Search results for: behavior against washing machine parameters

Authors: Asifa Amara, Prachi Singh, Kanishka, Debargho Pathak, Akshat Kumar, Jayakumar Eravelly

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Today's generation is totally dependent on technology that uses data as its fuel. The present study is all about innovations and developments in data science and gives an idea about how efficiently to use the data provided. This study will help to understand the core concepts of data science. The concept of artificial intelligence was introduced by Alan Turing in which the main principle was to create an artificial system that can run independently of human-given programs and can function with the help of analyzing data to understand the requirements of the users. Data science comprises business understanding, analyzing data, ethical concerns, understanding programming languages, various fields and sources of data, skills, etc. The usage of data science has evolved over the years. In this review article, we have covered a part of data science, i.e., machine learning. Machine learning uses data science for its work. Machines learn through their experience, which helps them to do any work more efficiently. This article includes a comparative study image between human understanding and machine understanding, advantages, applications, and real-time examples of machine learning. Data science is an important game changer in the life of human beings. Since the advent of data science, we have found its benefits and how it leads to a better understanding of people, and how it cherishes individual needs. It has improved business strategies, services provided by them, forecasting, the ability to attend sustainable developments, etc. This study also focuses on a better understanding of data science which will help us to create a better world.

Keywords: data science, machine learning, data analytics, artificial intelligence

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Authors: Ali Al-Yaaribi, Maria Kavussanu

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Prosocial and antisocial behavior occurs in sport. Prosocial behavior is voluntary behavior intended to help or benefit another individual, while antisocial behavior is behavior intended to harm or disadvantage another individual. Previous sport morality research has investigated primarily antecedents of prosocial and antisocial behavior. However, the potential consequences of these behaviors remain unexplored. The aims of this study were to examine whether: (a) perceived prosocial and antisocial teammate behavior predicts task cohesion and burnout; (b) affect mediate these relationships; and (c) motivational climate moderates any of these effects. Participants were male (n = 96) and female (n = 176) teams sport players (Mage = 21.86, SD = 4.36), who completed questionnaires measuring the aforementioned variables. Mediation analysis (Hayes, 2013) indicated that prosocial teammate behavior positively predicted task cohesion and negatively predicted burnout; these effects were mediated by positive affect. Also, mastery climate moderated the positive effect of prosocial teammate behavior on task cohesion: The effect of antisocial teammate behavior on task cohesion was stronger for players who perceived a higher mastery climate created by their coaches. Performance climate moderated the negative effect of prosocial teammate behavior on burnout: This effect was only significant for players who perceived moderate or low levels of performance team climate. Antisocial teammate behavior negatively predicted task cohesion and positively predicted burnout, and these effects were mediated by negative affect. Also, performance climate moderated the positive effect of antisocial teammate behavior on burnout, such that the effect of antisocial teammate behavior on burnout was stronger for players who perceived a lower performance climate. The research findings shed some light on the potential role of prosocial and antisocial teammate behaviors as well as coach-created motivational climate on influencing players’ affect, task cohesion, and burnout. Coaches should focus on creating a mastery motivational climate and rewarding prosocial behavior while at the same time trying to deter antisocial behavior among teammates in order to enhance positive affect, task cohesion, and prevent experience of negative affect and burnout.

Keywords: mediation, moderation, morality, teams sport

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Authors: Marziye Hadian, Alireza Jabbari

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Background: The World Health Organization has identified COVID-19 as a public health emergency and is urging governments to stop the virus transmission by adopting appropriate policies. In this regard, the countries have taken different approaches to cutting the chain or controlling the spread of the disease. Methods: The present study was a systematize review of publications relating to prevention strategies for covid-19 disease. The study was carried out based on the PRISMA guidelines and CASP for articles and AACODS for grey literature. Finding: The study findings showed that in order to confront the COVID-19 epidemic, in general, there are three approaches of "mitigation", "active control" and "suppression" and four strategies of "quarantine", "isolation", "social distance" as well as "lockdown" in both individual and social dimensions to deal with epidemics that the choice of each approach requires specific strategies and has different effects when it comes to controlling and inhibiting the disease. Conclusion: The only way to control the disease is to change your behavior and lifestyle. In addition to prevention strategies, use of masks, observance of personal hygiene principles such as regular hand washing and non-contact of contaminated hands with the face, as well as observance of public health principles such as control of sneezing and coughing, safe extermination of personal protective equipment, etc. have not been included in the category of prevention tools. However, it has a great impact on controlling the epidemic, especially the new coronavirus epidemic.

