Publisher: Hindawi   (Total: 343 journals)

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Showing 1 - 200 of 343 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 8, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 51, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 67)
Advances in Agriculture     Open Access   (Followers: 12)
Advances in Artificial Intelligence     Open Access   (Followers: 22)
Advances in Astronomy     Open Access   (Followers: 51, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 20, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 11)
Advances in Chemistry     Open Access   (Followers: 35)
Advances in Civil Engineering     Open Access   (Followers: 51, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 8)
Advances in Condensed Matter Physics     Open Access   (Followers: 11, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 4, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 52)
Advances in Electronics     Open Access   (Followers: 101)
Advances in Emergency Medicine     Open Access   (Followers: 16)
Advances in Endocrinology     Open Access   (Followers: 6)
Advances in Environmental Chemistry     Open Access   (Followers: 10)
Advances in Epidemiology     Open Access   (Followers: 9)
Advances in Fuzzy Systems     Open Access   (Followers: 5, SJR: 0.161, CiteScore: 1)
Advances in Geology     Open Access   (Followers: 19)
Advances in Geriatrics     Open Access   (Followers: 6)
Advances in Hematology     Open Access   (Followers: 13, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 3)
Advances in High Energy Physics     Open Access   (Followers: 26, SJR: 0.866, CiteScore: 2)
Advances in Human-Computer Interaction     Open Access   (Followers: 21, SJR: 0.186, CiteScore: 1)
Advances in Materials Science and Engineering     Open Access   (Followers: 31, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 9, SJR: 0.218, CiteScore: 1)
Advances in Medicine     Open Access   (Followers: 3)
Advances in Meteorology     Open Access   (Followers: 24, SJR: 0.48, CiteScore: 1)
Advances in Multimedia     Open Access   (Followers: 1, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 7)
Advances in Numerical Analysis     Open Access   (Followers: 9)
Advances in Nursing     Open Access   (Followers: 37)
Advances in Operations Research     Open Access   (Followers: 13, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 9)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 11, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological and Pharmaceutical Sciences     Open Access   (Followers: 9, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 13, SJR: 0.179, CiteScore: 1)
Advances in Polymer Technology     Open Access   (Followers: 14, SJR: 0.299, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 44, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 28)
Advances in Regenerative Medicine     Open Access   (Followers: 4)
Advances in Software Engineering     Open Access   (Followers: 11)
Advances in Statistics     Open Access   (Followers: 10)
Advances in Toxicology     Open Access   (Followers: 4)
Advances in Tribology     Open Access   (Followers: 15, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 13, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 8, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 2, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 3, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 4)
Anemia     Open Access   (Followers: 6, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 15, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 20, SJR: 0.451, CiteScore: 1)
Applied Bionics and Biomechanics     Open Access   (Followers: 7, SJR: 0.288, CiteScore: 1)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 15)
Archaea     Open Access   (Followers: 4, SJR: 0.852, CiteScore: 2)
Autism Research and Treatment     Open Access   (Followers: 36)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 9, SJR: 0.786, CiteScore: 2)
Biochemistry Research Intl.     Open Access   (Followers: 6, SJR: 0.437, CiteScore: 2)
Bioinorganic Chemistry and Applications     Open Access   (Followers: 11, SJR: 0.419, CiteScore: 2)
BioMed Research Intl.     Open Access   (Followers: 5, SJR: 0.935, CiteScore: 3)
Biotechnology Research Intl.     Open Access   (Followers: 1)
Bone Marrow Research     Open Access   (Followers: 2, SJR: 0.531, CiteScore: 1)
Canadian J. of Gastroenterology & Hepatology     Open Access   (Followers: 4, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 8, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 3, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 11, SJR: 1.237, CiteScore: 4)
Cardiovascular Therapeutics     Open Access   (Followers: 2, SJR: 1.075, CiteScore: 2)
Case Reports in Anesthesiology     Open Access   (Followers: 11)
Case Reports in Cardiology     Open Access   (Followers: 8, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 12)
Case Reports in Dentistry     Open Access   (Followers: 8, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 19)
Case Reports in Endocrinology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 2)
Case Reports in Hematology     Open Access   (Followers: 9)
Case Reports in Hepatology     Open Access   (Followers: 2)
Case Reports in Immunology     Open Access   (Followers: 6)
Case Reports in Infectious Diseases     Open Access   (Followers: 6)
Case Reports in Medicine     Open Access   (Followers: 3)
Case Reports in Nephrology     Open Access   (Followers: 5)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 11)
Case Reports in Oncological Medicine     Open Access   (Followers: 2, SJR: 0.204, CiteScore: 1)
Case Reports in Ophthalmological Medicine     Open Access   (Followers: 3)
Case Reports in Orthopedics     Open Access   (Followers: 6)
Case Reports in Otolaryngology     Open Access   (Followers: 7)
Case Reports in Pathology     Open Access   (Followers: 7)
Case Reports in Pediatrics     Open Access   (Followers: 8)
Case Reports in Psychiatry     Open Access   (Followers: 18)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 12)
Case Reports in Rheumatology     Open Access   (Followers: 10)
