interpretable intrinsic mode functions (IMFs) and residue. Subsequently, the RVFL network is trained for each obtained IMF and residue. Finally, the prediction results of all the IMFs and residue are summed up to obtain the final predicted output. We validated this proposed method on the benchmark datasets of 304 respiratory motion traces obtained from 31 patients for various prediction lengths, which are equivalent to the latencies of radiotherapy systems. In direct comparison with existing prediction techniques, our hybrid architecture consistently delivers a robust and highly accurate prediction performance. This proof-of-concept study indicates that the proposed approach is feasible and has the potential to improve the accuracy and effectiveness of radiotherapy treatment.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040588
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 589: Evaluating Infinite Series Involving
           Harmonic Numbers by Integration

    • Authors: Chunli Li, Wenchang Chu
      First page: 589
      Abstract: Eight infinite series involving harmonic-like numbers are coherently and systematically reviewed. They are evaluated in closed form exclusively by integration together with calculus and complex analysis. In particular, a mysterious series W is introduced and shown to be expressible in terms of the trilogarithm function. Several remarkable integral values and difficult infinite series identities are shown as consequences.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040589
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 590: Revisiting the Dynamics of Two-Body
           Problem in the Framework of the Continued Fraction Potential

    • Authors: Sergey Ershkov, Ghada F. Mohamdien, Mohammad Javed Idrisi, Elbaz I. Abouelmagd
      First page: 590
      Abstract: In this analytical study, a novel solving method for determining the precise coordinates of a mass point in orbit around a significantly more massive primary body, operating within the confines of the restricted two-body problem (R2BP), has been introduced. Such an approach entails the utilization of a continued fraction potential diverging from the conventional potential function used in Kepler’s formulation of the R2BP. Furthermore, a system of equations of motion has been successfully explored to identify an analytical means of representing the solution in polar coordinates. An analytical approach for obtaining the function t = t(r), incorporating an elliptic integral, is developed. Additionally, by establishing the inverse function r = r(t), further solutions can be extrapolated through quasi-periodic cycles. Consequently, the previously elusive restricted two-body problem (R2BP) with a continued fraction potential stands fully and analytically solved.
      Citation: Mathematics
      PubDate: 2024-02-16
      DOI: 10.3390/math12040590
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 591: A Non-Parametric Sequential Procedure for
           the Generalized Partition Problem

    • Authors: Tumulesh K. S. Solanky, Jie Zhou
      First page: 591
      Abstract: In selection and ranking, the partitioning of treatments by comparing them to a control treatment is an important statistical problem. For over eighty years, this problem has been investigated by a number of researchers via various statistical designs to specify the partitioning criteria and optimal strategies for data collection. Many researchers have proposed designs in order to generalize formulations known at that time. One such generalization adopted the indifference-zone formulation to designate the region between the boundaries for “good” and “bad” treatments as the indifference zone. Since then, this formulation has been adopted by a number of researchers to study various aspects of the partition problem. In this paper, a non-parametric purely sequential procedure is formulated for the partition problem. The “first-order” asymptotic properties of the proposed non-parametric procedure are derived. The performance of the proposed non-parametric procedure for small and moderate sample sizes is studied via Monte Carlo simulations. An example is provided to illustrate the proposed procedure.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040591
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 592: Collocation Technique Based on Chebyshev
           Polynomials to Solve Emden–Fowler-Type Singular Boundary Value
           Problems with Derivative Dependence

    • Authors: Shabanam Kumari, Arvind Kumar Singh, Utsav Gupta
      First page: 592
      Abstract: In this work, an innovative technique is presented to solve Emden–Fowler-type singular boundary value problems (SBVPs) with derivative dependence. These types of problems have significant applications in applied mathematics and astrophysics. Initially, the differential equation is transformed into a Fredholm integral equation, which is then converted into a system of nonlinear equations using the collocation technique based on Chebyshev polynomials. Subsequently, an iterative numerical approach, such as Newton’s method, is employed on the system of nonlinear equations to obtain an approximate solution. Error analysis is included to assess the accuracy of the obtained solutions and provide insights into the reliability of the numerical results. Furthermore, we graphically compare the residual errors of the current method to the previously established method for various examples.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040592
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 593: RHCA: Robust HCA via Consistent Revoting

