Search results for: random effects model

Authors: Sidi Yang, Haiyi Zhang

Abstract:

Twitter is a microblogging platform, where millions of users daily share their attitudes, views, and opinions. Using a probabilistic Latent Dirichlet Allocation (LDA) topic model to discern the most popular topics in the Twitter data is an effective way to analyze a large set of tweets to find a set of topics in a computationally efficient manner. Sentiment analysis provides an effective method to show the emotions and sentiments found in each tweet and an efficient way to summarize the results in a manner that is clearly understood. The primary goal of this paper is to explore text mining, extract and analyze useful information from unstructured text using two approaches: LDA topic modelling and sentiment analysis by examining Twitter plain text data in English. These two methods allow people to dig data more effectively and efficiently. LDA topic model and sentiment analysis can also be applied to provide insight views in business and scientific fields.

Keywords: Text mining, Twitter, topic model, sentiment analysis.

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Authors: Changjin Xu, Peiluan Li

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In this paper, we study the stability of a fractional order delayed predator-prey model. By using the Laplace transform, we introduce a characteristic equation for the above system. It is shown that if all roots of the characteristic equation have negative parts, then the equilibrium of the above fractional order predator-prey system is Lyapunov globally asymptotical stable. An example is given to show the effectiveness of the approach presented in this paper.

Keywords: Fractional predator-prey model, laplace transform, characteristic equation.

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Authors: M. R. Dolatian, A. Jalilian

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The behavior of three phase five leg transformer under voltage sag is studied in this paper. This paper proposes a simple, practical model of a three phase-five leg, saturated transformer with accurate performance. Transformer saturation is produced when the voltage sag is recovered and it causes inrush current in transformer. Effects of voltage sag depth, duration and initial point on wave have been analyzed in this paper. Initial point on wave can produce maximum inrush current in five leg transformers while comparing with three leg transformers. The magnetic circuit symmetry of five leg transformer produces the more symmetrical shape of inrush current curves versus initial point on wave and sag duration than three leg transformer. The simulations show that current peak has a periodical dependence on sag duration and linear dependence on sag depth. Inrush current that is produced in three phase five leg transformer is higher than three phase three leg transformer.

Keywords: Inrush current, three phase five leg transformer, saturation, voltage sag.

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Authors: Chaudhuri Manoj Kumar Swain, Susmita Das

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This paper explores a detailed procedure of predicting a path loss (PL) model and its application in estimating the coverage probability in a WiMAX network. For this a hybrid approach is followed in predicting an empirical PL model of a 2.65 GHz WiMAX network deployed in a suburban environment. Data collection, statistical analysis, and regression analysis are the phases of operations incorporated in this approach and the importance of each of these phases has been discussed properly. The procedure of collecting data such as received signal strength indicator (RSSI) through experimental set up is demonstrated. From the collected data set, empirical PL and RSSI models are predicted with regression technique. Furthermore, with the aid of the predicted PL model, essential parameters such as PL exponent as well as the coverage probability of the network are evaluated. This research work may assist in the process of deployment and optimisation of any cellular network significantly.

Keywords: WiMAX, RSSI, path loss, coverage probability, regression analysis.

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Authors: Onesmus Muvengei, John Kihiu, Bernard Ikua

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This paper numerically investigates the effects of input speed on the overall dynamic characteristics of a multi-body system with differently located revolute clearance joints without friction. A typical planar slider-crank mechanism is used as a demonstration case in which the effects of the input speed on the dynamic performance of the mechanism with a revolute clearance joint between the crank and connecting rod, and between the connecting rod and slider are separately investigated with comprehensive observations numerically presented. It is observed that, changing the driving speed of a multibody system makes the behavior of the system to change from either periodic to chaotic, or chaotic to periodic depending on which joint has clearance. The location of the clearance revolute joint and the operating speed of a multi-body system play a crucial role in predicting accurately the dynamic responses of the system. Therefore the dynamic behavior of one clearance revolute joint cannot be used as a general case for a mechanical system.