Case Reports in Surgery     Open Access   (Followers: 12)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 12)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Child Development Research     Open Access   (Followers: 21, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Chromatography Research Intl.     Open Access   (Followers: 5)
Complexity     Hybrid Journal   (Followers: 8, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Biology J.     Open Access   (Followers: 7)
Computational Intelligence and Neuroscience     Open Access   (Followers: 15, SJR: 0.326, CiteScore: 1)
Concepts in Magnetic Resonance Part A     Open Access   (Followers: 1, SJR: 0.354, CiteScore: 1)
Concepts in Magnetic Resonance Part B, Magnetic Resonance Engineering     Open Access   (Followers: 1, SJR: 0.26, CiteScore: 1)
Conference Papers in Science     Open Access   (Followers: 2)
Contrast Media & Molecular Imaging     Open Access   (Followers: 2, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 13, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 10, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 19, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 4, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 6, SJR: 0.279, CiteScore: 1)
Disease Markers     Open Access   (Followers: 1, SJR: 0.9, CiteScore: 2)
Economics Research Intl.     Open Access   (Followers: 1)
Education Research Intl.     Open Access   (Followers: 19)
Emergency Medicine Intl.     Open Access   (Followers: 9, SJR: 0.298, CiteScore: 1)
Enzyme Research     Open Access   (Followers: 5, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 30, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 1, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 5, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
Heteroatom Chemistry     Open Access   (Followers: 3, SJR: 0.333, CiteScore: 1)
HPB Surgery     Open Access   (Followers: 9, SJR: 0.824, CiteScore: 2)
Infectious Diseases in Obstetrics and Gynecology     Open Access   (Followers: 5, SJR: 1.27, CiteScore: 2)
Interdisciplinary Perspectives on Infectious Diseases     Open Access   (Followers: 1, SJR: 0.627, CiteScore: 2)
Intl. J. of Aerospace Engineering     Open Access   (Followers: 81, SJR: 0.232, CiteScore: 1)
Intl. J. of Agronomy     Open Access   (Followers: 6, SJR: 0.311, CiteScore: 1)
Intl. J. of Alzheimer's Disease     Open Access   (Followers: 12, SJR: 0.787, CiteScore: 3)
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 13, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 3)
Intl. J. of Biomaterials     Open Access   (Followers: 5, SJR: 0.511, CiteScore: 2)
Intl. J. of Biomedical Imaging     Open Access   (Followers: 3, SJR: 0.501, CiteScore: 2)
Intl. J. of Breast Cancer     Open Access   (Followers: 14, SJR: 1.025, CiteScore: 2)
Intl. J. of Cell Biology     Open Access   (Followers: 4, SJR: 1.887, CiteScore: 4)
Intl. J. of Chemical Engineering     Open Access   (Followers: 8, SJR: 0.327, CiteScore: 1)
Intl. J. of Chronic Diseases     Open Access   (Followers: 1)
Intl. J. of Combinatorics     Open Access   (Followers: 1)
Intl. J. of Computer Games Technology     Open Access   (Followers: 10, SJR: 0.287, CiteScore: 2)
Intl. J. of Corrosion     Open Access   (Followers: 11, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 8, SJR: 0.649, CiteScore: 2)
Intl. J. of Differential Equations     Open Access   (Followers: 8, SJR: 0.191, CiteScore: 0)
Intl. J. of Digital Multimedia Broadcasting     Open Access   (Followers: 5, SJR: 0.296, CiteScore: 2)
Intl. J. of Electrochemistry     Open Access   (Followers: 10)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 7)
Intl. J. of Food Science     Open Access   (Followers: 5, SJR: 0.44, CiteScore: 2)
Intl. J. of Forestry Research     Open Access   (Followers: 3, SJR: 0.373, CiteScore: 1)
Intl. J. of Genomics     Open Access   (Followers: 2, SJR: 0.868, CiteScore: 3)
Intl. J. of Geophysics     Open Access   (Followers: 5, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 4, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 8, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 4)
Intl. J. of Manufacturing Engineering     Open Access   (Followers: 2)
Intl. J. of Mathematics and Mathematical Sciences     Open Access   (Followers: 3, SJR: 0.177, CiteScore: 0)
Intl. J. of Medicinal Chemistry     Open Access   (Followers: 6, SJR: 0.31, CiteScore: 1)
Intl. J. of Metals     Open Access   (Followers: 7)
Intl. J. of Microbiology     Open Access   (Followers: 8, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 6, SJR: 0.136, CiteScore: 1)
Intl. J. of Navigation and Observation     Open Access   (Followers: 20, SJR: 0.267, CiteScore: 2)
Intl. J. of Nephrology     Open Access   (Followers: 2, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 8)
Intl. J. of Optics     Open Access   (Followers: 10, SJR: 0.231, CiteScore: 1)
Intl. J. of Otolaryngology     Open Access   (Followers: 3)
Intl. J. of Partial Differential Equations     Open Access   (Followers: 2)
Intl. J. of Pediatrics     Open Access   (Followers: 6)
Intl. J. of Peptides     Open Access   (Followers: 2, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 3, SJR: 0.341, CiteScore: 1)
Intl. J. of Plant Genomics     Open Access   (Followers: 4, SJR: 0.583, CiteScore: 1)
Intl. J. of Polymer Science     Open Access   (Followers: 28, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 4)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 17)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 6)
Intl. J. of Rheumatology     Open Access   (Followers: 4, SJR: 0.645, CiteScore: 2)
Intl. J. of Rotating Machinery     Open Access   (Followers: 2, SJR: 0.193, CiteScore: 1)
Intl. J. of Spectroscopy     Open Access   (Followers: 8)
Intl. J. of Stochastic Analysis     Open Access   (Followers: 3, SJR: 0.279, CiteScore: 1)
Intl. J. of Surgical Oncology     Open Access   (Followers: 1, SJR: 0.573, CiteScore: 2)
Intl. J. of Telemedicine and Applications     Open Access   (Followers: 7, SJR: 0.403, CiteScore: 2)
Intl. J. of Vascular Medicine     Open Access   (SJR: 0.782, CiteScore: 2)
Intl. J. of Zoology     Open Access   (Followers: 2, SJR: 0.209, CiteScore: 1)
Intl. Scholarly Research Notices     Open Access   (Followers: 230)