Keywords: novel corona virus, COVID-19, prevention tools, prevention strategies

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner

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The aim of this paper is to compare and discuss better classifier algorithm options for credit risk assessment by applying different Machine Learning techniques. Using records from a Brazilian financial institution, this study uses a database of 5,432 companies that are clients of the bank, where 2,600 clients are classified as non-defaulters, 1,551 are classified as defaulters and 1,281 are temporarily defaulters, meaning that the clients are overdue on their payments for up 180 days. For each case, a total of 15 attributes was considered for a one-against-all assessment using four different techniques: Artificial Neural Networks Multilayer Perceptron (ANN-MLP), Artificial Neural Networks Radial Basis Functions (ANN-RBF), Logistic Regression (LR) and finally Support Vector Machines (SVM). For each method, different parameters were analyzed in order to obtain different results when the best of each technique was compared. Initially the data were coded in thermometer code (numerical attributes) or dummy coding (for nominal attributes). The methods were then evaluated for each parameter and the best result of each technique was compared in terms of accuracy, false positives, false negatives, true positives and true negatives. This comparison showed that the best method, in terms of accuracy, was ANN-RBF (79.20% for non-defaulter classification, 97.74% for defaulters and 75.37% for the temporarily defaulter classification). However, the best accuracy does not always represent the best technique. For instance, on the classification of temporarily defaulters, this technique, in terms of false positives, was surpassed by SVM, which had the lowest rate (0.07%) of false positive classifications. All these intrinsic details are discussed considering the results found, and an overview of what was presented is shown in the conclusion of this study.

Keywords: artificial neural networks (ANNs), classifier algorithms, credit risk assessment, logistic regression, machine Learning, support vector machines

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Authors: Rim Messaoudi, Nogaye-Gueye Gning, François Azelart

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Automatic text classification applies mostly natural language processing (NLP) and other AI-guided techniques to automatically classify text in a faster and more accurate manner. This paper discusses the subject of using predictive maintenance to manage incident tickets inside the sociality. It focuses on proposing a tool that treats and analyses comments and notes written by administrators after resolving an incident ticket. The goal here is to increase the quality of these comments. Additionally, this tool is based on NLP and machine learning techniques to realize the textual analytics of the extracted data. This approach was tested using real data taken from the French National Railways (SNCF) company and was given a high-quality result.

Keywords: machine learning, text classification, NLP techniques, semantic representation

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Authors: Yi-Min Lan, Hung-Wen Cheng, Hsuan-Ling Chang, Jou-Ping Yu

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The study applies machine learning models to construct momentum strategies and utilizes the information coefficient as an indicator for selecting stocks with strong and weak momentum characteristics. Through this approach, the study has built investment portfolios capable of generating superior returns and conducted a thorough analysis. Compared to existing research on momentum strategies, machine learning is incorporated to capture non-linear interactions. This approach enhances the conventional stock selection process, which is often impeded by difficulties associated with timeliness, accuracy, and efficiency due to market risk factors. The study finds that implementing bidirectional momentum strategies outperforms unidirectional ones, and momentum factors with longer observation periods exhibit stronger correlations with returns. Optimizing the number of stocks in the portfolio while staying within a certain threshold leads to the highest level of excess returns. The study presents a novel framework for momentum strategies that enhances and improves the operational aspects of asset management. By introducing innovative financial technology applications to traditional investment strategies, this paper can demonstrate significant effectiveness.

Keywords: information coefficient, machine learning, momentum, portfolio, return prediction

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Authors: Mohammad Hossein Bayati Chaleshtari, Hadi Khoramishad

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Minimizing the stress concentration around triangular cutout in infinite perforated plates subjected to a uniform heat flux induces thermal stresses is an important consideration in engineering design. Furthermore, understanding the effective parameters on stress concentration and proper selection of these parameters enables the designer to achieve a reliable design. In the analysis of thermal stress, the effective parameters on stress distribution around cutout include fiber angle, flux angle, bluntness and rotation angle of the cutout for orthotropic materials. This paper was tried to examine effect of these parameters on thermal stress analysis of infinite perforated plates with central triangular cutout. In order to achieve the least amount of thermal stress around a triangular cutout using a novel swarm intelligence optimization technique called dragonfly optimizer that inspired by the life method and hunting behavior of dragonfly in nature. In this study, using the two-dimensional thermoelastic theory and based on the Likhnitskiiʼ complex variable technique, the stress analysis of orthotropic infinite plate with a circular cutout under a uniform heat flux was developed to the plate containing a quasi-triangular cutout in thermal steady state condition. To achieve this goal, a conformal mapping function was used to map an infinite plate containing a quasi- triangular cutout into the outside of a unit circle. The plate is under uniform heat flux at infinity and Neumann boundary conditions and thermal-insulated condition at the edge of the cutout were considered.

