Search results for: PU decision

Authors: Musyimi Mbathi

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Information regarding social, political, demographic, economic and other attributes of human settlement is important for decision makers at all levels of planning, as they have to grapple with dynamic environments often associated with settlements. At the local level, it is particularly important for both communities and urban managers to have accurate and reliable information regarding all planning attributes. Settlement mapping, in particular, informal settlements mapping in Kenya, has over the past few years been carried out using modern tools like Geographic information systems (GIS) and remote sensing for spatial data analysis and planning. GIS tools offer a platform for integration of spatial and non-spatial data as well as visualisation of the settlements. The capabilities offered by these tools have enabled communities to participate especially in the planning and management of new infrastructure as well as settlement upgrading. Land tenure based projects within informal settlements have also relied on GIS and related tools with considerable success. Additionally, the adoption of participatory approaches and use of geo-information tools helped to provide a basis for all inclusive planning thus promoting accountability, transparency, legitimacy, and other dimensions of governance within human settlement planning. The paper examines the context and application of geo-information tools for planning within low-income settlements of Kenya. A case study of Kiambiu settlement will be used to demonstrate how the tools have been applied for planning and decision-making purposes.

Keywords: informal settlements, GIS, governance, modern tools

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Authors: Kiatkulchai Jitt-Aer, Graham Wall, Dylan Jones

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As a result of the Indian Ocean tsunami in 2004, logistics applied to disaster relief operations has received great attention in the humanitarian sector. As learned from such disaster, preparing and responding to the aspect of delivering essential items from distribution centres to affected locations are of the importance for relief operations as the nature of disasters is uncertain especially in suffering figures, which are normally proportional to quantity of supplies. Thus, this study proposes a spatial decision support system (SDSS) for humanitarian logistics by integrating Geographical Information Systems (GIS) and the capacitated vehicle routing problem (CVRP). The GIS is utilised for acquiring demands simulated from the tsunami flooding model of the affected area in the first stage, and visualising the simulation solutions in the last stage. While CVRP in this study encompasses designing the relief routes of a set of homogeneous vehicles from a relief centre to a set of geographically distributed evacuation points in which their demands are estimated by using both simulation and randomisation techniques. The CVRP is modeled as a multi-objective optimization problem where both total travelling distance and total transport resources used are minimized, while demand-cost efficiency of each route is maximized in order to determine route priority. As the model is a NP-hard combinatorial optimization problem, the Clarke and Wright Saving heuristics is proposed to solve the problem for the near-optimal solutions. The real-case instances in the coastal area of Phuket, Thailand are studied to perform the SDSS that allows a decision maker to visually analyse the simulation scenarios through different decision factors.

Keywords: demand simulation, humanitarian logistics, geographical information systems, relief operations, capacitated vehicle routing problem

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Authors: Siqi Qiu, Yijian Zheng, Xin Guo Ming

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In many reliability and risk analysis, failures of components are supposed to be independent. However, in reality, the ignorance of failure dependencies among components may render the results of reliability and risk analysis incorrect. There are two principal ways to incorporate failure dependencies in system reliability and risk analysis: implicit and explicit methods. In the implicit method, failure dependencies can be modeled by joint probabilities, correlation values or conditional probabilities. In the explicit method, certain types of dependencies can be modeled in a fault tree as mutually independent basic events for specific component failures. In this paper, explicit and implicit methods based on BDD will be proposed to evaluate the reliability of systems considering failure dependencies. The obtained results prove the equivalence of the proposed implicit and explicit methods. It is found that the consideration of failure dependencies decreases the reliability of systems. This observation is intuitive, because more components fail due to failure dependencies. The consideration of failure dependencies helps designers to reduce the dependencies between components during the design phase to make the system more reliable.

Keywords: reliability assessment, risk assessment, failure dependencies, binary decision diagram

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Authors: Yin-Yann Chen, Jia-Ying Li

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Assembly line balancing problems can be categorized into one-sided, two-sided, and multi-manned ones by using the number of operators deployed at workstations. This study explores the balancing problem of a resource-constrained assembly line with multi-manned workstations. Resources include machines or tools in assembly lines such as jigs, fixtures, and hand tools. A mathematical programming model was developed to carry out decision-making and planning in order to minimize the numbers of workstations, resources, and operators for achieving optimal production efficiency. To improve the solution-finding efficiency, a genetic algorithm (GA) and a simulated annealing algorithm (SA) were designed and developed in this study to be combined with a practical case in car making. Results of the GA/SA and mathematics programming were compared to verify their validity. Finally, analysis and comparison were conducted in terms of the target values, production efficiency, and deployment combinations provided by the algorithms in order for the results of this study to provide references for decision-making on production deployment.

Keywords: heuristic algorithms, line balancing, multi-manned workstation, resource-constrained

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Authors: Begum Koru, Isik Aybay, Demet Celik Ulusoy

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The discipline of law is quite complex and has its own terminology. Apart from written legal rules, there is also living law, which refers to legal practice. Basic legal rules aim at the happiness of individuals in social life and have different characteristics in different branches such as public or private law. On the other hand, law is a national phenomenon. The law of one nation and the legal system applied on the territory of another nation may be completely different. People who are experts in a particular field of law in one country may have insufficient expertise in the law of another country. Today, in addition to the local nature of law, international and even supranational law rules are applied in order to protect basic human values and ensure the protection of human rights around the world. Systems that offer algorithmic solutions to legal problems using artificial intelligence (AI) tools will perhaps serve to produce very meaningful results in terms of human rights. However, algorithms to be used should not be developed by only computer experts, but also need the contribution of people who are familiar with law, values, judicial decisions, and even the social and political culture of the society to which it will provide solutions. Otherwise, even if the algorithm works perfectly, it may not be compatible with the values of the society in which it is applied. The latest developments involving the use of AI techniques in legal systems indicate that artificial law will emerge as a new field in the discipline of law. More AI systems are already being applied in the field of law, with examples such as predicting judicial decisions, text summarization, decision support systems, and classification of documents. Algorithms for legal systems employing AI tools, especially in the field of prediction of judicial decisions and decision support systems, have the capacity to create automatic decisions instead of judges. When the judge is removed from this equation, artificial intelligence-made law created by an intelligent algorithm on its own emerges, whether the domain is national or international law. In this work, the aim is to make a general analysis of this new topic. Such an analysis needs both a literature survey and a perspective from computer experts' and lawyers' point of view. In some societies, the use of prediction or decision support systems may be useful to integrate international human rights safeguards. In this case, artificial law can serve to produce more comprehensive and human rights-protective results than written or living law. In non-democratic countries, it may even be thought that direct decisions and artificial intelligence-made law would be more protective instead of a decision "support" system. Since the values of law are directed towards "human happiness or well-being", it requires that the AI algorithms should always be capable of serving this purpose and based on the rule of law, the principle of justice and equality, and the protection of human rights.

