Search results for: nonlinear hysteretic model

Authors: Amit Saraswat, Ruchi Tewari

Abstract:

Purpose: The research paper aims at understanding the alignment of business and IT in the Indian context and the business value attached to such an alignment. Methodology: The study is conducted in two stages. Stage one: Bibliographic research was conducted to evolve the parameters for defining alignment. Stage two: Evolving a model for strategic alignment to conduct an empirical study. The model is defined in terms of four fundamental domains of strategic management choice – business strategy, information strategy, organizational structure, and information technology structure. A survey through a questionnaire was conducted across organizations from 4 different industries and Structure Equation Modelling (SEM) technique is used for validating the model. Findings: In the Indian scenario all the subscales of alignment could not be validated. It could be validated that organizational strategy impacts information strategy and information technology structure. Research Limitations: The study is limited to the Indian context. Business IT alignment may be culture dependent so further research is required to validate the model in other cultures. Originality/Value: In the western world several models of alignment of business strategy and information systems is available but they do not measure the extent of alignment which the current study in the Indian context. Findings of the study can be used by managers in strategizing and understanding their business and information systems needs holistically and cohesively leading to efficient use of resources and output.

Keywords: business strategy, information technology (IT), business IT alignment, SEM

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Authors: Gholamhosein Khosravi, Mohammad Azadi, Hamidreza Ghezavati

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In this paper, vibration of a nonlinear composite beam is analyzed and then an active controller is used to control the vibrations of the system. The beam is resting on a Winkler-Pasternak elastic foundation. The composite beam is reinforced by single walled carbon nanotubes. Using the rule of mixture, the material properties of functionally graded carbon nanotube-reinforced composites (FG-CNTRCs) are determined. The beam is cantilever and the free end of the beam is under follower force. Piezoelectric layers are attached to the both sides of the beam to control vibrations as sensors and actuators. The governing equations of the FG-CNTRC beam are derived based on Euler-Bernoulli beam theory Lagrange- Rayleigh-Ritz method. The simulation results are presented and the effects of some parameters on stability of the beam are analyzed.

Keywords: carbon nanotubes, vibration control, piezoelectric layers, elastic foundation

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Authors: Gak-Gyu Kim, Won Il Jung

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According to a remarkable development of science and technology, function and role of the system of engineering fields has recently been diversified. The system has become increasingly more complex and precise, and thus, system designers intended to maximize reliability concentrate more effort at the design stage. This study deals with the reliability redundancy optimization problem (RROP) for k-out-of-n: G system configuration with cold standby and warm standby components. This paper further intends to present the optimal mathematical model through which the following three elements of (i) multiple components choices, (ii) redundant components quantity and (iii) the choice of redundancy strategies may be combined in order to maximize the reliability of the system. Therefore, we focus on the following three issues. First, we consider RROP that there exists warm standby state as well as cold standby state of the component. Second, as eliminating an approximation approach of the previous RROP studies, we construct a precise model for system reliability. Third, given transition time when the state of components changes, we present not simply a workable solution but the advanced method. For the wide applicability of RROPs, moreover, we use absorbing continuous time Markov chain and matrix analytic methods in the suggested mathematical model.

Keywords: RROP, matrix analytic methods, k-out-of-n: G system, MTTF, absorbing continuous time Markov Chain

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Authors: Ella Sèdé Maforikan