Keywords: infinite perforated plate, complex variable method, thermal stress, optimization method

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Authors: Debalkie Demissie Addisu, Ejigu Alemu Abebe, Tsegay Tensay Assefa

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This study examines the indirect effect of servant leadership on organizational citizenship behavior through perceptions of organizational politics moderated by political skill. This study reports the responses of 321 respondents from six federal public service organizations in Ethiopia. A multi-stage random sampling procedure was employed to select the sampled federal public service organizations. To test hypotheses, the study employed structural equation modeling using AMOS version-26 software. The result revealed that all direct effects have a significant effect. Specifically, servant leadership has a positive effect on organizational citizenship behavior. Likewise, servant leadership has a negative effect on perceptions of organizational politics. Also, a perception of organizational politics has a negative effect on organizational citizenship behavior. Moreover, perceptions of organizational politics competitively mediated the effect of servant leadership on organizational citizenship behavior. As well, political skill moderated the effect of perceptions of organizational politics on organizational citizenship behavior but not the indirect effect. To the best of our knowledge, no one else employs perceptions of organizational politics as a mediating effect between servant leadership and organizational citizenship behavior. Furthermore, we are not aware of anyone else employing political skill as a moderating role in the indirect effect of servant leadership on organizational citizenship behavior through perceptions of organizational politics.

Keywords: servant leadership, organizational citizenship behavior, perceptions of organizational politics, political skill, public service organization, Ethiopia

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Authors: Iqbal Abd. Muhbir Hadi Anam

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The purpose of the study is to discuss the relationship between organizational culture, teacher psychological empowerment, and organizational citizenship behavior at the University of Bangkalan District. The data was obtained using a survey of 100 respondents tested for validity and reliability. The analytical technique used is a hierarchical regression test. The results showed that the organizational culture of the university had a strong influence on the psychological empowerment of teachers and the psychological empowerment of teachers and that the organizational culture and psychological empowerment of teachers provided effective predictions of the psychological empowerment of the university. In addition, organizational culture directly or indirectly influences teachers' organizational citizenship behavior through psychological empowerment. Given these results, universities need to build an organizational culture that reflects the nature of the university.

Keywords: organizational behavior, teacher psychological empowerment, organizational citizenship behavior, universities

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Authors: Labar Hocine, Kelaiaia Mounia Samira, Mesbah Tarek, Kelaiaia Samia

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Maximum power point tracking (MPPT) techniques are used in photovoltaic (PV) systems to maximize the PV array output power by tracking continuously the maximum power point(MPP) which depends on panels temperature and on irradiance conditions. The main drawback of P&O is that, the operating point oscillates around the MPP giving rise to the waste of some amount of available energy; moreover, it is well known that the P&O algorithm can be confused during those time intervals characterized by rapidly changing atmospheric conditions. In this paper, it is shown that in order to limit the negative effects associated to the above drawbacks, the P&O MPPT parameters must be customized to the dynamic behavior of the specific converter adopted. A theoretical analysis allowing the optimal choice of such initial set parameters is also carried out. The fast convergence of the proposal is proven.

Keywords: P&O, Taylor’s series, MPPT, photovoltaic panel

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Authors: Kanyarat Bussaban, Kunyanuth Kularbphettong

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This research aims to create a model for analysis of student behavior using Library resources based on data mining technique in case of Suan Sunandha Rajabhat University. The model was created under association rules, apriori algorithm. The results were found 14 rules and the rules were tested with testing data set and it showed that the ability of classify data was 79.24 percent and the MSE was 22.91. The results showed that the user’s behavior model by using association rule technique can use to manage the library resources.

