RFOS - Repository of Faculty of Organizational Sciences Univ. of Belgrade
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The integrated prospect theory with consensus model for risk analysis of human error factors in the clinical use of medical devices
No full textThe risk analysis of human error factors is one of the most significant procedures for preventing and reducing risk in the clinical use of medical devices. Human Factor Analysis and Classification System (HFACS) framework, a systematic human error analysis tool, is widely used for human error factors risk analysis. However, the traditional HFACS framework is insufficient to deal with the scenario with complex and uncertain risk information and conflicting opinions among experts caused by their heterogeneous risk preferences and diverse knowledge. To address these limitations, this paper integrated the prospect theory with the consensus model under Interval Type-2 Fuzzy Sets (IT2FSs) environment for addressing the HFACS-based human error factors risk analysis problem. This integrated method enabled the HFACS to yield highly acceptable risk analysis results considering experts' heterogeneous risk preferences. Specifically, the IT2FSs are utilized to represent highly complex and uncertain risk assessment information of human error factors. Secondly, the prospect theory is applied to model the heterogeneous risk preferences of experts and eliminate their impact on risk evaluation results. After obtaining the risk evaluation matrix by prospect theory, the consensual risk evaluation matrix is yielded by a consensus model that can balance the group aim of reaching a consensus and the individual aim of keeping original risk evaluation information as much as possible. Then, the risk ranking of human error factors is determined based on the distance of IT2FSs. Finally, a case study of the clinical use of ventilators, including sensitivity analysis and comparative analysis, is presented to illustrate the efficiency of the proposed method
Input-output scaling factors tuning of type-2 fuzzy PID controller using multi-objective optimization technique
No full textThe PID controller is a popular controller that is widely used in various industrial applications. On the other hand, the control problems in microgrids (MGs) are so challenging, because of natural disturbances such as wind speed changes, load variation, and changes in other sources. This paper proposes an input-output scaling factor tuning of interval type-2 fuzzy (IT2F) PID controller using a multi-objective optimization technique. The suggested controller is applied to an MG frequency regulation problem. In the introduced controller the effect of variations of renewable energies (REs) scheme, some factors such as least overshoot, and minimum settling/rising time are considered. achieved, such that the frequency trajectory shows the desired overshoot, and settling/rising time
A readiness assessment framework for the adoption of 5G based smart-living services
No full textThe subject of this article is to analyze the users' attitude towards new, 5G-enabled smart living services before their commercial launch. The goal is to offer a framework for the analysis and evaluation of influential factors in the early adoption of 5G residential services. Additionally, the paper examines how mobile operators can leverage their existing infrastructure and services to boost the acceptance of both 5G as a technology and the provided smart-living services. To ascertain the potential impact that mobile operators can have on the adoption of such services, loyalty programs were taken into account as a separate factor in the acceptance study. The study was conducted in Serbia in the form of a survey. The analysis of the results yielded some notable conclusions such as trust in technology playing the leading role in influencing the behavior intention, while loyalty programs showed that they can influence attitudes towards individual smart living services. The presented results can be used to shape any future implementation of 5G-based services in Serbia, or any other country whose 5G infrastructure and services for the residential customer segment are yet to be established
Evaluation of key indicators affecting the performance of healthcare supply chain agility
No full textIn this study, essential factors of healthcare supply chain have been investigated. Factors were selected through an integrated approach, in which experts played a pivotal and decisive role in each phase. A novel hybrid methodology comprising Best-Worst-Method (BWM) and Interpretive structural modelling (ISM) is employed. Best-Worst-Method is utilised to determine the different weights of healthcare supply chain agility factors, and ISM and MICMAC analysis are utilising to examine interrelations among final selected factors. A case study in local pharmacies examined the effectiveness of the proposed hybrid model in the real world. The application of the hybrid BWM-ISM method demonstrates that 'Proper IT infrastructure' and 'Strategic planning' are the most significant factors, respectively. They will facilitate local pharmacies to accomplish agility practices in the healthcare supply chain thus, increasing effectiveness and adaptability to a variety of situations. This research helps public healthcare decision-makers by changing the organisation's response to critical situations and unexpected events by implementing corrective measures within local pharmacies
Gaussian conditional random fields for classification
No full textGaussian conditional random fields (GCRF) are a well-known structured model for continuous outputs that uses multiple unstructured predictors to form its features and at the same time exploits dependence structure among outputs, which is provided by a similarity measure. In this paper, a Gaussian conditional random field model for structured binary classification (GCRFBC) is proposed. The model is applicable to classification problems with undirected graphs, intractable for standard classification CRFs. The model representation of GCRFBC is extended by latent variables which yield some appealing properties. Thanks to the GCRF latent structure, the model becomes tractable, efficient and open to improvements previously applied to GCRF regression models. In addition, the model allows for reduction of noise, that might appear if structures were defined directly between discrete outputs. Two different forms of the algorithm are presented: GCRFBCb (GCRGBC - Bayesian) and GCRFBCnb (GCRFBC - non-Bayesian). The extended method of local variational approximation of sigmoid function is used for solving empirical Bayes in Bayesian GCRFBCb variant, whereas MAP value of latent variables is the basis for learning and inference in the GCRFBCnb variant. The inference in GCRFBCb is solved by Newton-Cotes formulas for one-dimensional integration. Both models are evaluated on synthetic data and real-world data. We show that both models achieve better prediction performance than unstructured predictors. Furthermore, computational and memory complexity is evaluated. Advantages and disadvantages of the proposed GCRFBCb and GCRFBCnb are discussed in detail
Applying OptaPlanner in the implementation of doctors' schedule of duty hours
