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Publisher: Hindawi   (Total: 339 journals)

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Showing 1 - 200 of 339 Journals sorted alphabetically
Abstract and Applied Analysis     Open Access   (Followers: 3, SJR: 0.343, CiteScore: 1)
Active and Passive Electronic Components     Open Access   (Followers: 7, SJR: 0.136, CiteScore: 0)
Advances in Acoustics and Vibration     Open Access   (Followers: 36, SJR: 0.147, CiteScore: 0)
Advances in Aerospace Engineering     Open Access   (Followers: 54)
Advances in Agriculture     Open Access   (Followers: 10)
Advances in Artificial Intelligence     Open Access   (Followers: 15)
Advances in Astronomy     Open Access   (Followers: 39, SJR: 0.257, CiteScore: 1)
Advances in Bioinformatics     Open Access   (Followers: 17, SJR: 0.565, CiteScore: 2)
Advances in Biology     Open Access   (Followers: 9)
Advances in Chemistry     Open Access   (Followers: 24)
Advances in Civil Engineering     Open Access   (Followers: 43, SJR: 0.539, CiteScore: 1)
Advances in Computer Engineering     Open Access   (Followers: 4)
Advances in Condensed Matter Physics     Open Access   (Followers: 11, SJR: 0.315, CiteScore: 1)
Advances in Decision Sciences     Open Access   (Followers: 3, SJR: 0.303, CiteScore: 1)
Advances in Electrical Engineering     Open Access   (Followers: 33)
Advances in Electronics     Open Access   (Followers: 76)
Advances in Emergency Medicine     Open Access   (Followers: 12)
Advances in Endocrinology     Open Access   (Followers: 5)
Advances in Environmental Chemistry     Open Access   (Followers: 7)
Advances in Epidemiology     Open Access   (Followers: 8)
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: 5)
Advances in Hematology     Open Access   (Followers: 11, SJR: 0.661, CiteScore: 2)
Advances in Hepatology     Open Access   (Followers: 2)
Advances in High Energy Physics     Open Access   (Followers: 19, 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: 30, SJR: 0.315, CiteScore: 1)
Advances in Mathematical Physics     Open Access   (Followers: 5, 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: 2, SJR: 0.173, CiteScore: 1)
Advances in Nonlinear Optics     Open Access   (Followers: 6)
Advances in Numerical Analysis     Open Access   (Followers: 7)
Advances in Nursing     Open Access   (Followers: 32)
Advances in Operations Research     Open Access   (Followers: 12, SJR: 0.205, CiteScore: 1)
Advances in Optical Technologies     Open Access   (Followers: 4, SJR: 0.214, CiteScore: 1)
Advances in Optics     Open Access   (Followers: 5)
Advances in OptoElectronics     Open Access   (Followers: 6, SJR: 0.141, CiteScore: 0)
Advances in Orthopedics     Open Access   (Followers: 8, SJR: 0.922, CiteScore: 2)
Advances in Pharmacological Sciences     Open Access   (Followers: 8, SJR: 0.591, CiteScore: 2)
Advances in Physical Chemistry     Open Access   (Followers: 10, SJR: 0.179, CiteScore: 1)
Advances in Power Electronics     Open Access   (Followers: 33, SJR: 0.184, CiteScore: 0)
Advances in Preventive Medicine     Open Access   (Followers: 6)
Advances in Public Health     Open Access   (Followers: 24)
Advances in Regenerative Medicine     Open Access   (Followers: 3)
Advances in Software Engineering     Open Access   (Followers: 10)
Advances in Statistics     Open Access   (Followers: 4)
Advances in Toxicology     Open Access   (Followers: 2)
Advances in Tribology     Open Access   (Followers: 14, SJR: 0.265, CiteScore: 1)
Advances in Urology     Open Access   (Followers: 9, SJR: 0.51, CiteScore: 1)
Advances in Virology     Open Access   (Followers: 7, SJR: 0.838, CiteScore: 2)
AIDS Research and Treatment     Open Access   (Followers: 3, SJR: 0.758, CiteScore: 2)
Analytical Cellular Pathology     Open Access   (Followers: 2, SJR: 0.886, CiteScore: 2)
Anatomy Research Intl.     Open Access   (Followers: 2)
Anemia     Open Access   (Followers: 5, SJR: 0.669, CiteScore: 2)
Anesthesiology Research and Practice     Open Access   (Followers: 14, SJR: 0.501, CiteScore: 1)
Applied and Environmental Soil Science     Open Access   (Followers: 17, 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: 14)
Archaea     Open Access   (Followers: 3, SJR: 0.852, CiteScore: 2)
Arthritis     Open Access   (Followers: 6, SJR: 0.454, CiteScore: 1)
Autism Research and Treatment     Open Access   (Followers: 27)
Autoimmune Diseases     Open Access   (Followers: 3, SJR: 0.805, CiteScore: 2)
Behavioural Neurology     Open Access   (Followers: 10, 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: 4, 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: 6, SJR: 0.867, CiteScore: 1)
Canadian J. of Infectious Diseases and Medical Microbiology     Open Access   (Followers: 5, SJR: 0.548, CiteScore: 1)
Canadian Respiratory J.     Open Access   (Followers: 1, SJR: 0.474, CiteScore: 1)
Cardiology Research and Practice     Open Access   (Followers: 8, SJR: 1.237, CiteScore: 4)
Case Reports in Anesthesiology     Open Access   (Followers: 10)
Case Reports in Cardiology     Open Access   (Followers: 4, SJR: 0.219, CiteScore: 0)
Case Reports in Critical Care     Open Access   (Followers: 9)
Case Reports in Dentistry     Open Access   (Followers: 5, SJR: 0.229, CiteScore: 0)
Case Reports in Dermatological Medicine     Open Access   (Followers: 2)
Case Reports in Emergency Medicine     Open Access   (Followers: 14)
Case Reports in Endocrinology     Open Access   (Followers: 1, SJR: 0.209, CiteScore: 1)
Case Reports in Gastrointestinal Medicine     Open Access   (Followers: 3)
Case Reports in Genetics     Open Access   (Followers: 1)
Case Reports in Hematology     Open Access   (Followers: 4)
Case Reports in Hepatology     Open Access   (Followers: 2)
Case Reports in Immunology     Open Access   (Followers: 4)