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Similar Journals
Journal Cover
Complexity
Journal Prestige (SJR): 0.531
Citation Impact (citeScore): 2
Number of Followers: 8  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1076-2787 - ISSN (Online) 1099-0526
Published by Hindawi Homepage  [343 journals]
  • Hamilton Connectivity of Convex Polytopes with Applications to Their
           Detour Index
    • Abstract: A connected graph is called Hamilton-connected if there exists a Hamiltonian path between any pair of its vertices. Determining whether a graph is Hamilton-connected is an NP-complete problem. Hamiltonian and Hamilton-connected graphs have diverse applications in computer science and electrical engineering. The detour index of a graph is defined to be the sum of lengths of detours between all the unordered pairs of vertices. The detour index has diverse applications in chemistry. Computing the detour index for a graph is also an NP-complete problem. In this paper, we study the Hamilton-connectivity of convex polytopes. We construct three infinite families of convex polytopes and show that they are Hamilton-connected. An infinite family of non-Hamilton-connected convex polytopes is also constructed, which, in turn, shows that not all convex polytopes are Hamilton-connected. By using Hamilton connectivity of these families of graphs, we compute exact analytical formulas of their detour index.
      PubDate: Mon, 25 Jan 2021 08:05:00 +000
       
  • Improved Hierarchical Convolutional Features for Robust Visual Object
           Tracking
    • Abstract: The target and background will change continuously in the long-term tracking process, which brings great challenges to the accurate prediction of targets. The correlation filter algorithm based on manual features is difficult to meet the actual needs due to its limited feature representation ability. Thus, to improve the tracking performance and robustness, an improved hierarchical convolutional features model is proposed into a correlation filter framework for visual object tracking. First, the objective function is designed by lasso regression modeling, and a sparse, time-series low-rank filter is learned to increase the interpretability of the model. Second, the features of the last layer and the second pool layer of the convolutional neural network are extracted to realize the target position prediction from coarse to fine. In addition, using the filters learned from the first frame and the current frame to calculate the response maps, respectively, the target position is obtained by finding the maximum response value in the response map. The filter model is updated only when these two maximum responses meet the threshold condition. The proposed tracker is evaluated by simulation analysis on TC-128/OTB2015 benchmarks including more than 100 video sequences. Extensive experiments demonstrate that the proposed tracker achieves competitive performance against state-of-the-art trackers. The distance precision rate and overlap success rate of the proposed algorithm on OTB2015 are 0.829 and 0.695, respectively. The proposed algorithm effectively solves the long-term object tracking problem in complex scenes.
      PubDate: Mon, 25 Jan 2021 07:05:00 +000
       
  • Self-Confirming Biased Beliefs in Organizational “Learning by
           Doing”
    • Abstract: Learning by doing, a change in beliefs (and consequently behaviour) due to experience, is crucial to the adaptive behaviours of organizations as well as the individuals that inhabit them. In this review paper, we summarise different pathologies of learning noted in past literature using a common underlying mechanism based on self-confirming biased beliefs. These are inaccurate beliefs about the environment that are self-confirming because acting upon these beliefs prevents their falsification. We provide a formal definition for self-confirming biased beliefs as an attractor that can lock learning by doing systems into suboptimal actions and provide illustrations based on simulations. We then compare and distinguish self-confirming biased beliefs from other related theoretical constructs, including confirmation bias, self-fulfilling prophecies, and sticking points, and underscore that self-confirming biased beliefs underlie inefficient self-confirming equilibria and hot-stove effects. Lastly, we highlight two fundamental ways to escape self-confirming biased beliefs: taking actions inconsistent with beliefs (i.e., exploration) and getting information on unchosen actions (i.e., counterfactuals).
      PubDate: Mon, 25 Jan 2021 06:50:00 +000
       
  • Complexity Measures for Maxwell–Boltzmann Distribution
    • Abstract: This work presents a discussion about the application of the Kolmogorov; López-Ruiz, Mancini, and Calbet (LMC); and Shiner, Davison, and Landsberg (SDL) complexity measures to a common situation in physics described by the Maxwell–Boltzmann distribution. The first idea about complexity measure started in computer science and was proposed by Kolmogorov, calculated similarly to the informational entropy. Kolmogorov measure when applied to natural phenomena, presents higher values associated with disorder and lower to order. However, it is considered that high complexity must be associated to intermediate states between order and disorder. Consequently, LMC and SDL measures were defined and used in attempts to model natural phenomena but with the inconvenience of being defined for discrete probability distributions defined over finite intervals. Here, adapting the definitions to a continuous variable, the three measures are applied to the known Maxwell–Boltzmann distribution describing thermal neutron velocity in a power reactor, allowing extension of complexity measures to a continuous physical situation and giving possible discussions about the phenomenon.
      PubDate: Sat, 23 Jan 2021 12:20:00 +000
       
  • Multicriteria-Based Crowd Selection Using Ant Colony Optimization
    • Abstract: Internet-enabled technologies have provided a way for people to communicate and collaborate with each other. The collaboration and communication made crowdsourcing an efficient and effective activity. Crowdsourcing is a modern paradigm that employs cheap labors (crowd) for accomplishing different types of tasks. The task is usually posted online as an open call, and members of the crowd self-select a task to be carried out. Crowdsourcing involves initiators or crowdsourcers (an entity usually a person or an organization who initiate the crowdsourcing process and seek out the ability of crowd for a task), the crowd (online participant who is a having a particular background, qualification, and experience for accomplishing task in crowdsourcing activity), crowdsourcing task (the activity in which the crowd contribute), the process (how the activity is carried out), and the crowdsourcing platform (software or market place) where requesters offer various tasks and crowd workers complete these tasks. As the crowdsourcing is carried out in the online environment, it gives rise to certain challenges. The major problem is the selection of crowd that is becoming a challenging issue with the growth in crowdsourcing popularity. Crowd selection has been significantly investigated in crowdsourcing processes. Nonetheless, it has observed that the selection is based only on a single feature of the crowd worker which was not sufficient for appropriate crowd selection. For addressing the problem of crowd selection, a novel “ant colony optimization-based crowd selection method” (ACO-CS) is presented in this paper that selects a crowd worker based on multicriteria features. By utilizing the proposed model, the efficiency and effectiveness of crowdsourcing activity will be increased.
      PubDate: Sat, 23 Jan 2021 12:20:00 +000
       
