Search results for: decision experts

Authors: Nora Alkharji, Wafa Fallatah

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Introduction: One of the major issues to consider when is deciding on what to feed a baby is the quality of the food itself. Whilst commercially prepared infant formulas are a nutritious alternative to breast milk, and even contain some vitamins and nutrients, most major medical organizations consider breastfeeding the best nutritional option for babies. Choosing whether to breastfeed or formula feed your baby is one of the first decisions expectant parents will make. The American Academy of Pediatrics (AAP) is in agreement with other organizations such as the American Medical Association (AMA), the American Dietetic Association (ADA), and the World Health Organization (WHO) in recommending breastfeeding as the best nutrition for babies and best suited for a baby's digestive system. In addition, breastfeeding helps in the combatting of infections, prevention of allergies, and protection against various chronic conditions. The decision to breastfeed or formula feed one’s baby is a very personal one. However, certain points need to be clarified regarding the nutritional value of breastfeeding versus formula feeding to allow for informed decision-making. Methodology: -A formal debate about whether to breastfeed or formula feed babies as the better choice. -There will be two debaters, both lactation consultants -Arguments will be based on evidence-based medicine -Duration period of debated: 45 min Result: Clarification and heightened awareness of the benefits of breastfeeding. Conclusion: This debate will make the choice between breastfeeding or formula feeding a relatively easy one to make by both health worker and parents.

Keywords: breastmilk, formula milk, nutritional, comparison

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Authors: Suarez-Puello Alejandro, Baquero-Ruiz Andrés F, Suarez-Puello Rodrigo

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This paper discusses the results of a foresight exercise which analyzes Bogota’s fruit, vegetable and tuber supply chain strategy- described at the Food Supply and Security Master Plan (FSSMP)-to provide the inhabitants of Bogotá, Colombia, with basic food products at a fair price. The methodology consisted of using quantitative and qualitative foresight tools such as system dynamics and variable selection methods to better represent interactions among stakeholders and obtain more integral results that could shed light on this complex situation. At first, the Master Plan is an input to establish the objectives and scope of the exercise. Then, stakeholders and their relationships are identified. Later, system dynamics is used to model product, information and money flow along the fruit, vegetable and tuber supply chain. Two scenarios are presented, discussing actions by the public sector and the reactions that could be expected from the whole food supply system. Finally, these impacts are compared to the Food Supply and Security Master Plan’s objectives suggesting recommendations that could improve its execution. This foresight exercise performed at a governmental level is intended to promote the widen the use of foresight as an anticipatory, decision-making tool that offers solutions to complex problems.

Keywords: decision making, foresight, public policies, supply chain, system dynamics

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Authors: Rene Hellmuth

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The requirements for the factory planning and the building concerned have changed in the last years. Factory planning has the task of designing products, plants, processes, organization, areas, and the building of a factory. Regular restructuring gains more importance in order to maintain the competitiveness of a factory. Restrictions regarding new areas, shorter life cycles of product and production technology as well as a VUCA (volatility, uncertainty, complexity and ambiguity) world cause more frequently occurring rebuilding measures within a factory. Restructuring of factories is the most common planning case today. Restructuring is more common than new construction, revitalization and dismantling of factories. The increasing importance of restructuring processes shows that the ability to change was and is a promising concept for the reaction of companies to permanently changing conditions. The factory building is the basis for most changes within a factory. If an adaptation of a construction project (factory) is necessary, the inventory documents must be checked and often time-consuming planning of the adaptation must take place to define the relevant components to be adapted, in order to be able to finally evaluate them. The different requirements of the planning participants from the disciplines of factory planning (production planner, logistics planner, automation planner) and industrial construction planning (architect, civil engineer) come together during reconstruction and must be structured. This raises the research question: Which requirements do the disciplines involved in the reconstruction planning place on a digital factory model? A subordinate research question is: How can model-based decision support be provided for a more efficient design of the conversion within a factory? Because of the high adaptation rate of factories and its building described above, a methodology for rescheduling factories based on the requirements engineering method from software development is conceived and designed for practical application in factory restructuring projects. The explorative research procedure according to Kubicek is applied. Explorative research is suitable if the practical usability of the research results has priority. Furthermore, it will be shown how to best use a digital factory model in practice. The focus will be on mobile applications to meet the needs of factory planners on site. An augmented reality (AR) application will be designed and created to provide decision support for planning variants. The aim is to contribute to a shortening of the planning process and model-based decision support for more efficient change management. This requires the application of a methodology that reduces the deficits of the existing approaches. The time and cost expenditure are represented in the AR tablet solution based on a building information model (BIM). Overall, the requirements of those involved in the planning process for a digital factory model in the case of restructuring within a factory are thus first determined in a structured manner. The results are then applied and transferred to a construction site solution based on augmented reality.

Keywords: augmented reality, digital factory model, factory planning, restructuring

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Siti Azrina B. A. Aziz, Siti Hafizah A. Hamid

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New approaches to analyze and visualize data stream in real-time basis is important in making a prompt decision by the decision maker. Financial market trading and surveillance, large-scale emergency response and crowd control are some example scenarios that require real-time analytic and data visualization. This situation has led to the development of techniques and tools that support humans in analyzing the source data. With the emergence of Big Data and social media, new techniques and tools are required in order to process the streaming data. Today, ranges of tools which implement some of these functionalities are available. In this paper, we present chronological evolution evaluation of technologies for supporting of real-time analytic and visualization of the data stream. Based on the past research papers published from 2002 to 2014, we gathered the general information, main techniques, challenges and open issues. The techniques for streaming text visualization are identified based on Text Visualization Browser in chronological order. This paper aims to review the evolution of streaming text visualization techniques and tools, as well as to discuss the problems and challenges for each of identified tools.