Keywords: behavior, data mining technique, a priori algorithm, knowledge discovery

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Authors: J.-Y. Lee, H.-S. Lim, S.-E. Kim

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Recently, the use of high-strength materials is increasing as the construction of large structures and high-rise structures increases. This paper presents an analysis of the shear behavior of prestressed concrete members with various types of materials by simulating a finite element (FE) analysis. The analytical results indicated that the shear strength and shear failure mode were strongly influenced by not only the shear reinforcement ratio but also the yield strength of shear reinforcement and the compressive strength of concrete. Though the yield strength of shear reinforcement increased the shear strength of prestressed concrete members, there was a limit to the increase in strength because of the change of shear failure modes. According to the results of FE analysis on various parameters, the maximum yield strength of the steel stirrup that can be applied to prestressed concrete members was about 860 MPa.

Keywords: prestressed concrete members, high strength reinforcing bars, high strength concrete, shear behavior

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Authors: Kanmanas Muensak, Somphoom Saweangkun

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The objectives of this research were to study the consumer behavior that affects the customer loyalty of CP Fresh Mart in Bangkok province. The sample of the study comprised 400 consumers over 15 years old who made the purchase through CP Fresh Mart in Bangkok. The questionnaires were used as the data gathering instrument, and the data were analyzed applying Percentage, Mean, Standard Deviation, Independent Sample t-test, Two- Way ANOVA, and Least Significant Difference, and Pearson’s Correlation Coefficient also. The result of hypothesis testing showed that the respondents of different gender, age, level of education, income, marital status and occupation had differences in consumer behavior through customer loyalty of CP Fresh Mart and the factors on customer loyalty in the aspects of re-purchase, word of mouth and price sensitive, promotion, process, and personnel had positive relationship with the consumer behavior through of CP Fresh Mart in Bangkok as well as.

Keywords: consumers in Bangkok, consumer behavior, customer loyalty, CP Fresh Mart, operating budget

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Authors: Y. Kim, H. Lee, M. Park, B. Lee

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Planning is understood as the determination of what has to be performed, how, in which sequence, when, what resources are needed, and their cost within the organization before execution. In most construction project, it is evident that the inherent nature of planning is dynamic, and initial planning is subject to be changed due to various uncertain conditions of construction project. Planners take a continuous revision process during the course of a project and until the very end of project. However, current practice lacks reliable, systematic tool for setting variance thresholds to determine when and what corrective actions to be taken. Rather it is heavily dependent on the level of experience and knowledge of the planner. Thus, this paper introduces a machine learning approach to evaluate project control threshold reliability incorporating project-specific data and presents a method to automate the process. The results have shown that the model improves the efficiency and accuracy of the monitoring process as an early warning.

Keywords: machine learning, project control, project progress monitoring, schedule

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Authors: Abeer A. Aljohani

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COVID-19 virus spread has been one of the extreme pandemics across the globe. It is also referred to as coronavirus, which is a contagious disease that continuously mutates into numerous variants. Currently, the B.1.1.529 variant labeled as omicron is detected in South Africa. The huge spread of COVID-19 disease has affected several lives and has surged exceptional pressure on the healthcare systems worldwide. Also, everyday life and the global economy have been at stake. This research aims to predict COVID-19 disease in its initial stage to reduce the death count. Machine learning (ML) is nowadays used in almost every area. Numerous COVID-19 cases have produced a huge burden on the hospitals as well as health workers. To reduce this burden, this paper predicts COVID-19 disease is based on the symptoms and medical history of the patient. This research presents a unique architecture for COVID-19 detection using ML techniques integrated with feature dimensionality reduction. This paper uses a standard UCI dataset for predicting COVID-19 disease. This dataset comprises symptoms of 5434 patients. This paper also compares several supervised ML techniques to the presented architecture. The architecture has also utilized 10-fold cross validation process for generalization and the principal component analysis (PCA) technique for feature reduction. Standard parameters are used to evaluate the proposed architecture including F1-Score, precision, accuracy, recall, receiver operating characteristic (ROC), and area under curve (AUC). The results depict that decision tree, random forest, and neural networks outperform all other state-of-the-art ML techniques. This achieved result can help effectively in identifying COVID-19 infection cases.