  • Parallel Computing for Efficient and Intelligent Industrial Internet of
           Health Things: An Overview
    • Abstract: Internet of Things (IoT) is expanding and evolves into all aspects of the society. Research and developments in the field of IoT have shown the possibility of producing huge volume of data and computation among different devices of the IoT. The data collected from IoT devices are transferred to a central server which can further be retrieved and accessed by the service providers for analyzing, processing, and using. Industrial Internet of Health Things (IIoHT) is the expansion of the Internet of Health Things (IoHT) which plays an important role in observing, consulting, monitoring, and treatment process of remote exchange data processes. The linkage of computation and interoperability are supported through various intelligent sensors, controllers, and actuators. The role of parallel computing for efficient and Intelligent Industrial Internet of Health Things is obvious to analyze and process different healthcare situations. A detailed overview of this existing literature is needed through which the research community will provide new solutions for efficient healthcare with the help of IoT based on parallel computing. Therefore, the current study presents a detailed overview of the existing literature for facilitating IIoHT.
      PubDate: Sat, 23 Jan 2021 11:35:00 +000
       
  • Development of a Complex Network-Based Integrated Multilayer Urban Growth
           and Optimisation Model for an Efficient Urban Traffic Network
    • Abstract: Previous research studies of traffic networks are mainly based on planar networks and less considered the influence of multilayer networks, which illustrate and represent different appropriate urban traffic modes. Development of rail and road networks is inseparable from the development of a prosperous urban area; thus, research on multilayer networks has scientific potential and fulfils a real need. In this paper, a framework of complex network based integrated multilayer urban growth and optimisation model (CNIMUGOM) is proposed, to analyse the complex relationships between the traffic network structure, the population growth, and the urban land-use. The innovation of this paper is the combination of the traffic complex multilayer networks and the “Four Step Model” (which stands for trip generation, trip distribution, model split, and traffic assignment steps). With the multiobjective, multilayer network coevolution and optimisation model, a more efficient traffic network layout was generated based on different land-use, population density, and travel speed scenarios. Then, this paper has proved that the proposed CNIMUGOM can save the traffic network construction investment, reduce the travel cost, make the urban traffic network more efficient, and decrease the total traffic flow amount. This research has connected the recent complex multilayer network related study and traditional urban economic model based study. The findings of the study afford to improve the current land-use and traffic integrated models and can provide traffic network planning suggestions for urban agglomeration development.
      PubDate: Sat, 23 Jan 2021 11:35:00 +000
       
  • A Nonintrusive Load Monitoring Method for Microgrid EMS Using Bi-LSTM
           Algorithm
    • Abstract: Nonintrusive load monitoring in smart microgrids aims to obtain the energy consumption of individual appliances from the aggregated energy data, which is generally confronted with the error identification of the load type for energy disaggregation in microgrid energy management system (EMS). This paper proposes a classification strategy for the nonintrusive load identification scheme based on the bilateral long-term and short-term memory network (Bi-LSTM) algorithm. The sliding window algorithm is used to extract the detected load event features and obtain the load features of data samples. In order to accurately identify these load features, the steady state information is combined as the input of the Bi-LSTM model during training. Comprising long-term and short-term memory (LSTM) network and recurrent neural network (RNN), Bi-LSTM has the advantages of stronger recognition ability. Finally, precision (P), recall (R), accuracy (A), and F1 values are used as the evaluation method for nonintrusive load identification. The experimental results show the accuracy of the Bi-LSTM identification method for load start and stop state feature matching; moreover, the method can identify relatively low-power and multistate appliances.
      PubDate: Sat, 23 Jan 2021 11:20:00 +000
       
  • Exploring Research Trends and Building a Multidisciplinary Framework
           Related to Brownfield: A Visual Analysis Using CiteSpace
    • Abstract: Brownfield has become one of the critical issues in modern cities. Over the past few decades, a considerable number of papers on brownfield research have been published. This study reviewed 773 documents themed with “brownfield” in the Web of Science core database between 1980 and 2020 and used the CiteSpace software to sort out the spatial and temporal distribution, knowledge groups, subject structures and hotspot fields, and evolutionary trends of global brownfield research. The analysis focuses on distribution of lead authors and their institutions, high-frequency categories and keywords, high influential journals, author contribution, and evolutionary trends based on coword analysis, coauthor analysis, cocitation analysis, and cluster analysis of documents. On the basis of the aforementioned keywords, clusters, and citation bursts analysis, this paper establishes a multidisciplinary framework for brownfield research, suggesting the main research directions for the future development, which provides theoretical support and practical guidance for the research direction of future brownfield research.
      PubDate: Fri, 22 Jan 2021 17:20:00 +000
       
  • Assessing Nonlinear Dynamics and Trends in Precipitation by Ensemble
           Empirical Mode Decomposition (EEMD) and Fractal Approach in Benin Republic
           (West Africa)
    • Abstract: Climate dynamics and trends have significant environmental and socioeconomic impacts; however, in the Benin Republic, they are generally studied with diverse statistical methods ignoring the nonstationarity, nonlinearity, and self-similarity characteristics contained in precipitation time series. This can lead to erroneous conclusions and an unclear understanding of climatic dynamics. Based on daily precipitation data observed in the six synoptic stations of Benin Republic, in the period from 1951 to 2010, we have proposed (i) determining the local trends of precipitations, (ii) investigating precipitation nonlinear dynamics, and (iii) assessing climatic shift in the study period by Ensemble Empirical Mode Decomposition (EEMD) and Multifractal Detrended Fluctuation Analysis (MFDFA) method. To overcome the detrending issue in the standard MFDFA method, the EEMD algorithm is embedded into the MFDFA. The study period is subdivided into three subperiods: 1951–1970, 1971–1990, and 1991–2010. Intrinsic Mode Functions (IMFs) are obtained according to the climatic region in which the stations are located. Results show that precipitation variation is significantly governed by the five first IMFs, in which oscillation periods vary from 1 to 25 days. The trend curves decrease at all the synoptic stations, and their slope values vary accordingly to the subperiods. Referring to the values of the multifractal spectrum parameters, , and the width of the spectrum , consistent changes are observed regardless of the subperiods and the concerned stations. The spatial and temporal variability of precipitation indicates that the multifractal properties are good indicators for assessing changes in precipitation dynamics, and the changes in their features could be explained by the global change than by the local climate variation (climatic zones). Despite the observed differences in multifractal spectra properties from the three subperiods, it is not possible to verify the subdivision of 1951–2010 in three subperiods as it is done by previous studies in West Africa. Our findings can be used in the validation of global and regional climate models since a valid model should explain empirically detected scaling properties in observed data.
      PubDate: Fri, 22 Jan 2021 17:05:00 +000
       