Keywords: information visualization, visual analytics, text mining, visual text analytics tools, big data visualization

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Authors: Siti N. I. Mat Kamal, Othman Ibrahim, Mehrbakhsh Nilashi, Jafalizan M. Jali

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The use of digital forensics (DF) is nowadays essential for law enforcement agencies to identify analysis and interpret the digital information derived from digital sources. In Malaysia, the engagement of Malaysian Law Enforcement Agencies (MLEA) with this new technology is not evenly distributed. To investigate the factors influencing the adoption of DF in Malaysia law enforcement agencies’ operational environment, this study proposed the initial theoretical framework based on the integration of technology organization environment (TOE), institutional theory, and human organization technology (HOT) fit model. A questionnaire survey was conducted on selected law enforcement agencies in Malaysia to verify the validity of the initial integrated framework. Relative advantage, compatibility, coercive pressure, normative pressure, vendor support and perceived technical competence of technical staff were found as the influential factors on digital forensics adoption. In addition to the only moderator of this study (agency size), any significant moderating effect on the perceived technical competence and the decision to adopt digital forensics by Malaysian law enforcement agencies was found insignificant. Thus, these results indicated that the developed integrated framework provides an effective prediction of the digital forensics adoption by Malaysian law enforcement agencies.

Keywords: digital forensics, digital forensics adoption, digital information, law enforcement agency

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Authors: Shameka Stanford

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This presentation will discuss how cognitive and communication disorders in the areas of executive functioning, receptive and expressive language can impact the problem-solving and decision making of individuals with such impairments. More specifically, this presentation will discuss approaches the legal defense team of capital case lawyers can add to their experience when servicing individuals who have a history of educational decline, special education, and limited intervention and treatment. The objective of the research is to explore and identify the correlations between impaired executive function skills and decision making and competency for individuals facing death penalty charges. To conduct this research, experimental design, randomized sampling, qualitative analysis was employed. This research contributes to the legal and criminal justice system related to how they view, defend, and characterize, and judge individuals with documented cognitive and communication disorders who are eligible for capital case charges. More importantly, this research contributes to the increased ability of death penalty lawyers to successfully defend clients with a history of academic difficulty, special education, and documented disorders that impact educational progress and academic success.

Keywords: communication disorders, cognitive disorders, capital murder, death penalty, executive function

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Authors: Gajendra Kumar Mourya, Dinesh Bhatia, Akash Handique, Sunita Warjri, Syed Achaab Amir

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Nowadays, Medical imaging has become an integral part of modern healthcare. Abdominal CT images are an invaluable mean for abdominal organ investigation and have been widely studied in the recent years. Diagnosis of liver pathologies is one of the major areas of current interests in the field of medical image processing and is still an open problem. To deeply study and diagnose the liver, segmentation of liver is done to identify which part of the liver is mostly affected. Manual segmentation of the liver in CT images is time-consuming and suffers from inter- and intra-observer differences. However, automatic or semi-automatic computer aided segmentation of the Liver is a challenging task due to inter-patient Liver shape and size variability. In this paper, we present a technique for automatic segmenting the liver from CT images using Random Forest Classifier. Random forests or random decision forests are an ensemble learning method for classification that operate by constructing a multitude of decision trees at training time and outputting the class that is the mode of the classes of the individual trees. After comparing with various other techniques, it was found that Random Forest Classifier provide a better segmentation results with respect to accuracy and speed. We have done the validation of our results using various techniques and it shows above 89% accuracy in all the cases.

Keywords: CT images, image validation, random forest, segmentation

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Authors: Jan Van Uden

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The paper reviews classical agency-theory and its contributions to the analysis of complex business negotiations and gives an approach for the modification of the basic agency-model in order to examine the negotiation specific dimensions of agency-problems. By illustrating fundamental potentials for the modification of agency-theory in context of business negotiations the paper highlights recent empirical research that investigates agent-based negotiations and inter-team constellations. A general theoretical analysis of complex negotiation would be based on a two-level approach. First, the modification of the basic agency-model in order to illustrate the organizational context of business negotiations (i.e., multi-agent issues, common-agencies, multi-period models and the concept of bounded rationality). Second, the application of the modified agency-model on complex business negotiations to identify agency-problems and relating areas of risk in the negotiation process. The paper is placed on the first level of analysis – the modification. The method builds on the one hand on insights from behavior decision research (BRD) and on the other hand on findings from agency-theory as normative directives to the modification of the basic model. Through neoclassical assumptions concerning the fundamental aspects of agency-relationships in business negotiations (i.e., asymmetric information, self-interest, risk preferences and conflict of interests), agency-theory helps to draw solutions on stated worst-case-scenarios taken from the daily negotiation routine. As agency-theory is the only universal approach able to identify trade-offs between certain aspects of economic cooperation, insights obtained provide a deeper understanding of the forces that shape business negotiation complexity. The need for a modification of the basic model is illustrated by highlighting selected issues of business negotiations from agency-theory perspective: Negotiation Teams require a multi-agent approach under the condition that often decision-makers as superior-agents are part of the team. The diversity of competences and decision-making authority is a phenomenon that overrides the assumptions of classical agency-theory and varies greatly in context of certain forms of business negotiations. Further, the basic model is bound to dyadic relationships preceded by the delegation of decision-making authority and builds on a contractual created (vertical) hierarchy. As a result, horizontal dynamics within the negotiation team playing an important role for negotiation success are therefore not considered in the investigation of agency-problems. Also, the trade-off between short-term relationships within the negotiation sphere and the long-term relationships of the corporate sphere calls for a multi-period perspective taking into account the sphere-specific governance-mechanisms already established (i.e., reward and monitoring systems). Within the analysis, the implementation of bounded rationality is closely related to findings from BRD to assess the impact of negotiation behavior on underlying principal-agent-relationships. As empirical findings show, the disclosure and reservation of information to the agent affect his negotiation behavior as well as final negotiation outcomes. Last, in context of business negotiations, asymmetric information is often intended by decision-makers acting as superior-agents or principals which calls for a bilateral risk-approach to agency-relations.