    • Authors: Zijian Zhang, Kaiyu Feng, Xi Chen, Xuyang Liu, Haibo Sun
      First page: 593
      Abstract: Since the emergence of blockchain, how to improve its transaction throughput and reduce transaction latency has always been an important issue. Hostuff has introduced a pipeline mechanism and combined it with a chain structure to improve the performance of blockchain networks. HCA has introduced a revoting mechanism on the basis of Hostuff, further reducing transaction latency, but it has also brought some problems. In HCA, if the leader is malicious, it would be possible to continuously call on the replica nodes to revote, which can lead to network congestion. This paper employs the global perfect coin technology to guarantee that every replica can obtain a globally consistent and the freshest candidate proposal during the Revote phase, thereby improving the robustness of the HCA protocol. The performance improvement of RHCA in attack scenarios has been verified through experiments.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040593
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 594: Design of a Novel Chaotic Horse Herd
           Optimizer and Application to MPPT for Optimal Performance of Stand-Alone
           Solar PV Water Pumping Systems

    • Authors: Rabeh Abbassi, Salem Saidi
      First page: 594
      Abstract: A significant part of agricultural farms in the Kingdom of Saudi Arabia (KSA) are in off-grid sites where there is a lack of sufficient water supply despite its availability from groundwater resources in several regions of the country. Since abundant agricultural production is mainly dependent on water, farmers are forced to pump water using diesel generators. This investigation deals with the increase in the effectiveness of a solar photovoltaic water pumping system (SPVWPS). It investigated, from a distinct perspective, the nonlinear behavior of photovoltaic modules that affects the induction motor-pump because of the repeated transitions between the current and the voltage. A new chaotic Horse Herd Optimization (CHHO)-based Maximum Power Point Tracking technique (MPPT) is proposed. This algorithm integrates the capabilities of chaotic search methods to solve the model with a boost converter to maximize power harvest while managing the nonlinear and unpredictable dynamical loads. The analytical modeling for the proposed SPVWPS components and the implemented control strategies of the optimal duty cycle of the DC–DC chopper duty cycle and the Direct Torque Control (DTC) of the Induction Motor (IM) has been conducted. Otherwise, the discussions and evaluations of the proposed model performance in guaranteeing the maximum water flow rate and the operation at MPP of the SPVWPS under partial shading conditions (PSC) and changing weather conditions have been carried out. A comparative study with competitive algorithms was conducted, and the proposed control system’s accuracy and its significant appropriateness to improve the tracking ability for SPVWPS application have been proven in steady and dynamic operating climates and PSC conditions.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040594
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 595: Statistical Depth in Spatial Point
           Process

    • Authors: Xinyu Zhou, Wei Wu
      First page: 595
      Abstract: Statistical depth is widely used as a powerful tool to measure the center-outward rank of multivariate and functional data. Recent studies have introduced the notion of depth to the temporal point process, which exhibits randomness in the cardinality as well as distribution in the observed events. The proposed methods can well capture the rank of a point process in a given time interval, where a critical step is to measure the rank by using inter-arrival events. In this paper, we propose to extend the depth concept to multivariate spatial point process. In this case, the observed process is in a multi-dimensional location and there are no conventional inter-arrival events in the temporal process. We adopt the newly developed depth in metric space by defining two different metrics, namely the penalized metric and the smoothing metric, to fully explore the depth in the spatial point process. The mathematical properties and the large sample theory, as well as depth-based hypothesis testings, are thoroughly discussed. We then use several simulations to illustrate the effectiveness of the proposed depth method. Finally, we apply the new method in a real-world dataset and obtain desirable ranking performance.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040595
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 596: Incompatible Deformations in Hyperelastic
           Plates

    • Authors: Sergey Lychev, Alexander Digilov, Vladimir Bespalov, Nikolay Djuzhev
      First page: 596
      Abstract: The design of thin-walled structures is commonly based on the solutions of linear boundary-value problems, formulated within well-developed theories for elastic plates and shells. However, in modern appliances, especially in MEMS design, it is necessary to take into account non-linear mechanical effects that become decisive for flexible elements. Among the substantial non-linear effects that significantly change the deformation properties of thin plates are the effects of residual stresses caused by the incompatibility of deformations, which inevitably arise during the manufacture of ultrathin elements. The development of new methods of mathematical modeling of residual stresses and incompatible finite deformations in plates is the subject of this paper. To this end, the local unloading hypothesis is used. This makes it possible to define smooth fields of local deformations (inverse implant field) for the mathematical formalization of incompatibility. The main outcomes are field equations, natural boundary conditions and conservation laws, derived from the least action principle and variational symmetries taking account of the implant field. The derivations are carried out in the framework of elasticity theory for simple materials and, in addition, within Cosserat’s theory of a two-dimensional continuum. As illustrative examples, the distributions of incompatible deformations in a circular plate are considered.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040596
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 597: An Improved Fault Diagnosis Algorithm for
           Highly Scalable Data Center Networks