  • An Iterative Algorithm for Solving -Order Fractional Differential Equation
           with Mixed Integral and Multipoint Boundary Conditions
    • Abstract: In this paper, we consider the iterative algorithm for a boundary value problem of -order fractional differential equation with mixed integral and multipoint boundary conditions. Using an iterative technique, we derive an existence result of the uniqueness of the positive solution, then construct the iterative scheme to approximate the positive solution of the equation, and further establish some numerical results on the estimation of the convergence rate and the approximation error.
      PubDate: Fri, 22 Jan 2021 17:05:00 +000
       
  • The Role of Artificial and Nonartificial Intelligence in the New Product
           Success with Moderating Role of New Product Innovation: A Case of
           Manufacturing Companies in China
    • Abstract: Currently, there is an increasing trend in the organizations towards examining the artificial intelligence and nonartificial intelligence for the innovation and success of the new product, as well as getting the intentions of the upcoming researchers. Thus, the purpose of the ongoing study is to examine the role of artificial and nonartificial intelligence in the new product success along with the moderating role of new product innovation in the manufacturing organizations of China. The quantitative methods have been followed by the study and gathered the responses from the respondents using questionnaires, and analysis has been conducted by using the smart-PLS. The results exposed that artificial intelligence and nonartificial intelligence have positive and significant nexus with the new product success. The outcomes also revealed that the new product innovation significantly moderated the links among the nonartificial intelligence and new product success, but it insignificantly moderated the links among the artificial intelligence and new product success in the manufacturing organizations of China. These findings have provided the guidelines to the manufacturing companies and their policies developing authorities that they should be developed and implement the suitable policies regarding the adoption of artificial intelligence and nonartificial intelligence that enhance the success of the new product, which ultimately enhances the success of the organization.
      PubDate: Fri, 22 Jan 2021 16:35:00 +000
       
  • Exploring Coevolution of Emotional Contagion and Behavior for Microblog
           Sentiment Analysis: A Deep Learning Architecture
    • Abstract: This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to build the corresponding weighted network. Secondly, in the weighted network, the Deepwalk algorithm is used to build the sequence representation of microblogs which are similar to the target microblog. Next, a CNN-BiLSTM-Attention network (the convolutional neural network and bidirectional long short-term memory network with a multihead attention mechanism) is designed to analyze the sentiment analysis of target and similar microblogs. Finally, the experimental results on two real Twitter datasets demonstrate that the proposed MSA-UITC has advanced performance compared with the existing state-of-the-art methods.
      PubDate: Fri, 22 Jan 2021 15:20:00 +000
       
  • Cross-Market Infection Research on Stock Herding Behavior Based on DGC-MSV
           Models and Bayesian Network
    • Abstract: This paper is concerned with the multivariate stochastic volatility modeling of the stock market. We investigate a DGC-t-MSV model to find the historical volatility spillovers between nine markets, including S&P, Nasdaq, SSE, SZSE, HSI, FTSE, CAC, DAX, and Nikkei indices. We use the Bayesian network to analyze the spreading of herd behavior between nine markets. The main results are as follows: (1) the DGC-t-MSV model we considered is a useful way to estimate the parameter and fit the data well in the stock market; (2) our computational analysis shows that the S&P and Nasdaq have higher volatility spillovers to the Shanghai and Shenzhen stock markets; (3) the results also show that there is a strong correlation between stock markets in the same region.
      PubDate: Fri, 22 Jan 2021 15:05:00 +000
       
  • A New Lifetime Distribution: Properties, Copulas, Applications, and
           Different Classical Estimation Methods
    • Abstract: A new continuous version of the inverse flexible Weibull model is proposed and studied. Some of its properties such as quantile function, moments and generating functions, incomplete moments, mean deviation, Lorenz and Bonferroni curves, the mean residual life function, the mean inactivity time, and the strong mean inactivity time are derived. The failure rate of the new model can be “increasing-constant,” “bathtub-constant,” “bathtub,” “constant,” “J-HRF,” “upside down bathtub,” “increasing,” “upside down-increasing-constant,” and “upside down.” Different copulas are used for deriving many bivariate and multivariate type extensions. Different non-Bayesian well-known estimation methods under uncensored scheme are considered and discussed such as the maximum likelihood estimation, Anderson Darling estimation, ordinary least square estimation, Cramér-von-Mises estimation, weighted least square estimation, and right tail Anderson Darling estimation methods. Simulation studies are performed for comparing these estimation methods. Finally, two real datasets are analyzed to illustrate the importance of the new model.
      PubDate: Thu, 21 Jan 2021 16:20:01 +000
       
  • Dynamic Large-Scale Server Scheduling for IVF Queuing Network in Cloud
           Healthcare System
    • Abstract: As one of the most effective medical technologies for the infertile patients, in vitro fertilization (IVF) has been more and more widely developed in recent years. However, prolonged waiting for IVF procedures has become a problem of great concern, since this technology is only mastered by the large general hospitals. To deal with the insufficiency of IVF service capacity, this paper studies an IVF queuing network in an integrated cloud healthcare system, where the two key medical services, that is, egg retrieval and transplantation, are assigned to accomplish in the general hospital, while the routine medical tests are assigned into the community hospital. Based on continuous-time Markov procedure, a dynamic large-scale server scheduling problem in this complicated service network is modeled with consideration of different arrival rates of multiple type of patients and different service capacities of multiple servers that can be defined as doctors of the general hospital. To solve this model, a reinforcement learning (RL) algorithm is proposed, where the reward functions are designed for four conflicting subcosts: setup cost, patient waiting cost, penalty cost for unsatisfied patient personal preferences, and medical cost of patient. The experimental results show that the optimal service rule of each server’s queue obtained by the RL method is significantly superior to the traditional service rule.
      PubDate: Thu, 21 Jan 2021 08:20:00 +000
       