Keywords: AI and law, artificial law, protection of human rights, AI tools for legal systems

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Authors: Golshid Gilani, Albert De La Fuente, Ana Blanco

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Sustainability assessment of new and existing buildings has generated a growing interest due to the evident environmental, social and economic impacts during their construction and service life. Façades, as one of the most important exterior elements of a building, may contribute to the building sustainability by reducing the amount of energy consumption and providing thermal comfort for the inhabitants, thus minimizing the environmental impact on both the building and on the environment. Various methods have been used for the sustainability assessment of buildings due to the importance of this issue. However, most of the existing methods mainly concentrate on environmental and economic aspects, disregarding the third pillar of sustainability, which is the social aspect. Besides, there is a little focus on comprehensive sustainability assessment of facades, as an important element of a building. This confirms the need of developing methods for assessing the sustainable performance of building façades as an important step in achieving building sustainability. In this respect, this paper aims at presenting a model for assessing the global sustainability of façade systems. for that purpose, the Integrated Value Model for Sustainable Assessment (MIVES), a Multi-Criteria Decision Making model that integrates the main sustainability requirements (economic, environmental and social) and includes the concept of value functions, used as an assessment tool.

Keywords: façade, MCDM, MIVES, sustainability

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Authors: Zahid Ullah, Atlas Khan

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In today's data-driven world, the role of mathematics in bridging the gap between theory and applications is becoming increasingly vital. This abstract highlights the significance of mathematics as a powerful tool for analyzing, interpreting, and extracting meaningful insights from vast amounts of data. By integrating mathematical principles with real-world applications, researchers can unlock the full potential of data-driven decision-making processes. This abstract delves into the various ways mathematics acts as a bridge connecting theoretical frameworks to practical applications. It explores the utilization of mathematical models, algorithms, and statistical techniques to uncover hidden patterns, trends, and correlations within complex datasets. Furthermore, it investigates the role of mathematics in enhancing predictive modeling, optimization, and risk assessment methodologies for improved decision-making in diverse fields such as finance, healthcare, engineering, and social sciences. The abstract also emphasizes the need for interdisciplinary collaboration between mathematicians, statisticians, computer scientists, and domain experts to tackle the challenges posed by the data-driven landscape. By fostering synergies between these disciplines, novel approaches can be developed to address complex problems and make data-driven insights accessible and actionable. Moreover, this abstract underscores the importance of robust mathematical foundations for ensuring the reliability and validity of data analysis. Rigorous mathematical frameworks not only provide a solid basis for understanding and interpreting results but also contribute to the development of innovative methodologies and techniques. In summary, this abstract advocates for the pivotal role of mathematics in bridging theory and applications in a data-driven world. By harnessing mathematical principles, researchers can unlock the transformative potential of data analysis, paving the way for evidence-based decision-making, optimized processes, and innovative solutions to the challenges of our rapidly evolving society.

Keywords: mathematics, bridging theory and applications, data-driven world, mathematical models

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Authors: Katharina Eberhardt, Florian Klaus Kaiser, Frank Schultmann

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Last-mile distribution is one of the most critical parts of a disaster relief operation. Various uncertainties, such as infrastructure conditions, resource availability, and fluctuating beneficiary demand, render last-mile distribution challenging in disaster relief operations. The need to balance critical performance criteria like response time, meeting demand and cost-effectiveness further complicates the task. The occurrence of disasters cannot be controlled, and the magnitude is often challenging to assess. In summary, these uncertainties create a need for additional flexibility, agility, and preparedness in logistics operations. As a result, strategic planning and efficient network design are critical for an effective and efficient response. Furthermore, the increasing frequency of disasters and the rising cost of logistical operations amplify the need to provide robust and resilient solutions in this area. Therefore, we formulate a scenario-based bi-objective optimization model that integrates pre-positioning, allocation, and distribution of relief supplies extending the general form of a covering location problem. The proposed model aims to minimize underlying logistics costs while maximizing demand coverage. Using a set of disruption scenarios, the model allows decision-makers to identify optimal network solutions to address the risk of disruptions. We provide an empirical case study of the public authorities’ emergency food storage strategy in Germany to illustrate the potential applicability of the model and provide implications for decision-makers in a real-world setting. Also, we conduct a sensitivity analysis focusing on the impact of varying stockpile capacities, single-site outages, and limited transportation capacities on the objective value. The results show that the stockpiling strategy needs to be consistent with the optimal number of depots and inventory based on minimizing costs and maximizing demand satisfaction. The strategy has the potential for optimization, as network coverage is insufficient and relies on very high transportation and personnel capacity levels. As such, the model provides decision support for public authorities to determine an efficient stockpiling strategy and distribution network and provides recommendations for increased resilience. However, certain factors have yet to be considered in this study and should be addressed in future works, such as additional network constraints and heuristic algorithms.