Keywords: Chaotic behavior, Contact-impact forces, Dynamic response, Multi-body mechanical system, Periodic behavior, Poincare maps, Quasi-periodic behavior, Revolute clearance joint

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Authors: Jae Kyung Son, Wan Shik Jang, Sung hyun Shim, Yoon Gyung Sung

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This paper is concerned with the application of the vision control algorithm for robot's point placement task in discontinuous trajectory caused by obstacle. The presented vision control algorithm consists of four models, which are the robot kinematic model, vision system model, parameters estimation model, and robot joint angle estimation model.When the robot moves toward a target along discontinuous trajectory, several types of obstacles appear in two obstacle regions. Then, this study is to investigate how these changes will affect the presented vision control algorithm.Thus, the practicality of the vision control algorithm is demonstrated experimentally by performing the robot's point placement task in discontinuous trajectory by obstacle.

Keywords: Vision control algorithm, location of obstacle region, range of obstacle region, point placement.

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Authors: Babak H. Tabrizi, Seyed Farid Ghaderi

Abstract:

Concurrent planning of project scheduling and material ordering has been increasingly addressed within last decades as an approach to improve the project execution costs. Therefore, we have taken the problem into consideration in this paper, aiming to maximize schedules quality robustness, in addition to minimize the relevant costs. In this regard, a bi-objective mathematical model is developed to formulate the problem. Moreover, it is possible to utilize the all-unit discount for materials purchasing. The problem is then solved by the E-constraint method, and the Pareto front is obtained for a variety of robustness values. The applicability and efficiency of the proposed model is tested by different numerical instances, finally.

Keywords: E-constraint method, material ordering, project management, project scheduling.

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Authors: Petr Gurný

Abstract:

One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the creditscoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: Credit-scoring Models, Multidimensional Subordinated Lévy Model, Probability of Default.

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Authors: S. Balaji, V. Swaminathan, K. Kannan

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The Minimum Vertex Cover (MVC) problem is a classic graph optimization NP - complete problem. In this paper a competent algorithm, called Vertex Support Algorithm (VSA), is designed to find the smallest vertex cover of a graph. The VSA is tested on a large number of random graphs and DIMACS benchmark graphs. Comparative study of this algorithm with the other existing methods has been carried out. Extensive simulation results show that the VSA can yield better solutions than other existing algorithms found in the literature for solving the minimum vertex cover problem.

Keywords: vertex cover, vertex support, approximation algorithms, NP - complete problem.

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Authors: Thanat Rungsirathana, Vorathit Rungsetthaphat, Shogo Azuma, Nobuhiro Harada

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This paper presents a 2-D hydrodynamic model of the ablated plasma when irradiating a 50 μm Al solid target with a single pulsed ion beam. The Lagrange method is used to solve the moving fluid for the ablated plasma production and formation mechanism. In the calculations, a 10-ns-single-pulsed of ion beam with a total energy density of 120 J/cm2, is used. The results show that the ablated plasma was formed after 2 ns of ion beam irradiation and it started to expand right after 4-6 ns. In addition, the 2-D model give a better understanding of pulsed ion beam-solid target ablated plasma production and expansion process clearer.

Keywords: Ablated plasma, pulse ion beam, thin foil solid target, two-dimensional model

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Authors: Faraj El Dabee, Romeo Marian, Yousef Amer

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Just-In-Time (JIT) is a lean manufacturing tool, which provides the benefits of efficiency, and of minimizing unnecessary costs for many organisations. However, the risks arising from these benefits have been disregarded. These risks impact on system processes disrupting the whole supply chain. This paper proposes an inventory model that can simultaneously reduce costs and risks in JIT systems. This model is developed to ascertain an optimal ordering strategy for procuring raw materials by using regular multi-external and local backup suppliers to reduce the total cost of the products, and at the same time to reduce the risks arising from this cost reduction within production systems. Some results that will be illustrated in the second part of this paper are presented.

Keywords: Lean manufacturing, Just-in-Time (JIT), production system, cost-risk reduction, inventory model, eternal supplier, local backup supplier.

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Authors: N. Khatiashvili, Ch. Pirumova, V. Akhobadze

Abstract:

In the paper the mathematical model of tumor growth is considered. New capillary network formation, which supply cancer cells with the nutrients, is taken into the account. A formula estimating a tumor growth in connection with the number of capillaries is obtained.

