Search results for: multi variable decision making

Authors: Joanna Stawska

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The main objective of this article is to examine the impact of interest rates on investments in Poland in the context of financial crisis. The paper also investigates the dependence of bank loans to enterprises on interbank market rates. The article studies the impact of interbank market rate on the level of investments in Poland. Besides, this article focuses on the research of the correlation between the level of corporate loans and the amount of investments in Poland in order to determine the indirect impact of central bank interest rates through the transmission mechanism of monetary policy on the real economy. To achieve the objective we have used econometric and statistical research methods like: econometric model and Pearson correlation coefficient. This analysis suggests that the central bank reference rate inversely proportionally affects the level of investments in Poland and this dependence is moderate. This is also important issue because it is related to preparing of Poland to accession to euro area. The research is important from both theoretical and empirical points of view. The formulated conclusions and recommendations determine the practical significance of the paper which may be used in the decision making process of monetary and economic authorities of the country.

Keywords: central bank, financial crisis, interest rate, investments

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Authors: Asmae Chraibi, Rachid Lghoul, Nabil Rhiati

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In the era of Industry 4.0, enterprises are increasingly employing digital technologies in order to improve their product development processes. This research focuses on the strategic deployment of Product Lifecycle Management (PLM) solutions during production as a key tracker of traceability and digital transformation activities. The study explores the integration of PLM within a larger organizational framework, examining its impact on product lifecycle efficiency, corporation, and innovation. Through a comprehensive analysis of a real case study from the automotive industry, this project evaluates the critical success factors and challenges associated with implementing PLM solutions for digital transformation. Moreover, it explores the synergic relationship between PLM and emerging technologies such as 3D experience and SOLIDWORKS, elucidating their combined potential in optimizing production workflows and enabling data-driven decision-making. The study's findings provide global approaches for firms looking to embark on a digital transformation journey by implementing PLM technologies. This research contributes to a better understanding of how PLM can be effectively used to foster innovation and competitiveness in the changing landscape of modern industry by shining light on best practices, critical considerations, and potential obstacles.

Keywords: product lifecyle management (PLM), industry 4.0, traceability, digital transformation, solution, innovation, 3D experience, SOLIDWORKS

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Authors: Antonia Tsaknaki

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There are many and divergent views on how the science of translation should be taught in academic institutions or colleges, meaning as an independent study area or as part of Linguistics, Literature or Foreign Languages Departments. A much more debated issue refers to the question of whether translation theory should be included in syllabuses and study programs or the focus should be solely on practicing the profession, that is translating texts. This dissertation examines prevailing views on the significance of translation theory in translation studies in order to design an open course on moodle. Taking into account that there is a remarkable percentage of translation professionals who are self-taught without having any specific studies, the course aims at helping either translation students or professional translators familiarize with concepts, methods and problem-solving strategies that are considered necessary during the process. It is organized in four modules where the learner is guided through a series of topics (register, equivalence, decision-making, level of naturalness, Skopos theory etc); after completing these topics, they are given assignments (further reading) and texts to work on in order to practice the skills obtained. The course does not focus on a specific language pair and therefore is suitable for every individual who needs a theoretical background to boost their performance or for institutions seeking to save classroom time but not at the expense of learners’ skills.

Keywords: MOOCs, moodle, online learning, open courses, translation, translation theory

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Authors: C. Elavarasi, T. Shanmuganantham

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A coplanar waveguide (CPW) feed is presented, and comprising a split ring resonator (SRR) loaded fractal with water lily shape is used for multi band applications. The impedance matching of the antenna is determined by the number of Koch curve fractal unit cells. The antenna is designed on a FR4 substrate with a permittivity of ε_{r =} 4.4 and size of 14 x 16 x 1.6 mm³ to generate multi resonant mode at 3.8 GHz covering S band, 8.68 GHz at X band, 13.96 GHz at Ku band, and 19.74 GHz at K band with reflection coefficient better than -10 dB. Simulation results show that the antenna exhibits the desired voltage standing wave ratio (VSWR) level and radiation patterns across the wide frequency range. The fundamental parameters of the antenna such as return loss, VSWR, good radiation pattern with reasonable gain across the operating bands are obtained.