Keywords: humanitarian logistics, bi-objective optimization, pre-positioning, last mile distribution, decision support, disaster relief networks

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Authors: Yu-Jia Jian, Emily Chia-Yu Su, Hui-Ling Hsu, Jian-Jhih Chen

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Coronary artery is the major supplier of myocardial blood flow. When fat and cholesterol are deposit in the coronary arterial wall, narrowing and stenosis of the artery occurs, which may lead to myocardial ischemia and eventually infarction. According to the World Health Organization (WHO), estimated 740 million people have died of coronary heart disease in 2015. According to Statistics from Ministry of Health and Welfare in Taiwan, heart disease (except for hypertensive diseases) ranked the second among the top 10 causes of death from 2013 to 2016, and it still shows a growing trend. According to American Heart Association (AHA), the risk factors for coronary heart disease including: age (> 65 years), sex (men to women with 2:1 ratio), obesity, diabetes, hypertension, hyperlipidemia, smoking, family history, lack of exercise and more. We have collected a dataset of 421 patients from a hospital located in northern Taiwan who received coronary computed tomography (CT) angiography. There were 300 males (71.26%) and 121 females (28.74%), with age ranging from 24 to 92 years, and a mean age of 56.3 years. Prior to coronary CT angiography, basic data of the patients, including age, gender, obesity index (BMI), diastolic blood pressure, systolic blood pressure, diabetes, hypertension, hyperlipidemia, smoking, family history of coronary heart disease and exercise habits, were collected and used as input variables. The output variable of the prediction module is the degree of coronary artery stenosis. The output variable of the prediction module is the narrow constriction of the coronary artery. In this study, the dataset was randomly divided into 80% as training set and 20% as test set. Four machine learning algorithms, including logistic regression, stepwise regression, neural network and decision tree, were incorporated to generate prediction results. We used area under curve (AUC) / accuracy (Acc.) to compare the four models, the best model is neural network, followed by stepwise logistic regression, decision tree, and logistic regression, with 0.68 / 79 %, 0.68 / 74%, 0.65 / 78%, and 0.65 / 74%, respectively. Sensitivity of neural network was 27.3%, specificity was 90.8%, stepwise Logistic regression sensitivity was 18.2%, specificity was 92.3%, decision tree sensitivity was 13.6%, specificity was 100%, logistic regression sensitivity was 27.3%, specificity 89.2%. From the result of this study, we hope to improve the accuracy by improving the module parameters or other methods in the future and we hope to solve the problem of low sensitivity by adjusting the imbalanced proportion of positive and negative data.

Keywords: decision support, computed tomography, coronary artery, machine learning

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Authors: Gultekin Gurcay

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It is known that the most crucial influence of the psychological, social and emotional factors that affect any human behavior is to corrupt the rational decision making mechanism of the individuals and cause them to display irrational behaviors. In this regard, the social context of human beings influences the rationality of our decisions, and people tend to display different behaviors when they were alone compared to when they were surrounded by others. At this point, the interaction and interdependence of the behavioral finance and economics with the area of social psychology comes, where intentions and the behaviors of the individuals are being analyzed in the actual or implied presence of others comes into prominence. Within the context of this study, the prevalent theories of behavioral finance, which are The Prospect Theory, The Utility Theory Given Uncertainty and the Five Axioms of Choice under Uncertainty, Veblen’s Hidden Utility Theory, and the concept of ‘Overreaction’ has been examined and demonstrated; and the meaning, existence and validity of these theories together with the social context has been assessed. Finally, in this study the behavior of the individuals in financial crisis situations where the majority of the society is being affected from the same negative conditions at the same time has been analyzed, by taking into account how individual behavior will change according to the presence of the others.

Keywords: conditional variance coefficient, financial crisis, garch model, stock market

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Authors: Faith-Michael E. Uzoka, Boluwaji A. Akinnuwesi, Taiwo Amoo, Flora Aladi, Stephen Fashoto, Moses Olaniyan, Joseph Osuji

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The overarching aim of this study is to develop a soft-computing system for the differential diagnosis of tropical diseases. These conditions are of concern to health bodies, physicians, and the community at large because of their mortality rates, and difficulties in early diagnosis due to the fact that they present with symptoms that overlap, and thus become ‘confusable’. We report on the first phase of our study, which focuses on the development of a fuzzy cognitive map model for early differential diagnosis of tropical diseases. We used malaria as a case disease to show the effectiveness of the FCM technology as an aid to the medical practitioner in the diagnosis of tropical diseases. Our model takes cognizance of manifested symptoms and other non-clinical factors that could contribute to symptoms manifestations. Our model showed 85% accuracy in diagnosis, as against the physicians’ initial hypothesis, which stood at 55% accuracy. It is expected that the next stage of our study will provide a multi-disease, multi-symptom model that also improves efficiency by utilizing a decision support filter that works on an algorithm, which mimics the physician’s diagnosis process.

Keywords: medical diagnosis, tropical diseases, fuzzy cognitive map, decision support filters, malaria differential diagnosis

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Authors: Halima Akhtar, Abhijeet Chandra

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The paper attempts to analyze the factors that influence the Consumer Behavior to purchase apparel through the internet. The intentions to buy apparels online were based on in terms of user style, orientation, size and reputation of the merchant, social influence, perceived information utility, perceived ease of use, perceived pleasure and attractiveness and perceived trust and risk. The basic framework used was Technology acceptance model to explain apparels acceptance. A survey was conducted to gather the data from 200 people. The measures and hypotheses were analyzed using Correlation testing and would be further validated by the Structural Equation Modelling. The implications of the findings for theory and practice could be used by marketers of online apparel websites. Based on the values obtained, we can conclude that the factors such as social influence, Perceived information utility, attractiveness and trust influence the decision for a user to buy apparels online. The major factors which are found to influence an online apparel buying decision are ease of use, attractiveness that a website can offer and the trust factor which a user shares with the website.