Case Reports in Infectious Diseases     Open Access   (Followers: 5)
Case Reports in Medicine     Open Access   (Followers: 2)
Case Reports in Nephrology     Open Access   (Followers: 4)
Case Reports in Neurological Medicine     Open Access   (Followers: 1)
Case Reports in Obstetrics and Gynecology     Open Access   (Followers: 10)
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: 5)
Case Reports in Otolaryngology     Open Access   (Followers: 6)
Case Reports in Pathology     Open Access   (Followers: 5)
Case Reports in Pediatrics     Open Access   (Followers: 7)
Case Reports in Psychiatry     Open Access   (Followers: 13)
Case Reports in Pulmonology     Open Access   (Followers: 3)
Case Reports in Radiology     Open Access   (Followers: 9)
Case Reports in Rheumatology     Open Access   (Followers: 6)
Case Reports in Surgery     Open Access   (Followers: 11)
Case Reports in Transplantation     Open Access  
Case Reports in Urology     Open Access   (Followers: 9)
Case Reports in Vascular Medicine     Open Access  
Case Reports in Veterinary Medicine     Open Access   (Followers: 5)
Child Development Research     Open Access   (Followers: 18, SJR: 0.144, CiteScore: 0)
Chinese J. of Engineering     Open Access   (Followers: 2, SJR: 0.114, CiteScore: 0)
Chinese J. of Mathematics     Open Access  
Cholesterol     Open Access   (Followers: 1, SJR: 0.424, CiteScore: 1)
Chromatography Research Intl.     Open Access   (Followers: 6)
Complexity     Hybrid Journal   (Followers: 6, SJR: 0.531, CiteScore: 2)
Computational and Mathematical Methods in Medicine     Open Access   (Followers: 2, SJR: 0.403, CiteScore: 1)
Computational Intelligence and Neuroscience     Open Access   (Followers: 12, SJR: 0.326, CiteScore: 1)
Contrast Media & Molecular Imaging     Open Access   (Followers: 3, SJR: 0.842, CiteScore: 3)
Critical Care Research and Practice     Open Access   (Followers: 12, SJR: 0.499, CiteScore: 1)
Current Gerontology and Geriatrics Research     Open Access   (Followers: 9, SJR: 0.512, CiteScore: 2)
Depression Research and Treatment     Open Access   (Followers: 15, SJR: 0.816, CiteScore: 2)
Dermatology Research and Practice     Open Access   (Followers: 3, SJR: 0.806, CiteScore: 2)
Diagnostic and Therapeutic Endoscopy     Open Access   (Followers: 1, SJR: 0.201, CiteScore: 1)
Discrete Dynamics in Nature and Society     Open Access   (Followers: 5, 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: 4, SJR: 0.653, CiteScore: 3)
Evidence-based Complementary and Alternative Medicine     Open Access   (Followers: 22, SJR: 0.683, CiteScore: 2)
Game Theory     Open Access   (Followers: 1)
Gastroenterology Research and Practice     Open Access   (Followers: 3, SJR: 0.768, CiteScore: 2)
Genetics Research Intl.     Open Access   (Followers: 1, SJR: 0.61, CiteScore: 2)
Geofluids     Open Access   (Followers: 4, SJR: 0.952, CiteScore: 2)
Hepatitis Research and Treatment     Open Access   (Followers: 6, SJR: 0.389, CiteScore: 2)
HPB Surgery     Open Access   (Followers: 6, 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: 74, 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: 11, SJR: 0.787, CiteScore: 3)
Intl. J. of Analysis     Open Access  
Intl. J. of Analytical Chemistry     Open Access   (Followers: 22, SJR: 0.285, CiteScore: 1)
Intl. J. of Antennas and Propagation     Open Access   (Followers: 11, SJR: 0.233, CiteScore: 1)
Intl. J. of Atmospheric Sciences     Open Access   (Followers: 21)
Intl. J. of Biodiversity     Open Access   (Followers: 4)
Intl. J. of Biomaterials     Open Access   (Followers: 4, 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: 13, 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: 10, SJR: 0.194, CiteScore: 1)
Intl. J. of Dentistry     Open Access   (Followers: 6, 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: 8)
Intl. J. of Endocrinology     Open Access   (Followers: 4, SJR: 1.012, CiteScore: 3)
Intl. J. of Engineering Mathematics     Open Access   (Followers: 5)
Intl. J. of Food Science     Open Access   (Followers: 4, 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: 4, SJR: 0.182, CiteScore: 1)
Intl. J. of Hepatology     Open Access   (Followers: 5, SJR: 0.874, CiteScore: 2)
Intl. J. of Hypertension     Open Access   (Followers: 6, SJR: 0.578, CiteScore: 1)
Intl. J. of Inflammation     Open Access   (SJR: 1.264, CiteScore: 3)
Intl. J. of Inorganic Chemistry     Open Access   (Followers: 3)
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: 4)
Intl. J. of Microbiology     Open Access   (Followers: 4, SJR: 0.662, CiteScore: 2)
Intl. J. of Microwave Science and Technology     Open Access   (Followers: 3, 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: 1, SJR: 0.697, CiteScore: 1)
Intl. J. of Oceanography     Open Access   (Followers: 7)
Intl. J. of Optics     Open Access   (Followers: 7, 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: 4, SJR: 0.46, CiteScore: 1)
Intl. J. of Photoenergy     Open Access   (Followers: 2, 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: 24, SJR: 0.298, CiteScore: 1)
Intl. J. of Population Research     Open Access   (Followers: 3)
Intl. J. of Quality, Statistics, and Reliability     Open Access   (Followers: 16)
Intl. J. of Reconfigurable Computing     Open Access   (SJR: 0.123, CiteScore: 1)
Intl. J. of Reproductive Medicine     Open Access   (Followers: 4)
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: 5, 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: 198)
ISRN Astronomy and Astrophysics     Open Access   (Followers: 7)
J. of Addiction     Open Access   (Followers: 14)
J. of Advanced Transportation     Hybrid Journal   (Followers: 13, SJR: 0.581, CiteScore: 1)
J. of Aerodynamics     Open Access   (Followers: 12)