Keywords: fractal, metamaterial, split ring resonator, waterlily shape

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Authors: Yanping Liao, Zenan Wu, Ruigang Zhao

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Frequency diverse array (FDA) beamforming is a technology developed in recent years, and its antenna pattern has a unique angle-distance-dependent characteristic. However, the beam is always required to have strong concentration, high resolution and low sidelobe level to form the point-to-point interference in the concentrated set. In order to eliminate the angle-distance coupling of the traditional FDA and to make the beam energy more concentrated, this paper adopts a multi-carrier FDA structure based on proposed power exponential frequency offset to improve the array structure and frequency offset of the traditional FDA. The simulation results show that the beam pattern of the array can form a dot-shape beam with more concentrated energy, and its resolution and sidelobe level performance are improved. However, the covariance matrix of the signal in the traditional adaptive beamforming algorithm is estimated by the finite-time snapshot data. When the number of snapshots is limited, the algorithm has an underestimation problem, which leads to the estimation error of the covariance matrix to cause beam distortion, so that the output pattern cannot form a dot-shape beam. And it also has main lobe deviation and high sidelobe level problems in the case of limited snapshot. Aiming at these problems, an adaptive beamforming technique based on exponential correction for multi-carrier FDA is proposed to improve beamforming robustness. The steps are as follows: first, the beamforming of the multi-carrier FDA is formed under linear constrained minimum variance (LCMV) criteria. Then the eigenvalue decomposition of the covariance matrix is ​​performed to obtain the diagonal matrix composed of the interference subspace, the noise subspace and the corresponding eigenvalues. Finally, the correction index is introduced to exponentially correct the small eigenvalues ​​of the noise subspace, improve the divergence of small eigenvalues ​​in the noise subspace, and improve the performance of beamforming. The theoretical analysis and simulation results show that the proposed algorithm can make the multi-carrier FDA form a dot-shape beam at limited snapshots, reduce the sidelobe level, improve the robustness of beamforming, and have better performance.

Keywords: adaptive beamforming, correction index, limited snapshot, multi-carrier frequency diverse array, robust

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Authors: Till Gramberg, Max Kellner, Erwin Gross

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This paper presents a comprehensive investigation into the topic of smart services and IIoT-Platforms, focusing on their selection and prioritization in manufacturing organizations. First, a literature review is conducted to provide a basic understanding of the current state of research in the area of smart services. Based on discussed and established definitions, a definition approach for this paper is developed. In addition, value propositions for smart services are identified based on the literature and expert interviews. Furthermore, the general requirements for the provision of smart services are presented. Subsequently, existing approaches for the selection and development of smart services are identified and described. In order to determine the requirements for the selection of smart services, expert opinions from successful companies that have already implemented smart services are collected through semi-structured interviews. Based on the results, criteria for the evaluation of existing methods are derived. The existing methods are then evaluated according to the identified criteria. Furthermore, a novel method for the selection of smart services in manufacturing companies is developed, taking into account the identified criteria and the existing approaches. The developed concept for the method is verified in expert interviews. The method includes a collection of relevant smart services identified in the literature. The actual relevance of the use cases in the industrial environment was validated in an online survey. The required data and sensors are assigned to the smart service use cases. The value proposition of the use cases is evaluated in an expert workshop using different indicators. Based on this, a comparison is made between the identified value proposition and the required data, leading to a prioritization process. The prioritization process follows an established procedure for evaluating technical decision-making processes. In addition to the technical requirements, the prioritization process includes other evaluation criteria such as the economic benefit, the conformity of the new service offering with the company strategy, or the customer retention enabled by the smart service. Finally, the method is applied and validated in an industrial environment. The results of these experiments are critically reflected upon and an outlook on future developments in the area of smart services is given. This research contributes to a deeper understanding of the selection and prioritization process as well as the technical considerations associated with smart service implementation in manufacturing organizations. The proposed method serves as a valuable guide for decision makers, helping them to effectively select the most appropriate smart services for their specific organizational needs.

Keywords: smart services, IIoT, industrie 4.0, IIoT-platform, big data

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Authors: Yun Hang Stanley Cheung

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In recent years, many educators and entrepreneurs have often suggested that students’ and workers’ ability of the adversity response is very important, it would affect problem-solving strategies and ultimate success in their career or life. The meaning of resilience is discussed as the process of bouncing back and the ability to adapt well in adversity’s response, being resilient does not mean to live without any stress and difficulty, but to grow and thrive under pressure. The purpose of this study is to describe the process of resilience and adversity’s response. The use of the narrative inquiry aims for understanding the experiential process of adversity response, and the problem-solving strategies (such as emotion control, motivation, decisions making process), as well as making the experience become life story, which may be evaluated by its teller and its listeners. The narrative study describes the researcher’s self-experience of adversity’s response to the recovery of the seriously burnt injury from a hill fire at his 12 years old, as well as the adversities and obstacles related to the tragedy after the physical recovery. Sense-Making Theory and McCormack’s Lenses were used for constructive perspective and data analyzing. To conclude, this study has described the life story of fighting the adversities, also, those narratives come out some suggestions, which point out positive thinking is necessary to build up resilience and the ability of immediate adversity response. Also, some problem-solving strategies toward adversities are discussed, which are helpful for resilience education for youth and young adult.