Keywords: E-tailers, consumer behaviour, technology acceptance model, structural modelling

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Authors: Michel Patiño-Sáenz, Martín Haissiner, Jorge Martínez-Cotrina, Daniel Pastor, Hernando Santamaría-García, Maria-Alejandra Tangarife, Agustin Ibáñez, Sandra Baez

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It has been shown that gruesome descriptions of harm can increase the punishment given to a transgressor. This biasing effect is mediated by negative emotions, which are elicited upon the presentation of gruesome descriptions. However, there is a lack of studies inquiring the influence of such descriptions on moral decision-making in people involved in the criminal justice system. Such populations are of special interest since they have experience dealing with gruesome evidence, but also formal education on how to assess evidence and gauge the appropriate punishment according to the law. Likewise, they are expected to be objective and rational when performing their duty, because their decisions can impact profoundly people`s lives. Considering these antecedents, the objective of this study was to explore the influence gruesome written descriptions on moral decision-making in this group of people. To that end, we recruited attorneys, judges and public prosecutors (Criminal justice group, CJ, n=30) whose field of specialty is criminal law. In addition, we included a control group of people who did not have a formal education in law (n=30), but who were paired in age and years of education with the CJ group. All participants completed an online, Spanish-adapted version of a moral decision-making task, which was previously reported in the literature and also standardized and validated in the Latin-American context. A series of text-based stories describing two characters, one inflicting harm on the other, were presented to participants. Transgressor's intentionality (accidental vs. intentional harm) and language (gruesome vs. plain) used to describe harm were manipulated employing a within-subjects and a between-subjects design, respectively. After reading each story, participants were asked to rate (a) the harmful action's moral adequacy, (b) the amount of punishment deserving the transgressor and (c) how damaging was his behavior. Results showed main effects of group, intentionality and type of language on all dependent measures. In both groups, intentional harmful actions were rated as significantly less morally adequate, were punished more severely and were deemed as more damaging. Moreover, control subjects deemed more damaging and punished more severely any type of action than the CJ group. In addition, there was an interaction between intentionality and group. People in the control group rated harmful actions as less morally adequate than the CJ group, but only when the action was accidental. Also, there was an interaction between intentionality and language on punishment ratings. Controls punished more when harm was described using gruesome language. However, that was not the case of people in the CJ group, who assigned the same amount of punishment in both conditions. In conclusion, participants with job experience in the criminal justice system or criminal law differ in the way they make moral decisions. Particularly, it seems that they are less sensitive to the biasing effect of gruesome evidence, which is probably explained by their formal education or their experience in dealing with such evidence. Nonetheless, more studies are needed to determine the impact this phenomenon has on the fulfillment of their duty.

Keywords: criminal justice system, emotions, gruesome descriptions, intentionality, moral decision-making

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Authors: Dona Primasari

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This study examines the antecedent variables of Government Financial Acccounting System (SAKD) implementation and its consequence. The antecedent variables are: decentralization of decision making, adaptation, and the manager support. The consequences are satisfaction and performance officer. This research represents the empirical test which used convenience sampling technics in data collection. The data were collected from 167 officers of local government in the Regional Coordinating Agency for Development of Cross County/City Region III Central Java Province. Data analysis used Structural Equation Model (SEM) with the AMOS 18.0 program. The result of hypothesis examination indicates that six raised hypothesis are accepted and two hypothesis are rejected.

Keywords: decentralization of decision making, adaptation officer, manager support, implementation of Government Accounting Financial System (SAKD), satisfaction and performance officer

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Authors: M. Lopez-Pereyra, I. Cisneros Alvarado, M. Del Socorro Lobato Alba

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This paper aims to discuss a conceptual, pedagogical approach that foster inclusive education and that create an awareness of the use of assistive technology in Mexico. Interdisciplinary understanding of disabilities and the use of assistive technology as a frame for an inclusive education have challenged the reality of the researchers’ participation in decision-making. Drawing upon a pedagogical inquiry process within an interdisciplinary academic project that involved the sciences, design, biotechnology, psychology and education fields, this paper provides a discussion on the challenges of assistive technology and inclusive education in interdisciplinary research on disabilities and technology project. This study is frame on an educational action research design where the team is interested in integrating, disability, technology, and inclusion, theory, and practice. Major findings include: (1) the concept of inclusive education as a strategy for interdisciplinary research; (2) inclusion as a pedagogical approach that challenges the creation of assistive technology from diverse academic fields; and, (3) inclusion as a frame, problem-focused, for decision-making. The findings suggest that inclusive pedagogical approaches provide a unique insight into interdisciplinary teams on disability and assistive technology in education.

Keywords: assistive technology, inclusive education, inclusive pedagogy, interdisciplinary research

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Authors: E. R. Swart, S. J. Gismondi, N. R. Swart, C. E. Bell

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We present an heuristic algorithm that decides graph non-Hamiltonicity. All graphs are directed, each undirected edge regarded as a pair of counter directed arcs. Each of the n! Hamilton cycles in a complete graph on n+1 vertices is mapped to an n-permutation matrix P where p(u,i)=1 if and only if the ith arc in a cycle enters vertex u, starting and ending at vertex n+1. We first create exclusion set E by noting all arcs (u, v) not in G, sufficient to code precisely all cycles excluded from G i.e. cycles not in G use at least one arc not in G. Members are pairs of components of P, {p(u,i),p(v,i+1)}, i=1, n-1. A doubly stochastic-like relaxed LP formulation of the Hamilton cycle decision problem is constructed. Each {p(u,i),p(v,i+1)} in E is coded as variable q(u,i,v,i+1)=0 i.e. shrinks the feasible region. We then implement the Weak Closure Algorithm (WCA) that tests necessary conditions of a matching, together with Boolean closure to decide 0/1 variable assignments. Each {p(u,i),p(v,j)} not in E is tested for membership in E, and if possible, added to E (q(u,i,v,j)=0) to iteratively maximize |E|. If the WCA constructs E to be maximal, the set of all {p(u,i),p(v,j)}, then G is decided non-Hamiltonian. Only non-Hamiltonian G share this maximal property. Ten non-Hamiltonian graphs (10 through 104 vertices) and 2000 randomized 31 vertex non-Hamiltonian graphs are tested and correctly decided non-Hamiltonian. For Hamiltonian G, the complement of E covers a matching, perhaps useful in searching for cycles. We also present an example where the WCA fails.