Keywords: business negotiations, agency-theory, negotiation analysis, interteam negotiations

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Authors: Rikson Gultom

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Hate Speech and Abusive language on social media is difficult to detect, usually, it is detected after it becomes viral in cyberspace, of course, it is too late for prevention. An early detection system that has a fairly good accuracy is needed so that it can reduce conflicts that occur in society caused by postings on social media that attack individuals, groups, and governments in Indonesia. The purpose of this study is to find an early detection model on Twitter social media using machine learning that has high accuracy from several machine learning methods studied. In this study, the support vector machine (SVM), Naïve Bayes (NB), and Random Forest Decision Tree (RFDT) methods were compared with the Support Vector machine with genetic algorithm (SVM-GA), Nave Bayes with genetic algorithm (NB-GA), and Random Forest Decision Tree with Genetic Algorithm (RFDT-GA). The study produced a comparison table for the accuracy of the hate speech and abusive language detection model, and presented it in the form of a graph of the accuracy of the six algorithms developed based on the Indonesian-language Twitter dataset, and concluded the best model with the highest accuracy.

Keywords: abusive language, hate speech, machine learning, optimization, social media

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Sevde D. Karayel, Ediz Atmaca

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Supplier selection subject is vital because of development competitiveness and performance of firms which have right, rapid and with low cost procurement. Considering the fact that competition between firms is no longer on their supply chains, hence it is very clear that performance of the firms’ not only depend on their own success but also success of all departments in supply chain. For this purpose, firms want to work with suppliers which are cost effective, flexible in terms of demand and high quality level for customer satisfaction. However, diversification and redundancy of their expectations from suppliers, supplier selection problems need to be solved as a hard problem. In this study, supplier selection problem is discussed for critical piece, which is using almost all production of products in and has troubles with lead time from supplier, in a firm that produces medical devices. Analyzing policy in the current situation of the firm in the supplier selection indicates that supplier selection is made based on the purchasing department experience and other authorized persons’ general judgments. Because selection do not make based on the analytical methods, it is caused disruptions in production, lateness and extra cost. To solve the problem, AHP and TOPSIS which are multi-criteria decision making techniques, which are effective, easy to implement and can analyze many criteria simultaneously, are used to make a selection among alternative suppliers.

Keywords: AHP-TOPSIS methods, multi-criteria decision making, supplier selection problem, supply chain management

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Authors: Wlodzimierz Ogryczak, Tomasz Sliwinski, Jaroslaw Hurkala, Mariusz Kaleta, Bartosz Kozlowski, Piotr Palka

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Implementation concepts of a decision support system for planning and management of mobile personnel tasks (sales representatives and others) are discussed. Large-scale periodic time-dependent vehicle routing and scheduling problems with complex constraints are solved for this purpose. Complex nonuniform constraints with respect to frequency, time windows, working time, etc. are taken into account with additional fast adaptive procedures for operational rescheduling of plans in the presence of various disturbances. Five individual solution quality indicators with respect to a single personnel person are considered. This paper deals with modeling issues corresponding to the problem and general solution concepts. The research was supported by the European Union through the European Regional Development Fund under the Operational Programme ‘Innovative Economy’ for the years 2007-2013; Priority 1 Research and development of modern technologies under the project POIG.01.03.01-14-076/12: 'Decision Support System for Large-Scale Periodic Vehicle Routing and Scheduling Problems with Complex Constraints.'

Keywords: mobile personnel management, multiple criteria, time dependent, time windows, vehicle routing and scheduling

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Authors: Aydin M. Torkabadi, Ehsan Pourjavad

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A decade after the introduction of Lean in Saskatchewan’s public healthcare system, its effectiveness remains a controversial subject among health researchers, workers, managers, and politicians. Therefore, developing a framework to quantitatively assess the Lean achievements is significant. This study investigates the success of initiatives across Saskatchewan health regions by recognizing the Lean healthcare criteria, measuring the success levels, comparing the regions, and identifying the areas for improvements. This study proposes an integrated intelligent computing approach by applying Fuzzy Inference System (FIS) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). FIS is used as an efficient approach to assess the Lean healthcare criteria, and TOPSIS is applied for ranking the values in regards to the level of leanness. Due to the innate uncertainty in decision maker judgments on criteria, principals of the fuzzy theory are applied. Finally, FIS-TOPSIS was established as an efficient technique in determining the lean merit in healthcare systems.

Keywords: lean healthcare, intelligent computing, fuzzy inference system, healthcare evaluation, technique for order of preference by similarity to ideal solution, multi-criteria decision making, MCDM

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Authors: MinHee Chung, BoYeob Lee, Yuri Kim, Eon Ku Rhee

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With an increase in the interest in the energy conservation for buildings, and the emergence of many methods and easily-understandable approaches to it, energy conservation has now become the public’s main interest, as compared to in the past when it was only focused upon by experts. This study aims to help the occupants of a building to understand the energy efficiency and consumption of the building by providing them information on the building’s energy efficiency through a mobile application. The energy performance assessment models are proposed on the basis of the actual energy usage and building characteristics such as the architectural scheme and the building equipment. The university buildings in Korea are used as a case to demonstrate the mobile application.