Keywords: adversity response, life story, narrative inquiry, resilience

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Authors: Mohammad Mahdi Doustdar, Mohammad Mojtahedpoor

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The effects of flame-holder position, the ratio of flame holder diameter to combustion chamber diameter and injection angle on fuel propulsive droplets sizing and effective mass fraction have been studied by a cold flow. We named the mass of fuel vapor inside the flammability limit as the effective mass fraction. An empty cylinder as well as a flame-holder which are as a simulator for duct combustion has been considered. The airflow comes into the cylinder from one side and injection operation will be done by four nozzles which are located on the entrance of cylinder. To fulfill the calculations a modified version of KIVA-3V code which is a transient, three-dimensional, multi phase, multi component code for the analysis of chemically reacting flows with sprays, is used.

Keywords: KIVA-3V, flame-holder, duct combustion, effective mass fraction, mean diameter of droplets

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Authors: Partha Saha, Uttam Kumar Banerjee

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Maternal and Child Healthcare (MCH) has always been regarded as one of the important issues globally. Reduction of maternal and child mortality rates and increase of healthcare service coverage were declared as one of the targets in Millennium Development Goals till 2015 and thereafter as an important component of the Sustainable Development Goals. Over the last decade, worldwide MCH indicators have improved but could not match the expected levels. Progress of both maternal and child mortality rates have been monitored by several researchers. Each of the studies has stated that only less than 26% of low-income and middle income countries (LMICs) were on track to achieve targets as prescribed by MDG4. Average worldwide annual rate of reduction of under-five mortality rate and maternal mortality rate were 2.2% and 1.9% as on 2011 respectively whereas rates should be minimum 4.4% and 5.5% annually to achieve targets. In spite of having proven healthcare interventions for both mothers and children, those could not be scaled up to the required volume due to fragmented health systems, especially in the developing and under-developed countries. In this research, a knowledge discovery based interactive Decision Support System (DSS) has been developed on web platform which would assist healthcare policy makers to develop evidence-based policies. To achieve desirable results in MCH, efficient resource planning is very much required. In maximum LMICs, resources are big constraint. Knowledge, generated through this system, would help healthcare managers to develop strategic resource planning for combatting with issues like huge inequity and less coverage in MCH. This system would help healthcare managers to accomplish following four tasks. Those are a) comprehending region wise conditions of variables related with MCH, b) identifying relationships within variables, c) segmenting regions based on variables status, and d) finding out segment wise key influential variables which have major impact on healthcare indicators. Whole system development process has been divided into three phases. Those were i) identifying contemporary issues related with MCH services and policy making; ii) development of the system; and iii) verification and validation of the system. More than 90 variables under three categories, such as a) educational, social, and economic parameters; b) MCH interventions; and c) health system building blocks have been included into this web-based DSS and five separate modules have been developed under the system. First module has been designed for analysing current healthcare scenario. Second module would help healthcare managers to understand correlations among variables. Third module would reveal frequently-occurring incidents along with different MCH interventions. Fourth module would segment regions based on previously mentioned three categories and in fifth module, segment-wise key influential interventions will be identified. India has been considered as case study area in this research. Data of 601 districts of India has been used for inspecting effectiveness of those developed modules. This system has been developed by importing different statistical and data mining techniques on Web platform. Policy makers would be able to generate different scenarios from the system before drawing any inference, aided by its interactive capability.

Keywords: maternal and child heathcare, decision support systems, data mining techniques, low and middle income countries