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Sustained water management requires quantitative information and the knowledge of spatiotemporal dynamics of hydrological system within the basin. This can be achieved through the research. Several studies have investigated both surface water and groundwater in Beterou catchment. However, there are few published papers on the application of the SWAT modeling in Beterou catchment. The objective of this study was to evaluate the performance of SWAT to simulate the water balance within the watershed. The inputs data consist of digital elevation model, land use maps, soil map, climatic data and discharge records. The model was calibrated and validated using the Sequential Uncertainty Fitting (SUFI2) approach. The calibrated started from 1989 to 2006 with four years warming up period (1985-1988); and validation was from 2007 to 2020. The goodness of the model was assessed using five indices, i.e., Nash–Sutcliffe efficiency (NSE), the ratio of the root means square error to the standard deviation of measured data (RSR), percent bias (PBIAS), the coefficient of determination (R²), and Kling Gupta efficiency (KGE). Results showed that SWAT model successfully simulated river flow in Beterou catchment with NSE = 0.79, R2 = 0.80 and KGE= 0.83 for the calibration process against validation process that provides NSE = 0.78, R2 = 0.78 and KGE= 0.85 using site-based streamflow data. The relative error (PBIAS) ranges from -12.2% to 3.1%. The parameters runoff curve number (CN2), Moist Bulk Density (SOL_BD), Base Flow Alpha Factor (ALPHA_BF), and the available water capacity of the soil layer (SOL_AWC) were the most sensitive parameter. The study provides further research with uncertainty analysis and recommendations for model improvement and provision of an efficient means to improve rainfall and discharges measurement data.

Keywords: watershed, water balance, SWAT modeling, Beterou

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Authors: Cheng Fang Hsu

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Call center is the core of building customer relationship management system. Under the strong competitive stress, it becomes a new profiting challenge for a successful enterprise. Call center is a department not only to provide customer service but also to bring business profit. This is the qualitative case study in Taiwan bank service industry which goes on deeper exploration, and analysis by business interviews and industrial analysis. This study starts from the establishment, development, and management after the reforming of the case call center. Through SWOT analysis, and industrial analysis, this study adopted 7S model to explain how the call center reforms from service oriented to profit oriented and from cost management to profit management. The results indicated how service innovation management promotes call center to be operated as a market profit competition center. The recommendations are indicated to support the call center on marketing profit by service innovation management.

Keywords: call center, 7S model, service innovation management, bioinformatics

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Authors: Lizhi Ma, Chengcheng Hu, Fuxian Wong

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Partial reduce (P-Reduce) has set a state-of-the-art performance on distributed machine learning in the heterogeneous environment over the All-Reduce architecture. The dynamic P-Reduce based on the exponential moving average (EMA) approach predicts all the intermediate model parameters, which raises unreliability. It is noticed that the approximation trick leads the wrong way to obtaining model parameters in all the nodes. In this paper, SAP-Reduce is proposed, which is a variant of the All-Reduce distributed training model with staleness-aware dynamic P-Reduce. SAP-Reduce directly utilizes the EMA-like algorithm to generate the normalized weights. To demonstrate the effectiveness of the algorithm, the experiments are set based on a number of deep learning models, comparing the single-step training acceleration ratio and convergence time. It is found that SAP-Reduce simplifying dynamic P-Reduce outperforms the intermediate approximation one. The empirical results show SAP-Reduce is 1.3× −2.1× faster than existing baselines.

Keywords: collective communication, decentralized distributed training, machine learning, P-Reduce

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Authors: Fadi Eleiwi, Taous Meriem Laleg-Kirati

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This paper presents a complete dynamic modeling of a membrane distillation process. The model contains two consistent dynamic models. A 2D advection-diffusion equation for modeling the whole process and a modified heat equation for modeling the membrane itself. The complete model describes the temperature diffusion phenomenon across the feed, membrane, permeate containers and boundary layers of the membrane. It gives an online and complete temperature profile for each point in the domain. It explains heat conduction and convection mechanisms that take place inside the process in terms of mathematical parameters, and justify process behavior during transient and steady state phases. The process is monitored for any sudden change in the performance at any instance of time. In addition, it assists maintaining production rates as desired, and gives recommendations during membrane fabrication stages. System performance and parameters can be optimized and controlled using this complete dynamic model. Evolution of membrane boundary temperature with time, vapor mass transfer along the process, and temperature difference between membrane boundary layers are depicted and included. Simulations were performed over the complete model with real membrane specifications. The plots show consistency between 2D advection-diffusion model and the expected behavior of the systems as well as literature. Evolution of heat inside the membrane starting from transient response till reaching steady state response for fixed and varying times is illustrated.