Keywords: energy consumption, energy performance assessment, mobile application, university buildings

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Authors: Olayemi Bakre

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The majoritant of the South African rural populace are directly or indirectly engaged in agricultural practices for a livelihood. However, impediments such as the climate change and inadequacy of governmental support has undermined the once thriving subsistence farming communities of South Africa. Furthermore, the poor leadership in hydrology, coupled with lack of depths in skills to facilitate the understanding and acceptance of groundwater from national level to local governance has made it near impossible for subsistence farmers to optimally benefit from the groundwater beneath their feet. The 2012 drought experienced in South Africa paralysed the farming activities across several subsistence farming communities across the KwaZulu-Natal Province. To revamp subsistence farming, a variety of interventions and strategies such as the Resource Poor Farmers (RPF) and Water Allocation Reforms (WAR) have been launched by the Department of Water and Sanitation (DWS) as an agendum to galvanising the defunct subsistence farming communities of KwaZulu-Natal as well as other subsistence farming communities across South Africa. Despite the enormous resources expended on the subsistence farming communities whom often fall under the Historically Disadvantaged Individuals (HDI); indicators such as the unsustainable farming practices, poor crop yield, pitiable living condition as well as the poor standard of living, are evidential to the claim that these afore cited interventions and a host of other similar strategies indicates that these initiatives have not yield the desired result. Thus, this paper seeks to suggest practicable interventions aimed at salvaging the vulnerability of subsistence farmers within the province understudy. The study pursued a qualitative approach as the view of experts on ground water and similarly related fields from the DWS were solicited as an agendum to obtaining in-depth perspective into the current study. Some of the core challenges undermining the sustainability and growth of subsistence farming in the area of study were - inadequacy of experts (engineers, scientist, researchers) in ground water; water shortages; lack of political will as well as lack of coordination among stakeholders. As an agendum to optimising the ground water usage for subsistence farming, this paper advocates the strengthening of geohydrological skills, development of technical training capacity, interactive participation among stakeholders as well as the initiation of Participatory Action Research as an agenda to optimising the available ground water in KwaZulu-Natal which is intended to orchestrate a sustainable and viable subsistence farming practice within the province.

Keywords: subsistence farming, ground water optimisation, resource poor farmers, and water allocation reforms, hydrology

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Authors: Khomsan Laosillapacharoen

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This research paper was aimed to analyze the current situation of the furniture industry and embark a plan to enhance the export volume of Thai furniture. This is a qualitative research which utilized meta-analysis and focus group. A total of 24 experts in both government and private sectors were interviewed. The findings revealed that Thai furniture had some advantages of access to raw material, high quality of labors, and have a unique skill. However, the threat included a tendency to have more foreign competitors in domestic market. In addition, the strategies to enhance the level of export included increase the standard quality of Thai furniture, offer new and modern designs, use marketing on the internet, use modern technology, and gain tax incentive from the government.

Keywords: export, furniture, strategies, marketing

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Authors: Gulnaz Erkomaishvili

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Introduction: The paper assesses the impact of the COVID-19 pandemic on the activities of small and medium-sized enterprises in Georgia, identifies their problems, and analyzes the state economic policy measures. During the pandemic, entrepreneurs named the imposition of restrictions, access to financial resources, shortage of qualified personnel, high tax rates, unhealthy competition in the market, etc. as the main challenges. The Georgian government has had to take special measures to mitigate the crisis impact caused by the pandemic. For example - in 2020, they mobilized more than 1,6 billion Gel for various eventsto support entrepreneurs. Small and medium-sized entrepreneurship development strategy is presented based on the research; Corresponding conclusions are made, and recommendations are developed. Objectives: The object of research is small and medium-sized enterprises and economic-political decisions aimed at their promotion.Methodology: This paper uses general and specific methods, in particular, analysis, synthesis, induction, deduction, scientific abstraction, comparative and statistical methods, as well as experts’ evaluation. In-depth interviews with experts were conducted to determine quantitative and qualitative indicators; Publications of the National Statistics Office of Georgia are used to determine the regularity between analytical and statistical estimations. Also, theoretical and applied research of international organizations and scientist-economists are used. Contributions: The COVID-19pandemic has had a significant impact on small and medium-sized enterprises. For them, Lockdown is a major challenge. Total sales volume decreased. At the same time, the innovative capabilities of enterprises and the volume of sales in remote channels have increased. As for the assessment of state support measures by small and medium-sizedentrepreneurs, despite the existence of support programs, a large number of entrepreneurs still do not evaluate the measures taken by the state positively. Among the desirable measures to be taken by the state, which would improve the activities of small and medium-sized entrepreneurs, who negatively or largely negatively assessed the activity of the state, named: tax incentives/exemption from certain taxes at the initial stage; Need for periodic trainings/organization of digital technologies, marketing training courses to improve the qualification of employees; Logic and adequacy of criteria when awarding grants and funding; Facilitating the finding of investors; Less bureaucracy, etc.

Keywords: small and medium enterprises, small and medium entrepreneurship, economic policy for small and medium entrepreneurship development, government regulations in Georgia, COVID-19 pandemic

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Authors: G. Singer, M. Golan

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Providing students with learning disabilities (LD) with extra time to grant them equal access to the exam is a necessary but insufficient condition to compensate for their LD; there should also be a clear indication that the additional time was actually used. For example, if students with LD use more time than students without LD and yet receive lower grades, this may indicate that a different accommodation is required. If they achieve higher grades but use the same amount of time, then the effectiveness of the accommodation has not been demonstrated. The main goal of this study is to evaluate the effect of including parameters related to LD and extended exam time, along with other commonly-used characteristics (e.g., student background and ability measures such as high-school grades), on the ability of ordinal decision-tree algorithms to predict exam performance. We use naturally-occurring data collected from hundreds of undergraduate engineering students. The sub-goals are i) to examine the improvement in prediction accuracy when the indicator of exam performance includes 'actual time used' in addition to the conventional indicator (exam grade) employed in most research; ii) to explore the effectiveness of extended exam time on exam performance for different courses and for LD students with different profiles (i.e., sets of characteristics). This is achieved by using the patterns (i.e., subgroups) generated by the algorithms to identify pairs of subgroups that differ in just one characteristic (e.g., course or type of LD) but have different outcomes in terms of exam performance (grade and time used). Since grade and time used to exhibit an ordering form, we propose a method based on ordinal decision-trees, which applies a weighted information-gain ratio (WIGR) measure for selecting the classifying attributes. Unlike other known ordinal algorithms, our method does not assume monotonicity in the data. The proposed WIGR is an extension of an information-theoretic measure, in the sense that it adjusts to the case of an ordinal target and takes into account the error severity between two different target classes. Specifically, we use ordinal C4.5, random-forest, and AdaBoost algorithms, as well as an ensemble technique composed of ordinal and non-ordinal classifiers. Firstly, we find that the inclusion of LD and extended exam-time parameters improves prediction of exam performance (compared to specifications of the algorithms that do not include these variables). Secondly, when the indicator of exam performance includes 'actual time used' together with grade (as opposed to grade only), the prediction accuracy improves. Thirdly, our subgroup analyses show clear differences in the effect of extended exam time on exam performance among different courses and different student profiles. From a methodological perspective, we find that the ordinal decision-tree based algorithms outperform their conventional, non-ordinal counterparts. Further, we demonstrate that the ensemble-based approach leverages the strengths of each type of classifier (ordinal and non-ordinal) and yields better performance than each classifier individually.