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Authors: Liying Li, Han Guo

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Considering the environmental issues generated by the building sector for its energy consumption, solid waste generation, water use, land use, and global greenhouse gas (GHG) emissions, this review pointed out to LCA as a decision-support tool to substantially improve the sustainability in the building and construction industry. The comprehensiveness and simplicity of LCA make it one of the most promising decision support tools for the sustainable design and construction of future buildings. This paper contains a comprehensive review of existing studies related to LCAs with a focus on their advantages and limitations when applied in the building sector. The aim of this paper is to enhance the understanding of a building life-cycle analysis, thus promoting its application for effective, sustainable building design and construction in the future. Comparisons and discussions are carried out between four categories of LCA methods: building material and component combinations (BMCC) vs. the whole process of construction (WPC) LCA，attributional vs. consequential LCA, process-based LCA vs. input-output (I-O) LCA, traditional vs. hybrid LCA. Classical case studies are presented, which illustrate the effectiveness of LCA as a tool to support the decisions of practitioners in the design and construction of sustainable buildings. (i) BMCC and WPC categories of LCA researches tend to overlap with each other, as majority WPC LCAs are actually developed based on a bottom-up approach BMCC LCAs use. (ii) When considering the influence of social and economic factors outside the proposed system by research, a consequential LCA could provide a more reliable result than an attributional LCA. (iii) I-O LCA is complementary to process-based LCA in order to address the social and economic problems generated by building projects. (iv) Hybrid LCA provides a more superior dynamic perspective than a traditional LCA that is criticized for its static view of the changing processes within the building’s life cycle. LCAs are still being developed to overcome their limitations and data shortage (especially data on the developing world), and the unification of LCA methods and data can make the results of building LCA more comparable and consistent across different studies or even countries.

Keywords: decision support tool, life-cycle analysis, LCA tools and data, sustainable building design

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Authors: Efrizal, Ferdiansyah, Noorlailie Soewarno, Bambang Tjahjadi

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Tax revenue plays an important role in the State Budget of the Government of Indonesia (GOI). The GOI keeps raising tax revenue portion of the Budget from year to year. The low tax ratio of 11 percent in Indonesia shows a big opportunity to collect taxes in the future. The Directorate General of Taxation (DGT) is the institution mandated by the Law to collect tax revenue. This is a case study using quantitative and qualitative approaches. This study introduces contingent factors of taxpayers’ compliance as the mediating variable and internal business process as the independent variable. This study aims to empirically test the contingency theory, especially the mediating effect of taxpayers’ compliance on internal business process-tax revenue relationship. Internal business processes of the DGT include servicing, counseling, expanding, supervising, inspecting, and enforcing. The secondary data of 31 regional offices representing 293 tax offices in Indonesia was collected and analyzed using Partial Least Square. The result showed the following: (1) internal business process affected tax revenue; (2) taxpayers’ compliance did not mediate internal business processes - tax revenue relationship, and (3) taxpayers’ compliance affected tax revenue. In-depth interviews revealed that the DGT needs to make more innovations in business processes in the future.

Keywords: innovations, internal business process, taxpayers’ compliance, tax revenue

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Authors: Yiming Jin, Ping Dong

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The third super-harmonic resonance of a pipeline close to the seabed is investigated in this paper. To analyse the vortex-induced vibration (VIV) of the pipeline close to the seabed, the classic Van der Pol equation is extended with a nonlinear item. Then, on the base of the multi-scale method, the frequency-response curves of the pipeline with regard to the third super-harmonic resonance are studied with a series of parameters, such as the mass ratio, frequency, damp ratio and gap ratio. On the whole, the numerical results show that the characters of third super-harmonic resonance are quite from that of primary resonance, though with the same trend that the larger is the mass ratio, the smaller impact the gap ratio has on the frequency-response curves of the third super-harmonic resonance.

Keywords: the third super-harmonic resonance, gap ratio, vortex-induced vibration, multi-scale method

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Vhuhwavho Gadisi, Rebecca Alowo, German Nkhonjera

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It has been noted that technical programming for handling groundwater resources is not accessible. The lack of these systems hinders groundwater management processes necessary for decision-making through monitoring and evaluation regarding the Jukskei River of the Crocodile River (West) Basin in Johannesburg, South Africa. Several challenges have been identified in South Africa's Jukskei Catchment concerning groundwater management. Some of those challenges will include the following: Gaps in data records; there is a need for training and equipping of monitoring staff; formal accreditation of monitoring capacities and equipment; there is no access to regulation terms (e.g., meters). Taking into consideration necessities and human requirements as per typical densities in various regions of South Africa, there is a need to construct several groundwater level monitoring stations in a particular segment; the available raw data on groundwater level should be converted into consumable products for example, short reports on delicate areas (e.g., Dolomite compartments, wetlands, aquifers, and sole source) and considering the increasing civil unrest there has been vandalism and theft of groundwater monitoring infrastructure. GIS was employed at the catchment level to plot the relationship between those identified groundwater parameters in the catchment area and the identified borehole. GIS-based maps were designed for groundwater monitoring to be pretested on one borehole in the Jukskei catchment. This data will be used to establish changes in the borehole compared to changes in the catchment area according to identified parameters.