Keywords: membrane distillation, dynamical modeling, advection-diffusion equation, thermal equilibrium, heat equation

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Authors: D. Muley, H. S. Al-Mandhari

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This paper evaluates the performance of a multi-lane four-legged modern roundabout operating in Muscat using SIDRA model. The performance measures include Degree of Saturation (DOS), average delay, and queue lengths. The geometric and traffic data were used for model preparation. Gap acceptance parameters, critical gap, and follow-up headway were used for calibration of SIDRA model. The results from the analysis showed that currently the roundabout is experiencing delays up to 610 seconds with DOS 1.67 during peak hour. Further, sensitivity analysis for general and roundabout parameters was performed, amongst lane width, cruise speed, inscribed diameter, entry radius, and entry angle showed that inscribed diameter is the most crucial factor affecting delay and DOS. Upgradation of the roundabout to the fully signalized junction was found as the suitable solution which will serve for future years with LOS C for design year having DOS of 0.9 with average control delay of 51.9 seconds per vehicle.

Keywords: performance analysis, roundabout, sensitivity analysis, SIDRA

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Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

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Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

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The Intelligent transportation system is essential to build smarter cities. Machine learning based transportation prediction could be highly promising approach by delivering invisible aspect visible. In this context, this research aims to make a prototype model that predicts transportation network by using big data and machine learning technology. In detail, among urban transportation systems this research chooses bus system.  The research problem that existing headway model cannot response dynamic transportation conditions. Thus, bus delay problem is often occurred. To overcome this problem, a prediction model is presented to fine patterns of bus delay by using a machine learning implementing the following data sets; traffics, weathers, and bus statues. This research presents a flexible headway model to predict bus delay and analyze the result. The prototyping model is composed by real-time data of buses. The data are gathered through public data portals and real time Application Program Interface (API) by the government. These data are fundamental resources to organize interval pattern models of bus operations as traffic environment factors (road speeds, station conditions, weathers, and bus information of operating in real-time). The prototyping model is designed by the machine learning tool (RapidMiner Studio) and conducted tests for bus delays prediction. This research presents experiments to increase prediction accuracy for bus headway by analyzing the urban big data. The big data analysis is important to predict the future and to find correlations by processing huge amount of data. Therefore, based on the analysis method, this research represents an effective use of the machine learning and urban big data to understand urban dynamics.

Keywords: big data, machine learning, smart city, social cost, transportation network

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Authors: Kaoutar Jraida

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Growing evidence demonstrates that thermal conditions in office buildings broadly influence productivity of workers. The purpose of this study is to evaluate and analyze the indoor thermal comfort in an office space based on the calculation of predicted mean vote and predicted percentage of dissatisfied (PMV-PPD) model and field survey.

Keywords: Office, Predicted Mean Vote (PMV), Percentage People Dissatisfied (PPD), Thermal comfort

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Authors: Kgolagano Mpejane

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The study applies a New Institutional Economics (NIE) analytical framework to trade in developing economies by assessing the impacts of institutional quality and institutional quality distance on agricultural trade using a panel data of 15 Southern African Development Community (SADC) countries from the years 1991-2010. The issue of institutions on agricultural trade has not been accorded the necessary attention in the literature, particularly in developing economies. Therefore, the paper empirically tests the gravity model of international trade by measuring the impact of political, economic and legal institutions on intra SADC agricultural trade. The gravity model is noted for its exploratory power and strong theoretical foundation. However, the model has statistical shortcomings in dealing with zero trade values and heteroscedasticity residuals leading to biased results. Therefore, this study employs a two stage Heckman selection model with a Probit equation to estimate the influence of institutions on agricultural trade. The selection stages include the inverse Mills ratio to account for the variable bias of the gravity model. The Heckman model accounts for zero trade values and is robust in the presence of heteroscedasticity. The empirical results of the study support the NIE theory premise that institutions matter in trade. The results demonstrate that institutions determine bilateral agricultural trade on different margins with political institutions having positive and significant influence on bilateral agricultural trade flows within the SADC region. Legal and economic institutions have significant and negative effects on SADC trade. Furthermore, the results of this study confirm that institutional quality distance influences agricultural trade. Legal and political institutional distance have a positive and significant influence on bilateral agricultural trade while the influence of economic, institutional quality is negative and insignificant. The results imply that nontrade barriers, in the form of institutional quality and institutional quality distance, are significant factors limiting intra SADC agricultural trade. Therefore, gains from intra SADC agricultural trade can be attained through the improvement of institutions within the region.

