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Abstract: Abstract This paper explores advancements in multiscale computational models for understanding arterial mechanics and diseases. Arteries, as dynamic structures, must adapt to constant blood flow and pressure, with their layered composition playing a crucial role in maintaining functionality. Recent research highlights the importance of both macroscopic properties and microstructural elements, such as collagen fibers, elastin, smooth muscle cells, and the extracellular matrix. Multiscale modeling bridges these scales, providing insights into how microstructural changes influence arterial behavior under various conditions, including hypertension, atherosclerosis, and aneurysms. This paper emphasizes the utility of these models in simulating arterial conditions, predicting disease progression, and designing medical devices. Key challenges, such as computational complexity, biological integration, and the need for advanced imaging, are addressed alongside suggestions for future directions, including real-time simulations and nanoscale modeling. By combining biological and mechanical perspectives, multiscale approaches offer a comprehensive framework for advancing both scientific understanding and clinical applications in arterial health. PubDate: 2025-02-05 DOI: 10.1007/s11831-025-10241-8
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Abstract: Abstract Lung cancer remains a critical global health challenge, with its prognosis heavily dependent on the timing of diagnosis. This literature review critically examines Artificial Intelligence and Computer-Aided Diagnosis (CADx) systems for lung cancer detection using Computed Tomography (CT) images, guided by seven pivotal research questions. Adhering to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 standards and focusing on high-impact studies from 2013 to 2023, we provide an exhaustive assessment of current methodologies, underscore the variety and efficacy of algorithms and datasets, and evaluate preprocessing and performance evaluation strategies. Our findings reveal significant advancements in integrating machine learning and deep learning techniques, highlighting the importance of machine learning and deep learning methods and scrutinizing their goals, strengths, and limitations. Through a comprehensive meta-analysis, we offer insights into the state-of-the-art in lung cancer CADx, emphasizing data handling, model robustness, and avenues for enhancing diagnostic accuracy and reliability. This review not only critically relates varied methodologies and validates them against established metrics but also offers insights into future research trajectories aimed at enhancing early and accurate lung cancer diagnosis, thereby markedly improving patient outcomes. Targeting broad audiences, from experts in biomedical engineering to those across engineering and clinical sciences, we pave the way for future innovations in this vital domain. PubDate: 2025-02-03 DOI: 10.1007/s11831-025-10239-2
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Abstract: Abstract Anti-cancer peptides (ACPs) represent promising candidates for cancer therapy because they can target cancer cells selectively while leaving healthy cells unaffected. ACPs offer a multifaceted approach to cancer treatment by combining targeted cytotoxicity, immune system activation, and the potential to overcome drug resistance. Their development is aided by computational tools that expedite the discovery of promising candidates. As a result, they have received significant attention and broadly studied by many researchers. Currently, numerous peptide-based drugs are undergoing evaluation in preclinical and clinical trials. Accurately identifying ACPs has become a major focus of research, leading to the construction of diverse methods for their detection in silico. These methods implemented different training/testing datasets, classifiers, feature engineering, and feature selection techniques. Thus, it is indispensable to highlight the strengths and weaknesses of current methods and provide insights to improve novel computational tools for identification of ACPs. To address this, we conducted a comprehensive investigation of 26 available existing methods for ACPs, examining their feature engineering methods, classification learning algorithms, performance validation parameters, and availability of web servers. Subsequently, we performed a thorough performance assessment to examine the robustness of these studies using different benchmark datasets. Based on our findings, we offer potential strategies for enhancing model performance and effectiveness. PubDate: 2025-02-03 DOI: 10.1007/s11831-025-10237-4
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Abstract: Abstract This review paper examines the evolution of shape factors for the bearing capacity of shallow foundations, with a specific focus on rectangular and circular footings. Through a critical examination of methodologies from early empirical approaches to the sophisticated analyses enabled by recent technological advancements, this paper highlights the transformative impact of computational modeling on the field. Specifically, the review utilizes 3D finite element and finite difference analyses to validate and recalibrate shape factors against modern and reliable data. The quantitative findings confirm the reliability of the shape factors developed by Zhu and Michalowski in 2005 through classical finite element analysis in Abaqus. Their \({s}_{\gamma }\) factor, for example, was validated using Flac3D. Particularly notable is the finding that shape factors for circular footings can be effectively expressed by adjusting those for square footings using a simple geometric ratio, \(4/\pi \) . This adjustment, based on the