Keywords: GIS, monitoring, Jukskei, catchment

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Authors: Rhoann Kerh, Chen-Fu Chien, Kuo-Yi Lin

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In the era of rapidly increasing notebook market, consumer electronics manufacturers are facing a highly dynamic and competitive environment. In particular, the product appearance is the first part for user to distinguish the product from the product of other brands. Notebook product should differ in its appearance to engage users and contribute to the user experience (UX). The UX evaluates various product concepts to find the design for user needs; in addition, help the designer to further understand the product appearance preference of different market segment. However, few studies have been done for exploring the relationship between consumer background and the reaction of product appearance. This study aims to propose a data mining framework to capture the user’s information and the important relation between product appearance factors. The proposed framework consists of problem definition and structuring, data preparation, rules generation, and results evaluation and interpretation. An empirical study has been done in Taiwan that recruited 168 subjects from different background to experience the appearance performance of 11 different portable computers. The results assist the designers to develop product strategies based on the characteristics of consumers and the product concept that related to the UX, which help to launch the products to the right customers and increase the market shares. The results have shown the practical feasibility of the proposed framework.

Keywords: consumers decision making, product design, rough set theory, user experience

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Authors: Mansour Al Qubeissi, Sergei S. Sazhin, Cyril Crua, Morgan R. Heikal

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This paper presents the application of the Discrete Component Model for heating and evaporation to multi-component biodiesel fuel droplets in direct injection internal combustion engines. This model takes into account the effects of temperature gradient, recirculation and species diffusion inside droplets. A distinctive feature of the model used in the analysis is that it is based on the analytical solutions to the temperature and species diffusion equations inside the droplets. Nineteen types of biodiesel fuels are considered. It is shown that a simplistic model, based on the approximation of biodiesel fuel by a single component or ignoring the diffusion of components of biodiesel fuel, leads to noticeable errors in predicted droplet evaporation time and time evolution of droplet surface temperature and radius.

Keywords: heat/mass transfer, biodiesel, multi-component fuel, droplet

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Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: Roman Asatryan

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The decision-making processes in Corporate Social Responsibility (CSR) among firms in general and airlines in particular have to do with the benefits that accrue through those investments. The crux of the matter is not whether to invest in CSR or not, but rather, how firms can quantify the benefits derived from such investments. This paper analyzes the cost benefit adjustment strategies for firms in the airline industry in their CSR strategy adoption and implementation. The adjustment strategies identified will enable firms in the airline industry to have a basis for determining the worth of such CSR investments. This paper discusses the cost and benefit analysis model in order to understand the ways airlines can reduce costs and increase returns on CSR, or balance the cost and benefits. The analysis from this study points to the fact that economic concepts especially the CBA are useful, though they are not without challenges. The challenge arises when it is problematic to express the real impact of the externality in monetary terms. The use of rational maximization of the gains may seem to be a rather optimistic goal mainly because of environmental variability, perceptual uncertainty, and imperfect knowledge about the potential externality. This paper concludes that the CBA model gives a basic understanding of the motivations for investing in intangible assets like CSR. Consequently, it sets the tone for formulating relevant hypothesis in empirical studies in investment in CSR in particular and other intangible assets in business operations.

Keywords: cost-benefit analysis, corporate social responsibility, airline industry

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Authors: Dina Ziadlou, Anthony Sunjaya, Joyzen Cortez Ramos, Romario Muñoz Ramos, Richard Dasselaar

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This study aimed to evaluate the opinions of users using digital health technologies to prevent, promote, and maintain their health and well-being with or without the support of a healthcare provider to delineate an overview of the future patient journey while considering the strategic initiatives in the digital transformation era. This research collected the opinions of healthcare clients through a structural questionnaire to collect user accessibility, user knowledge, user experience, user engagement, and personalized medicine to investigate the mindset of the users and illustrate their opinions, expectations, needs, and voices about digital self-care expansion. In the realm of digital transformation, the accessibility of users to the internet, digital health platforms, tools, and mobile health applications have revolutionized the healthcare ecosystem toward nurturing informed and empowered patients who are tech-savvy and can take the initiative to be in charge of their health, involved in medical decision-making, and seek digital health innovations to prevent diseases and promote their healthy lifestyles. Therefore, the future of the patient journey is digital self-care intervention in a healthcare ecosystem where the partnership of patients in healthcare services is tied to their health information and action ownership.