Keywords: Hamilton cycle decision problem, computational complexity theory, graph theory, theoretical computer science

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Authors: Iva Buchberger, Vesna Kovač

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Global education policy trends and recommendations underline the importance of (distributive) school leadership as a school effectiveness key factor. In this context, the broader aim of this research (supported by the Croatian Science Foundation) is to identify school leadership characteristics in Croatian schools and to examine the correlation between school leadership and school effectiveness. The aim of the proposed conference paper is to focus on the school leadership characteristics which are additionally explained with school leadership facilitators that contribute to (distributive) school leadership development. The aforementioned school leadership characteristics include the following dimensions: (a) participation in the process of making different types of decisions, (b) influence in the decision making process, (c) social interactions between different stakeholders in the decision making process in schools. Further, the school leadership facilitators are categorized as follows: (a) principal’s activities (such as providing support to different stakeholders and developing mutual trust among them), (b) stakeholders’ characteristics (such as developed stakeholders’ interest and competence to participate in decision-making process), (c) organizational and material resources (such as school material conditions, the necessary information and time as resources for making decisions). The data were collected by a constructed and validated questionnaire for examining the school leadership characteristics and facilitators from teachers’ perspective. The main population in this study consists of all primary schools in Croatia while the sample is comprised of 100 primary schools, selected by random sampling. Furthermore, the sample of teachers was selected by an additional procedure taking into consideration the independent variables of sex, work experience, etc. Data processing was performed by standard statistical methods of descriptive and inferential statistics. Statistical program IBM SPSS 20.0 was used for data processing. The results of this study show that there is a (positive) correlation between school leadership characteristics and school leadership facilitators. Specifically, it is noteworthy to mention that all the dimensions of school leadership characteristics are in positive correlation with the categories of school leadership facilitators. These results are indicative for the education policy creators who should ensure positive and supportive environment for the school leadership development including the development of school leadership characteristics and school leadership facilitators.

Keywords: distributive school leadership, school effectiveness , school leadership characteristics, school leadership facilitators

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Authors: Ahmad Alqatan

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Purpose: The purpose of this study is to analyze how the adoption of cloud accounting systems influences the governance practices and performance of boards of directors in MENA countries. The research aims to identify the benefits and challenges associated with cloud accounting and its role in improving board efficiency and oversight. Methodology: This research employs a mixed-method approach, combining quantitative surveys and qualitative interviews with board members and financial officers from a diverse range of companies in the MENA region. The quantitative data is analyzed to determine patterns and correlations, while qualitative insights provide a deeper understanding of the contextual factors influencing cloud accounting adoption and its impacts. Findings: The findings indicate that cloud accounting significantly enhances the decision-making capabilities of boards by providing real-time financial information and facilitating better communication among board members. Companies using cloud accounting reports improved financial oversight and more timely and accurate financial reporting. However, the research also identifies challenges such as cybersecurity concerns, resistance to change, and the need for ongoing training and support. Practical Implications: The study suggests that MENA companies can benefit from investing in cloud accounting technologies to improve board governance and strategic decision-making. It highlights the importance of addressing cybersecurity issues and providing adequate training for board members to maximize the advantages of cloud accounting. Originality: This research contributes to the limited literature on cloud accounting in the MENA region, offering valuable insights for policymakers, business leaders, and academics. It underscores the transformative potential of cloud accounting for enhancing board performance and corporate governance in emerging markets.

Keywords: cloud accounting, board of directors, MENA region, corporate governance, financial transparency, real-time data, decision-making, cybersecurity, technology adoption

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Authors: Charles Davidson

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This paper presents the Stoplight Analysis System of Partnering Organizations Readiness, offering a structured framework to evaluate conflict resolution collaboration feasibility, especially crucial in conflict areas, employing a colour-coded approach and specific assessment points, with implications for more informed decision-making and improved outcomes in peacebuilding initiatives. Derived from at total of 40 years of practical peacebuilding experience from the project’s two researchers as well as interviews of various other peacebuilding actors, this paper introduces the Stoplight Analysis System of Partnering Organizations Readiness, a comprehensive framework designed to facilitate effective collaboration in international/local peacebuilding partnerships by evaluating the readiness of both potential partner organisations and the location of the proposed project. ^The system employs a colour-coded approach, categorising potential partnerships into three distinct indicators: Red (no-go), Yellow (requires further research), and Green (promising, go ahead). Within each category, specific points are identified for assessment, guiding decision-makers in evaluating the feasibility and potential success of collaboration. The Red category signals significant barriers, prompting an immediate stoppage in the consideration of partnership. The Yellow category encourages deeper investigation to determine whether potential issues can be mitigated, while the Green category signifies organisations deemed ready for collaboration. This systematic and structured approach empowers decision-makers to make informed choices, enhancing the likelihood of successful and mutually beneficial partnerships. Methodologically, this paper utilised interviews from peacebuilders from around the globe, scholarly research of extant strategies, and a collaborative review of programming from the project’s two authors from their own time in the field. This method as a formalised model has been employed for the past two years across a litany of partnership considerations, and has been adjusted according to its field experimentation. This research holds significant importance in the field of conflict resolution as it provides a systematic and structured approach to peacebuilding partnership evaluation. In conflict-affected regions, where the dynamics are complex and challenging, the Stoplight Analysis System offers decision-makers a practical tool to assess the readiness of partnering organisations. This approach can enhance the efficiency of conflict resolution efforts by ensuring that resources are directed towards partnerships with a higher likelihood of success, ultimately contributing to more effective and sustainable peacebuilding outcomes.