  • Toward Pleomorphic Reconfigurable Robots for Optimum Coverage
    • Abstract: Buildings are constructed for accommodating living and industrial needs. Floor cleaning robots have been developed to cater to the demand of these buildings. Area coverage and coverage time are crucial performance factors of a floor cleaning robot. Reconfigurable tiling robots have been introduced over fixed shape robots to improve area coverage in floor cleaning applications compared to robots with fixed morphologies. However, area coverage and coverage time of a tiling robot compromised one another. This study proposes a novel concept that considers the ability of a tiling robot to configure both its morphology and size according to the environment. This concept is inspired by the pleomorphism that could be seen in bacteria. In this regard, P-hTetro, a pleomorphic tiling robot that can reconfigure its morphology and size, is considered. A novel coverage strategy for realizing the size reconfiguration is also proposed. According to this strategy, the robot covers obstacle-free areas with its maximum size, while an obstacle cluster is covered after shrinking to an optimum size. The optimum size for reconfiguration is determined by the genetic algorithm based on the arrangement of the environment. The performance and behavior of the proposed P-hTetro have been compared against that of an existing tiling robot which has a fixed size. According to the statistical outcomes, a tiling robot with the ability to reconfigure its size can significantly improve the performance in the aspects of area coverage and coverage time compared to a tiling robot with no ability to reconfigure its size.
      PubDate: Thu, 21 Jan 2021 08:05:01 +000
       
  • Channel Contention-Based Routing Protocol for Wireless Ad Hoc Networks
    • Abstract: With the development of wireless technology, two basic wireless network models that are commonly used, known as infrastructure and wireless ad hoc networks (WANETs), have been developed. In the literature, it has been observed that channel contention is one of the main reasons for packet drop in WANETs. To handle this problem, this paper presents a routing protocol named CCBR (Channel Contention Based Routing). CCBR tries to determine a least contended path between the endpoints to increase packet delivery ratio and to reduce packet delay and normalized routing overhead. Moreover, throughout the active data section, each intermediate node computes its channel contention value. If an intermediate node detects an increase in channel contention, it notifies the source node. Then the source node determines another least contended route for transmission. The advantages of CCBR are verified in our NS2-based performance study, and the results show that CCBR outperforms ad hoc on-demand distance vector (AODV) in terms of packet delivery ratio, end-to-end delay, and routing overhead by 4% to 9%.
      PubDate: Thu, 21 Jan 2021 07:50:00 +000
       
  • GAN-Holo: Generative Adversarial Networks-Based Generated Holography Using
           Deep Learning
    • Abstract: Current development in a deep neural network (DNN) has given an opportunity to a novel framework for the reconstruction of a holographic image and a phase recovery method with real-time performance. There are many deep learning-based techniques that have been proposed for the holographic image reconstruction, but these deep learning-based methods can still lack in performance, time complexity, accuracy, and real-time performance. Due to iterative calculation, the generation of a CGH requires a long computation time. A novel deep generative adversarial network holography (GAN-Holo) framework is proposed for hologram reconstruction. This novel framework consists of two phases. In phase one, we used the Fresnel-based method to make the dataset. In the second phase, we trained the raw input image and holographic label image data from phase one acquired images. Our method has the capability of the noniterative process of computer-generated holograms (CGHs). The experimental results have demonstrated that the proposed method outperforms the existing methods.
      PubDate: Thu, 21 Jan 2021 06:35:00 +000
       
  • Research on Sustainable Development Ability and Spatial-Temporal
           Differentiation of Urban Human Settlements in China and Japan Based on
           SDGs, Taking Dalian and Kobe as Examples
    • Abstract: The sustainable development of the human settlements (HS) has become a global universal program. The comparison of cities in different countries is of great significance to provide international experience for future urban construction. Combined with the UN 2030 Sustainable Development Goals (SDGs), this paper establishes an evaluation index system for the sustainable development ability of urban HS and constructs a three-dimensional research framework of “development-coordination-sustainability,” which compares the sustainable development ability of the HS of Dalian, China, and Kobe, Japan, from 2005 to 2018 and explores the spatial evolution characteristics and obstacle factors of the HS of the two cities. The results show that (1) the development degree of the HS of the two cities is on the rise. The development level of Kobe is always higher than that of Dalian, and the gap is gradually narrowing; Kobe has advantages in natural and residential environment, while Dalian has advantages in cultural and economic environment. (2) The coordination degree of the development of the HS of the two cities has improved steadily, and the coordination degree of Kobe is better than that of Dalian. (3) The sustainability of the development of the HS of the two cities is fluctuating, and the average sustainable growth rate of Dalian is higher than that of Kobe. (4) The sustainable development space of the HS in Dalian presents a pattern of “high in the south and low in the north,” and the spatial characteristics of the subsystems are different; the main obstacles have changed from economic-natural to economic-natural-cultural-public services, and the obstacles to development in districts are different. (5) The sustainable development space of the HS in Kobe has a high level of development in the southeast, radiating to the surrounding area, and the spatial characteristics of the subsystems are different; the main obstacles have changed from economic-cultural-natural to economic-natural-population, and the obstacles to development in districts are different. Finally, it puts forward targeted suggestions for the sustainable construction of Dalian. This paper can provide methodological reference for quantitative assessment of the sustainable development of HS and provide policy reference for scientific planning of the construction of HS.
      PubDate: Wed, 20 Jan 2021 17:50:00 +000
       