    • Authors: Wanling Lin, Xiao-Yan Li, Jou-Ming Chang, Xiangke Wang
      First page: 597
      Abstract: Fault detection and localization are vital for ensuring the stability of data center networks (DCNs). Specifically, adaptive fault diagnosis is deemed a fundamental technology in achieving the fault tolerance of systems. The highly scalable data center network (HSDC) is a promising structure of server-centric DCNs, as it exhibits the capacity for incremental scalability, coupled with the assurance of low cost and energy consumption, low diameter, and high bisection width. In this paper, we first determine that both the connectivity and diagnosability of the m-dimensional complete HSDC, denoted by HSDCm(m), are m. Further, we propose an efficient adaptive fault diagnosis algorithm to diagnose an HSDCm(m) within three test rounds, and at most N+4m(m−2) tests with m≥3 (resp. at most nine tests with m=2), where N=m·2m is the total number of nodes in HSDCm(m). Our experimental outcomes demonstrate that this diagnosis scheme of HSDC can achieve complete diagnosis and significantly reduce the number of required tests.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040597
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 598: A Privacy-Preserving Multilingual
           Comparable Corpus Construction Method in Internet of Things

    • Authors: Yu Weng, Shumin Dong, Chaomurilige Chaomurilige
      First page: 598
      Abstract: With the expansion of the Internet of Things (IoT) and artificial intelligence (AI) technologies, multilingual scenarios are gradually increasing, and applications based on multilingual resources are also on the rise. In this process, apart from the need for the construction of multilingual resources, privacy protection issues like data privacy leakage are increasingly highlighted. Comparable corpus is important in multilingual language information processing in IoT. However, the multilingual comparable corpus concerning privacy preserving is rare, so there is an urgent need to construct a multilingual corpus resource. This paper proposes a method for constructing a privacy-preserving multilingual comparable corpus, taking Chinese–Uighur–Tibetan IoT based news as an example, and mapping the different language texts to a unified language vector space to avoid sensitive information, then calculates the similarity between different language texts and serves as a comparability index to construct comparable relations. Through the decision-making mechanism of minimizing the impossibility, it can identify a comparable corpus pair of multilingual texts based on chapter size to realize the construction of a privacy-preserving Chinese–Uighur–Tibetan comparable corpus (CUTCC). Evaluation experiments demonstrate the effectiveness of our proposed provable method, which outperforms in accuracy rate by 77%, recall rate by 34% and F value by 47.17%. The CUTCC provides valuable privacy-preserving data resources support and language service for multilingual situations in IoT.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040598
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 599: Carbon Reduction Incentives under
           Multi-Market Interactions: Supply Chain Vertical Cooperation Perspective

    • Authors: Xiaohui Huang, Juan He, Lin Mao
      First page: 599
      Abstract: The greening trend in consumer markets and the marketization and financialization of carbon emission rights have begun to revitalize carbon assets. However, solitary efforts and the spillover of environmental protection effects still hamper enterprises’ enthusiasm for carbon emission reduction. To tackle this challenge, two vertical cooperation mechanisms, cost cooperation and alliance cooperation, are proposed. The mathematical models and solutions are developed for both of the two mechanisms, and their values and applicability are explored, respectively. In addition, the impact of fluctuations in consumer markets, financial markets, and carbon markets on cooperation is examined. The results show that both cooperation models effectively motivate enterprises to enhance carbon reduction and boost market demand. However, cost cooperation may result in inflated product prices and even weaken the profitability of the supply chain. In contrast, alliance cooperation can enhance product price performance and effectively increase supply chain profits. Concerning environmental performance, the initial market is better suited for alliance cooperation, whereas cost cooperation fits the mid-to-late market. The higher financing costs of the financial market and the trading price of the carbon market will strengthen the applicability of cost cooperation. This study offers managerial insights for collaborative decision-making in the context of a multi-market cross-section.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040599
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 600: High Dynamic Bipedal Robot with
           Underactuated Telescopic Straight Legs

    • Authors: Haiming Mou, Jun Tang, Jian Liu, Wenqiong Xu, Yunfeng Hou, Jianwei Zhang
      First page: 600
      Abstract: Bipedal robots have long been a focal point of robotics research with an unwavering emphasis on platform stability. Achieving stability necessitates meticulous design considerations at every stage, encompassing resilience against environmental disturbances and the inevitable wear associated with various tasks. In pursuit of these objectives, here, the bipedal L04 Robot is introduced. The L04 Robot employs a groundbreaking approach by compactly enclosing the hip joints in all directions and employing a coupled joint design. This innovative approach allows the robot to attain the traditional 6 degrees of freedom in the hip joint while using only four motors. This design not only enhances energy efficiency and battery life but also safeguards all vulnerable motor reducers. Moreover, the double-slider leg design enables the robot to simulate knee bending and leg height adjustment through leg extension. This simulation can be mathematically modeled as a linear inverted pendulum (LIP), rendering the L04 Robot a versatile platform for research into bipedal robot motion control. A dynamic analysis of the bipedal robot based on this structural innovation is conducted accordingly. The design of motion control laws for forward, backward, and lateral movements are also presented. Both simulation and physical experiments corroborate the excellent bipedal walking performance, affirming the stability and superior walking capabilities of the L04 Robot.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040600
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 601: Local Second Order Sobolev Regularity for
           p-Laplacian Equation in Semi-Simple Lie Group