Keywords: actual exam time usage, ensemble learning, learning disabilities, ordinal classification, time extension

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Authors: Noa Nelson, Orit Appel, Rachel Ben-ari

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Organizational Citizenship Behavior (OCB) is voluntary employee behavior that contributes to the organization beyond formal job requirements. It can take different forms, such as helping teammates (OCB toward individuals; hence, OCB-I), or staying after hours to attend a task force (OCB toward the organization; hence, OCB-O). Generally, OCB contributes substantially to organizational climate, goals, productivity, and resilience, so organizations need to understand what encourages it. This is particularly challenging in security organizations. Security work is characterized by high levels of stress and burnout, which is detrimental to OCB, and security organizational design emphasizes formal rules and clear hierarchies, leaving employees with less freedom for voluntary behavior. The current research explored the role of Perceived Procedural Justice (PPJ) in enhancing OCB in a security organization. PPJ refers to how fair decision-making processes are perceived to be. It involves the sense that decision makers are objective, attentive to everyone's interests, respectful in their communications and participatory - allowing individuals a voice in decision processes. Justice perceptions affect motivation, and it was specifically suggested that PPJ creates an attachment to one's organization and personal interest in its success. Accordingly, PPJ had been associated with OCB, but hardly any research tested their association with security organizations. The current research was conducted among prison guards in the Israel Prison Service, to test a correlational and a causal association between PPJ and OCB. It differentiated between perceptions of direct commander procedural justice (CPJ), and perceptions of organization procedural justice (OPJ), hypothesizing that CPJ would relate to OCB-I, while OPJ would relate to OCB-O. In the first study, 336 prison guards (305 male) from 10 different prisons responded to questionnaires measuring their own CPJ, OPJ, OCB-I, and OCB-O. Hierarchical linear regression analyses indicated the significance of commander procedural justice (CPJ): It associated with OCB-I and also associated with OPJ, which, in turn, associated with OCB-O. The second study tested CPJ's causal effects on prison guards' OCB-I and OCB-O; 311 prison guards (275 male) from 14 different prisons read scenarios that described either high or low CPJ, and then evaluated the likelihood of that commander's prison guards performing OCB-I and OCB-O. In this study, CPJ enhanced OCB-O directly. It also contributed to OCB-I, indirectly: CPJ enhanced the motivation for collaboration with the commander, which respondents also evaluated after reading scenarios. Collaboration, in turn, associated with OCB-I. The studies demonstrate that procedural justice, especially commander's PJ, promotes OCB in security work environments. This is important because extraordinary teamwork and motivation are needed to deal with emergency situations and with delicate security challenges. Following the studies, the Israel Prison Service implemented personal procedural justice training for commanders and unit level programs for procedurally just decision processes. From a theoretical perspective, the studies extend the knowledge on PPJ and OCB to security work environments and contribute evidence on PPJ's causal effects. They also call for further research, to understand the mechanisms through which different types of PPJ affect different types of OCB.

Keywords: organizational citizenship behavior, perceived procedural justice, prison guards, security organizations

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Authors: Corinne Zurmuehle, Andreas Christoph Weber

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In 2019, the Swiss Federal Institute of Sport Magglingen (SFISM) conducted a mixed-methods study on the Swiss elite sports system, which yielded a large quantity of research data. In a quantitative online survey, 1151 elite sports athletes, 542 coaches, and 102 Performance Directors of national sports federations (NF) have submitted their perceptions of the national support measures of the Swiss elite sports system. These data provide an essential database for the further development of the Swiss elite sports system. The results were published in a report presenting the results divided into 40 Olympic summer and 14 winter sports (Olympic classification). The authors of this paper assume that, in practice, this division is too unspecific to assess where further measures would be needed. The aim of this paper is to find appropriate parameters for data visualization in order to identify disparities in sports promotion that allow an assessment of where further interventions by Swiss Olympic (NF umbrella organization) are required. Method: First, the variable 'salary earned from sport' was defined as a variable to measure the impact of elite sports promotion. This variable was chosen as a measure as it represents an important indicator for the professionalization of elite athletes and therefore reflects national level sports promotion measures applied by Swiss Olympic. Afterwards, the variable salary was tested with regard to the correlation between Olympic classification [a], calculating the Eta coefficient. To estimate the appropriate parameters for data visualization, the correlation between salary and four further parameters was analyzed by calculating the Eta coefficient: [a] sport; [b] prioritization (from 1 to 5) of the sports by Swiss Olympic; [c] gender; [d] employment level in sports. Results & Discussion: The analyses reveal a very small correlation between salary and Olympic classification (ɳ² = .011, p = .005). Gender demonstrates an even small correlation (ɳ² = .006, p = .014). The parameter prioritization was correlating with small effect (ɳ² = .017, p = .001) as did employment level (ɳ² = .028, p < .001). The highest correlation was identified by the parameter sport with a moderate effect (ɳ² = .075, p = .047). The analyses show that the disparities in sports promotion cannot be determined by a particular parameter but presumably explained by a combination of several parameters. We argue that the possibility of combining parameters for data visualization should be enabled when the analysis is provided to Swiss Olympic for further strategic decision-making. However, the inclusion of multiple parameters massively multiplies the number of graphs and is therefore not suitable for practical use. Therefore, we suggest to apply interactive dashboards for data visualization using Business Intelligence Software. Practical & Theoretical Contribution: This contribution provides the first attempt to use Business Intelligence Software for strategic decision-making in national level sports regarding the prioritization of national resources for sports and athletes. This allows to set specific parameters with a significant effect as filters. By using filters, parameters can be combined and compared against each other and set individually for each strategic decision.