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Journal Cover
Complexity
Journal Prestige (SJR): 0.531
Citation Impact (citeScore): 2
Number of Followers: 6  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1076-2787 - ISSN (Online) 1099-0526
Published by Hindawi Homepage  [339 journals]
  • Optimal Policies for the Pricing and Replenishment of Fashion Apparel
           considering the Effect of Fashion Level
    • Abstract: Fashion apparel, with short product lifecycles and highly volatile demand, requires careful attention during both the initial ordering periods before the selling season and during the selling season, with its decisions regarding price and replenishment. Using Pontryagin’s maximum principle method, this study investigates the problem of the dynamic pricing strategy and replenishment cycle for fashion apparel by considering the effect of fashion level on demand. First, we provide a framework for fashion apparel by formulating a model that includes both price and demand at different fashion levels. We then provide an algorithm to derive the optimal dynamic pricing strategy and replenishment cycle. Numerical examples and sensitivity analyses of the main system parameters are provided to demonstrate the obtained results, which form the basis for managerial insights. It is shown that the apparel retailer has three types of optimal dynamic pricing strategies and that the optimal strategy is independent of the replenishment cycle. The apparel retailer is able to realize the profit advantage of a continuously variable price policy by adjusting the sales price periodically.
      PubDate: Sun, 17 Feb 2019 13:05:19 +000
       
  • Resolution of Max-Product Fuzzy Relation Equation with Interval-Valued
           Parameter
    • Abstract: Considering the application background on P2P network system, we investigate the max-product fuzzy relation equation with interval-valued parameter in this paper. Order relation on the set of all interval-valued numbers plays key role in the construction and resolution of the interval-valued-parameter fuzzy relation equation (IPFRE). The basic operations supremum () and infimum () in the IPFRE should be defined depending on the order relation. A novel total order is introduced for establishing the IPFRE. We also discuss some properties of the IPFRE system, including the consistency and structure of the complete solution set. Concepts of close index set and open index set are defined, helping us to construct the resolution method of the IPFRE system. We further provide a detailed algorithm for obtaining the complete solution set. Besides, the solution set is compared to that of the classical max- fuzzy relation equations system.
      PubDate: Sun, 17 Feb 2019 13:05:17 +000
       
  • Public Policy Modeling and Applications
    • PubDate: Sun, 17 Feb 2019 12:05:13 +000
       
  • An Agent-Based Model of a Pricing Process with Power Law, Volatility
           Clustering, and Jumps
    • Abstract: In this paper, we propose a new model of security price dynamics in order to explain the stylized facts of the pricing process such as power law distribution, volatility clustering, jumps, and structural changes. We assume that there are two types of agents in the financial market: speculators and fundamental investors. Speculators use past prices to predict future prices and only buy assets whose prices are expected to rise. Fundamental investors attach a certain value to each asset and buy when the asset is undervalued by the market. When the expectations of agents are exogenously driven, that is, entirely shaped by exogenous news, then they can be modeled as following a random walk. We assume that the information related to the two types of agents in the model will arrive randomly with a certain probability distribution and change the viewpoint of the agents according to a certain percentage. Our simulated results show that this model can simulate well the random walk of asset prices and explain the power-law tail distribution of returns, volatility clustering, jumps, and structural changes of asset prices.
      PubDate: Sun, 17 Feb 2019 12:05:11 +000
       
  • A Parameter-Free Model Comparison Test Using Differential Algebra
    • Abstract: We present a method for rejecting competing models from noisy time-course data that does not rely on parameter inference. First we characterize ordinary differential equation models in only measurable variables using differential-algebra elimination. This procedure gives input-output equations, which serve as invariants for time series data. We develop a model comparison test using linear algebra and statistics to reject incorrect models from their invariants. This algorithm exploits the dynamic properties that are encoded in the structure of the model equations without recourse to parameter values, and, in this sense, the approach is parameter-free. We demonstrate this method by discriminating between different models from mathematical biology.
      PubDate: Sun, 17 Feb 2019 08:05:08 +000
       