Keywords: Differential Equations, Mathematical Models, Vascular Endothelial, Tumor

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Authors: Rossella Arcucci, Luisa D’Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

Abstract:

This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: Data Assimilation, Parallel Algorithm, GPU architectures, Ocean Models.

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Authors: R. Tandlich, S. Hoossein, K. A. Tagwira, M. M. Marais, T. A. Ludwig, R. P. Chidziva, M. N. Munodawafa, W. M. Wrench

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Response to the public health-related emergencies is analysed here for a rural university in South Africa. The structure of the designated emergency plan covers all the phases of the disaster management cycle. The plan contains elements of the vulnerability model and the technocratic model of emergency management. The response structures are vertically and horizontally integrated, while the planning contains elements of scenario-based and functional planning. The available number of medical professionals at the Rhodes University, along with the medical insurance rates, makes the staff and students potentially more medically vulnerable than the South African population. The main improvements of the emergency management are required in the tornado response and the information dissemination during health emergencies. The latter should involve the increased use of social media and e-mails, following the Taylor model of communication. Infrastructure must be improved in the telecommunication sector in the face of unpredictable electricity outages.

Keywords: Public health, Rural university, Taylor model of communication.

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Authors: Samiah Alammari, Nassim Ammour

Abstract:

When providing a massive number of tasks successively to a deep learning process, a good performance of the model requires preserving the previous tasks data to retrain the model for each upcoming classification. Otherwise, the model performs poorly due to the catastrophic forgetting phenomenon. To overcome this shortcoming, we developed a successful continual learning deep model for remote sensing hyperspectral image regions classification. The proposed neural network architecture encapsulates two trainable subnetworks. The first module adapts its weights by minimizing the discrimination error between the land-cover classes during the new task learning, and the second module tries to learn how to replicate the data of the previous tasks by discovering the latent data structure of the new task dataset. We conduct experiments on hyperspectral image (HSI) dataset on Indian Pines. The results confirm the capability of the proposed method.

Keywords: Continual learning, data reconstruction, remote sensing, hyperspectral image segmentation.

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Authors: S. K. Tomar, R. Prasad, S. Panda, C. Ardil

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Reduction of Single Input Single Output (SISO) discrete systems into lower order model, using a conventional and an evolutionary technique is presented in this paper. In the conventional technique, the mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method the original discrete system is, first, converted into equivalent continuous system by applying bilinear transformation. The denominator of the equivalent continuous system and its reciprocal are differentiated successively, the reduced denominator of the desired order is obtained by combining the differentiated polynomials. The numerator is obtained by matching the quotients of MCF. The reduced continuous system is converted back into discrete system using inverse bilinear transformation. In the evolutionary technique method, Particle Swarm Optimization (PSO) is employed to reduce the higher order model. PSO method is based on the minimization of the Integral Squared Error (ISE) between the transient responses of original higher order model and the reduced order model pertaining to a unit step input. Both the methods are illustrated through numerical example.

Keywords: Discrete System, Single Input Single Output (SISO), Bilinear Transformation, Reduced Order Model, Modified CauerForm, Polynomial Differentiation, Particle Swarm Optimization, Integral Squared Error.

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Authors: M. Hadi Mashinchi, M. Reza Mashinchi, Siti Mariyam H. J. Shamsuddin

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In this paper a genetic algorithms approach for solving the linear and quadratic fuzzy equations Ãx̃=B̃ and Ãx̃² + B̃x̃=C̃ , where Ã, B̃, C̃ and x̃ are fuzzy numbers is proposed by genetic algorithms. Our genetic based method initially starts with a set of random fuzzy solutions. Then in each generation of genetic algorithms, the solution candidates converge more to better fuzzy solution x̃_b . In this proposed method the final reached x̃_b is not only restricted to fuzzy triangular and it can be fuzzy number.

Keywords: Fuzzy coefficient, fuzzy equation, genetic algorithms.