Keywords: data visualization, business intelligence, Swiss elite sports system, strategic decision-making

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Authors: Abadi Berhane, Walelign Worku, Berhanu Abrha, Gebre Hadgu, Tigray

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Sesame is an important oilseed crop in Ethiopia; which is the second most exported agricultural commodity next to coffee. However, there is poor soil fertility management and a research-led farming system for the crop. The AquaCrop model was applied as a decision-support tool; which performs a semi-quantitative approach to simulate the yield of crops under different soil fertility levels. The objective of this experiment was to calibrate and validated the AquaCrop model for simulating the growth and yield of sesame under different nitrogen fertilizer levels and to test the performance of the model as a decision-support tool for improved sesame cultivation in the study area. The experiment was laid out as a randomized complete block design (RCBD) in a factorial arrangement in the 2016, 2017, and 2018 main cropping seasons. In this experiment, four nitrogen fertilizer rates; 0, 23, 46, and 69 Kg/ha nitrogen, and three improved varieties (Setit-1, Setit-2, and Humera-1). In the meantime, growth, yield, and yield components of sesame were collected from each treatment. Coefficient of determination (R2), Root mean square error (RMSE), Normalized root mean square error (N-RMSE), Model efficiency (E), and Degree of agreement (D) were used to test the performance of the model. The results indicated that the AquaCrop model successfully simulated soil water content with R2 varying from 0.92 to 0.98, RMSE 6.5 to 13.9 mm, E 0.78 to 0.94, and D 0.95 to 0.99; and the corresponding values for AB also varied from 0.92 to 0.98, 0.33 to 0.54 tons/ha, 0.74 to 0.93, and 0.9 to 0.98, respectively. The results on the canopy cover of sesame also showed that the model acceptably simulated canopy cover with R2 varying from 0.95 to 0.99, and a RMSE of 5.3 to 8.6%. The AquaCrop model was appropriately calibrated to simulate soil water content, canopy cover, aboveground biomass, and sesame yield; the results indicated that the model adequately simulated the growth and yield of sesame under the different nitrogen fertilizer levels. The AquaCrop model might be an important tool for improved soil fertility management and yield enhancement strategies of sesame. Hence, the model might be applied as a decision-support tool in soil fertility management in sesame production.

Keywords: aquacrop model, sesame, normalized water productivity, nitrogen fertilizer

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Authors: Lior Davidovitch

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Traditional training based on static models and case studies is the backbone of most teaching and training programs of engineering education. However, project management learning is characterized by dynamics models that requires new and enhanced learning method. The results of empirical experiments evaluating the effectiveness and efficiency of using cognitive simulator as a new training technique are reported. The empirical findings are focused on the impact of keeping and reviewing learning history in a dynamic and interactive simulation environment of engineering education. The cognitive simulator for engineering project management learning had two learning history keeping modes: manual (student-controlled), automatic (simulator-controlled) and a version with no history keeping. A group of industrial engineering students performed four simulation-runs divided into three identical simple scenarios and one complicated scenario. The performances of participants running the simulation with the manual history mode were significantly better than users running the simulation with the automatic history mode. Moreover, the effects of using the undo enhanced further the learning process. The findings indicate an enhancement of engineering students’ learning and decision making when they use the record functionality of the history during their engineering training process. Furthermore, the cognitive simulator as educational innovation improves students learning and training. The practical implications of using simulators in the field of engineering education are discussed.

Keywords: cognitive simulator, decision making, engineering learning, project management

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Authors: Recep Sait Arpat, Hakan Güreşci

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Emergency, disaster and crisis management terms are generally perceived as the same processes. This conflict effects the approach and delegating policy of the political order. Crisis management starts in the aftermath of the mismanagement of disaster and emergency. In the light of the information stated above in this article Turkey and United Kingdom(UK)’s crisis management systems are analyzed. This article’s main aim is to clarify the main points of the emergency management system of United Kingdom and Turkey’s disaster management system by comparing them. To do this: A prototype model of the political decision making processes of the countries is drawn, decision making mechanisms and the planning functions are compared. As a result it’s found that emergency management policy in Turkey is reactive whereas it’s proactive in UK; as the delegating policy Turkey’s system is similar to UK; levels of emergency situations are similar but not the same; the differences are stemming from the civil order and nongovernmental organizations effectiveness; UK has a detailed government engagement model to emergencies, which shapes the doctrine of the approach to emergencies, and it’s successful in gathering and controlling the whole state’s efforts; crisis management is a sub-phase of UK emergency management whereas it’s accepted as a outmoded management perception and the focal point of crisis management perception in UK is security crisis and natural disasters while in Turkey it is natural disasters. In every anlysis proposals are given to Turkey.