Keywords: collaboration, conflict resolution, partnerships, peacebuilding

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should properly evaluate their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, Neural Networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable to offer an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Manpreet Singh, V. P. Agrawal, Gurmanjot Singh Bhatti

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From the last decades, there is a significant technological advancement in the field of robotics, and a number of modular self-reconfigurable robots were introduced that can help in space exploration, bucket to stuff, search, and rescue operation during earthquake, etc. As there are numbers of self-reconfigurable robots, choosing the optimum one is always a concern for robot user since there is an increase in available features, facilities, complexity, etc. The objective of this research work is to present a multiple attribute decision making based methodology for coding, evaluation, comparison ranking and selection of modular self-reconfigurable robots using a technique for order preferences by similarity to ideal solution approach. However, 86 attributes that affect the structure and performance are identified. A database for modular self-reconfigurable robot on the basis of different pertinent attribute is generated. This database is very useful for the user, for selecting a robot that suits their operational needs. Two visual methods namely linear graph and spider chart are proposed for ranking of modular self-reconfigurable robots. Using five robots (Atron, Smores, Polybot, M-Tran 3, Superbot), an example is illustrated, and raking of the robots is successfully done, which shows that Smores is the best robot for the operational need illustrated, and this methodology is found to be very effective and simple to use.

Keywords: self-reconfigurable robots, MADM, TOPSIS, morphogenesis, scalability

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Authors: Mane Darbinyan, Lusine Hayrapetyan, Elen Matevosyan

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Artificial Intelligence in Education (AIED) has been created to help students get ready for the workforce, and over the past 25 years, it has grown significantly, offering a variety of technologies to support academic, institutional, and administrative services. However, this is still challenging, especially considering the labor market's rapid change. While choosing a career, people face various obstacles because they do not take into consideration their own preferences, which might lead to many other problems like shifting jobs, work stress, occupational infirmity, reduced productivity, and manual error. Besides preferences, people should evaluate properly their technical and non-technical skills, as well as their personalities. Professional counseling has become a difficult undertaking for counselors due to the wide range of career choices brought on by changing technological trends. It is necessary to close this gap by utilizing technology that makes sophisticated predictions about a person's career goals based on their personality. Hence, there is a need to create an automated model that would help in decision-making based on user inputs. Improving career guidance can be achieved by embedding machine learning into the career consulting ecosystem. There are various systems of career guidance that work based on the same logic, such as the classification of applicants, matching applications with appropriate departments or jobs, making predictions, and providing suitable recommendations. Methodologies like KNN, neural networks, K-means clustering, D-Tree, and many other advanced algorithms are applied in the fields of data and compute some data, which is helpful to predict the right careers. Besides helping users with their career choice, these systems provide numerous opportunities which are very useful while making this hard decision. They help the candidate to recognize where he/she specifically lacks sufficient skills so that the candidate can improve those skills. They are also capable of offering an e-learning platform, taking into account the user's lack of knowledge. Furthermore, users can be provided with details on a particular job, such as the abilities required to excel in that industry.

Keywords: career guidance system, machine learning, career prediction, predictive decision, data mining, technical and non-technical skills

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Authors: Mark E. Lokanan, Liz Lee

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This paper aims to examine the role of accountants as gatekeepers in anti-money laundering compliance in real estate transactions. The paper seeks to answer questions on ways in which accountants are involved in real estate transactions and mandatory compliance with regulatory authorities in Canada. The data for the study came from semi-structured interviews with accountants, lawyers, and government officials. Preliminary results reveal that there is a conflict between accountants’ obligation to disclose and loyalty to their clients. Accountants often do not see why they are obligated to disclose their clients' information to government agencies. The importance of the client in terms of the amount of revenue contributed to the accounting firm also plays a significant role in accountants' reporting decision-making process. Although the involvement of accountants in real estate purchase and sale transactions is limited to lawyers or notaries, they are often involved in designing financing schemes, which may involve money laundering activities. The paper is of wider public policy interests to both accountants and regulators. It is hard not to see Chartered Professional Accountant (CPA) Canada and government regulators using the findings to better understand the decision-making processes of accountants in their reporting practices to regulatory authorities.

Keywords: money laundering, real estate, disclosure, legislation, compliance

Procedia PDF Downloads 231

Authors: Lars Andersen

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The development of design and engineering in large construction projects are characterized by an increased degree of flattening out of formal structures, extended use of parallel and integrated processes (‘Integrated Concurrent Engineering’) and an increased number of expert disciplines. The integration process is based on ongoing collaborations, dialogues, intercommunication and comments on each other’s work (iterations). This process based on reciprocal communication between actors and disciplines triggers value creation. However, communication between equals is not in itself sufficient to create effective decision making. The complexity of the process and time pressure contribute to an increased risk of a deficit of decisions and loss of process control. The paper refers to a study that aims at developing a resilient decision-making system that does not come in conflict with communication processes based on equality between the disciplines in the process. The study includes the construction of a hospital, following the phases design, engineering and physical building. The Research method is a combination of formative process research, process tracking and phenomenological analyses. The study tracked challenges and problems in the building process to the projection substrates (drawing and models) and further to the organization of the engineering and design phase. A comparative analysis of traditional and new ways of organizing the projecting made it possible to uncover an implicit material order or structure in the process. This uncovering implied a development of a material process perspective. According to this perspective the complexity of the process is rooted in material-functional differentiation. This differentiation presupposes a structuring material (the skeleton of the building) that coordinates the other types of material. Each expert discipline´s competence is related to one or a set of materials. The architect, consulting engineer construction etc. have their competencies related to structuring material, and inherent in this; coordination competence. When dialogues between the disciplines concerning the coordination between them do not result in agreement, the disciplines with responsibility for the structuring material decide the interface issues. Based on these premises, this paper develops a self-organized expert-driven interdisciplinary decision-making system.