Keywords: digital health, patient engagement, self-care intervention, digital self-care intervention, digital transformation

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Authors: Francisco José Plácido da Cunha, Tassio Ferenzini Martins Sirqueira, Marx Leles Viana, Carlos José Pereira de Lucena

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Open Multiagent Systems (MASs) are societies in which heterogeneous and independently designed entities (agents) work towards similar, or different ends. Software agents are autonomous and the diversity of interests among different members living in the same society is a fact. In order to deal with this autonomy, these open systems use mechanisms of social control (norms) to ensure a desirable social order. This paper considers the following types of norms: (i) obligation — agents must accomplish a specific outcome; (ii) permission — agents may act in a particular way, and (iii) prohibition — agents must not act in a specific way. All of these characteristics mean to encourage the fulfillment of norms through rewards and to discourage norm violation by pointing out the punishments. Once the software agent decides that its priority is the satisfaction of its own desires and goals, each agent must evaluate the effects associated to the fulfillment of one or more norms before choosing which one should be fulfilled. The same applies when agents decide to violate a norm. This paper also introduces a framework for the development of MASs that provide support mechanisms to the agent’s decision-making, using norm-based reasoning. The applicability and validation of this approach is demonstrated applying a traffic intersection scenario.

Keywords: BDI agent, BDI4JADE framework, multiagent systems, normative agents

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Authors: Abdul Razzaq

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It is known that every human society throughout the world and throughout history, the various religions and their theologies, ethics, and traditions influence everything in their life, shaping socio-economic and political ideas, attitudes and institutions. It is observed that religious teachings and traditions shape how people respond to each other in their daily social inter-course and interaction in the community at large. The majorities of us preserves and protect our own religious beliefs and traditions as generally they symbolize our essential identities, theologically, historically, culturally, socially, and even politically. Our religious faiths symbolize our dignity as persons and our very souls as communities and individuals. It thus goes without saying that in our multi-racial and multi-religious society, the only way for us to live in peace and harmony is for us to live in peaceful co-existence. It is important for us to recognize, understand, accept and respect each other regardless of our respective belief. The history of interfaith is as ancient as the religions since men and women when not at war with their neighbors have always made an effort to understand them (not least because understanding is a strategy for defense, but also because for as long as there is dialogue wars are delayed).

Keywords: Islam, religion, peace, society

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Authors: Taner Ersoz, Filiz Ersoz, Seyma Ozbilge

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In this study, the clients who applied to a bank branch for loan were analyzed through data mining. The study was composed of the information such as amounts of loans received by personal and SME clients working with the bank branch, installment numbers, number of delays in loan installments, payments available in other banks and number of banks to which they are in debt between 2010 and 2013. The client risk profile was examined through Classification and Regression Tree (CART) analysis, one of the decision tree classification methods. At the end of the study, 5 different types of customers have been determined on the decision tree. The classification of these types of customers has been created with the rating of those posing a risk for the bank branch and the customers have been classified according to the risk ratings.

Keywords: client classification, loan suitability, risk rating, CART analysis

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Authors: Alan (Avi) Kirschenbaum, Carmit Rapaport

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The behavior of bystanders in emergencies and disasters have been examined for over 50 years. Such acts have been cited as contributing to saving lives in terms of providing first responder help until official emergency units can arrive. Several reasons have been suggested for this type of behavior but most focused on a broad segment of individual psychological decision-making processes. Recent theoretical evidence suggests that the external factors for such bystander decisions, mainly disaster community based social contexts factors, are also important. We aim to test these competing arguments. Specifically, we examine alternative explanatory perspectives by focusing on self-efficacy as a proxy for the accepted individual psychological case and contrast it with potential bystander characteristics of the individual as well factors as embedded in the social context of the disaster community. To do so, we will utilize a random sampling of the population from a field study of an urban community in Israel that experienced five years of continuous terror attacks. The results strongly suggest that self-efficacy, as well as external factors: preparedness and having skills for intervention during emergencies along with gender best, predict potential helping behaviors. These results broaden our view of bystander behavior and open a window for enhancing this phenomenon as another element in disaster and crisis management.

Keywords: bystander behavior, disasters emergencies, psychological motivation to help, social context for helping

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Authors: Ryan Brisbin, Hassan Harb, Justin Debow, Hrant Hratchian, Ryan Baxter

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Donor-Acceptor (DA) frameworks are crucial parts of any technology requiring charge transport. This type of behavior is ubiquitous across technologies from semi conductors to solar panels. Currently, most DA systems involve metallic components, but progressive research is being pursued to design fully organic DA systems to be used as both organic semi-conductors and light emitting diodes. These systems are currently comprised of conductive polymers and salts. However, little is known about the effect of various physical aspects (size, torsional angle, electron density) have on the act of reversible charge transfer. Herein, the effect of torsional angle on reductive stability in bis(imino)pyridines is analyzed using a combination of single crystal analysis and electro-chemical peak current ratios from cyclic voltammetry. The computed free energies of reduction and electron attachment points were also investigated through density functional theory and natural ionization orbital theory to gain greater understanding of the global effect torsional angles have on electron transfer in bis(imino)pyridines. Findings indicated that torsional angles are a multi-variable parameter affected by both local steric constraints and resonant electronic contributions. Local steric impacted torsional angles demonstrated a negligible effect on electrochemical reversibility, while resonant affected torsional angles were observed to significantly alter the electrochemical reversibility.