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Authors: Agnes Achs

Abstract:

The basic aim of our study is to give a possible model for handling uncertain information. This model is worked out in the framework of DATALOG. The concept of multivalued knowledgebase will be defined as a quadruple of any background knowledge; a deduction mechanism; a connecting algorithm, and a function set of the program, which help us to determine the uncertainty levels of the results. At first the concept of fuzzy Datalog will be summarized, then its extensions for intuitionistic- and interval-valued fuzzy logic is given and the concept of bipolar fuzzy Datalog is introduced. Based on these extensions the concept of multivalued knowledge-base will be defined. This knowledge-base can be a possible background of a future agent-model.

Keywords: Fuzzy-, intuitionistic-, bipolar datalog, multivalued knowledge-base

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Authors: Nik Kamariah Nik-Mat, Noor Hasmini Abd-Ghani, Jamal Mohammed Esmail Al-Ekam

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The Malaysian government had consistently revived its campaign for “Buy Malaysian Goods” from time to time. The purpose of the campaign is to remind consumers to be ethnocentric and patriotic when purchasing product and services. This is necessary to ensure high demand for local products and services compared to foreign products. However, the decline of domestic investment in 2012 has triggered concern for the Malaysian economy. Hence, this study attempts to determine the drivers of actual purchasing behavior, intention to purchase domestic products and ethnocentrism. The study employs the cross-sectional primary data, self-administered on household, selected using stratified random sampling in four Malaysian regions. A nine factor driver of actual domestic purchasing behavior (culture openness, conservatism, collectivism, patriotism, control belief, interest in foreign travel, attitude, ethnocentrism and intention) were measured utilizing 60 items, using 7-point Likertscale. From 1000 questionnaires distributed, a sample of 486 were returned representing 48.6 percent response rate. From the fit generated structural model (SEM analysis), it was found that the drivers of actual purchase behavior are collectivism, cultural openness and patriotism; the drivers of intention to purchase domestic product are attitude, control belief, collectivism and conservatism; and drivers of ethnocentrism are cultural openness, control belief, foreign travel and patriotism. It also shows that Malaysian consumers scored high in ethnocentrism and patriotism. The findings are discussed in the perspective of its implication to Malaysian National Agenda.

Keywords: Actual purchase, ethnocentrism, culture openness, conservatism, collectivism, patriotism.

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Authors: Aber Salem Aboalgasm, Rupert Ward

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Teaching art by digital means is a big challenge for the majority of teachers of art and design in primary schools, yet it allows relationships between art, technology and creativity to be clearly identified. The aim of this article is to present a modern way of teaching art, using digital tools in the art classroom to improve creative ability in pupils aged between nine and eleven years. It also presents a conceptual model for creativity based on digital art. The model could be useful for pupils interested in learning to draw by using an e-drawing package, and for teachers who are interested in teaching modern digital art in order to improve children’s creativity. By illustrating the strategy of teaching art through technology, this model may also help education providers to make suitable choices about which technological approaches are most effective in enhancing students’ creative ability, and which digital art tools can benefit children by developing their technical skills. It is also expected that use of this model will help to develop skills of social interaction, which may in turn improve intellectual ability.

Keywords: Digital tools, motivation, creative activity.

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Authors: K. Manikandan, T. Viruthagiri

Abstract:

Studies on Simultaneous Saccharification and Fermentation (SSF) of corn flour, a major agricultural product as the substrate using starch digesting glucoamylase enzyme derived from Aspergillus niger and non starch digesting and sugar fermenting Saccharomyces cerevisiae in a batch fermentation. Experiments based on Central Composite Design (CCD) were conducted to study the effect of substrate concentration, pH, temperature, enzyme concentration on Ethanol Concentration and the above parameters were optimized using Response Surface Methodology (RSM). The optimum values of substrate concentration, pH, temperature and enzyme concentration were found to be 160 g/l, 5.5, 30°C and 50 IU respectively. The effect of inoculums age on ethanol concentration was also investigated. The corn flour solution equivalent to 16% initial starch concentration gave the highest ethanol concentration of 63.04 g/l after 48 h of fermentation at optimum conditions of pH and temperature. Monod model and Logistic model were used for growth kinetics and Leudeking – Piret model was used for product formation kinetics.

Keywords: Simultaneous Saccharification and Fermentation(SSF), Corn Starch, Ethanol, Logisitic Model.