  • Modelling Multilevel Interdependencies for Resilience in Complex
           Organisation
    • Abstract: This paper aims to model multilevel interdependencies in complex organisational systems and proposes application for resilience analysis. Most of the existing research studied interdependencies only at the single-level and overlooked their multilevel character. In response to this gap, we propose a multilevel approach to better comprehend the complexity of interdependencies in organisational systems. More specifically, the study focuses on how interdependencies are shaped across multiple organisational levels. To understand the research problem, we use multilevel and social network theories to elaborate the concept at five organisational levels, namely, individual, intraunit, interunit, intraorganisational, and interorganisational. Further, we show the application of multilevel interdependencies into analysis of organisational resilience. To this end, we construct a multiplex model of a real world organisational system that comprises formal and informal relations of different social exchange strength. Using the agent-based simulations of the organisational system, we investigate the relations between organisational interdependencies and organisational performance in normal and disrupted conditions. With the results, we argue that managing multilevel interdependencies is crucial to reduce vulnerability of organisational systems. By introducing the multilevel conceptualisation of interdependencies and presenting their influence on organisational resilience, we hope to pave a path to managing the complexity of interdependencies and strategic resilience enhancement in organisational systems.
      PubDate: Thu, 14 Feb 2019 13:05:02 +000
       
  • Sign Prediction on Unlabeled Social Networks Using Branch and Bound
           Optimized Transfer Learning
    • Abstract: Sign prediction problem aims to predict the signs of links for signed networks. Currently it has been widely used in a variety of applications. Due to the insufficiency of labeled data, transfer learning has been adopted to leverage the auxiliary data to improve the prediction of signs in target domain. Existing works suffer from two limitations. First, they cannot work if there is no target label available. Second, their generalization performance is not guaranteed due to that fact that the solution of their objective functions is not global optimal solution. To solve these problems, we propose a novel sign prediction on unlabeled social networks using branch and bound optimized transfer learning (SP_BBTL) sign prediction model. The main idea of SP_BBTL is to use target feature vectors to reconstruct source domain feature vectors based on relationship projection, which is a complicated optimal problem and is solved by proposed optimization based on branch and bound that can obtain global optimal solution. With this design, the target domain label information is not required for classifier. Finally, the experimental results on the large scale social signed networks validate the superiority of the proposed model.
      PubDate: Thu, 14 Feb 2019 12:05:05 +000
       
  • Influence of Duodenal–Jejunal Implantation on Glucose Dynamics: A Pilot
           Study Using Different Nonlinear Methods
    • Abstract: Diabetes is a disease of great and rising prevalence, with the obesity epidemic being a significant contributing risk factor. Duodenal–jejunal bypass liner (DJBL) is a reversible implant that mimics the effects of more aggressive surgical procedures, such as gastric bypass, to induce weight loss. We hypothesized that DJBL also influences the glucose dynamics in type II diabetes, based on the induced changes already demonstrated in other physiological characteristics and parameters. In order to assess the validity of this assumption, we conducted a quantitative analysis based on several nonlinear algorithms (Lempel–Ziv Complexity, Sample Entropy, Permutation Entropy, and modified Permutation Entropy), well suited to the characterization of biomedical time series. We applied them to glucose records drawn from two extreme cases available of DJBL implantation: before and after 10 months. The results confirmed the hypothesis and an accuracy of 86.4% was achieved with modified Permutation Entropy. Other metrics also yielded significant classification accuracy results, all above 70%, provided a suitable parameter configuration was chosen. With the Leave–One–Out method, the results were very similar, between 72% and 82% classification accuracy. There was also a decrease in entropy of glycaemia records during the time interval studied. These findings provide a solid foundation to assess how glucose metabolism may be influenced by DJBL implantation and opens a new line of research in this field.
      PubDate: Thu, 14 Feb 2019 07:05:08 +000
       
  • Numerical Simulation of a Class of Hyperchaotic System Using Barycentric
           Lagrange Interpolation Collocation Method
    • Abstract: Hyperchaotic system, as an important topic, has become an active research subject in nonlinear science. Over the past two decades, hyperchaotic system between nonlinear systems has been extensively studied. Although many kinds of numerical methods of the system have been announced, simple and efficient methods have always been the direction that scholars strive to pursue. Based on this problem, this paper introduces another novel numerical method to solve a class of hyperchaotic system. Barycentric Lagrange interpolation collocation method is given and illustrated with hyperchaotic system () as examples. Numerical simulations are used to verify the effectiveness of the present method.
      PubDate: Wed, 13 Feb 2019 11:05:04 +000
       
  • Robust Stabilization of Discrete-Time Switched Periodic Systems with Time
           Delays
    • Abstract: This paper studies the problems of robust stability and robust stabilization for discrete-time switched periodic systems with time-varying delays and parameter uncertainty. We obtain the novel sufficient conditions to ensure the switched system is robustly asymptotically stable in terms of linear matrix inequalities. To obtain these conditions, we utilize a descriptor system method and introduce a switched Lyapunov-Krasovskii functional. The robust stability results are then extended to solve problems of robust stabilization via periodic state feedback. Novel sufficient conditions are established to ensure that the uncertain switched periodic system is robustly asymptotically stabilizable. Finally, we give two numerical examples to illustrate the effectiveness of our method.
      PubDate: Wed, 13 Feb 2019 09:05:11 +000
       