perimeter or area ratios of the two shapes, suggests a more efficient approach that challenges the necessity for distinct shape factors for different footing types. Additionally, the review highlights historical gaps such as non-documented factors from early empirical research, limitations due to the scale effects of small-scale tests, and assumptions supporting shape factors derived from limit analysis. It also emphasizes that depending on the aspect ratio of the footing and the friction angle of the soil, the percentage error in bearing capacity calculations using non-acceptable shape factors, including those adopted by various design standards, could be several tens of percentage units. Additionally, the review identifies a gap in current research regarding large-scale experimental validation of these computational models, pointing to future directions in experimental research. PubDate: 2025-01-25 DOI: 10.1007/s11831-024-10184-6
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Abstract: Abstract Particle Swarm Optimization (PSO) is a key tool in Artificial Intelligence, is well-known to the public for its effectiveness in addressing complex and diverse problems. It possesses strong global search capabilities and robustness, serving as a powerful tool for problem-solving. PSO can handle multiple solutions simultaneously, accelerate problem-solving processes through parallel computing, and dynamically adjust search strategies based on the complexity and variability of problems, thereby adapting to different types of problems. As an efficient swarm intelligence-based algorithm, PSO has been a highly regarded Swarm Intelligence (SI) model since its establishment in 1995, undergoing numerous modifications and innovations to address various complex real-world problems. This article extensively investigates the variants and applications of PSO. Conducted based on a Systematic Review (SR) process, it delves deep into the research papers published in recent years, encompassing different algorithms, a wide range of application domains, potential issues, and future prospects. Specifically, this article reviews existing research methods and their applications, focusing on single-objective algorithms published from 2018 to the present, including but not limited to multiple swarms or multiple samples, learning mechanisms, hybrid algorithms, and their applications in various interdisciplinary fields such as mechanical engineering, civil engineering, power system, energy, and Internet of Things (IoT). Each paper contains practical guidance and inherent limitations, prompting discussions on their applications and outlining potential challenges of PSO, as well as guiding future research directions. PubDate: 2025-01-22 DOI: 10.1007/s11831-024-10185-5
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Abstract: Abstract This paper presents a comprehensive review of finite element methods (FEMs) applied to a diverse range of reaction-diffusion equations (RDEs). Beginning with a historical overview of FEMs, we then provide a summary of various FEMs, including standard Galerkin (both conforming and non-conforming), mixed Galerkin, discontinuous Galerkin, and weak Galerkin. Additionally, a priori and a posteriori error have been discussed for standard Galerkin. In further discussion related to RDEs, we provide insights into the evolution of these equations and their significance in various fields. We then systematically review these FEMs for solving different types of RDEs, including more recent advances pertaining to RDEs with nonlinear reaction terms, and advection reaction-diffusion equations. Finally, we briefly highlight the applications of machine learning and deep neural networks to FEM. PubDate: 2025-01-17 DOI: 10.1007/s11831-025-10222-x
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Abstract: Abstract This paper reviews modern approximate analytical methods for solving symmetric and non-symmetric dynamical problems, including the Perturbation Method using the Green function, the Regular Perturbation Method, the Adomian Decomposition Method, the Undetermined Coefficient Method, the Poincaré-Lindstedt Method, and Multiple-Scale Analysis. The applicability of each method is assessed based on its purpose, constraints, mathematical domain, and accessibility. Example applications demonstrate the solution process and the effectiveness of each method, with analytical solutions verified against numerical results for accuracy and stability. A comparison of the advantages, disadvantages, and suitable applications is presented in tabular form to aid in selecting the appropriate method for specific problems. Finally, this evaluation highlights future trends and potential applications in engineering and applied sciences. PubDate: 2025-01-17 DOI: 10.1007/s11831-025-10221-y
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Abstract: Abstract Cancer continues to be a primary cause of death worldwide, highlighting the critical need for early diagnosis methods. Automated, quick, and efficient technologies are critical to this endeavor, yet considerable gaps remain in this field. A comprehensive review was undertaken to examine seven cancer types characterized by elevated prevalence and mortality: lung, prostate, brain, skin, breast, leukemia, and colorectal cancer. The study aimed to reveal gaps in the existing research and compare traditional machine learning (TML) with deep learning (DL) methodologies, since such contrasts have been not much explored. A total of 320 publications were carefully chosen for study, including 150 that focused on TML methods and 170 that address DL techniques for the classification of cancer. Diverse parameters were used to assess these investigations, encompassing publication year, employed databases, data sample, classifier, modalities, and evaluation metrics. Separate evaluations were conducted for each cancer type and methodology, yielding 14 unique review tables. The assessment of each cancer type using ML/DL independently relied on four standard criteria: High performance (> 99%), Limited performance (< 85%), key findings, and key challenges. These studies were accompanied by a brief descriptive outline of the features, classifiers, public databases, and evaluation metrics that were utilized in the review process. The study concluded by offering general conclusions that highlighted the overall findings, overall challenges observed during the investigation. This thorough review seeks to improve clinical applications and guide future research initiatives in the persistent fight against cancer. PubDate: 2025-01-11 DOI: 10.1007/s11831-024-10219-y