Keywords: cyclic voltammetry, bis(imino)pyridines, structure-activity relationship, torsional angles

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Authors: Abdul Razzaq

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It is known that every human society throughout the world and throughout history, the various religions and their theologies, ethics, and traditions influence everything in their life, shaping socio-economic and political ideas, attitudes and institutions. It is observed that religious teachings and traditions shape how people respond to each other in their daily social inter-course and interaction in the community at large. The majorities of us preserves and protect our own religious beliefs and traditions as generally they symbolize our essential identities, theologically, historically, culturally, socially, and even politically. Our religious faiths symbolize our dignity as persons and our very souls as communities and individuals. It thus goes without saying that in our multi-racial and multi-religious society, the only way for us to live in peace and harmony is for us to live in peaceful co-existence. It is important for us to recognize, understand, accept and respect each other regardless of our respective belief. The history of interfaith is as ancient as the religions since men and women when not at war with their neighbors have always made an effort to understand them (not least because understanding is a strategy for defense, but also because for as long as there is dialogue wars are delayed).

Keywords: Islam, interfaith, Sects, world, piece

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Authors: Jian Wu

Abstract:

Since a few years, some French companies have integrated CRS (corporate social responsibility) criteria in their variable remuneration plans to ‘restore a good working atmosphere’ and ‘preserve the natural environment’. These CSR criteria are based on concerns on environment protection, social aspects, and corporate governance. In June 2012, a report on this practice has been made jointly by ORSE (which means Observatory on CSR in French) and PricewaterhouseCoopers. Facing this initiative from the business world, we need to examine whether it has a real economic utility. We adopt a theoretical approach for our study. First, we examine the debate between the ‘orthodox’ point of view in economics and the CSR school of thought. The classical economic model asserts that in a capitalist economy, exists a certain ‘invisible hand’ which helps to resolve all problems. When companies seek to maximize their profits, they are also fulfilling, de facto, their duties towards society. As a result, the only social responsibility that firms should have is profit-searching while respecting the minimum legal requirement. However, the CSR school considers that, as long as the economy system is not perfect, there is no ‘invisible hand’ which can arrange all in a good order. This means that we cannot count on any ‘divine force’ which makes corporations responsible regarding to society. Something more needs to be done in addition to firms’ economic and legal obligations. Then, we reply on some financial theories and empirical evident to examine the sound foundation of CSR. Three theories developed in corporate governance can be used. Stakeholder theory tells us that corporations owe a duty to all of their stakeholders including stockholders, employees, clients, suppliers, government, environment, and society. Social contract theory tells us that there are some tacit ‘social contracts’ between a company and society itself. A firm has to respect these contracts if it does not want to be punished in the form of fine, resource constraints, or bad reputation. Legitime theory tells us that corporations have to ‘legitimize’ their actions toward society if they want to continue to operate in good conditions. As regards empirical results, we present a literature review on the relationship between the CSR performance and the financial performance of a firm. We note that, due to difficulties in defining these performances, this relationship remains still ambiguous despite numerous research works realized in the field. Finally, we are curious to know whether the integration of CSR criteria in variable remuneration plans – which is practiced so far in big companies – should be extended to other ones. After investigation, we note that two groups of firms have the greatest need. The first one involves industrial sectors whose activities have a direct impact on the environment, such as petroleum and transport companies. The second one involves companies which are under pressures in terms of return to deal with international competition.