Keywords: crisis management, disaster management, emergency management, turkey, united kingdom

Procedia PDF Downloads 372

Authors: Marco F. Benaglia, Mei H. Chen, Kune M. Tsai, Chia H. Hung

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As societal and family structures, consumer dietary habits, and awareness about food safety and quality continue to evolve in most developed countries, the demand for refrigerated and frozen foods has been growing, and the issues related to their preservation have gained increasing attention. A well-established cold chain logistics system is essential to avoid any temperature abuse; therefore, assessing potential disruptions in the operational processes of cold chain logistics centers becomes pivotal. This study preliminarily employs HACCP to find disruption factors in cold chain logistics centers that may cause temperature abuse. Then, concept mapping is applied: selected experts engage in brainstorming sessions to identify any further factors. The panel consists of ten experts, including four from logistics and home delivery, two from retail distribution, one from the food industry, two from low-temperature logistics centers, and one from the freight industry. Disruptions include equipment-related aspects, human factors, management aspects, and process-related considerations. The areas of observation encompass freezer rooms, refrigerated storage areas, loading docks, sorting areas, and vehicle parking zones. The experts also categorize the disruption factors based on perceived similarities and build a similarity matrix. Each factor is evaluated for its impact, frequency, and investment importance. Next, multiple scale analysis, cluster analysis, and other methods are used to analyze these factors. Simultaneously, key disruption factors are identified based on their impact and frequency, and, subsequently, the factors that companies prioritize and are willing to invest in are determined by assessing investors’ risk aversion behavior. Finally, Cumulative Prospect Theory (CPT) is applied to verify the risk patterns. 66 disruption factors are found and categorized into six clusters: (1) "Inappropriate Use and Maintenance of Hardware and Software Facilities", (2) "Inadequate Management and Operational Negligence", (3) "Product Characteristics Affecting Quality and Inappropriate Packaging", (4) "Poor Control of Operation Timing and Missing Distribution Processing", (5) "Inadequate Planning for Peak Periods and Poor Process Planning", and (6) "Insufficient Cold Chain Awareness and Inadequate Training of Personnel". This study also identifies five critical factors in the operational processes of cold chain logistics centers: "Lack of Personnel’s Awareness Regarding Cold Chain Quality", "Personnel Not Following Standard Operating Procedures", "Personnel’s Operational Negligence", "Management’s Inadequacy", and "Lack of Personnel’s Knowledge About Cold Chain". The findings show that cold chain operators prioritize prevention and improvement efforts in the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster, particularly focusing on the factors of "Temperature Setting Errors" and "Management’s Inadequacy". However, through the application of CPT theory, this study reveals that companies are not usually willing to invest in the improvement of factors related to the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster due to its low occurrence likelihood, but they acknowledge the severity of the consequences if it does occur. Hence, the main implication is that the key disruption factors in cold chain logistics centers’ processes are associated with personnel issues; therefore, comprehensive training, periodic audits, and the establishment of reasonable incentives and penalties for both new employees and managers may significantly reduce disruption issues.

Keywords: concept mapping, cold chain, HACCP, cumulative prospect theory

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Authors: Lele Dominic Dummene

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Environmental issues are predominant in rural areas where indigenous people reside. These environmental issues cover environmental, health, social, economic, and political issues that emanate from poor environmental management and unfair distribution of environmental resources. These issues have greatly affected the lives of the indigenous people and their daily activities. As these environmental issues grow in communities, environmental experts, scientists, and theorists have proposed and developed methods, policies, and strategies to address these environmental-related issues in indigenous communities. Thus, this paper explores how the Ogoni indigenous knowledge and cultural practices could be used to address environmental issues such as oil pollution and other environmental-related issues that have destroyed the Ogoni environment.

Keywords: Ogoniland, indigenous knowledge, environment, environmental education

Procedia PDF Downloads 122

Authors: Rodríguez Sánchez Carla, Sancho-Esper Franco, Guillen-Davo Marina

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Tourism is one of the most important sectors worldwide since it is an important economic engine for today's society. It is also one of the sectors that most negatively affect the environment in terms of CO₂ emissions due to this expansion. In light of this, airlines are developing Voluntary Carbon Offset (VCO). There is important evidence focused on analyzing the features of these VCO programs and their efficacy in reducing CO₂ emissions, and findings are mixed without a clear consensus. Different research approaches have centered on analyzing factors and consequences of VCO programs, such as economic modelling based on panel data, survey research based on traveler responses or experimental research analyzing customer decisions in a simulated context. This study belongs to the latter group because it tries to understand how different characteristics of an online ticket purchase website affect the willingness of a traveler to choose a sustainable one. The proposed behavioral model is based on several theories, such as the nudge theory, the dual processing ELM and the cognitive dissonance theory. This randomized experiment aims at overcoming previous studies based on self-reported measures that mainly study sustainable behavioral intention rather than actual decision-making. It also complements traditional self-reported independent variables by gathering objective information from an eye-tracking device. This experiment analyzes the influence of two characteristics of the online purchase website: i) the type of information regarding flight CO₂ emissions (quantitative vs. qualitative) and the comparison framework related to the sustainable purchase decision (negative: alternative with more emissions than the average flight of the route vs. positive: alternative with less emissions than the average flight of the route), therefore it is a 2x2 experiment with four alternative scenarios. A pretest was run before the actual experiment to refine the experiment features and to check the manipulations. Afterward, a different sample of students answered the pre-test questionnaire aimed at recruiting the cases and measuring several pre-stimulus measures. One week later, students came to the neurolab at the University setting to be part of the experiment, made their decision regarding online purchases and answered the post-test survey. A final sample of 21 students was gathered. The committee of ethics of the institution approved the experiment. The results show that qualitative information generates more sustainable decisions (less contaminant alternative) than quantitative information. Moreover, evidence shows that subjects are more willing to choose the sustainable decision to be more ecological (comparison of the average with the less contaminant alternative) rather than to be less contaminant (comparison of the average with the more contaminant alternative). There are also interesting differences in the information processing variables from the eye tracker. Both the total time to make the choice and the specific times by area of interest (AOI) differ depending on the assigned scenario. These results allow for a better understanding of the factors that condition the decision of a traveler to be part of a VCO program and provide useful information for airline managers to promote these programs to reduce environmental impact.