No full textThe result of this work is to enable direct executors, within a medical institution, significantly improved performance when creating doctors' duty schedules compared to one that is created manually. A theoretical overview of the basic concepts related to this set of problems is given. The main task is to maximize the preferences of medical personnel for working in shifts when a set of restrictions is provided for the process of planning a schedule. They can be closely related to labor laws, hospital regulations, individual preferences, and many more. Based on these constraints a mathematical model is proposed. Since this is an NP-hard problem, metaheuristics are used. As this field includes a certain number of algorithms, this paper focuses on simulated annealing. Defining this, the theoretical background and usage of the OptaPlanner tool are presented. This tool is used to solve previously mentioned types of optimization problems and can be fully integrated into the Java programming language
Analysis of cybercrimes and security in FinTech industries using the novel concepts of interval-valued complex q-rung orthopair fuzzy relations
No full textModeling the uncertainty has long been a crux for mathematicians which was resolved by the theory of fuzzy sets and logic initiated by Zadeh. Since its introduction, many developments and improvements have been made in the field. This research defines several innovative theories such as the Cartesian product (CP) of interval-valued complex q-rung orthopair fuzzy sets (IVCqROFSs), IVCqROF relations (IVCqROFRs), and their types. After deep analysis, related results and worthwhile properties have been stated. Moreover, a Hasse diagram for IVCqROFR is introduced with its properties. The proposed concepts are capable of modeling a wide range of problems of uncertain nature. The involvement of complex numbers enables these structures to cope with phase-altering problems and multidimensional problems, ultimately making them extremely powerful tools for handling ambiguity. An application has been proposed as an illustration. Since, in modern days, every business and industry is being digitalized, the business and finance departments are no exception. Digital systems have numerous risks and threats. Henceforth, for the first time in the theory of fuzzy logic, cybercrimes and cyber security in the industry of FinTech have been modeled and analyzed through fuzzy mathematics. The effects of different cybersecurity techniques on different threats in FinTech are analyzed by using the proposed methods. Finally, the selection and use of the proposed theories in the analysis of Fintech cybersecurity is justified and explained in contrast to other available competing fuzzy structures in the literature
Evaluating the Performance of various Algorithms for Wind Energy Optimization: A Hybrid Decision-Making model
No full textWind resource is one of the most promising renewable energy, which has become a suitable replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is essential to obtain the maximum power output as other variables are uncontrollable. This paper presents four different optimization algorithms, namely ant lion optimization (ALO), whale optimization algorithm (WOA), particle swarm optimization (PSO), and crow search optimization (CSO), considering a hybrid decision-making model to compare the performances of wind energy optimization. In the first phase, the evolutionary algorithms are defined based on several factors to meet the need for wind energy based on volumetric and time reliability, reversibility, and vulnerability as well as evaluate optimized energy to the subscriber from the Gansu region. In the second phase, the ordinal priority approach (OPA) is coupled with VIseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method to rank the evolutionary algorithms. Then, the results are compared with the absolute optimal response based on the nonlinear programming method obtained from GAMS software. The results demonstrate that an ALO out-performs other algorithms. The average accuracy of ALO is 92%. CSO is the least accurate with 55% of the absolute optimal response. ALO is found to be faster, more efficient, and achieved economy and reliability as compared to other optimization algorithms for solving the problem under consideration. It is shown that the applied models are robust, effective, and able to save costs
Open badges and achievement goal orientation: a study with high-performing student programmers
No full textEarning Open Badges instead of regular grades and credits can be a motivating factor for high-performing students in terms of attending classes and completing assignments in extracurricular courses, but to what extent? And for what student profiles? To tackle these questions, we conducted a quantitative study with high-performing students. Each student involved in the study had consecutively attended two Java programming courses-one where credits and regular grades were issued for their achievements and performance in the course, and another extracurricular one where Open Badges were issued instead. The study compared the achievement goal orientation (AGO) of each student in the two courses (Wilcoxon paired test). It also examined how students' AGO scores in the Open-Badges-only course were associated with class attendance, completion of assignments and public display of their achievements (badges)-both as individual correlations with these variables (Spearman method), as well as associations with student profiles based on these variables (identified with Ward hierarchical clustering). The results indicate that high-performing students feel less motivated in terms of outperforming/under-performing others and have less fear of not learning enough if they receive Open Badges rather than regular grades. Also, a small portion of high-performers will be fully engaged in an Open-Badges-only course (attendance/completing assignments), while the majority will attend but complete a few assignments or just attend. Still, their AGO is not correlated with attending classes, completing assignments and displaying badges
A Hybrid Power Heronian Function-Based Multicriteria Decision-Making Model for Workplace Charging Scheduling Algorithms
No full textThis study proposes a new multicriteria decision-making (MCDM) model to determine the best smart charging scheduling that meets electric vehicle (EV) user considerations at workplaces. An optimal charging station model is incorporated into the decision-making for a quantitative evaluation. The proposed model is based on a hybrid power Heronian functions in which the linear normalization method is improved by applying the inverse sorting algorithm for rational and objective decision-making. This enables EV users to specify and evaluate multicriteria for considering their aspects at workplaces. Five different charging scheduling algorithms with ac dual-port L2 and dc fast charging (DCFC) EV supply equipment (EVSE) are investigated. Based on EV users from the field, the required charging time, EVSE occupancy, the number of EVSE units, and user flexibility are found to have the highest importance degree, while charging cost has the lowest importance degree. The experimental results show that in terms of meeting EV users' considerations at workplaces, scheduling EVs based on their charging energy needs performs better when compared with scheduling them by their arrival and departure times. While the scheduling alternatives display similar ranking behavior for both EVSE types, the best alternative may differ for the EVSE type. To validate the proposed model, a comparison against three traditional models is performed. It is demonstrated that the proposed model yields the same ranking order as the alternative approaches. Sensitivity analysis validates the best and worst scheduling alternatives