Keywords: supervised machine learning, COVID-19 prediction, healthcare analytics, random forest, neural network

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Authors: Haftay Abraha Tadesse

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Background: Staphylococcus is a genus of worldwide distributed bacteria correlated to several infectious of different sites in human and animals. They are among the most important causes of infection that are associated with the consumption of contaminated food. Objective: The objective of this study was to determine the isolates, antimicrobial susceptibility patterns and public health significance for Staphylococcus aureus in raw meat from butchery and abattoir houses of Mekelle, Northern Ethiopia. Methodology: A cross-sectional study was conducted from April to October 2019. Sociodemographic data and public health significance were collected using predesigned questionnaire. The raw meat samples were collected aseptically in the butchery and abattoir houses and transported using ice box to Mekelle University, College of Veterinary Sciences for isolating and identification of Staphylococcus aureus. Antimicrobial susceptibility tests were determined by disc diffusion method. Data obtained were cleaned and entered in to STATA 22.0 and logistic regression model with odds ratio were calculated to assess the association of risk factors with bacterial contamination. P-value < 0.05 was considered as statistically significant. Results: In present study, 88 out of 250 (35.2%) were found to be contamination with Staphylococcus aureus. Among the raw meat specimens to be positivity rate of Staphylococcus aureus were 37.6% (n=47) and (32.8% (n=41), butchery and abattoir houses, respectively. Among the associated risk factories not using gloves reduces risk was found to (AOR=0.222; 95% CI: 0.104-0.473), Strict Separation b/n clean & dirty (AOR= 1.37; 95% CI: 0.66-2.86) and poor habit of hand washing (AOR=1.08; 95%CI: 0.35-3.35) were found to be statistically significant and ha ve associated with Staphylococcus aureus contamination. All isolates thirty sevevn of Staphyloco ccus aureus were checked displayed (100%) sensitive to doxycycline, trimethoprim, gentamicin, sulphamethoxazole, amikacin, CN, Co trimoxazole and nitrofurantoi. whereas the showed resistance of cefotaxime (100%), ampicillin (87.5%), Penicillin (75%), B (75%), and nalidixic acid (50%) from butchery houses. On the other hand, all isolates of Staphylococcus aur eu isolate 100% (n= 10) showed sensitive chloramphenicol, gentamicin and nitrofurantoin whereas the showed 100% resistance of Penicillin, B, AMX, ceftriaxone, ampicillin and cefotaxime from abattoirs houses. The overall multi drug resistance pattern for Staphylococcus aureus were 90% and 100% of butchery and abattoirs houses, respectively. Conclusion: 35.3% Staphylococcus aureus isolated were recovered from the raw meat samples collected from the butchery and abattoirs houses. More has to be done in the developed of hand washing behavior, and availability of safe water in the butchery houses to reduce burden of bacterial contamination. The results of the present finding highlight the need to implement protective measures against the levels of food contamination and alternative drug options. The development of antimicrobial resistance is nearly always as a result of repeated therapeutic and/or indiscriminate use of them. Regular antimicrobial sensitivity testing helps to select effective antibiotics and to reduce the problems of drug resistance development towards commonly used antibiotics. Key words: abattoir houses, antimicrobial resistance, butchery houses, Ethiopia,

Keywords: abattoir houses, antimicrobial resistance, butchery houses, Ethiopia, staphylococcus aureuse, MDR

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Authors: Anjali Malik

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This paper presents a review of the literature on behavioral addictions with a particular focus on understanding online gaming habits among adolescents. Extant researches yielded many different sets of antecedent factors for developing pathological online gaming behavior. This paper draws findings from the most-cited publications most closely associated with factors explaining why individuals develop such kind of problematic behavior. What emerges as central to understanding this phenomenon is the presence of multiple variable causes that take into account the individual, the environment and their interaction to explain the risk behavior such as pathological online gaming. In addition to that role of some mediating factors and pull factors has also been discussed, along with the consequences on personal, social and academic performance resulting from such kind of addictive behavior. The paper also makes recommendations for future research including developing a deeper understanding of the phenomena studied here by examining the relative contribution of these multiple-risk contexts.

Keywords: pathological gaming, gaming addiction, adolescents, behavior

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Authors: Mustapha Rilwan Adewale, Salau Ayobami Muhammed