Keywords: collaboration, complexity, design, engineering, materiality

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Authors: M. Shahab Uddin, Pennung Warnitchai

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Currently more than half of the world’s populations are living in cities, and the number and sizes of cities are growing faster than ever. Cities rely on the effective functioning of complex and interdependent critical infrastructures networks to provide public services, enhance the quality of life, and save the community from hazards and disasters. In contrast, complex connectivity and interdependency among the urban critical infrastructures bring management challenges and make the urban system prone to the domino effect. Unplanned rapid growth, increased connectivity, and interdependency among the infrastructures, resource scarcity, and many other socio-political factors are affecting the typical state of an urban system and making it susceptible to numerous sorts of diversion. In addition to internal vulnerabilities, urban systems are consistently facing external threats from natural and manmade hazards. Cities are not just complex, interdependent system, but also makeup hubs of the economy, politics, culture, education, etc. For survival and sustainability, complex urban systems in the current world need to manage their vulnerabilities and hazardous incidents more wisely and more interactively. Coordinated management in such systems makes for huge potential when it comes to absorbing negative effects in case some of its components were to function improperly. On the other hand, ineffective management during a similar situation of overall disorder from hazards devastation may make the system more fragile and push the system to an ultimate collapse. Following the quantum, the current research hypothesizes that a hazardous event starts its journey as an emergency, and the system’s internal vulnerability and response capacity determine its destination. Connectivity and interdependency among the urban critical infrastructures during this stage may transform its vulnerabilities into dynamic damaging force. An emergency may turn into a disaster in the absence of effective management; similarly, mismanagement or lack of management may lead the situation towards a catastrophe. Situation awareness and factual decision-making is the key to win a battle. The current research proposed a contextual decision support system for an urban critical infrastructure system while integrating three different models: 1) Damage cascade model which demonstrates damage propagation among the infrastructures through their connectivity and interdependency, 2) Restoration model, a dynamic restoration process of individual infrastructure, which is based on facility damage state and overall disruptions in surrounding support environment, and 3) Optimization model that ensures optimized utilization and distribution of available resources in and among the facilities. All three models are tightly connected, mutually interdependent, and together can assess the situation and forecast the dynamic outputs of every input. Moreover, this integrated model will hold disaster managers and decision makers responsible when it comes to checking all the alternative decision before any implementation, and support to produce maximum possible outputs from the available limited inputs. This proposed model will not only support to reduce the extent of damage cascade but will ensure priority restoration and optimize resource utilization through adaptive and collaborative management. Complex systems predictably fail but in unpredictable ways. System understanding, situation awareness, and factual decisions may significantly help urban system to survive and sustain.

Keywords: disaster prevention, decision support system, emergency response, urban critical infrastructure system

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Authors: Oluwaseun Adeleke, Samuel Ikan, Anthony Nwala, Mopelola Raji, Fidelis Edet

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Background: The risks associated with each pregnancy is carried almost entirely by a woman; however, the decision about whether and when to get pregnant is a subject that several others contend with her to make. The self-care concept offers women of reproductive age the opportunity to take control of their health and its determinants with or without the influence of a healthcare provider, family, and friends. DMPA-SC Self-injection (SI) is becoming the cornerstone of contraceptive self-care and has the potential to expand access and create opportunities for women to take control of their reproductive health. Methodology: To obtain insight into the influences that interfere with a woman’s capacity to make contraceptive choices independently, the Delivering Innovations in Selfcare (DISC) project conducted two intensive rounds of qualitative data collection and triangulation that included provider, client, and community mobilizer interviews, facility observations, and routine program data collection. Respondents were sampled according to a convenience sampling approach and data collected analyzed using a codebook and Atlas-TI. The research team members came together for participatory analysis workshop to explore and interpret emergent themes. Findings: Insights indicate that women are increasingly finding their voice and independently seek services to prevent a deterioration of their economic situation and achieve personal ambitions. Women who hold independent decision-making power still prefer to share decision making power with their male partners. Male partners’ influence on women’s use of family planning and self-inject was most dominant. There were examples of men’s support for women’s use of contraception to prevent unintended pregnancy, as well as men withholding support. Other men outrightly deny their partners from obtaining contraceptive services and their partners cede this sexual and reproductive health right without objection. A woman’s decision to initiate family planning is affected by myths and misconceptions, many of which have cultural and religious origins. Some tribes are known for their reluctance to use contraception and often associate stigma with the pursuit of family planning (FP) services. Information given by the provider is accepted, and, in many cases, clients cede power to providers to shape their SI user journey. A provider’s influence on a client’s decision to self-inject is reinforced by their biases and concerns. Clients are inhibited by the presence of peers during group education at the health facility. Others are motivated to seek FP services by the interest expressed by peers. There is also a growing trend in the influence of social media on FP uptake, particularly Facebook fora. Conclusion: The convenience of self-administration at home is a benefit for those that contend with various forms of social influences as well as covert users. Beyond increasing choice and reducing barriers to accessing Sexual and Reproductive Health (SRH) services, it can initiate the process of self-discovery and agency in the contraceptive user journey.

Keywords: selfcare, self-empowerment, agency, DMPA-SC, contraception, family planning, influences

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Authors: Jingsi Li, Neil S. Ferguson