Keywords: agricultural trade, institutions, gravity model, SADC

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Authors: Yee Hooi Min, Mohdnasir Abdul Hadi

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Tensioned membrane structure in the form of Enneper minimal surface can be considered as a sustainable development for the green environment and technology, it also can be used to support the effectiveness used of energy and the structure. Soap film in the form of Enneper minimal surface model has been studied. The combination of shape and internal forces for the purpose of stiffness and strength is an important feature of membrane surface. For this purpose, form-finding using soap film model has been carried out for Enneper minimal surface models with variables u=v=0.6 and u=v=1.0. Enneper soap film models with variables u=v=0.6 and u=v=1.0 provides an alternative choice for structural engineers to consider the tensioned membrane structure in the form of Enneper minimal surface applied in the building industry. It is expected to become an alternative building material to be considered by the designer.

Keywords: Enneper, minimal surface, soap film, tensioned membrane structure

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Authors: Jamaiah H. Yahaya, Aziz Deraman, Abdul Razak Hamdan, Yusmadi Yah Jusoh

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One of the most essential issues in software products is to maintain it relevancy to the dynamics of the user’s requirements and expectation. Many studies have been carried out in quality aspect of software products to overcome these problems. Previous software quality assessment models and metrics have been introduced with strengths and limitations. In order to enhance the assurance and buoyancy of the software products, certification models have been introduced and developed. From our previous experiences in certification exercises and case studies collaborating with several agencies in Malaysia, the requirements for user based software certification approach is identified and demanded. The emergence of social network applications, the new development approach such as agile method and other varieties of software in the market have led to the domination of users over the software. As software become more accessible to the public through internet applications, users are becoming more critical in the quality of the services provided by the software. There are several categories of users in web-based systems with different interests and perspectives. The classifications and metrics are identified through brain storming approach with includes researchers, users and experts in this area. The new paradigm in software quality assessment is the main focus in our research. This paper discusses the classifications of users in web-based software system assessment and their associated factors and metrics for quality measurement. The quality model is derived based on IEEE structure and FCM model. The developments are beneficial and valuable to overcome the constraints and improve the application of software certification model in future.

Keywords: software certification model, user centric approach, software quality factors, metrics and measurements, web-based system

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Authors: M. Y. Ismail, M. Inam, A. F. M. Zain, N. Misran

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Progressive phase distribution is an important consideration in reflect array antenna design which is required to form a planar wave in front of the reflect array aperture. This paper presents a detailed mathematical model in order to determine the required reflection phase values from individual element of a reflect array designed in Ku-band frequency range. The proposed technique of obtaining reflection phase can be applied for any geometrical design of elements and is independent of number of array elements. Moreover the model also deals with the solution of reflect array antenna design with both centre and off-set feed configurations. The theoretical modeling has also been implemented for reflect arrays constructed on 0.508 mm thickness of different dielectric substrates. The results show an increase in the slope of the phase curve from 4.61°/mm to 22.35°/mm by varying the material properties.