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This study focuses on the heat transfer analysis of magneto-hydrodynamics (MHD) squeezing flow between parallel disks, considering a viscous incompressible fluid. The upper disk exhibits both upward and downward motion, while the lower disk remains stationary but permeable. By employing similarity transformations, a system of nonlinear ordinary differential equations is derived to describe the flow behavior. To solve this system, a numerical approach, namely the Chebyshev collocation method, is utilized. The study investigates the influence of flow parameters and compares the obtained results with existing literature. The significance of this research lies in understanding the heat transfer characteristics of MHD squeezing flow, which has practical implications in various engineering and industrial applications. By employing the similarity transformations, the complex governing equations are simplified into a system of nonlinear ordinary differential equations, facilitating the analysis of the flow behavior. To obtain numerical solutions for the system, the Chebyshev collocation method is implemented. This approach provides accurate approximations for the nonlinear equations, enabling efficient computations of the heat transfer properties. The obtained results are compared with existing literature, establishing the validity and consistency of the numerical approach. The study's major findings shed light on the influence of flow parameters on the heat transfer characteristics of the squeezing flow. The analysis reveals the impact of parameters such as magnetic field strength, disk motion amplitude, fluid viscosity on the heat transfer rate between the disks, the squeeze number(S), suction/injection parameter(A), Hartman number(M), Prandtl number(Pr), modified Eckert number(Ec), and the dimensionless length(δ). These findings contribute to a comprehensive understanding of the system's behavior and provide insights for optimizing heat transfer processes in similar configurations. In conclusion, this study presents a thorough heat transfer analysis of magneto-hydrodynamics squeezing flow between parallel disks. The numerical solutions obtained through the Chebyshev collocation method demonstrate the feasibility and accuracy of the approach. The investigation of flow parameters highlights their influence on heat transfer, contributing to the existing knowledge in this field. The agreement of the results with previous literature further strengthens the reliability of the findings. These outcomes have practical implications for engineering applications and pave the way for further research in related areas.

Keywords: squeezing flow, magneto-hydro-dynamics (MHD), chebyshev collocation method(CCA), parallel manifolds, finite difference method (FDM)

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Authors: Ladan Maijama’a, Jibril D. Jiya, Ejike C. Anene

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This paper presents Differential Evolution Algorithm (DEA) based Variable Structure Position Control (VSPC) of Laboratory DC servomotor (LDCSM). DEA is employed for the optimal tuning of Variable Structure Control (VSC) parameters for position control of a DC servomotor. The VSC combines the techniques of Sliding Mode Control (SMC) that gives the advantages of small overshoot, improved step response characteristics, faster dynamic response and adaptability to plant parameter variations, suppressed influences of disturbances and uncertainties in system behavior. The results of the simulation responses of the VSC parameters adjustment by DEA were performed in Matlab Version 2010a platform and yield better dynamic performance compared with the untuned VSC designed.

Keywords: differential evolution algorithm, laboratory DC servomotor, sliding mode control, variable structure control

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Authors: Bekim Berisha, Sebastian Hirsiger, Pavel Hora

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Advanced material models involving several sets of model parameters require a big experimental effort. As models are getting more and more complex like e.g. the so called “Homogeneous Anisotropic Hardening - HAH” model for description of the yielding behavior in the 2D/3D stress space, the number and complexity of the required experiments are also increasing continuously. In the context of sheet metal forming, these requirements are even more pronounced, because of the anisotropic behavior or sheet materials. In addition, some of the experiments are very difficult to perform e.g. the plane stress biaxial compression test. Accordingly, tensile tests in at least three directions, biaxial tests and tension-compression or shear-reverse shear experiments are performed to determine the parameters of the macroscopic models. Therefore, determination of the macroscopic model parameters based on virtual experiments is a very promising strategy to overcome these difficulties. For this purpose, in the framework of multiscale material modeling, a dislocation density based crystal plasticity model in combination with a FFT-based spectral solver is applied to perform virtual experiments. Modeling of the plastic behavior of metals based on crystal plasticity theory is a well-established methodology. However, in general, the computation time is very high and therefore, the computations are restricted to simplified microstructures as well as simple polycrystal models. In this study, a dislocation density based crystal plasticity model – including an implementation of the backstress – is used in a spectral solver framework to generate virtual experiments for three deep drawing materials, DC05-steel, AA6111-T4 and AA4045 aluminum alloys. For this purpose, uniaxial as well as multiaxial loading cases, including various pre-strain histories, has been computed and validated with real experiments. These investigations showed that crystal plasticity modeling in the framework of Representative Volume Elements (RVEs) can be used to replace most of the expensive real experiments. Further, model parameters of advanced macroscopic models like the HAH model can be determined from virtual experiments, even for multiaxial deformation histories. It was also found that crystal plasticity modeling can be used to model anisotropic hardening more accurately by considering the backstress, similar to well-established macroscopic kinematic hardening models. It can be concluded that an efficient coupling of crystal plasticity models and the spectral solver leads to a significant reduction of the amount of real experiments needed to calibrate macroscopic models. This advantage leads also to a significant reduction of computational effort needed for the optimization of metal forming process. Further, due to the time efficient spectral solver used in the computation of the RVE models, detailed modeling of the microstructure are possible.