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Authors: Heejung Lee, Hyo-Won Suh

Abstract:

Hybrid knowledge model is suggested as an underlying framework for product development management. It can support such hybrid features as ontologies and rules. Effective collaboration in product development environment depends on sharing and reasoning product information as well as engineering knowledge. Many studies have considered product information and engineering knowledge. However, most previous research has focused either on building the ontology of product information or rule-based systems of engineering knowledge. This paper shows that F-logic based knowledge model can support such desirable features in a hybrid way.

Keywords: Ontology, rule, F-logic, product development.

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Authors: Chiung-Hui Chen

Abstract:

The Internet of Things (IoT) was designed for widespread convenience. With the smart tag and the sensing network, a large quantity of dynamic information is immediately presented in the IoT. Through the internal communication and interaction, meaningful objects provide real-time services for users. Therefore, the service with appropriate decision-making has become an essential issue. Based on the science of human behavior, this study employed the environment model to record the time sequences and locations of different behaviors and adopted the probability module of the hierarchical Hidden Markov Model for the inference. The statistical analysis was conducted to achieve the following objectives: First, define user behaviors and predict the user behavior routes with the environment model to analyze user purposes. Second, construct the hierarchical Hidden Markov Model according to the logic framework, and establish the sequential intensity among behaviors to get acquainted with the use and activity fabric of the intelligent environment. Third, establish the intensity of the relation between the probability of objects’ being used and the objects. The indicator can describe the possible limitations of the mechanism. As the process is recorded in the information of the system created in this study, these data can be reused to adjust the procedure of intelligent design services.

Keywords: Behavior, big data, hierarchical Hidden Markov Model, intelligent object.

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Authors: B. L. Ho, L. Shi, D. F. Wang, V. C. T. Mok

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The brain’s functional connectivity while temporally non-stationary does express consistency at a macro spatial level. The study of stable resting state connectivity patterns hence provides opportunities for identification of diseases if such stability is severely perturbed. A mathematical model replicating the brain’s spatial connections will be useful for understanding brain’s representative geometry and complements the empirical model where it falls short. Empirical computations tend to involve large matrices and become infeasible with fine parcellation. However, the proposed analytical model has no such computational problems. To improve replicability, 92 subject data are obtained from two open sources. The proposed methodology, inspired by financial theory, uses multivariate regression to find relationships of every cortical region of interest (ROI) with some pre-identified hubs. These hubs acted as representatives for the entire cortical surface. A variance-covariance framework of all ROIs is then built based on these relationships to link up all the ROIs. The result is a high level of match between model and empirical correlations in the range of 0.59 to 0.66 after adjusting for sample size; an increase of almost forty percent. More significantly, the model framework provides an intuitive way to delineate between systemic drivers and idiosyncratic noise while reducing dimensions by more than 30 folds, hence, providing a way to conduct attribution analysis. Due to its analytical nature and simple structure, the model is useful as a standalone toolkit for network dependency analysis or as a module for other mathematical models.

Keywords: Functional magnetic resonance imaging, multivariate regression, network hubs, resting state functional connectivity.

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Authors: Wang Yichen, Haruka Yamashita

Abstract:

In recent years, in the field of sports, decision making such as member in the game and strategy of the game based on then analysis of the accumulated sports data are widely attempted. In fact, in the NBA basketball league where the world's highest level players gather, to win the games, teams analyze the data using various statistical techniques. However, it is difficult to analyze the game data for each play such as the ball tracking or motion of the players in the game, because the situation of the game changes rapidly, and the structure of the data should be complicated. Therefore, it is considered that the analysis method for real time game play data is proposed. In this research, we propose an analytical model for "determining the optimal lineup composition" using the real time play data, which is considered to be difficult for all coaches. In this study, because replacing the entire lineup is too complicated, and the actual question for the replacement of players is "whether or not the lineup should be changed", and “whether or not Small Ball lineup is adopted”. Therefore, we propose an analytical model for the optimal player selection problem based on Small Ball lineups. In basketball, we can accumulate scoring data for each play, which indicates a player's contribution to the game, and the scoring data can be considered as a time series data. In order to compare the importance of players in different situations and lineups, we combine RNN (Recurrent Neural Network) model, which can analyze time series data, and NN (Neural Network) model, which can analyze the situation on the field, to build the prediction model of score. This model is capable to identify the current optimal lineup for different situations. In this research, we collected all the data of accumulated data of NBA from 2019-2020. Then we apply the method to the actual basketball play data to verify the reliability of the proposed model.