Keywords: mathematical modeling, progressive phase distribution, reflect array antenna, reflection phase

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Authors: Michiko Miyamoto

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This paper empirically investigates whether information technology (IT) strategies, business strategies, and divisions are aligned to meet overall business goals for Korean Small and medium-sized enterprises (SMEs), based on structure based Strategic Alignment Model, and make comparison with those of Japanese SMEs. Using 2,869 valid responses of Korean Human Capital Corporate Panel survey, a result of this study suggests that Korean human resources (HR) departments have a major influence over IT strategy, which is the same as Japanese SMEs, even though their management styles are quite different. As for IT strategy, it is not related to other departments at all for Korean SMEs. The Korean management seems to possess a great power over each division, such as Sales/Service, Research and Development/Technical Experts, HR, and Production.

Keywords: IT-business alignment, structured based strategic alignment model, structural equation model, human resources department

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Authors: Mohammed Alonazi, Natalia Beloff, Martin White

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This paper presents a proposal for a mobile government adoption and utilization model (MGAUM), which is a framework designed to increase the adoption rate of m-government services in Saudi Arabia. Recent advances in mobile technologies such are Mobile compatibilities, The development of wireless communication, mobile applications and devices are enabling governments to deliver services in new ways to citizens more efficiently and economically. In the last decade, many governments around the globe are utilizing these advances effectively to develop their next generation of e-government services. However, a low adoption rate of m-government services by citizens is a common problem in Arabian countries, including Saudi Arabia. Yet, to our knowledge, very little research has been conducted focused on understanding the factors that influence citizen adoption of these m-government services in this part of the world. A set of social, cultural and technological factors have been identified in the literature, which has led to the formulation of associated research questions and hypotheses. These hypotheses will be tested on Saudi citizens using questionnaires and interview methods based around the technology acceptance model. A key objective of the MGAUM framework is to investigate and understand Saudi citizens perception towards adoption and utilization of m-government services.

Keywords: e-government, m-government, citizen services quality, technology acceptance model, Saudi Arabia, adoption framework.

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Authors: Khalid Hussein, Waleed Abdalati, Pakorn Petchprayoon, Khaula Alkaabi

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In this study, we integrated ASTER thermal data with an area-based spatial insolation model to identify and delineate geothermally active areas in Yellowstone National Park (YNP). Two pairs of L1B ASTER day- and nighttime scenes were used to calculate land surface temperature. We employed the Emissivity Normalization Algorithm which separates temperature from emissivity to calculate surface temperature. We calculated the incoming solar radiation for the area covered by each of the four ASTER scenes using an insolation model and used this information to compute temperature due to solar radiation. We then identified the statistical thermal anomalies using land surface temperature and the residuals calculated from modeled temperatures and ASTER-derived surface temperatures. Areas that had temperatures or temperature residuals greater than 2σ and between 1σ and 2σ were considered ASTER-modeled thermal anomalies. The areas identified as thermal anomalies were in strong agreement with the thermal areas obtained from the YNP GIS database. Also the YNP hot springs and geysers were located within areas identified as anomalous thermal areas. The consistency between our results and known geothermally active areas indicate that thermal remote sensing data, integrated with a spatial-based insolation model, provides an effective means for identifying and locating areas of geothermal activities over large areas and rough terrain.

Keywords: thermal remote sensing, insolation model, land surface temperature, geothermal anomalies

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Authors: Rouzbeh Ramezani, Renzo Di Felice

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Nowadays, removal of CO₂ as one of the major contributors to global warming using alternative solvents with high CO₂ absorption efficiency, is an important industrial operation. In this study, three amines, including 2-methylpiperazine, potassium sarcosinate and potassium lysinate as potential additives, were added to the potassium carbonate solution as a base solvent for CO₂ capture. In order to study the absorption performance of CO₂ in terms of loading capacity of CO₂ and absorption rate, the absorption experiments in a blend of additives with potassium carbonate were carried out using the vapor-liquid equilibrium apparatus at a temperature of 313.15 K, CO₂ partial pressures ranging from 0 to 50 kPa and at mole fractions 0.2, 0.3, and 0.4. Furthermore, the performance of CO₂ absorption in these blend solutions was compared with pure monoethanolamine and with pure potassium carbonate. Finally, a correlation with good accuracy was developed using the nonlinear regression analysis in order to predict CO₂ loading capacity.