Keywords: anisotropic hardening, crystal plasticity, micro structure, spectral solver

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Authors: Yang Yang, Dan Liu

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Anomaly detection models for network flow based on machine learning have poor detection performance under extremely unbalanced training data conditions and also have slow detection speed and large resource consumption when deploying on network edge devices. Embedding multi-teacher knowledge distillation (mKD) in anomaly detection can transfer knowledge from multiple teacher models to a single model. Inspired by this, we proposed a state-of-the-art model, mKDNAD, to improve detection performance. mKDNAD mine and integrate the knowledge of one-dimensional sequence and two-dimensional image implicit in network flow to improve the detection accuracy of small sample classes. The multi-teacher knowledge distillation method guides the train of the student model, thus speeding up the model's detection speed and reducing the number of model parameters. Experiments in the CICIDS2017 dataset verify the improvements of our method in the detection speed and the detection accuracy in dealing with the small sample classes.

Keywords: network flow anomaly detection (NAD), multi-teacher knowledge distillation, machine learning, deep learning

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Authors: Shuai Zhao, Shalendra D. Sharma, Jin Xu

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This research uses advanced machine learning models to learn a number of hypotheses regarding political executives. Specifically, it analyses the impact these powerful leaders have on economic growth by using leaders’ data from the Archigos database from 1835 to the end of 2015. The data is processed by the AutoGluon, which was developed by Amazon. Automated Machine Learning (AutoML) and AutoGluon can automatically extract features from the data and then use multiple classifiers to train the data. Use a linear regression model and classification model to establish the relationship between leaders and economic growth (GDP per capita growth), and to clarify the relationship between their characteristics and economic growth from a machine learning perspective. Our work may show as a model or signal for collaboration between the fields of statistics and artificial intelligence (AI) that can light up the way for political researchers and economists.

Keywords: comparative politics, political executives, leaders’ characteristics, artificial intelligence

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Authors: M. Gazzah, B. Jaouachi, F. Sakli

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This research focuses on the yarn-to-yarn and metal-to-fabric friction effects on the residual bagging behavior expressed by residual bagging height, volume and recovery of some denim fabrics. The results show, that both residual bagging height and residual bagging volume, which is determined using image analysis method, are significantly affected due to the most influential fabric parameter variations, the weft yarns density and the mean frictional coefficients. After the applied number of fatigue cycles, the findings revealed that the weft yarn rigidity contributes on fabric bagging behavior accurately. Among the tested samples, our results show that the elastic fabrics present a high recovery ability to give low bagging height and volume values.

Keywords: bagging recovery, denim fabric, metal-to-fabric friction, residual bagging height, yarn-to-yarn friction

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Authors: Nihal Abdel Hamid Taha

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Crack evaluation of flexure reinforced concrete (RC) member is considered an important step in the design process, since the formation of concrete cracks depends on the possibility of exposure to various conditions(pollution, humidity,..etc.). Because of the disparity between different grades of steel in the service load stresses, this affects the cracking behavior. This paper is concerned with the crack pattern and cracking load for concrete beams with T-section reinforced with two different grades of steel at the service load levels stages up to ultimate load. A practical program has been put up to investigate the difference between reinforced steel bars with yield strength 420 N/mm2 and 500 N/mm2 through six T-section reinforced beams. The beams were tested under static- monotonic two– point service loading up to ultimate failure under flexural stresses. The influence of parameters such as clear concrete cover and concrete compressive strength are considered for each of the two grades of steel used. Cracking load, spacing and width were determined. The experimental results demonstrated that increasing the concrete strength results in both of cracking and ultimate load increase, while no significant difference in yield load for the two steel grades used. It has also become obvious, that the number of cracks was more for the lower steel strength, which is followed by decrease in crack width and spacing.

Keywords: RC beams, cracking behavior, steel stress, crack width, crack spacing

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Authors: Florin Leon, Silvia Curteanu

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The Bacterial Foraging Optimization (BFO) algorithm is inspired by the behavior of bacteria such as Escherichia coli or Myxococcus xanthus when searching for food, more precisely the chemotaxis behavior. Bacteria perceive chemical gradients in the environment, such as nutrients, and also other individual bacteria, and move toward or in the opposite direction to those signals. The application example considered as a case study consists in establishing the dependency between the reaction yield of hydrogels based on polyacrylamide and the working conditions such as time, temperature, monomer, initiator, crosslinking agent and inclusion polymer concentrations, as well as type of the polymer added. This process is modeled with a neural network which is included in an optimization procedure based on BFO. An experimental study of BFO parameters is performed. The results show that the algorithm is quite robust and can obtain good results for diverse combinations of parameter values.