    • Authors: Chengwei Yu, Yue Zeng
      First page: 601
      Abstract: In this paper, we establish a structural inequality of the ∞-subLaplacian ▵0,∞ in a class of the semi-simple Lie group endowed with the horizontal vector fields X1,⋯,X2n. When 1<p≤4 with n=1 and 1<p<3+1n−1 with n≥2, we apply the structural inequality to obtain the local horizontal W2,2-regularity of weak solutions to p-Laplacian equation in the semi-simple Lie group. Compared to Euclidean spaces R2n with n≥2, the range of this p obtained is already optimal.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040601
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 602: An ETD Method for Vulnerable American
           Options

    • Authors: Rafael Company, Vera N. Egorova, Lucas Jódar
      First page: 602
      Abstract: This paper introduces the exponential time differencing (ETD) technique as a numerical method to efficiently solve vulnerable American options pricing. We address several challenges, including removing cross-derivative terms through appropriate transformations, treating early-exercise opportunities using the penalty method, and substituting fixed boundary conditions with corresponding one-sided finite differences. The proposed method is shown to be both accurate and efficient through numerical experiments, which also compare the results with existing methods and analyze the numerical stability and convergence rate.
      Citation: Mathematics
      PubDate: 2024-02-17
      DOI: 10.3390/math12040602
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 603: Reliability of Partitioning Metric Space
           Data

    • Authors: Yariv N. Marmor, Emil Bashkansky
      First page: 603
      Abstract: The process of sorting or categorizing objects or information about these objects into clusters according to certain criteria is a fundamental procedure in data analysis. Where it is feasible to determine the distance metric for any pair of objects, the significance and reliability of the separation can be evaluated by calculating the separation/segregation power (SP) index proposed herein. The latter index is the ratio of the average inter distance to the average intra distance, independent of the scale parameter. Here, the calculated SP value is compared to its statistical distribution obtained by a simulation study for a given partition under the homogeneity null hypothesis to draw a conclusion using standard statistical procedures. The proposed concept is illustrated using three examples representing different types of objects under study. Some general considerations are given regarding the nature of the SP distribution under the null hypothesis and its dependence on the number of divisions and the amount of data within them. A detailed modus operandi (working method) for analyzing a metric data partition is also offered.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040603
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 604: Optimal Coloring Strategies for the Max
           k-Cut Game

    • Authors: Andrea Garuglieri, Dario Madeo, Chiara Mocenni, Giulia Palma, Simone Rinaldi
      First page: 604
      Abstract: We explore strong Nash equilibria in the max k-cut game on an undirected and unweighted graph with a set of k colors. Here, the vertices represent players, and the edges denote their relationships. Each player, v, selects a color as its strategy, and its payoff (or utility) is determined by the number of neighbors of v who have chosen a different color. Limited findings exist on the existence of strong equilibria in max k-cut games. In this paper, we make advancements in understanding the characteristics of strong equilibria. Specifically, our primary result demonstrates that optimal solutions are seven-robust equilibria. This implies that for a coalition of vertices to deviate and shift the system to a different configuration, i.e., a different coloring, a number of coalition vertices greater than seven is necessary. Then, we establish some properties of the minimal subsets concerning a robust deviation, revealing that each vertex within these subsets will deviate toward the color of one of its neighbors.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040604
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 605: Some Generalized Entropy Ergodic Theorems
           for Nonhomogeneous Hidden Markov Models

    • Authors: Qifeng Yao, Longsheng Cheng, Wenhe Chen, Ting Mao
      First page: 605
      Abstract: Entropy measures the randomness or uncertainty of a stochastic process, and the entropy rate refers to the limit of the time average of entropy. The generalized entropy rate in the form of delayed averages can overcome the redundancy of initial information while ensuring stationarity. Therefore, it has better practical value. A Hidden Markov Model (HMM) contains two stochastic processes, a stochastic process in which all states can be observed and a Markov chain in which all states cannot be observed. The entropy rate is an important characteristic of HMMs. The transition matrix of a homogeneous HMM is unique, while a Nonhomogeneous Hidden Markov Model (NHMM) requires the transition matrices to be dependent on time variables. From the perspective of model structure, NHMMs are novel extensions of homogeneous HMMs. In this paper, the concepts of the generalized entropy rate and NHMMs are defined and fully explained, a strong limit theorem and limit properties of a norm are presented, and then generalized entropy ergodic theorems with an almost surely convergence for NHMMs are obtained. These results provide concise formulas for the computation and estimation of the generalized entropy rate for NHMMs.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040605
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 606: Allocation of Starting Points in Global
           Optimization Problems