Keywords: absorption rate, carbon dioxide, CO2 capture, global warming, loading capacity

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Authors: Warda Nasir, M. N. S. Qureshi

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Whistler waves are right handed circularly polarized waves and are frequently observed in space plasmas. The Low frequency branch of the Whistler waves having frequencies nearly around 100 Hz, known as Lion roars, are frequently observed in magnetosheath. Another feature of the magnetosheath is the observations of flat top electron distributions with single as well as two electron populations. In the past, lion roars were studied by employing kinetic model using classical bi-Maxwellian distribution function, however, could not be justified both on quantitatively as well as qualitatively grounds. We studied Whistler waves by employing kinetic model using non-Maxwellian distribution function such as the generalized (r,q) distribution function which is the generalized form of kappa and Maxwellian distribution functions by employing kinetic theory with single or two electron populations. We compare our results with the Cluster observations and found good quantitative and qualitative agreement between them. At times when lion roars are observed (not observed) in the data and bi-Maxwellian could not provide the sufficient growth (damping) rates, we showed that when generalized (r,q) distribution function is employed, the resulted growth (damping) rates exactly match the observations.

Keywords: kinetic model, whistler waves, non-maxwellian distribution function, space plasmas

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Authors: Jeonghwan Jeon

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With the advent of IPTV in the fierce competition with existing broadcasting system, it is emerged as an important issue to predict how much the adoption of IPTV service will be. This paper aims to suggest a prediction model for adopting IPTV using classification and Ranking Belief Simplex (CaRBS). A simplex plot method of representing data allows a clear visual representation to the degree of interaction of the support from the variables to the prediction of the objects. CaRBS is applied to the survey data on the IPTV adoption.

Keywords: prediction, adoption, IPTV, CaRBS

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Authors: Davide Fuscà, Kanan Rahimli, Roberto Leuzzi

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Preventing abuse and illegal activities in a given area of the sea is a very difficult and expensive task. Artificial intelligence offers the possibility to implement new methods to identify the vessel class type from the kinematic features of the vessel itself. The task strictly depends on the quality of the data. This paper explores the application of a deep, long short-term memory model by using AIS flow only with a relatively low quality. The proposed model reaches high accuracy on detecting nine vessel classes representing the most common vessel types in the Ionian-Adriatic Sea. The model has been applied during the Adriatic-Ionian trial period of the international EU ANDROMEDA H2020 project to identify vessels performing behaviors far from the expected one depending on the declared type.

Keywords: maritime surveillance, artificial intelligence, behavior analysis, LSTM

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Authors: Neslihan Sahin Celik, Ali Eraslan

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Mathematical model and modeling are one of the topics that have been intensively discussed in recent years. In line with the results of the PISA studies, researchers in many countries have begun to question how much students in school-education system are prepared to solve the real-world problems they encounter in their future professional lives. As a result, many mathematics educators have begun to emphasize the importance of new skills and understanding such as constructing, Hypothesizing, Describing, manipulating, predicting, working together for complex and multifaceted problems for success in beyond the school. When students increasingly face this kind of situations in their daily life, it is important to make sure that students have enough experience to work together and interpret mathematical situations that enable them to think in different ways and share their ideas with their peers. Thus, model eliciting activities are one of main tools that help students to gain experiences and the new skills required. This research study was carried on the town center of a big city located in the Black Sea region in Turkey. The participants were eighth-grade students in a middle school. After a six-week preliminary study, three students in an eighth-grade classroom were selected using criterion sampling technique and placed in a focus group. The focus group of three students was videotaped as they worked on a model eliciting activity, the Summer Job Problem. The conversation of the group was transcribed, examined with students’ written work and then qualitatively analyzed through the lens of Blum’s (1996) modeling processing cycle. The study results showed that eighth grade students can successfully work with the model eliciting, develop a model based on the two parameters and review the whole process. On the other hand, they had difficulties to relate parameters to each other and take all parameters into account to establish the model.