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Time pressure could have an influence on the productivity, quality of decision making, and the efficiency of problem-solving. This has been mostly stemmed from cognitive research or psychological literature. However, a salient scarce discussion has been held for transport adjacent fields. It is conceivable that in many activity-travel contexts, time pressure is a potentially important factor since an excessive amount of decision time may incur the risk of late arrival to the next activity. The activity-travel rescheduling behavior is commonly explained by costs and benefits of factors such as activity engagements, personal intentions, social requirements, etc. This paper hypothesizes that an additional factor of perceived time pressure could affect travelers’ rescheduling behavior, thus leading to an impact on travel demand management. Time pressure may arise from different ways and is assumed here to be essentially incurred due to travelers planning their schedules without an expectation of unforeseen elements, e.g., transport disruption. In addition to a linear-additive utility-maximization model, the less computationally compensatory heuristic models are considered as an alternative to simulate travelers’ responses. The paper will contribute to travel behavior modeling research by investigating the following questions: how to measure the time pressure properly in an activity-travel day plan context? How do travelers reschedule their plans to cope with the time pressure? How would the importance of the activity affect travelers’ rescheduling behavior? What will the behavioral model be identified to describe the process of making activity-travel rescheduling decisions? How do these identified coping strategies affect the transport network? In this paper, a Mixed Heuristic Model (MHM) is employed to identify the presence of different choice heuristics through a latent class approach. The data about travelers’ activity-travel rescheduling behavior is collected via a web-based interactive survey where a fictitious scenario is created comprising multiple uncertain events on the activity or travel. The experiments are conducted in order to gain a real picture of activity-travel reschedule, considering the factor of time pressure. The identified behavioral models are then integrated into a multi-agent transport simulation model to investigate the effect of the rescheduling strategy on the transport network. The results show that an increased proportion of travelers use simpler, non-compensatory choice strategies instead of compensatory methods to cope with time pressure. Specifically, satisfying - one of the heuristic decision-making strategies - is adopted commonly since travelers tend to abandon the less important activities and keep the important ones. Furthermore, the importance of the activity is found to increase the weight of negative information when making trip-related decisions, especially route choices. When incorporating the identified non-compensatory decision-making heuristic models into the agent-based transport model, the simulation results imply that neglecting the effect of perceived time pressure may result in an inaccurate forecast of choice probability and overestimate the affectability to the policy changes.

Keywords: activity-travel rescheduling, decision making under uncertainty, mixed heuristic model, perceived time pressure, travel demand management

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Authors: Maryam Mohammadi, Mohammadjavad Mahdavinejad, Mojtaba Ansari

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The main driving force for increasing movement towards the design of High-Performance Buildings (HPB) are building codes and rating systems that address the various components of the building and their impact on the environment and energy conservation through various methods like prescriptive methods or simulation-based approaches. The methods and tools developed to meet these needs, which are often based on building performance simulation tools (BPST), have limitations in terms of compatibility with the integrated design process (IDP) and HPB design, as well as use by architects in the early stages of design (when the most important decisions are made). To overcome these limitations in recent years, efforts have been made to develop Design Decision Support Systems, which are often based on artificial intelligence. Numerous needs and steps for designing and developing a Decision Support System (DSS), which complies with the early stages of energy-efficient architecture design -consisting of combinations of different methods in an integrated package- have been listed in the literature. While various review studies have been conducted in connection with each of these techniques (such as optimizations, sensitivity and uncertainty analysis, etc.) and their integration of them with specific targets; this article is a critical and holistic review of the researches which leads to the development of applicable systems or introduction of a comprehensive framework for developing models complies with the IDP. Information resources such as Science Direct and Google Scholar are searched using specific keywords and the results are divided into two main categories: Simulation-based DSSs and Meta-simulation-based DSSs. The strengths and limitations of different models are highlighted, two general conceptual models are introduced for each category and the degree of compliance of these models with the IDP Framework is discussed. The research shows movement towards Multi-Level of Development (MOD) models, well combined with early stages of integrated design (schematic design stage and design development stage), which are heuristic, hybrid and Meta-simulation-based, relies on Big-real Data (like Building Energy Management Systems Data or Web data). Obtaining, using and combining of these data with simulation data to create models with higher uncertainty, more dynamic and more sensitive to context and culture models, as well as models that can generate economy-energy-efficient design scenarios using local data (to be more harmonized with circular economy principles), are important research areas in this field. The results of this study are a roadmap for researchers and developers of these tools.

Keywords: integrated design process, design decision support system, meta-simulation based, early stage, big data, energy efficiency

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Authors: A. Aguezzoul

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The Logistics Services Providers (LSPs) selection and performance is a strategic decision that affects the overall performance of any company as well as its supply chain. It is a complex process, which takes into account various conflicting quantitative and qualitative factors, as well as outsourced logistics activities. This article focuses on the evolution of the weights associated to these factors over the last years in order to better understand the change in the importance that logistics professionals place on them criteria when choosing their LSPs. For that, an analysis of 17 main studies published during 2014-2017 period was carried out and the results are compared to those of a previous literature review on this subject. Our analysis allowed us to deduce the following observations: 1) the LSPs selection is a multi-criteria process; 2) the empirical character of the majority of studies, conducted particularly in Asian countries; 3) the criteria importance has undergone significant changes following the emergence of information technologies that have favored the work in close collaboration and in partnership between the LSPs and their customers, even on a worldwide scale; 4) the cost criterion is relatively less important than in the past; and finally 5) with the development of sustainable supply chains, the factors associated with the logistic activities of return and waste processing (reverse logistics) are becoming increasingly important in this multi-criteria process of selection and evaluation of LSPs performance.

Keywords: logistics outsourcing, logistics providers, multi-criteria decision making, performance

Procedia PDF Downloads 154

Authors: Seun Mayowa Sunday

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Loan prediction is one of the most significant and recognised fields of research in the banking, insurance, and the financial security industries. Some prediction systems on the market include the construction of static software. However, due to the fact that static software only operates with strictly regulated rules, they cannot aid customers beyond these limitations. Application of many machine learning (ML) techniques are required for loan prediction. Four separate machine learning models, random forest (RF), decision tree (DT), k-nearest neighbour (KNN), and logistic regression, are used to create the loan prediction model. Using the anaconda navigator and the required machine learning (ML) libraries, models are created and evaluated using the appropriate measuring metrics. From the finding, the random forest performs with the highest accuracy of 80.17% which was later implemented into the Django framework. For real-time testing, the web application is deployed on the Alibabacloud which is among the top 4 biggest cloud computing provider. Hence, to the best of our knowledge, this research will serve as the first academic paper which combines the model development and the Django framework, with the deployment into the Alibaba cloud computing application.

Keywords: k-nearest neighbor, random forest, logistic regression, decision tree, django, cloud computing, alibaba cloud

Procedia PDF Downloads 135