    • Authors: Oleg Khamisov, Eugene Semenkin, Vladimir Nelyub
      First page: 606
      Abstract: We propose new multistart techniques for finding good local solutions in global optimization problems. The objective function is assumed to be differentiable, and the feasible set is a convex compact set. The techniques are based on finding maximum distant points on the feasible set. A special global optimization problem is used to determine the maximum distant points. Preliminary computational results are given.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040606
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 607: Optimization of Magnetic Pump Impeller
           Based on Blade Load Curve and Internal Flow Study

    • Authors: Ruijie Zhang, Jiaqiong Wang, Wenfei Qian, Linlin Geng
      First page: 607
      Abstract: Compared to traditional centrifugal pumps, magnetic pumps are widely used in industries such as chemical, pharmaceutical, and petroleum due to their characteristics of leakage-free operation and the ability to transport toxic and corrosive fluids. However, the efficiency of magnetic pumps is relatively low. Improving the efficiency of pumps helps to reduce energy loss and lower industrial costs. In this study, a magnetic pump was chosen as the research subject. The study aims to improve the efficiency and stability of the magnetic pump by optimizing the impeller blades based on the load curve. A combined approach of a numerical simulation and experimental verification was used to investigate the impact of the anterior loading point (AL), posterior loading point (PL), and slope (SL) in the blade loading curve on the pump’s performance. The slope, which had the most significant impact on pump performance, was selected as the dependent variable to analyze the internal pressure pulsation and main shaft radial force of the magnetic pump. The research found that the hydraulic performance test results of the magnetic pump were in good agreement with the simulation results. When efficiency is used as the optimization objective, the anterior loading point should be moved as far back as possible, and the posterior loading point should be moved as far forward as possible. Through the study of internal pressure fluctuations and radial forces within the pump, the radial force distribution is sequentially as follows: the anterior loading method, posterior loading method, and middle loading method at a rated flow rate. The maximum pressure pulsation amplitude was near the volute casing diffuser area. Compared to the original pump, the optimized magnetic pump showed a 5.05% improvement in hydraulic efficiency under the rated conditions. This research contributes to enhancing the performance and efficiency of magnetic pumps, making them more suitable for various industrial applications.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040607
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 608: Stochastic Arbitrage Opportunities: Set
           Estimation and Statistical Testing

    • Authors: Stelios Arvanitis, Thierry Post
      First page: 608
      Abstract: We provide a formal statistical theory of consistent estimation of the set of all arbitrage portfolios that meet the description of being a stochastic arbitrage opportunity. Two empirical likelihood ratio tests are developed: one for the null that a given arbitrage portfolio is qualified, and another for the alternative that the portfolio is not qualified. Apart from considering generalized concepts and hypotheses based on multiple host portfolios, the statistical assumption framework is also more general than in earlier studies that focused on special cases with a single benchmark portfolio. Various extensions and generalizations of the theory are discussed. A Monte Carlo simulation study shows promising statistical size and power properties for testing the null, for representative data dimensions. The results of an empirical application illustrate the importance of selecting a proper blocking structure and moment estimation method.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040608
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 609: Toward Optimal Fitting Parameters for
           Multi-Exponential DWI Image Analysis of the Human Kidney: A Simulation
           Study Comparing Different Fitting Algorithms

    • Authors: Jonas Jasse, Hans-Joerg Wittsack, Thomas Andreas Thiel, Romans Zukovs, Birte Valentin, Gerald Antoch, Alexandra Ljimani
      First page: 609
      Abstract: In DWI, multi-exponential signal analysis can be used to determine signal underlying diffusion components. However, the approach is very complex due to the inherent low SNR, the limited number of signal decay data points, and the absence of appropriate acquisition parameters and standardized analysis methods. Within the scope of this work, different methods for multi-exponential analysis of the diffusion signal in the kidney were compared. To assess the impact of fitting parameters, a simulation was conducted comparing the free non-negative (NNLS) and rigid non-linear least square (NLLS) fitting methods. The simulation demonstrated improved accuracy for NNLS in combination with area-under-curve estimation. Furthermore, the accuracy and stability of the results were further enhanced utilizing optimized parameters, namely 350 logarithmically spaced diffusion coefficients within [0.7, 300] × 10−3 mm2/s and a minimal SNR of 100. The NNLS approach shows an improvement over the rigid NLLS method. This becomes apparent not only in terms of accuracy and omitting prior knowledge, but also in better representation of renal tissue physiology. By employing the determined fitting parameters, it is expected that more stable and reliable results for diffusion imaging in the kidney can be achieved. This might enable more accurate DWI results for clinical utilization.
      Citation: Mathematics
      PubDate: 2024-02-18
      DOI: 10.3390/math12040609
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 610: Single Machine Scheduling Proportionally
           Deteriorating Jobs with Ready Times Subject to the Total Weighted
           Completion Time Minimization