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Abstract: Abstract With the rapid evolution of technologies like IoT (Internet of Things) and ML (Machine Learning), applications are becoming much smarter, thus giving more opportunity for the exploitation of various sectors. With such connectivity of devices giving rise to abundant amount of data thus boosting the deployment of Machine learning. Though Machine learning has found immense applicability in various sector, transportation is one such sectors that has attracted many researchers for transforming the paradigm of transportation into intelligence and enhancement. During the review, we considered the various layers of Machine learning in Transportation and reviewed them one by one. The application area of ML in transportation is reviewed thoroughly, discussing the recent advancements carried out in areas such as route optimization, logistics, accident detection, and many others. The purpose of the review is to present a self-contained critical review discussing every aspect of the deployment of the approach in Transportation. From the review, it is found that though ML is a strong aspect for the future revolution of transportation systems, there are some challenges, such as privacy concerns, that cannot be ignored and need good research to overcome these challenges. PubDate: 2025-01-09 DOI: 10.1007/s11831-024-10208-1
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Abstract: Abstract Cancer detection has long been a continuous key performer in oncological research. The revolution of artificial intelligence (AI) and its application in the field of cancer turned out to be more promising in the recent years. This paper provides a detailed review of the various aspects of AI in different cancers and their staging. The role of AI in interpreting and processing the imaging data, its accuracy and sensitivity to detect the tumors is examined. The images obtained through imaging modalities like MRI, CT, ultrasound etc. are considered in this review. Further the review highlights the implementation of AI algorithms in 12 types of cancers like breast cancer, prostate cancer, lung cancer etc. as discussed in the recent oncological studies. The review served to summarize the challenges involved with AI application. It revealed the efficacy of AI in detecting the region, size, and grade of cancer. While CT and ultrasound proved to be the ideal imaging modalities for cancer detection, MRI was helpful for cancer staging. The review bestows a roadmap to fully utilize the potential of AI in early cancer detection and staging to enhance patient survival. PubDate: 2025-01-08 DOI: 10.1007/s11831-024-10209-0
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Abstract: Abstract Implementation of 3D concrete printing technology has transformed the construction sector by providing enhanced design freedom, accelerated building timelines, and decreased material waste. This systematic study offers an in-depth review of current progress in 3D printed concrete, emphasizing materials, technological innovations, and environmental and economic factors. The research investigates several 3D printing methodologies, including extrusion-based and powder-based processes, and analyses the impact of materials such as fibre-reinforced, geopolymers, and high-strength concrete on the mechanical characteristics and workability of printed structures. Critical technological issues, such as layer adhesion, rheological properties, and printability, are examined to ascertain existing constraints and prospective research avenues. The analysis underscores case examples that demonstrate the actual uses of 3DCP, including buildings, bridges, and ornamental features, highlighting the technology’s capacity to decrease building expenses and environmental effects. The future potential of 3D concrete printing in large-scale building projects is discussed along with suggestions for the advancement of materials and printing methodologies to enhance performance and sustainability. PubDate: 2025-01-08 DOI: 10.1007/s11831-024-10220-5
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Abstract: Abstract Model Order Reduction (MOR) techniques play a crucial role in reducing the computational complexity of high-dimensional mathematical models, enabling efficient simulations and analysis. In recent years, Artificial Intelligence (AI) has emerged as a powerful tool in various domains, including MOR. This survey paper provides an overview of AI-based MOR techniques, exploring how AI methods are being integrated into traditional MOR approaches. Different AI algorithms, such as machine learning, deep learning, and evolutionary computing, and their applications in MOR are discussed in this paper. The advantages, challenges, and future directions of AI-based MOR techniques are also highlighted. PubDate: 2025-01-06 DOI: 10.1007/s11831-024-10207-2