  • Fuzzy Integral Sliding Mode Control Based on Microbial Fuel Cell
    • Abstract: Microbial fuel cell (MFC) is a renewable clean energy. Microorganisms are used as catalysts to convert the chemical energy of organic matter in the sewage into electrical energy to realize sewage treatment and recover energy at the same time. It has good development prospects. However, the output power of MFC is affected by many factors, and it is difficult to achieve a stable voltage output. For the control-oriented single-chamber MFC, a fuzzy integral sliding mode control is designed. The continuous adjustment of the sliding surface ensures that the system only moves on the sliding surface, which eliminates the arrival stage and improves robustness. For chattering existing in the system, the control scheme is further optimized to obtain fuzzy integral sliding mode control, and the fuzzy module adaptively adjusts the control parameters according to the system state, which effectively reduces the system chattering. Experiments prove that the control scheme reduces chattering while ensuring the stable output of the system.
      PubDate: Wed, 20 Jan 2021 13:50:00 +000
       
  • Policy and Law Assessment of COVID-19 Based on Smooth Transition
           Autoregressive Model
    • Abstract: As of the end of October 2020, the cumulative number of confirmed cases of COVID-19 has exceeded 45 million and the cumulative number of deaths has exceeded 1.1 million all over the world. Faced with the fatal pandemic, countries around the world have taken various prevention and control measures. One of the important issues in epidemic prevention and control is the assessment of the prevention and control effectiveness. Changes in the time series of daily new confirmed cases can reflect the impact of policies in certain regions. In this paper, a smooth transition autoregressive (STAR) model is applied to investigate the intrinsic changes during the epidemic in certain countries and regions. In order to quantitatively evaluate the influence of the epidemic control measures, the sequence is fitted to the STAR model; then, comparisons between the dates of transition points and those of releasing certain policies are applied. Our model well fits the data. Moreover, the nonlinear smooth function within the STAR model reveals that the implementation of prevention and control policies is effective in some regions with different speeds. However, the ineffectiveness is also revealed and the threat of a second wave had already emerged.
      PubDate: Wed, 20 Jan 2021 09:20:00 +000
       
  • Claim Amount Forecasting and Pricing of Automobile Insurance Based on the
           BP Neural Network
    • Abstract: The BP neural network model is a hot issue in recent academic research, and it has been successfully applied to many other fields, but few researchers apply the BP neural network model to the field of automobile insurance. The main method that has been used in the prediction of the total claim amount in automobile insurance is the generalized linear model, where the BP neural network model could provide a different approach to estimate the total claim loss. This paper uses a genetic algorithm to optimize the structure of the BP neural network at first, and the calculation speed is significantly improved. At the same time, by considering the overfitting problem, an early stop method is introduced to avoid the overfitting problem. In the model, a three-layer BP neural network model, which includes the input layer, hidden layer, and output layer, is trained. With consideration of various factors, a total claim amount prediction model is established, and the trained BP neural network model is used to predict the total claim amount of automobile insurance based on the data of the training set. The results show that the accuracy of the prediction by using the BP neural network model to both the data of Shandong Province and to the data of six cities is over 95%. Then, the predicted total claim amount is used to calculate premiums for five cities in Shandong Province according to credibility theory. The results show that the average premium of the five cities is slightly higher than the actual claim amount of the city. The combination of BP neural network and credibility theory can perform accurate claim amount estimation and pricing for automobile insurance, which can effectively improve the current situation of the automobile insurance business and promote the development of insurance industry.
      PubDate: Wed, 20 Jan 2021 08:05:00 +000
       
  • Identification of Self-Organized Critical State on Twitter Based on the
           Retweets’ Time Series Analysis
    • Abstract: There is a number of studies, in which it is established that the observed flows of microposts generated by microblogging social networks (e.g., Twitter) are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution, with 1/f noise and long memory. Despite this, there are no studies devoted to the detection and analysis of self-organized critical state, subcritical phase, and supercritical phase. The presented paper is devoted to the detection and investigation of such critical states and phases. An algorithm is proposed that allowed to detect of critical phases and critical conditions on Twitter, based on the analysis of retweets time series corresponding to the three debates of the 2016 United States Presidential Election, as the most popular debate in the history of America, collecting 84 million live views.
      PubDate: Wed, 20 Jan 2021 07:50:00 +000
       
  • Robust Fixed-Time Inverse Dynamic Control for Uncertain Robot Manipulator
           System
    • Abstract: This paper proposes a novel robust fixed-time control for the robot manipulator system with uncertainties. Based on the uniform robust exact differentiator (URED) algorithm, a robust control term is constructed. Then, a robust fixed-time inverse dynamics control (IDC) is proposed. For the proposed control method, the fixed-time stability of a closed-loop system with uncertainties is strictly proved. The newly proposed method exhibits the following two attractive features. First, the proposed control scheme extends the existing fixed-time IDC for the robot manipulator system to the robust control scheme. Second, the proposed method is strictly nonsingular rather than the commonly used approximate approach. Simulation result demonstrates the effectiveness of the proposed control scheme.
      PubDate: Wed, 20 Jan 2021 07:35:00 +000
       
  • Active Realization of Fractional-Order Integrators and Their Application
           in Multiscroll Chaotic Systems
    • Abstract: This paper presents the design, simulation, and experimental verification of the fractional-order multiscroll Lü chaotic system. We base them on op-amp-based approximations of fractional-order integrators and saturated series of nonlinear functions. The integrators are first-order active realizations tuned to reduce the inaccuracy of the frequency response. By an exponential curve fitting, we got a convenient design equation for realizing fractional-order integrators of orders from 0.1 to 0.95. The results include simulations in SPICE of the mathematical description and the electronic implementation and experimental measurements that confirm them. Monte Carlo and sensitivity tests revealed a robust realization. Contrary to its passive counterparts, the suggested realizations significantly reduce design and implementation efforts by favoring resistors and capacitors with commercial values and reducing hardware requirements.
      PubDate: Wed, 20 Jan 2021 07:35:00 +000
       