Keywords: corporate social responsibility, corporate governance, variable remuneration, stakeholder theory

Procedia PDF Downloads 171

Authors: N. S. Yen, T. H. Yang, W. H. Huang, Y. F. Fang, H. W. Cho

Abstract:

Ultimatum game is an experimental paradigm to study human decision making. There are two players, a proposer and a responder, to split a fixed amount of money. According to the traditional economic theory on ultimatum game, proposer should propose the selfish offers to responder as much as possible to maximize proposer’s own outcomes. However, most evidences had showed that people chose more fair offers, hence two hypotheses – fairness favoring and strategic concern were proposed. In current study, we induced the motivation in participants to be either selfish or altruistic, and manipulated the task variables, the stake sizes (NT$100, 1000, 10000) and the share sizes (the 40%, 30%, 20%, 10% of the sum as selfish offers, and the 60%, 70%, 80%, 90% of the sum as altruistic offers), to examine the two hypotheses. The results showed that most proposers chose more fair offers with longer reaction times (RTs) no matter in choosing between the fair and selfish offers, or between the fair and altruistic offers. However, the proposers received explicit self-interest instruction chose more selfish offers accompanied with longer RTs in choosing between the fair and selfish offers. Therefore, the results supported the strategic concern hypothesis that previous proposers choosing the fair offers might be resulted from the fear of rejection by responders. Proposers would become more self-interest if the fear of being rejected is eliminated.

Keywords: ultimatum game, proposer, self-interest, fear of rejection

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Authors: Yari Vecchio, Margherita Masi, Jorgelina Di Pasquale

Abstract:

Adoption of Precision Agriculture (PA) is involved in a multidimensional and complex scenario. The process of adopting innovations is complex and social inherently, influenced by other producers, change agents, social norms and organizational pressure. Complexity depends on factors that interact and influence the decision to adopt. Farm and operator characteristics, as well as organizational, informational and agro-ecological context directly affect adoption. This influence has been studied to measure drivers and to clarify 'bottlenecks' of the adoption of agricultural innovation. Making decision process involves a multistage procedure, in which individual passes from first hearing about the technology to final adoption. Awareness is the initial stage and represents the moment in which an individual learns about the existence of the technology. 'Static' concept of adoption has been overcome. Awareness is a precondition to adoption. This condition leads to not encountering some erroneous evaluations, arose from having carried out analysis on a population that is only in part aware of technologies. In support of this, the present study puts forward an empirical analysis among Italian farmers, considering awareness as a prerequisite for adoption. The purpose of the present work is to analyze both factors that affect the probability to adopt and determinants that drive an aware individual to not adopt. Data were collected through a questionnaire submitted in November 2017. A preliminary descriptive analysis has shown that high levels of adoption have been found among younger farmers, better educated, with high intensity of information, with large farm size and high labor-intensive, and whose perception of the complexity of adoption process is lower. The use of a logit model permits to appreciate the weight played by the intensity of labor and complexity perceived by the potential adopter in PA adoption process. All these findings suggest important policy implications: measures dedicated to promoting innovation will need to be more specific for each phase of this adoption process. Specifically, they should increase awareness of PA tools and foster dissemination of information to reduce the degree of perceived complexity of the adoption process. These implications are particularly important in Europe where is pre-announced the reform of Common Agricultural Policy, oriented to innovation. In this context, these implications suggest to the measures supporting innovation to consider the relationship between various organizational and structural dimensions of European agriculture and innovation approaches.

Keywords: adoption, awareness, complexity, precision agriculture

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Authors: Pasala Gopi, P. Linga Reddy

Abstract:

The response of the load frequency control problem in an multi-area interconnected electrical power system is much more complex with increasing size, changing structure and increasing load. This paper deals with Load Frequency Control of three area interconnected Power system incorporating Reheat, Non-reheat and Reheat turbines in all areas respectively. The response of the load frequency control problem in an multi-area interconnected power system is improved by designing PID controller using different tuning techniques and proved that the PID controller which was designed by Simulink Design Optimization (SDO) Software gives the superior performance than other controllers for step perturbations. Finally the robustness of controller was checked against system parameter variations

Keywords: load frequency control, pid controller tuning, step load perturbations, inter connected power system

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Authors: Yahia. Kourd, N. Guersi D. Lefebvre

Abstract:

In this paper we propose to study the faults diagnosis in squirrel-cage induction motor using MLP neural networks. We use neural healthy and faulty models of the behavior in order to detect and isolate some faults in machine. In the first part of this work, we have created a neural model for the healthy state using Matlab and a motor located in LGEB by acquirins data inputs and outputs of this engine. Then we detected the faults in the machine by residual generation. These residuals are not sufficient to isolate the existing faults. For this reason, we proposed additive neural networks to represent the faulty behaviors. From the analysis of these residuals and the choice of a threshold we propose a method capable of performing the detection and diagnosis of some faults in asynchronous machines with squirrel cage rotor.

Keywords: faults diagnosis, neural networks, multi-models, squirrel-cage induction motor

Procedia PDF Downloads 620