Keywords: middle school, modeling, mathematical modeling, summer job problem

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Authors: Inese Trusina, Elita Jermolajeva

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Global challenges require a transition from the existing linear economic model to a model that will consider nature as a life support system for the developmenton the way to social well-being in the frame of the ecological economics paradigm. The article presentsbasic definitions for the development of formalized description of sustainabledevelopment monitoring. It provides examples of calculating the parameters of monitoring for the Baltic Sea region countries and their primary interpretation.

Keywords: sustainability, development, power, ecological economics, regional economic, monitoring

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Authors: Kais Ben-Ahmed, Mhamed Ali-El-Aroui

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This paper develops a Local Generalized Linear Spatial Model (LGLSM) to describe the spatial variation of Visceral Leishmaniasis (VL) infection risk in northern and central Tunisia. The response from each region is a number of affected children less than five years of age recorded from 1996 through 2006 from Tunisian pediatric departments and treated as a poison county level data. The model includes climatic factors, namely averages of annual rainfall, extreme values of low temperatures in winter and high temperatures in summer to characterize the climate of each region according to each continentality index, the pluviometric quotient of Emberger (Q2) to characterize bioclimatic regions and component for residual extra-poison variation. The statistical results show the progressive increase in the number of affected children in regions with high continentality index and low mean yearly rainfull. On the other hand, an increase in pluviometric quotient of Emberger contributed to a significant increase in VL incidence rate. When compared with the original GLSM, Bayesian locally modeling is improvement and gives a better approximation of the Tunisian VL risk estimation. According to the Bayesian approach inference, we use vague priors for all parameters model and Markov Chain Monte Carlo method.

Keywords: generalized linear spatial model, local model, extra-poisson variation, continentality index, visceral leishmaniasis, Tunisia

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Authors: Farhad Salek, Shahaboddin Resalati

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The production of electric vehicles (EVs) featuring lithium-ion battery technology has substantially escalated over the past decade, demonstrating a steady and persistent upward trajectory. The imminent retirement of electric vehicle (EV) batteries after approximately eight years underscores the critical need for their redirection towards recycling, a task complicated by the current inadequacy of recycling infrastructures globally. A potential solution for such concerns involves extending the operational lifespan of electric vehicle (EV) batteries through their utilization in stationary energy storage systems during secondary applications. Such adoptions, however, require addressing the safety concerns associated with batteries’ knee points and thermal runaways. This paper develops an accurate mathematical model representative of the second-life battery packs from a cell-to-pack scale using an equivalent circuit model (ECM) methodology. Neural network algorithms are employed to forecast the degradation parameters based on the EV batteries' aging history to develop a degradation model. The degradation model is integrated with the ECM to reflect the impacts of the cycle aging mechanism on battery parameters during operation. The developed model is tested under real-life load profiles to evaluate the life span of the batteries in various operating conditions. The methodology and the algorithms introduced in this paper can be considered the basis for Battery Management System (BMS) design and techno-economic analysis of such technologies.

Keywords: second life battery, electric vehicles, degradation, neural network

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Authors: Rohini Hariharan, Yazhini R., Blessy Maria Mathew

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The task of detecting email spam is a very important one in the era of digital technology that needs effective ways of curbing unwanted messages. This paper presents an approach aimed at making email spam categorization algorithms transparent, reliable and more trustworthy by incorporating Local Interpretable Model-agnostic Explanations (LIME). Our technique assists in providing interpretable explanations for specific classifications of emails to help users understand the decision-making process by the model. In this study, we developed a complete pipeline that incorporates LIME into the spam classification framework and allows creating simplified, interpretable models tailored to individual emails. LIME identifies influential terms, pointing out key elements that drive classification results, thus reducing opacity inherent in conventional machine learning models. Additionally, we suggest a visualization scheme for displaying keywords that will improve understanding of categorization decisions by users. We test our method on a diverse email dataset and compare its performance with various baseline models, such as Gaussian Naive Bayes, Multinomial Naive Bayes, Bernoulli Naive Bayes, Support Vector Classifier, K-Nearest Neighbors, Decision Tree, and Logistic Regression. Our testing results show that our model surpasses all other models, achieving an accuracy of 96.59% and a precision of 99.12%.