  • Forecasting Different Types of Droughts Simultaneously Using Multivariate
           Standardized Precipitation Index (MSPI), MLP Neural Network, and
           Imperialistic Competitive Algorithm (ICA)
    • Abstract: Precipitation deficit causes meteorological drought, and its continuation appears as other different types of droughts including hydrological, agricultural, economic, and social droughts. Multivariate Standardized Precipitation Index (MSPI) can show the drought status from the perspective of different drought types simultaneously. Forecasting multivariate droughts can provide good information about the future status of a region and will be applicable for the planners of different water divisions. In this study, the MLP model and its hybrid form with the Imperialistic Competitive Algorithm (MLP-ICA) have been investigated for the first time in multivariate drought studies. For this purpose, two semi-arid stations of western Iran were selected, and their precipitation data were provided from the Iranian Meteorological Organization (IRIMO), during the period of 1988–2017. MSPI was calculated in 5-time windows of the multivariate drought, including MSPI3–6 (drought in perspectives of soil moisture and surface hydrology simultaneously), MSPI6–12 (hydrological and agricultural droughts simultaneously), MSPI3–12 (soil moisture, surface hydrology, and agricultural droughts simultaneously), MSPI12–24 (drought in perspectives of agriculture and groundwater simultaneously), and MSPI24–48 (socio-economical droughts). The results showed acceptable performances in forecasting multivariate droughts. In both stations, the larger time windows (MSPI12–24 and MSPI24–48) had better predictions than the smaller ones (MSPI3–6, MSPI6–12, and MSPI3–12). Generally, it can be reported that, by decreasing the size of the time window, the gradual changes of the index give way to sudden jumps. This causes weaker autocorrelation and consequently weaker predictions, e.g., forecasting droughts from the perspective of soil moisture and surface hydrology simultaneously (MSPI3–6). The hybrid MLP-ICA shows stronger prediction results than the simple MLP model in all comparisons. The ICA optimizer could averagely improve MLP’s accuracy by 28.5%, which is a significant improvement. According to the evaluations (RMSE = 0.20; MAE = 0.15; R = 0.95), the results are hopeful for simultaneous forecasting of different drought types and can be tested for other similar areas.
      PubDate: Tue, 19 Jan 2021 15:35:00 +000
       
  • A Novel Chinese Entity Relationship Extraction Method Based on the
           Bidirectional Maximum Entropy Markov Model
    • Abstract: To identify relationships among entities in natural language texts, extraction of entity relationships technically provides a fundamental support for knowledge graph, intelligent information retrieval, and semantic analysis, promotes the construction of knowledge bases, and improves efficiency of searching and semantic analysis. Traditional methods of relationship extraction, either those proposed at the earlier times or those based on traditional machine learning and deep learning, have focused on keeping relationships and entities in their own silos: extracting relationships and entities are conducted in steps before obtaining the mappings. To address this problem, a novel Chinese relationship extraction method is proposed in this paper. Firstly, the triple is treated as an entity relation chain and can identify the entity before the relationship and predict its corresponding relationship and the entity after the relationship. Secondly, the Joint Extraction of Entity Mentions and Relations model is based on the Bidirectional Long Short-Term Memory and Maximum Entropy Markov Model (Bi-MEMM). Experimental results indicate that the proposed model can achieve a precision of 79.2% which is much higher than that of traditional models.
      PubDate: Tue, 19 Jan 2021 15:20:01 +000
       
  • Aiding Traffic Prediction Servers through Self-Localization to Increase
           Stability in Complex Vehicular Clustering
    • Abstract: The integration of cellular networks and vehicular networks is complex and heterogeneous. Synchronization among vehicles in heterogeneous vehicular clusters plays an important role in effective data sharing and the stability of the cluster. This synchronization depends on the smooth exchange of information between vehicles and remote servers over the Internet. The remote servers predict road traffic patterns by adopting deep learning methods to help drivers on the roads. At the same time, local data processing at the vehicular cluster level may increase the capabilities of remote servers. However, global positioning system (GPS) signal interruption, especially in the urban environment, plays a big part in the detritions of synchronization among the vehicles that lead to the instability of the cluster. Instability of connections is a major hurdle in developing cost-effective solutions for deriving assistance and route planning applications. To solve this problem, a self-localization scheme within the vehicular cluster is proposed. The proposed self-localization scheme handles GPS signal interruption to the vehicle within the cluster. A unique clustering criterion and a synchronization mechanism for sharing traffic information system (TIS) data among multiple vehicles are developed. The developed scheme is simulated and compared with existing known approaches. The results show the better performance of our proposed scheme over others.
      PubDate: Tue, 19 Jan 2021 15:20:00 +000
       
  • Establishment and Analysis of Spatiotemporal Variation Hydrological Model
           of Distributed Rainfall and Evaporation in Biliu River Basin
    • Abstract: The Biliu River originates from the southern foot of Qinling Mountain in Gaizhou city, with an elevation of 1047 m, and is the largest river in Dalian. The hydrological elements mainly include rainfall, runoff, temperature, evaporation, and other time series associated with the hydrological cycle. Among them, runoff is the most visible output performance, and the direct source of runoff is during rainfall. This paper establishes a reservoir scheduling model that considers the influence of multiple uncertainty factors and analyzes the influence of mixed uncertainty on reservoir scheduling and Xingli’s objectives based on probability box theory. In terms of uncertainties, the uncertainty of hydrological model parameters and the randomness of precipitation processes are mainly considered, with the former having an impact on river runoff simulation and the latter having an impact on both river runoff simulation and crop irrigation water demand. In the case of the Jing River basin, for example, the results show that, compared to the stochasticity of the precipitation process, the variation in precipitation has a significant effect on irrigation water demand in maize, followed by the frequency of precipitation, and the interaction between the two is not significant.
      PubDate: Tue, 19 Jan 2021 11:50:01 +000
       
 
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