    • Authors: Zheng-Guo Lv, Li-Han Zhang, Xiao-Yuan Wang, Ji-Bo Wang
      First page: 610
      Abstract: In this paper, we investigate a single machine scheduling problem with a proportional job deterioration. Under release times (dates) of jobs, the objective is to minimize the total weighted completion time. For the general condition, some dominance properties, a lower bound and an upper bound are given, then a branch-and-bound algorithm is proposed. In addition, some meta-heuristic algorithms (including the tabu search (TS), simulated annealing (SA) and heuristic (NEH) algorithms) are proposed. Finally, experimental results are provided to compare the branch-and-bound algorithm and another three algorithms, which indicate that the branch-and-bound algorithm can solve instances of 40 jobs within a reasonable time and that the NEH and SA are more accurate than the TS.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040610
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 611: The Meaning and Accuracy of the Improving
           Functions in the Solution of the CBQR by Krotov’s Method

    • Authors: Ido Halperin
      First page: 611
      Abstract: A new solution to the continuous-time bilinear quadratic regulator optimal control problem (CBQR) was recently developed using Krotov’s Method. This paper provides two theoretical results related to the properties of that solution. The first discusses the equivalent representation of the cost-to-go performance index. The second one breaks down this equivalence into smaller identities referencing the components of the performance index. The paper shows how these results can be used to verify the numerical accuracy of the computed solution. Additionally, the meaning of the improving function and the equivalent representation, which are the main elements in the discussed CBQR’s solution, are explained according to the derived notions. A numerical example of structural control application exemplifies the significance of these results and how they can be applied to a specific CBQR problem.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040611
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 612: A Novel Chaotic System with Only
           Quadratic Nonlinearities: Analysis of Dynamical Properties and Stability

    • Authors: Othman Abdullah Almatroud, Karthikeyan Rajagopal, Viet-Thanh Pham, Giuseppe Grassi
      First page: 612
      Abstract: In nonlinear dynamics, there is a continuous exploration of introducing systems with evidence of chaotic behavior. The presence of nonlinearity within system equations is crucial, as it allows for the emergence of chaotic dynamics. Given that quadratic terms represent the simplest form of nonlinearity, our study focuses on introducing a novel chaotic system characterized by only quadratic nonlinearities. We conducted an extensive analysis of this system’s dynamical properties, encompassing the examination of equilibrium stability, bifurcation phenomena, Lyapunov analysis, and the system’s basin of attraction. Our investigations revealed the presence of eight unstable equilibria, the coexistence of symmetrical strange repeller(s), and the potential for multistability in the system.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040612
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 613: Golden Laplacian Graphs

    • Authors: Sadia Akhter, Mattia Frasca, Ernesto Estrada
      First page: 613
      Abstract: Many properties of the structure and dynamics of complex networks derive from the characteristics of the spectrum of the associated Laplacian matrix, specifically from the set of its eigenvalues. In this paper, we show that there exist graphs for which the ratio between the length of the spectrum (that is, the difference between the largest and smallest eigenvalues of the Laplacian matrix) and its spread (the difference between the second smallest eigenvalue and the smallest one) is equal to the golden ratio. We call such graphs Golden Laplacian Graphs (GLG). In this paper, we first find all such graphs with a number of nodes n≤10. We then prove several graph-theoretic and algebraic properties that characterize these graphs. These graphs prove to be extremely robust, as they have large vertex and edge connectivity along with a large isoperimetric constant. Finally, we study the synchronization properties of GLGs, showing that they are among the top synchronizable graphs of the same size. Therefore, GLGs represent very good candidates for engineering and communication networks.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040613
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 614: Study on Exchange Rate Forecasting with
           Stacked Optimization Based on a Learning Algorithm