  • Discovering Travel Community for POI Recommendation on Location-Based
           Social Networks
    • Abstract: Point-of-interest (POI) recommendations are a popular form of personalized service in which users share their POI location and related content with their contacts in location-based social networks (LBSNs). The similarity and relatedness between users of the same POI type are frequently used for trajectory retrieval, but most of the existing works rely on the explicit characteristics from all users’ check-in records without considering individual activities. We propose a POI recommendation method that attempts to optimally recommend POI types to serve multiple users. The proposed method aims to predict destination POIs of a user and search for similar users of the same regions of interest, thus optimizing the user acceptance rate for each recommendation. The proposed method also employs the variable-order Markov model to determine the distribution of a user’s POIs based on his or her travel histories in LBSNs. To further enhance the user’s experience, we also apply linear discriminant analysis to cluster the topics related to “Travel” and connect to users with social links or similar interests. The probability of POIs based on users’ historical trip data and interests in the same topics can be calculated. The system then provides a list of the recommended destination POIs ranked by their probabilities. We demonstrate that our work outperforms collaborative-filtering-based and other methods using two real-world datasets from New York City. Experimental results show that the proposed method is better than other models in terms of both accuracy and recall. The proposed POI recommendation algorithms can be deployed in certain online transportation systems and can serve over 100,000 users.
      PubDate: Tue, 12 Feb 2019 08:05:17 +000
       
  • Hybrid Genetic Grey Wolf Algorithm for Large-Scale Global Optimization
    • Abstract: Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.
      PubDate: Tue, 12 Feb 2019 08:05:15 +000
       
  • Fuel Cell Output Current Prediction with a Hybrid Intelligent System
    • Abstract: A fuel cell is a complex system, which produces electricity through an electrochemical reaction. For the formal application of control strategies on a fuel cell, it is very important to have a precise dynamic model of it. In this paper, a dynamic model of a real hydrogen fuel cell is obtained to predict its response. The data used in this paper to obtain the model have been acquired from a real fuel cell subjected to different load patterns by means of a programmable electronic load. Using this data, a nonlinear model based on a hybrid intelligent system is obtained. This hybrid model uses artificial neural networks to predict the output current of the fuel cell in a very precise way. The use of a hybrid scheme improves the performance of neural networks reducing to half the mean squared error obtained for a global model of the fuel cell.
      PubDate: Tue, 12 Feb 2019 07:05:29 +000
       
  • A Continuous Approximation Approach Based on Regular Hexagon Partition for
           the Facility Location Problem under Disruptions Risk
    • Abstract: Today’s business environment is complex, dynamic, and uncertain which makes a supply chain facility increasingly vulnerable to disruption from various risk accidents as one of the main threats to the whole supply chain’s operation. However, in general, most of the studies on facility location problems assume that the facilities, once built, will always run availably and reliably. In fact, although the probability is very low, supply chain disruptions often incur disastrous consequences. Therefore, it is critical to account for disruptions during designing supply chain networks. To accomplish planned outcomes and greater supply chain resiliency, this article proposes a continuous approximation approach based on regular hexagon partition to address the reliable facility location problems with consideration of facility disruptions risk. The optimization goal is to determine the best facility location that minimizes the expected total system cost on the premise that the supply chain network is not disrupted as a whole when one or some facilities are subject to probabilistic failure. Our numerical experiment discusses the performance of the proposed solution approaches which demonstrates that the benefits of considering disruptions in the supply chain design can be significant. In addition, considering the impact of disruption probability estimation error on the optimal decision, the misestimating of the disruption probability is also investigated in this paper.
      PubDate: Tue, 12 Feb 2019 07:05:27 +000
       
  • Multilayer Architecture Model for Mobile Cloud Computing Paradigm
    • Abstract: Mobile Cloud Computing is one of today's more disruptive paradigms of computation due to its effects on the performance of mobile computing and the development of Internet of Things. It is able to enhance the capabilities of devices by outsourcing the workload to external computing platforms deployed along the network, such as cloud servers, cloudlets, or other edge platforms. The research described in this work presents a computational model of a multilayer architecture for increasing the performance of devices using the Mobile Cloud Computing paradigm. The main novelty of this work lies in defining a comprehensive model where all the available computing platforms along the network layers are involved to perform the outsourcing of the application workload. This proposal provides a generalization of the Mobile Cloud Computing paradigm which allows handling the complexity of scheduling tasks in such complex scenarios. The behaviour of the model and its ability of generalization of the paradigm are exemplified through simulations. The results show higher flexibility for making offloading decisions.
      PubDate: Mon, 11 Feb 2019 13:05:04 +000
       
  • Human Sensitivity to Community Structure Is Robust to Topological
           Variation
    • Abstract: Despite mounting evidence that human learners are sensitive to community structure underpinning temporal sequences, this phenomenon has been studied using an extremely narrow set of network ensembles. The extent to which behavioral signatures of learning are robust to changes in community size and number is the focus of the present work. Here we present adult participants with a continuous stream of novel objects generated by a random walk along graphs of 1, 2, 3, 4, or 6 communities comprised of N = 24, 12, 8, 6, and 4 nodes, respectively. Nodes of the graph correspond to a unique object and edges correspond to their immediate succession in the stream. In short, we find that previously observed processing costs associated with community boundaries persist across an array of graph architectures. These results indicate that statistical learning mechanisms can flexibly accommodate variation in community structure during visual event segmentation.
      PubDate: Mon, 11 Feb 2019 09:05:10 +000
       