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Abstract: Abstract Maximum power point tracking (MPPT) is an essential technique used to extract the maximum power from a photovoltaic (PV) system. Fuzzy logic-based control is one of the popular methods used for MPPT because it provides excellent performance under varying environmental conditions. The world is now facing a challenge in terms of energy. Solar energy is a key to solve that issue, so it must be optimized. Among the MPPT optimization methods used to improve the photovoltaic modules efficiency, fuzzy logic control (FLC) seems to be the one that is really adapted. However, it has a lot of drawbacks like the complexity of implementation and its performance depends not only on the chosen error, but also on the established inference rules. To solve these problems this method has been modified in various ways. The present work makes a diversified review of MPPT algorithms using fuzzy logic control for PV applications. It is subdivided into three main parts. The first part deals with modified FLC algorithms. The second part deals with FLC algorithms associated with other classical algorithms and the third with MPPT algorithms associated with intelligent methods. The different works analyzed have tested their innovative approaches by simulation and have for the most part validated them by an. It can be noted that the third category is the one that offers a better increase in efficiency even if it has a higher complexity. The second category is more suitable for variable weather conditions and the first one is recommended especially for its very low cost. The suggested asymmetrical fuzzy logic-based MPPT technique uses an asymmetric membership function and a rule-based controller to improve the tracking accuracy and speed. The performance of the suggested technique was evaluated and compared with two existing MPPT techniques. The evaluation was conducted through simulations with MATLAB/Simulink. Overall, the results suggest that the proposed asymmetrical fuzzy logic-based MPPT technique is a promising approach for improving the speed and tracking accuracy MPPT in photovoltaic systems. PubDate: 2025-01-04 DOI: 10.1007/s11831-024-10210-7
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Abstract: Abstract This study provides an in-depth review and analysis of the nature-inspired Sand Cat Swarm Optimization (SCSO) algorithm. The SCSO algorithm effectively focuses on exploring solution areas inspired by sand cat hearing and finding the most suitable solutions for their hunting behavior. This algorithm is easily adaptable to various problems due to its stability, low-cost, flexibility, simple implementation, simplicity, derivative-free mechanism, and reasonable computation time. For these reasons, although it was published recently, it has begun to attract the attention of researchers. SCSO-based research has been presented in prestigious international journals such as Elsevier, Springer, MDPI, and IEEE since its inception in 2022. The studies cited in this paper are examined in three categories: improved, hybrid, and adapted. Research trends show that 39, 21, and 40% of SCSO-based studies fall into these three categories, respectively. Additionally, research on solving various problems inspired by the SCSO algorithm is discussed from two different perspectives: global optimizations and real-world applications. Analysis of the applications shows that 15 and 85% of the studies belong to these two fields, respectively. PubDate: 2025-01-03 DOI: 10.1007/s11831-024-10217-0
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Abstract: Abstract As researchers sought for new methods to decrease noxious emissions and improve engine performance, they discovered biodiesel as a promising biofuel. However, traditional study methodologies were deemed inadequate, prompting the need for computational methods to offer numerical solutions. This approach was seen as a creative and practical solution to the problem at hand. In response to the limitations of conventional modeling approaches, researchers turned towards the innovative solution of using machine-learning techniques as data processing systems. This creative approach has proven effective in addressing a broad variety of technical and scientific concerns, particularly in fields where traditional modeling approaches have fallen short of expectations. This review discusses using machine learning algorithms for predicting biodiesel performance and emissions with nanoparticles. Researchers have solved these problems with the application of machine learning to anticipate engine efficiency and emissions. The machine-learning algorithm predicts engine performance very precisely, proving its efficacy. Nanotechnology and biodiesel engine technologies are quickly advancing, making this review vital. Previous studies have examined nanoparticles' influence on engine performance and emissions. This review uniquely focuses on the application of machine learning techniques. Through the utilization of machine-learning algorithms, it is possible for gaining deeper understanding of intricate connections existing between the properties of nanoparticles and the behavior of engines. This methodology provides extensive comprehension of an impact of nanoparticles upon performance and emissions of biodiesel engines, hence enabling a development of more effectual and sustainable engine designs. PubDate: 2025-01-01 DOI: 10.1007/s11831-024-10144-0