Keywords: text classification, LIME (local interpretable model-agnostic explanations), stemming, tokenization, logistic regression.

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Authors: Fadwa Haraka, Ahmad Elouatouati, Mourad Taha Janan

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In this study, we propose a neural network based method in order to calculate the heat source temperature of isotropic plate with lower surface forced cooling. To validate the proposed model, the heat source temperatures values will be compared to the analytical method -variables separation- and finite element model. The mathematical simulation is done through 3D numerical simulation by COMSOL software considering three different materials: Aluminum, Copper, and Graphite. The proposed method will lead to a formulation of the heat source temperature based on the thermal and geometric properties of the base plate.

Keywords: thermal model, thermal resistance, finite element simulation, neural network

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Authors: Sung-Won Seo, Jang-Young Choi

Abstract:

The rare earth magnets used in synchronous generators offer many advantages, including high efficiency, greatly reduced the size, and weight. The permanent magnet linear synchronous generator (PMLSG) allows for direct drive without the need for a mechanical device. Therefore, the PMLSG is well suited to translational applications, such as wave energy converters and free piston energy converters. This manuscript compares the effects of different magnetization patterns on the characteristics of double-sided PMLSGs in slotless stator structures. The Halbach array has a higher flux density in air-gap than the Vertical array, and the advantages of its performance and efficiency are widely known. To verify the advantage of Halbach array, we apply a finite element method (FEM) and analytical method. In general, a FEM and an analytical method are used in the electromagnetic analysis for determining model characteristics, and the FEM is preferable to magnetic field analysis. However, the FEM is often slow and inflexible. On the other hand, the analytical method requires little time and produces accurate analysis of the magnetic field. Therefore, the flux density in air-gap and the Back-EMF can be obtained by FEM. In addition, the results from the analytical method correspond well with the FEM results. The model of the Halbach array reveals less copper loss than the model of the Vertical array, because of the Halbach array’s high output power density. The model of the Vertical array is lower core loss than the model of Halbach array, because of the lower flux density in air-gap. Therefore, the current density in the Vertical model is higher for identical power output. The completed manuscript will include the magnetic field characteristics and structural features of both models, comparing various results, and specific comparative analysis will be presented for the determination of the best model for application in a wave energy converting system.

Keywords: wave energy converter, permanent magnet linear synchronous generator, finite element method, analytical method

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Authors: R. Hellmuth

Abstract:

The method of factory planning has changed a lot, especially when it is about planning the factory building itself. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity and Ambiguity) lead to more frequent restructuring measures within a factory. A digital factory model is the planning basis for rebuilding measures and becomes an indispensable tool. Furthermore, digital building models are increasingly being used in factories to support facility management and manufacturing processes. The main research question of this paper is, therefore: What kind of digital factory model is suitable for the different areas of application during the operation of a factory? First, different types of digital factory models are investigated, and their properties and usabilities for use cases are analysed. Within the scope of investigation are point cloud models, building information models, photogrammetry models, and these enriched with sensor data are examined. It is investigated which digital models allow a simple integration of sensor data and where the differences are. Subsequently, possible application areas of digital factory models are determined by means of a survey and the respective digital factory models are assigned to the application areas. Finally, an application case from maintenance is selected and implemented with the help of the appropriate digital factory model. It is shown how a completely digitalized maintenance process can be supported by a digital factory model by providing information. Among other purposes, the digital factory model is used for indoor navigation, information provision, and display of sensor data. In summary, the paper shows a structuring of digital factory models that concentrates on the geometric representation of a factory building and its technical facilities. A practical application case is shown and implemented. Thus, the systematic selection of digital factory models with the corresponding application cases is evaluated.

Keywords: building information modeling, digital factory model, factory planning, maintenance

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