  • Opinion Dynamics Model Based on Cognitive Styles: Field-Dependence and
           Field-Independence
    • Abstract: Two distinct cognitive styles exist from the perspective of cognition: field-dependence and field-independence. In most public opinion dynamics models, people only consider that individuals update their opinions through interactions with other individuals. This represents the field-dependent cognitive style of the individual. The field-independent cognitive style is ignored in such cases. We consider both cognitive styles in public opinion dynamics and propose a public opinion evolution model based on cognitive styles (CS model). The opinions of neighbors and experiences of the individual represent field-dependent cognition and field-independent cognition, respectively, and the individual combines both cognitive styles to update his/her own opinion. In the proposed model, the experience parameter is designed to represent the weight of the current opinion in terms of the individual’s experiences and the cognitive parameter is proposed to represent the tendencies of his/her cognitive styles. We experimentally verify that the CS and Hegselmann–Krause (HK) models are similar in terms of public opinion evolution trends; with an increase in radius of confidence, the steady state of a social system shifts from divergence to polarization and eventually reaches consensus. Considering that individuals from different cultures have different degrees of inclination for the two styles, we present experiments focusing on cognitive parameter and experience parameter and analyze the evolutionary trends of opinion dynamics in different styles. We find that when an individual has a greater tendency toward the field-independent cognitive style under the influence of culture, the time required for a social system to reach a steady state will increase; the system will have greater difficultly in reaching consensus, mirroring the evolutionary trends in public opinion in the context of eastern and western cultures. The CS model constitutes an opinion dynamics model that is more consistent with the real world and may also serve as a basis for future cross-cultural research.
      PubDate: Mon, 11 Feb 2019 09:05:08 +000
       
  • Jiles-Atherton Based Hysteresis Identification of Shape Memory
           Alloy-Actuating Compliant Mechanism via Modified Particle Swarm
           Optimization Algorithm
    • Abstract: Shape memory alloy- (SMA-) based actuators are widely applied in the compliant actuating systems. However, the measured data of the SMA-based compliant actuating system reveal the input-output hysteresis behavior, and the actuating precision of the compliant actuating system could be degraded by such hysteresis nonlinearities. To characterize such nonlinearities in the SMA-based compliant actuator precisely, a Jiles-Atherton model is adopted in this paper, and a modified particle swarm optimization (MPSO) algorithm is proposed to identify the parameters in the Jiles-Atherton model, which is a combination of several differential nonlinear equations. Compared with the basic PSO identification algorithm, the designed MPSO algorithm can reduce the local optimum problem so that the Jiles-Atherton model with the identified parameters can show good agreements with the measured experimental data. The good capture ability of the proposed identification algorithm is also examined through the comparisons with Jiles-Atherton model using the basic PSO identification algorithm.
      PubDate: Mon, 11 Feb 2019 08:05:23 +000
       
  • Self-Organized Fission-Fusion Control Algorithm for Flocking Systems Based
           on Intermittent Selective Interaction
    • Abstract: In nature, gregarious animals, insects, or bacteria usually exhibit paradoxical behaviors in the form of group fission and fusion, which exerts an important influence on group’s pattern formation, information transfer, and epidemiology. However, the fission-fusion dynamics have received little attention compared to other flocking behavior. In this paper, an intermittent selective interaction based control algorithm for the self-organized fission-fusion behavior of flocking system is proposed, which bridges the gap between the two conflicting behaviors in a unified fashion. Specifically, a hybrid velocity coordination strategy that includes both the egalitarian and selective interactions is proposed, where the egalitarian interaction is to maintain the flock’s order and achieve the fusion behavior while the selective interaction strategy is for the response to external stimulus information and generates the fission behavior. Numerical simulations demonstrate that the proposed control algorithm can realize the self-organized fission-fusion behavior of flocking system under a unified framework. The influences of the main control parameters on the performance of the fission-fusion behavior are also discussed. In particular, the trade-off parameter α balances the exploration (fission) and exploitation (fusion) behaviors of flocking system and significantly enhances its movement flexibility and environmental adaptivity.
      PubDate: Mon, 11 Feb 2019 08:05:19 +000
       
  • Two-Phase Incremental Kernel PCA for Learning Massive or Online Datasets
    • Abstract: As a powerful nonlinear feature extractor, kernel principal component analysis (KPCA) has been widely adopted in many machine learning applications. However, KPCA is usually performed in a batch mode, leading to some potential problems when handling massive or online datasets. To overcome this drawback of KPCA, in this paper, we propose a two-phase incremental KPCA (TP-IKPCA) algorithm which can incorporate data into KPCA in an incremental fashion. In the first phase, an incremental algorithm is developed to explicitly express the data in the kernel space. In the second phase, we extend an incremental principal component analysis (IPCA) to estimate the kernel principal components. Extensive experimental results on both synthesized and real datasets showed that the proposed TP-IKPCA produces similar principal components as conventional batch-based KPCA but is computationally faster than KPCA and its several incremental variants. Therefore, our algorithm can be applied to massive or online datasets where the batch method is not available.
      PubDate: Mon, 11 Feb 2019 07:05:18 +000
       