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Abstract: Abstract There is a chromosomal defect that significantly affects an individual’s life is Down syndrome. Early identification of Down syndrome is crucial for an accurate assessment of the fetus. The process of assess the fetus includes measurement of the crown rump length, fetal heart rate, short arm or thighs bones length, nasal bone present or absent and the thickness of fluid behind neck. And the process are done during first and second trimester of pregnancy. Various invasive and noninvasive screenings are used for Down syndrome diagnosis. Research on diagnosing Down syndrome has been extensively documented. Additionally, this survey includes various techniques using deep learning for detecting the availability of Down Syndrome and does analysis of image processing methods and formulas for its diagnosis. PubDate: 2025-01-01 DOI: 10.1007/s11831-024-10158-8
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Abstract: Abstract Recently, the mechanical performance of various mechanical, electrical, and civil structures, including static and dynamic analysis, has been widely studied. Due to the neuroma's advanced technology in various engineering fields and applications, developing small-size structures has become highly demanded for several structural geometries. One of the most important is the nano/micro-plate structure. However, the essential nature of highly lightweight material with extraordinary mechanical, electrical, physical, and material characterizations makes researchers more interested in developing composite/laminated-composite-plate structures. To comprehend the dynamical behavior, precisely the linear/nonlinear-free vibrational responses, and to represent the enhancement of several parameters such as nonlocal, geometry, boundary condition parameters, etc., on the free vibrational performance at nano/micro scale size, it is revealed that to employ all various parameters into various mathematical equations and to solve the defined governing equations by analytical, numerical, high order, and mixed solutions. Thus, the presented literature review is considered the first work focused on investigating the linear/nonlinear free vibrational behavior of plates on a small scale and the impact of various parameters on both dimensional/dimensionless natural/fundamental frequency and Eigen-value. The literature is classified based on solution type and with/without considering the size dependency effect. As a key finding, most research in the literature implemented analytical or numerical solutions. The drawback of classical plate theory can be overcome by utilizing and developing the elasticity theories. The nonlocality, weight fraction of porosity, or the reinforcements, and its distribution type of elastic foundation significantly influence the frequencies. PubDate: 2025-01-01 DOI: 10.1007/s11831-024-10132-4
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Abstract: Abstract In the last few decades, metaheuristic algorithms that use the laws of nature have been used dramatically in numerous and complex optimization problems. The artificial hummingbird algorithm (AHA) is one of the metaheuristic algorithms that was invented in 2022 based on the foraging and migration behavior of the hummingbird for modeling and solving optimization problems. The algorithm initially starts with an initial random population of solutions. It then uses iterative processes and hummingbird position updates to balance exploration and exploitation toward the most optimal solutions. This paper has a detailed and extensive review of the AHA algorithm considering the aspects of hybrid, improved, binary, multi-objective, and optimization problems. In addition, a wide range of applications of AHA in various fields such as feature selection, image processing, scheduling, Internet of Things, classification, clustering, financial and economic issues, forecasting, wireless sensor networks, and many engineering challenges are explored. The statistical and numerical results showed that the AHA algorithm with deep learning methods, Levy flight, and opposition-based learning had the best performance. Also, the AHA algorithm is most widely used in solving multimodal optimization problems and continuous functions. PubDate: 2025-01-01 DOI: 10.1007/s11831-024-10135-1
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Abstract: Abstract Breast Cancer Disease is identified as one of the prime causes of death in women around the globe standing next to lung cancer. Breast cancer represents the development of malignant neoplasm from the breast cells. This breast cancer can be treated when it is identified at an early stage. Several researchers have contributed different machine learning approaches for maximizing the accuracy during the process of predicting breast cancer. Optimization of selected features is another important step essential for attaining maximized accuracy during the process of detection during the use of Artificial Neural Network. The utilization of optimization algorithm also helps in fine-tuning the hyperparameters of ANN such that the process of classification can be achieved with better precision and less computational time. In this paper, a Review on Swarm Intelligent metaheuristic optimization algorithms-based Artificial Neural Network-based Breast Cancer Diagnosis Schemes is presented for comparing different approaches depending on their efficacy in achieving the classification process. It presents the potentiality of wrapper and filter methods generally used for classifying cancer cells from normal cells. This review specifically concentrates on highlighting the significance of the swarm intelligent algorithms-based optimized ANN models which are contributed with its limitations. This review also demonstrates the future scope of research which could be concentrated from the identified extract of the literature. This review also highlighted the different kinds of evaluation metrics considered for assessing the potentiality of the existing ANN-based Breast Cancer Diagnosis Schemes with its need in utilization during evaluation. PubDate: 2025-01-01 DOI: 10.1007/s11831-024-10142-2
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