    • Authors: Weiwei Xie, Haifeng Wu, Boyu Liu, Shengdong Mu, Nedjah Nadia
      First page: 614
      Abstract: The time series of exchange rate fluctuations are characterized by non-stationary and nonlinear features, and forecasting using traditional linear or single-machine models can cause significant bias. Based on this, the authors propose the combination of the advantages of the EMD and LSTM models to reduce the complexity by analyzing and decomposing the time series and forming a new model, EMD-LSTM-SVR, with a stronger generalization ability. More than 30,000 units of data on the USD/CNY exchange rate opening price from 2 January 2015 to 30 April 2022 were selected for an empirical demonstration of the model’s accuracy. The empirical results showed that the prediction of the exchange rate fluctuation with the EMD-LSTM-SVR model not only had higher accuracy, but also ensured that most of the predicted positions deviated less from the actual positions. The new model had a stronger generalization ability, a concise structure, and a high degree of ability to fit nonlinear features, and it prevented gradient vanishing and overfitting to achieve a higher degree of prediction accuracy.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040614
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 615: Image Steganography and Style
           Transformation Based on Generative Adversarial Network

    • Authors: Li Li, Xinpeng Zhang, Kejiang Chen, Guorui Feng, Deyang Wu, Weiming Zhang
      First page: 615
      Abstract: Traditional image steganography conceals secret messages in unprocessed natural images by modifying the pixel value, causing the obtained stego to be different from the original image in terms of the statistical distribution; thereby, it can be detected by a well-trained classifier for steganalysis. To ensure the steganography is imperceptible and in line with the trend of art images produced by Artificial-Intelligence-Generated Content (AIGC) becoming popular on social networks, this paper proposes to embed hidden information throughout the process of the generation of an art-style image by designing an image-style-transformation neural network with a steganography function. The proposed scheme takes a content image, an art-style image, and messages to be embedded as inputs, processing them with an encoder–decoder model, and finally, generates a styled image containing the secret messages at the same time. An adversarial training technique was applied to enhance the imperceptibility of the generated art-style stego image from plain-style-transferred images. The lack of the original cover image makes it difficult for the opponent learning steganalyzer to identify the stego. The proposed approach can successfully withstand existing steganalysis techniques and attain the embedding capacity of three bits per pixel for a color image, according to the experimental results.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040615
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 616: A Convolutional Neural Network-Based
           Auto-Segmentation Pipeline for Breast Cancer Imaging

    • Authors: Lucas Jian Hoong Leow, Abu Bakr Azam, Hong Qi Tan, Wen Long Nei, Qi Cao, Lihui Huang, Yuan Xie, Yiyu Cai
      First page: 616
      Abstract: Medical imaging is crucial for the detection and diagnosis of breast cancer. Artificial intelligence and computer vision have rapidly become popular in medical image analyses thanks to technological advancements. To improve the effectiveness and efficiency of medical diagnosis and treatment, significant efforts have been made in the literature on medical image processing, segmentation, volumetric analysis, and prediction. This paper is interested in the development of a prediction pipeline for breast cancer studies based on 3D computed tomography (CT) scans. Several algorithms were designed and integrated to classify the suitability of the CT slices. The selected slices from patients were then further processed in the pipeline. This was followed by data generalization and volume segmentation to reduce the computation complexity. The selected input data were fed into a 3D U-Net architecture in the pipeline for analysis and volumetric predictions of cancer tumors. Three types of U-Net models were designed and compared. The experimental results show that Model 1 of U-Net obtained the highest accuracy at 91.44% with the highest memory usage; Model 2 had the lowest memory usage with the lowest accuracy at 85.18%; and Model 3 achieved a balanced performance in accuracy and memory usage, which is a more suitable configuration for the developed pipeline.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040616
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 617: Novel
           Proportional–Integral–Derivative Control Framework on
           Continuous-Time Positive Systems Using Linear Programming

    • Authors: Qingbo Li, Xiaoyue Zhou, Fengyu Lin, Yahao Yang, Junfeng Zhang
      First page: 617
      Abstract: This paper considers the proportional–integral–derivative (PID) control for continuous-time positive systems. A three-stage strategy is introduced to design the PID controller. In the first stage, the proportional and integral components of the PID control are designed. A matrix decomposition approach is used to describe the gain matrices of the proportional and integral components. The positivity and stability of the closed-loop systems without the derivative component of PID control are achieved by the properties of a Metzler and Hurwitz matrix. In the second stage, a non-negative inverse matrix is constructed to maintain the Metzler and Hurwitz properties of the closed-loop system matrix in the first stage. To deal with the inverse of the derivative component of PID control, a matrix decomposition approach is further utilized to design a non-negative inverse matrix. Then, the derivative component is obtained by virtue of the designed inverse matrix. All the presented conditions can be solved by virtue of a linear programming approach. Furthermore, the three-stage PID design is developed for a state observer-based PID controller. Finally, a simulation example is provided to verify the effectiveness and validity of the proposed design.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040617
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 618: Poncelet Porisms and Loci of Centers in
           the Isotropic Plane