  • Opinion Polarization during a Dichotomous Electoral Process
    • Abstract: Political polarization can emerge on electoral campaigns where the population faces a dichotomous decision, with only two voting alternatives. In this paper, we analyze the Twitter conversation around the second round of the 2017 Chilean elections, where voters had to choose between the final two candidates. First, we have estimated the opinions of Twitter users obtaining a distribution of opinions for each day. Next, we have measured the resulting political polarization from the cited opinion distributions and track its evolution during a full week that includes the voting day. We found the conversation to be highly polarized, reaching its maximum during the previous day to the election and significantly decreasing the day after due to the presence of new users who only participated during that day.
      PubDate: Sun, 10 Feb 2019 14:05:02 +000
       
  • Improved Monarch Butterfly Optimization Algorithm Based on
           Opposition-Based Learning and Random Local Perturbation
    • Abstract: Many optimization problems have become increasingly complex, which promotes researches on the improvement of different optimization algorithms. The monarch butterfly optimization (MBO) algorithm has proven to be an effective tool to solve various kinds of optimization problems. However, in the basic MBO algorithm, the search strategy easily falls into local optima, causing premature convergence and poor performance on many complex optimization problems. To solve the issues, this paper develops a novel MBO algorithm based on opposition-based learning (OBL) and random local perturbation (RLP). Firstly, the OBL method is introduced to generate the opposition-based population coming from the original population. By comparing the opposition-based population with the original population, the better individuals are selected and pass to the next generation, and then this process can efficiently prevent the MBO from falling into a local optimum. Secondly, a new RLP is defined and introduced to improve the migration operator. This operation shares the information of excellent individuals and is helpful for guiding some poor individuals toward the optimal solution. A greedy strategy is employed to replace the elitist strategy to eliminate setting the elitist parameter in the basic MBO, and it can reduce a sorting operation and enhance the computational efficiency. Finally, an OBL and RLP-based improved MBO (OPMBO) algorithm with its complexity analysis is developed, following on which many experiments on a series of different dimensional benchmark functions are performed and the OPMBO is applied to clustering optimization on several public data sets. Experimental results demonstrate that the proposed algorithm can achieve the great optimization performance compared with a few state-of-the-art algorithms in most of the test cases.
      PubDate: Sun, 10 Feb 2019 13:05:11 +000
       
  • Topological Influence-Aware Recommendation on Social Networks
    • Abstract: Users in online networks exert different influence during the process of information propagation, and the heterogeneous influence may contribute to personalized recommendations. In this paper, we analyse the topology of social networks to investigate users’ influence strength on their neighbours. We also exploit the user-item rating matrix to find the importance of users’ ratings and determine their influence on entire social networks. Based on the local influence between users and global influence over the whole network, we propose a recommendation method with indirect interactions that makes adequate use of users’ relationships on social networks and users’ rating data. The two kinds of influence are incorporated into a matrix factorization framework. We also consider indirect interactions between users who do not have direct links with each other. Experimental results on two real-world datasets demonstrate that our proposed framework performs better than other state-of-the-art methods for all users and cold-start users. Compared with node degrees, betweenness, and clustering coefficients, coreness constitutes the best topological descriptor to identify users’ local influence, and recommendations with the measure of coreness outperform other descriptors of user influence.
      PubDate: Sun, 10 Feb 2019 10:05:10 +000
       
  • Interval State Estimation of Linear Multicellular Systems
    • Abstract: Linear multicellular system is a type of differential inclusion system, which can be deemed as an extension of linear control system with set-valued mapping. As an important issue in existing control systems, interval state estimation has been widely applied in engineering practices. Over the years, the objects of the studies on interval state estimation have been extended from the initial linear time-invariant systems to linear time-varying systems, chaotic systems, feedback linearization systems, and nonlinear Lipschitz systems. However, there is no report on the design of interval observer for linear multicellular system. To make up for this gap, this chapter attempts to explore the design of an interval observer for linear multicellular systems.
      PubDate: Thu, 07 Feb 2019 12:05:20 +000
       
  • Nonlinear Backstepping Control Design for Coupled Nonlinear Systems under
           External Disturbances
    • Abstract: A nonlinear backstepping control is proposed for the coupled normal form of nonlinear systems. The proposed method is designed by combining the sliding-mode control and backstepping control with a disturbance observer (DOB). The key idea behind the proposed method is that the linear terms of state variables of the second subsystem are lumped into the virtual input in the first subsystem. A DOB is developed to estimate the external disturbances. Auxiliary state variables are used to avoid amplification of the measurement noise in the DOB. For output tracking and unmatched disturbance cancellation in the first subsystem, the desired virtual input is derived via the backstepping procedure. The actual input in the second subsystem is developed to guarantee the convergence of the virtual input to the desired virtual input by using a sliding-mode control. The stability of the closed-loop is verified by using the input-to-state stable (ISS) property. The performance of the proposed method is validated via numerical simulations and an application to a vehicle system based on CarSim platform.
      PubDate: Thu, 07 Feb 2019 09:05:22 +000
       