Keywords: bacterial foraging, hydrogels, modeling and optimization, neural networks

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Authors: Bessaad Taieb, Benbouali Abderrahmen

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Fuzzy controllers are a powerful tool for controlling complex processes. However, its robustness capacity remains moderately limited because it loses its property for large ranges of parametric variations. In this paper, the proposed control method is designed, based on a fuzzy adaptive controller used as a remedy for this problem. For increase the robustness of the vector control and to maintain the performance of the five-phase asynchronous machine despite the presence of disturbances (variation of rotor resistance, rotor inertia variations, sudden variations in the load etc.), by applying the method of behaviour model control (BMC). The results of simulation show that the fuzzy adaptive control provides best performance and has a more robustness as the fuzzy (FLC) and as a conventional (PI) controller.

Keywords: fuzzy adaptive control, behaviour model control, vector control, five-phase asynchronous machine

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Authors: Sadixa Sanjel, Ahmed Sadek, Naseef Mansoor, Zelalem Denekew

Abstract:

Collegiate ice hockey (NCAA) sports analytics is different from the national level hockey (NHL). We apply and compare multiple machine learning models such as Linear Regression, Random Forest, and Neural Networks to predict the win ratio for a team based on their statistics. Data exploration helps determine which statistics are most useful in increasing the win ratio, which would be beneficial to coaches and team managers. We ran experiments to select the best model and chose Random Forest as the best performing. We conclude with how to bridge the gap between the college and national levels of sports analytics and the use of machine learning to enhance team performance despite not having a lot of metrics or budget for automatic tracking.

Keywords: NCAA, NHL, sports analytics, random forest, regression, neural networks, game predictions

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Authors: Daniel Y. Abebe, Jaehyouk Choi

Abstract:

The idea of adding metallic energy dissipaters to a structure to absorb a large part of the seismic energy began four decades ago. There are several types of metal-based devices conceived as dampers for the seismic energy absorber whereby damages to the major structural components could be minimized for both new and existing structures. This paper aimed to develop and evaluate structural performance of both stiffened and non stiffened circular shear panel damper for passive seismic energy protection by inelastic deformation. Structural evaluation was done using commercially available nonlinear FE simulation program. Diameter-to-thickness ratio is employed as main parameter to investigate the hysteresis performance of stiffened and unstiffened circular shear panel. Depending on these parameters three different buckling mode and hysteretic behavior was found: yielding prior to buckling without strength degradation, yielding prior to buckling with strength degradation and yielding with buckling and strength degradation which forms pinching at initial displacement. Hence, the hysteresis behavior is identified, specimens which deform without strength degradation so it will be used as passive energy dissipating device in civil engineering structures.

Keywords: circular shear panel damper, FE analysis, hysteretic behavior, large deformation

Procedia PDF Downloads 388

Authors: Marisa Hakim

Abstract:

After all, collectivism moderate is one of the biggest issues that drive complicit behavior toward luxury in Indonesia and Thailand. The nature of collectivism that we find on this research would probably break the problems of the gap about the nature of complicit behavior. Precisely, we could probably drive to the further research about: 'Is there any pattern to describe consumer behavior toward counterfeit luxury goods among market in developing countries? Furthermore, is there any possibility to manipulate that pattern and bring the new concept of local/traditional luxury teste toward consumers in developing countries?'

Keywords: complicity, consumer complicity, counterfeit, consumer behavior, luxury goods, marketing, Indonesia, Thailand

Procedia PDF Downloads 269

Authors: Jing Zhang, Jie Chen

Abstract:

In this paper, we deal with the optimal I/O point location in an automated parking system. In this system, the S/R machine (storage and retrieve machine) travels independently in vertical and horizontal directions. Based on the characteristics of the parking system and the basic principle of AS/RS system (Automated Storage and Retrieval System), we obtain the continuous model in units of time. For the single command cycle using the randomized storage policy, we calculate the probability density function for the system travel time and thus we develop the travel time model. And we confirm that the travel time model shows a good performance by comparing with discrete case. Finally in this part, we establish the optimal model by minimizing the expected travel time model and it is shown that the optimal location of the I/O point is located at the middle of the left-hand above corner.

Keywords: parking system, optimal location, response time, S/R machine

Procedia PDF Downloads 409