    • Authors: Ema Jurkin
      First page: 618
      Abstract: Any triangle in an isotropic plane has a circumcircle u and incircle i. It turns out that there are infinitely many triangles with the same circumcircle u and incircle i. This one-parameter family of triangles is called a poristic system of triangles. We study the trace of the centroid, the Feuerbach point, the symmedian point, the Gergonne point, the Steiner point and the Brocard points for such a system of triangles. We also study the traces of some further points associated with the triangles of the poristic family, and we prove that the vertices of the contact triangle, tangential triangle and anticomplementary triangle move on circles while the initial triangle traverses the poristic family.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040618
      Issue No: Vol. 12, No. 4 (2024)
       
  • Mathematics, Vol. 12, Pages 619: An Algorithm Based on Non-Negative Matrix
           Factorization for Detecting Communities in Networks

    • Authors: Chenze Huang, Ying Zhong
      First page: 619
      Abstract: Community structure is a significant characteristic of complex networks, and community detection has valuable applications in network structure analysis. Non-negative matrix factorization (NMF) is a key set of algorithms used to solve the community detection issue. Nevertheless, the localization of feature vectors in the adjacency matrix, which represents the characteristics of complex network structures, frequently leads to the failure of NMF-based approaches when the data matrix has a low density. This paper presents a novel algorithm for detecting sparse network communities using non-negative matrix factorization (NMF). The algorithm utilizes local feature vectors to represent the original network topological features and learns regularization matrices. The resulting feature matrices effectively reveal the global structure of the data matrix, demonstrating enhanced feature expression capabilities. The regularized data matrix resolves the issue of localized feature vectors caused by sparsity or noise, in contrast to the adjacency matrix. The approach has superior accuracy in detecting community structures compared to standard NMF-based community detection algorithms, as evidenced by experimental findings on both simulated and real-world networks.
      Citation: Mathematics
      PubDate: 2024-02-19
      DOI: 10.3390/math12040619
      Issue No: Vol. 12, No. 4 (2024)
       
 
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  Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (714 journals)
    - MATHEMATICS (GENERAL) (45 journals)
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MATHEMATICS (GENERAL) (45 journals)

Showing 1 - 35 of 35 Journals sorted alphabetically
Acta Universitatis Sapientiae, Mathematica     Open Access  
Algebra Letters     Open Access   (Followers: 1)
American Journal of Computational Mathematics     Open Access   (Followers: 4)
American Journal of Mathematics and Statistics     Open Access   (Followers: 9)
Annals of Global Analysis and Geometry     Hybrid Journal   (Followers: 2)
Archiv der Mathematik     Hybrid Journal  
Beiträge zur Algebra und Geometrie / Contributions to Algebra and Geometry     Partially Free   (Followers: 2)
Bulletin of the American Mathematical Society     Open Access   (Followers: 5)
Communications in Mathematics     Open Access  
Communications in Mathematics and Statistics     Hybrid Journal   (Followers: 3)
Conformal Geometry and Dynamics     Full-text available via subscription  
Difficoltà in Matematica     Full-text available via subscription  
Ergodic Theory and Dynamical Systems     Hybrid Journal   (Followers: 3)
International Journal of Applied Metaheuristic Computing     Full-text available via subscription   (Followers: 2)
International Journal of Computing Science and Mathematics     Hybrid Journal   (Followers: 1)
International Journal of Mathematics and Statistics     Full-text available via subscription   (Followers: 2)
Journal of Elliptic and Parabolic Equations     Hybrid Journal  
Journal of Mathematical Physics     Hybrid Journal   (Followers: 26)
Journal of Physics A : Mathematical and Theoretical     Hybrid Journal   (Followers: 23)
Journal of the American Mathematical Society AMS     Full-text available via subscription   (Followers: 6)
Jurnal Fourier     Open Access   (Followers: 1)
Mathematical Journal of Interdisciplinary Sciences     Open Access   (Followers: 1)
Mathematical Programming     Hybrid Journal   (Followers: 15)
Mathematics     Open Access   (Followers: 3)
Mathematics of Computation     Full-text available via subscription   (Followers: 6)
Mathematika     Full-text available via subscription  
Memoirs of the American Mathematical Society AMS     Full-text available via subscription   (Followers: 2)
Optimization: A Journal of Mathematical Programming and Operations Research     Hybrid Journal   (Followers: 6)
Pesquimat     Open Access  
Pro Mathematica     Open Access  
Proceedings of the American Mathematical Society AMS     Full-text available via subscription   (Followers: 4)
Representation Theory     Full-text available via subscription   (Followers: 1)
St. Petersburg Mathematical Journal     Full-text available via subscription   (Followers: 1)
Theoretical Mathematics & Applications     Open Access  
Transactions of the Moscow Mathematical Society     Full-text available via subscription   (Followers: 1)
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School of Mathematical and Computer Sciences
Heriot-Watt University
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