Keywords: voluntary carbon offset, airline, online purchase, carbon emission, sustainability, randomized experiment

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Authors: Zahid Ullah, Atlas Khan

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The rapid growth of data in diverse domains has created an urgent need for effective utilization of mathematical and statistical sciences in data analytics. This abstract explores the journey from theory to practice, emphasizing the importance of harnessing mathematical and statistical innovations to unlock the full potential of data analytics. Drawing on a comprehensive review of existing literature and research, this study investigates the fundamental theories and principles underpinning mathematical and statistical sciences in the context of data analytics. It delves into key mathematical concepts such as optimization, probability theory, statistical modeling, and machine learning algorithms, highlighting their significance in analyzing and extracting insights from complex datasets. Moreover, this abstract sheds light on the practical applications of mathematical and statistical sciences in real-world data analytics scenarios. Through case studies and examples, it showcases how mathematical and statistical innovations are being applied to tackle challenges in various fields such as finance, healthcare, marketing, and social sciences. These applications demonstrate the transformative power of mathematical and statistical sciences in data-driven decision-making. The abstract also emphasizes the importance of interdisciplinary collaboration, as it recognizes the synergy between mathematical and statistical sciences and other domains such as computer science, information technology, and domain-specific knowledge. Collaborative efforts enable the development of innovative methodologies and tools that bridge the gap between theory and practice, ultimately enhancing the effectiveness of data analytics. Furthermore, ethical considerations surrounding data analytics, including privacy, bias, and fairness, are addressed within the abstract. It underscores the need for responsible and transparent practices in data analytics, and highlights the role of mathematical and statistical sciences in ensuring ethical data handling and analysis. In conclusion, this abstract highlights the journey from theory to practice in harnessing mathematical and statistical sciences in data analytics. It showcases the practical applications of these sciences, the importance of interdisciplinary collaboration, and the need for ethical considerations. By bridging the gap between theory and practice, mathematical and statistical sciences contribute to unlocking the full potential of data analytics, empowering organizations and decision-makers with valuable insights for informed decision-making.

Keywords: data analytics, mathematical sciences, optimization, machine learning, interdisciplinary collaboration, practical applications

Procedia PDF Downloads 93

Authors: Diana F. Lopes, Jorge Simoes, José Martins, Eduardo Castro

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Health professionals are the keystone of any health system, by delivering health services to the population. Given the time and cost involved in training new health professionals, the planning process of the health workforce is particularly important as it ensures a proper balance between the supply and demand of these professionals and it plays a central role on the Health 2020 policy. In the past 40 years, the planning of the health workforce in Portugal has been conducted in a reactive way lacking a prospective vision based on an integrated, comprehensive and valid analysis. This situation may compromise not only the productivity and the overall socio-economic development but the quality of the healthcare services delivered to patients. This is even more critical given the expected shortage of the health workforce in the future. Furthermore, Portugal is facing an aging context of some professional classes (physicians and nurses). In 2015, 54% of physicians in Portugal were over 50 years old, and 30% of all members were over 60 years old. This phenomenon associated to an increasing emigration of young health professionals and a change in the citizens’ illness profiles and expectations must be considered when planning resources in healthcare. The perspective of sudden retirement of large groups of professionals in a short time is also a major problem to address. Another challenge to embrace is the health workforce imbalances, in which Portugal has one of the lowest nurse to physician ratio, 1.5, below the European Region and the OECD averages (2.2 and 2.8, respectively). Within the scope of the HEALTH 2040 project – which aims to estimate the ‘Future needs of human health resources in Portugal till 2040’ – the present study intends to get a comprehensive dynamic approach of the problem, by (i) estimating the needs of physicians and nurses in Portugal, by specialties and by quinquenium till 2040; (ii) identifying the training needs of physicians and nurses, in medium and long term, till 2040, and (iii) estimating the number of students that must be admitted into medicine and nursing training systems, each year, considering the different categories of specialties. The development of such approach is significantly more critical in the context of limited budget resources and changing health care needs. In this context, this study presents the drivers of the healthcare needs’ evolution (such as the demographic and technological evolution, the future expectations of the users of the health systems) and it proposes a Bayesian methodology, combining the best available data with experts opinion, to model such evolution. Preliminary results considering different plausible scenarios are presented. The proposed methodology will be integrated in a user-friendly decision support system so it can be used by politicians, with the potential to measure the impact of health policies, both at the regional and the national level.

Keywords: bayesian estimation, health economics, health workforce planning, human health resources planning

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Authors: Susanne Ferschl, Peter Holzmüller, Elisabeth Wacker

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Ageing populations, advances in medical sciences and digitalization, diversity and social disparities, as well as the increasing need for skilled healthcare professionals, are challenging healthcare systems around the globe. To address these challenges, future healthcare systems need to center on human needs taking into account the living environments that shape individuals’ knowledge of and opportunities to access healthcare. Moreover, health should be considered as a common good and an integral part of securing livelihoods for all people. Therefore, the adoption of a systems approach, as well as inter-disciplinary and inter-sectoral cooperation among healthcare providers, are essential. Additionally, the active engagement of target groups in the planning and design of healthcare structures is indispensable to understand and respect individuals’ health and livelihood needs. We will present the research project b4 – identifying needs | building bridges | developing health care in the social space, which is situated within this reasoning and accompanies the cross-sectoral cooperation project Brückenschlag (building bridges) in a Bavarian district. Brückenschlag seeks to explore effective ways of health care linking university medicine (Maximalversorgung | maximum care) with regional inpatient, outpatient, rehabilitative, and preventive care structures (Regionalversorgung | regional care). To create advantages for both (potential) patients and the involved cooperation partners, project b4 qualitatively assesses needs and motivations among professionals, population groups, and political stakeholders at individual and collective levels. Besides providing an overview of the project structure as well as of regional population and healthcare characteristics, the first results of qualitative interviews conducted with different health experts will be presented. Interviewed experts include managers of participating hospitals, nurses, medical specialists working in the hospital and registered doctors operating in practices in rural areas. At the end of the project life and based on the identified factors relevant to the success -and also for failure- of participatory cooperation in health care, the project aims at informing other districts embarking on similar systems-oriented and human-centered healthcare projects. Individuals’ health care needs in dependence on the social space in which they live will guide the development of recommendations.

Keywords: cross-sectoral collaboration in health care, human-centered health care, regional health care, individual and structural health conditions

Procedia PDF Downloads 101