Keywords: Recurrent Neural Network, players lineup, basketball data, decision making model.

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Authors: Mohamed A. Alsharif, Peter A. Wallace, Donald M. Hepburn, Chengke Zhou

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Worldwide, most PILC MV underground cables in use are approaching the end of their design life; hence, failures are likely to increase. This paper studies the electric field and potential distributions within the PILC insulted cable containing common void-defect. The finite element model of the performance of the belted PILC MV underground cable is presented. The variation of the electric field stress within the cable using the Finite Element Method (FEM) is concentrated. The effects of the void-defect within the insulation are given. Outcomes will lead to deeper understanding of the modeling of Paper Insulated Lead Covered (PILC) and electric field response of belted PILC insulted cable containing void defect.

Keywords: MV PILC cables, Finite Element Method /COMSOL Multiphysics, Electric Field Stress, Partial Discharge Degradation.

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Authors: Wookyong Kwon, Sangmoon Lee

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In this paper, a sampled-data model predictive tracking control method is presented for mobile robots which is modeled as constrained continuous-time linear parameter varying (LPV) systems. The presented sampled-data predictive controller is designed by linear matrix inequality approach. Based on the input delay approach, a controller design condition is derived by constructing a new Lyapunov function. Finally, a numerical example is given to demonstrate the effectiveness of the presented method.

Keywords: Model predictive control, sampled-data control, linear parameter varying systems, LPV.

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Authors: Z. Fang, H.Y. Yu, W.J. Liu, N. Singh, G.Q. Lo

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High performance Resistive Random Access Memory (RRAM) based on HfOx has been prepared and its temperature instability has been investigated in this work. With increasing temperature, it is found that: leakage current at high resistance state increases, which can be explained by the higher density of traps inside dielectrics (related to trap-assistant tunneling), leading to a smaller On/Off ratio; set and reset voltages decrease, which may be attributed to the higher oxygen ion mobility, in addition to the reduced potential barrier to create / recover oxygen ions (or oxygen vacancies); temperature impact on the RRAM retention degradation is more serious than electrical bias.

Keywords: RRAM, resistive switching, temperature instability.

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Authors: Hermenegilde Nkurunziza, Albrecht Gebhardt, Juergen Pilz

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The focus in this work is to assess which method allows a better forecasting of malaria cases in Bujumbura ( Burundi) when taking into account association between climatic factors and the disease. For the period 1996-2007, real monthly data on both malaria epidemiology and climate in Bujumbura are described and analyzed. We propose a hierarchical approach to achieve our objective. We first fit a Generalized Additive Model to malaria cases to obtain an accurate predictor, which is then used to predict future observations. Various well-known forecasting methods are compared leading to different results. Based on in-sample mean average percentage error (MAPE), the multiplicative exponential smoothing state space model with multiplicative error and seasonality performed better.

Keywords: Burundi, Forecasting, Malaria, Regressionmodel, State space model.

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Authors: Sedigheh Mirzaei S., Debasis Sengupta

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Parametric models have been quite popular for studying human growth, particularly in relation to biological parameters such as peak size velocity and age at peak size velocity. Longitudinal data are generally considered to be vital for fittinga parametric model to individual-specific data, and for studying the distribution of these biological parameters in a human population. However, cross-sectional data are easier to obtain than longitudinal data. In this paper, we present a method of combining longitudinal and cross-sectional data for the purpose of estimating the distribution of the biological parameters. We demonstrate, through simulations in the special case ofthePreece Baines model, how estimates based on longitudinal data can be improved upon by harnessing the information contained in cross-sectional data.We study the extent of improvement for different mixes of the two types of data, and finally illustrate the use of the method through data collected by the Indian Statistical Institute.

Keywords: Preece-Baines growth model, MCMC method, Mixed effect model

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