  • STEAM-ME: A Novel Model for Successful Kaizen Implementation and
           Sustainable Performance of SMEs in Vietnam
    • Abstract: The current trend of international integration urges every business organizations to continuously improve their competitive advantage for their survival and sustainable growth. And Kaizen has been a preferable approach in practice. Due to the special role of SMEs in the Vietnam economy, improving their competitiveness is critical. Thus, this study is aimed at identifying determinants of the successful Kaizen implementation and sustainable performance so that SMEs can have proper actions and prioritize their operations within their available resources. Through a formal survey of 213 participants from 62 SMEs which have been successful in implementing Kaizen and appropriate statistical analyses, seven important determinants have been identified, namely, (1) supports from senior management; (2) training; (3) environment; (4) assessment; (5) motivation; (6) mindset; and (7) engagement of all members in the organization. Among them, “mindset” is newly proposed in this study through a qualitative research and found as crucial component in the model. The finding obviously fulfills the existing literature. Moreover, the first letters of the identified factors are orderly congregated as “STEAM-ME” which is a novel model for the successful Kaizen implementation and the sustainable performance of SMEs in Vietnam. “STEAM-ME” implies that organizations need to have a new airflow as “steam” to make all of its members refreshed and brimful of energy to gain significant success in implementing Kaizen, and improve their business performance as well as competitive advantage for their sustainable development. Notably, the novel model can efficiently demonstrate organic relationships among its components which all have positive and significant impacts on the successful Kaizen implementation and sustainable performance of SMEs in Vietnam.
      PubDate: Thu, 07 Feb 2019 08:05:06 +000
       
  • A Block Object Detection Method Based on Feature Fusion Networks for
           Autonomous Vehicles
    • Abstract: Nowadays, automatic multi-objective detection remains a challenging problem for autonomous vehicle technologies. In the past decades, deep learning has been demonstrated successful for multi-objective detection, such as the Single Shot Multibox Detector (SSD) model. The current trend is to train the deep Convolutional Neural Networks (CNNs) with online autonomous vehicle datasets. However, network performance usually degrades when small objects are detected. Moreover, the existing autonomous vehicle datasets could not meet the need for domestic traffic environment. To improve the detection performance of small objects and ensure the validity of the dataset, we propose a new method. Specifically, the original images are divided into blocks as input to a VGG-16 network which add the feature map fusion after CNNs. Moreover, the image pyramid is built to project all the blocks detection results at the original objects size as much as possible. In addition to improving the detection method, a new autonomous driving vehicle dataset is created, in which the object categories and labelling criteria are defined, and a data augmentation method is proposed. The experimental results on the new datasets show that the performance of the proposed method is greatly improved, especially for small objects detection in large image. Moreover, the proposed method is adaptive to complex climatic conditions and contributes a lot for autonomous vehicle perception and planning.
      PubDate: Wed, 06 Feb 2019 08:05:10 +000
       
  • Passive Initialization Method Based on Motion Characteristics for
           Monocular SLAM
    • Abstract: Visual SLAM techniques have proven to be effective methods for estimating robust position and attitude in the field of robotics. However, current monocular SLAM algorithms cannot guarantee timeliness of system startup due to the problematic initialization time and the low success rates. This paper introduces a rectilinear platform motion hypothesis and thereby converts the estimation problem into a verification problem to achieve fast monocular SLAM initialization. The proposed method is simulation tested on a fixed-wing UAV. Tests show that the proposed method can produce faster initialization of visual SLAM and that the advantages are more profound on systems with sparse image features.
      PubDate: Tue, 05 Feb 2019 13:30:04 +000
       
  • Improved Coupled Tensor Factorization with Its Applications in Health Data
           Analysis
    • Abstract: Coupled matrix and tensor factorizations have been successfully used in many data fusion scenarios where datasets are assumed to be exactly coupled. However, in the real world, not all the datasets share the same factor matrices, which makes joint analysis of multiple heterogeneous sources challenging. For this reason, approximate coupling or partial coupling is widely used in real-world data fusion, with exact coupling as a special case of these techniques. However, to fully address the challenge of tensor factorization, in this paper, we propose two improved coupled tensor factorization methods: one for approximately coupled datasets and the other for partially coupled datasets. A series of experiments using both simulated data and three real-world datasets demonstrate the improved accuracy of these approaches over existing baselines. In particular, when experiments on MRI data is conducted, the performance of our method is improved even by 12.47% in terms of accuracy compared with traditional methods.
      PubDate: Tue, 05 Feb 2019 08:05:07 +000
       
  • IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks
           Parallel-Hybrid Execution Cloud Service Composition for Cloud
           Manufacturing
    • Abstract: Cloud manufacturing (CMfg) is a new service-oriented smart manufacturing paradigm, and it provides a new product development model in which users are enabled to configure, select, and utilize customized manufacturing service on-demand. Because of the massive manufacturing resources, various users with individualized demands, heterogeneous manufacturing system or platform, and different data type or file type, CMfg is fully recognized as a kind of complex manufacturing system in complex environment and has received considerable attention in recent years. In practical scenarios of CMfg, the amount of manufacturing task may be very large, and there are always quite a lot of candidate manufacturing services in cloud pool for corresponding subtasks. These candidate services will be selected and composed together to complete a complex manufacturing task. Obviously, manufacturing service composition plays a very important role in CMfg lifecycle and thus enables complex manufacturing system to be stable, safe, reliable, and efficient and effective. In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. To address the problem, a novel optimization method named as Improved Hybrid Differential Evolution and Teaching Based Optimization (IHDETBO) is proposed and introduced in detail. The results obtained by simulation experiments and case study validate the effectiveness and feasibility of the proposed algorithm.
      PubDate: Mon, 04 Feb 2019 13:05:06 +000
       
 
 
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