Search results for: data driven decision making

Authors: Samipraj Mishra

Abstract:

Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which is climatically belongs to tropical humid and possessed high quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefit sharing, collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economical potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counter-productive to promote the equitable benefit sharing in the areas of heterogeneous socio-economic conditions with high value forests. The low pricing strategy has been increasing accessibility of better off households at higher rate than poor; as such households always have higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerment of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: community forest, livelihood, socio-economy, pricing system, Nepal

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Authors: Mitra Ghiasi, Roghayeh Ghorbani Bousari

Abstract:

Nowadays, the aim of the organizations is to gain sustainable competitive. So, developing their intellectual capital, encouraging innovation, increasing suitable performance can be done by knowledge management. Knowledge turns into science if knowledge is used to improve decision making, decision quality and make effective decisions. The current research intends to investigate the relationship between cybernetics and knowledge management from the perspective of users who are members of electronic discussion groups (ALA, ALIA). The research methodology is survey method, and it is a type of correlation research. Cybernetics and knowledge management questionnaires used for collecting data. The questionnaire that was designed in electronic format, distributed among two electronic discussion groups during 30 days and completed by 100 members of each electronic discussion groups. The finding of this research showed that although cybernetics has an impact on knowledge management, there is no significant difference between the ALA and ALIA user's view regard to effect of cybernetics on knowledge management. The results also indicated that this conceptual model is consistent with the data collected from the sample.

Keywords: ALA discussion group, ALIA discussion group, cybernetics, knowledge management

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Authors: Mahdi Alshamasin, Riad Al-Kasasbeh, Nikolay Korenevskiy

Abstract:

We apply mathematical models for the interaction of the internal and biologically active points of meridian structures. Amongst the diseases for which reflex diagnostics are effective are those of the stomach disease. It is shown that use of fuzzy logic decision-making yields good results for the prediction and early diagnosis of gastrointestinal tract diseases, depending on the reaction energy of biologically active points (acupuncture points). It is shown that good results for the prediction and early diagnosis of diseases from the reaction energy of biologically active points (acupuncture points) are obtained by using fuzzy logic decision-making.

Keywords: acupuncture points, fuzzy logic, diagnostically important points (DIP), confidence factors, membership functions, stomach diseases

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Authors: Hannah Gutmann, Rick Richardson, Richard Bryant

Abstract:

Intrusive memories following a traumatic event are not uncommon. However, in some individuals, these memories become maladaptive and lead to prolonged stress reactions. A seminal model of PTSD explains that aberrant processing during trauma may lead to prolonged stress reactions and intrusive memories. This model explains that elevated arousal at the time of the trauma promotes data driven processing, leading to fragmented and intrusive memories. This study investigated the role of elevated arousal on the development of intrusive memories. We measured salivary markers of arousal and investigated what impact this had on data driven processing, memory fragmentation, and subsequently, the development of intrusive memories. We assessed 100 healthy participants to understand their processing style, arousal, and experience of intrusive memories. Participants were randomised to a control or experimental condition, the latter of which was designed to increase their arousal. Based on current theory, participants in the experimental condition were expected to engage in more data driven processing and experience more intrusive memories than participants in the control condition. This research aims to shed light on the mechanisms underlying the development of intrusive memories to illustrate ways in which therapeutic approaches for PTSD may be augmented for greater efficacy.

Keywords: stress, cortisol, SAA, PTSD, intrusive memories

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Authors: Dragana Calic

Abstract:

The openness, availability and the sheer volume of big data have provided, what some regard as, an invaluable and rich dataset. Researchers, businesses, advertising agencies, medical institutions, to name only a few, collect, share, and analyze this data to enable their processes and decision making. However, there are important ethical considerations associated with the use of big data. The rapidly evolving nature of online technologies has overtaken the many legislative, privacy, and ethical frameworks and principles that exist. For example, should we obtain consent to use people’s online data, and under what circumstances can privacy considerations be overridden? Current guidance on how to appropriately and ethically handle big data is inconsistent. Consequently, this paper focuses on two quite distinct but related ethical considerations that are at the core of the use of big data for research purposes. They include empowering the producers of data and empowering researchers who want to study big data. The first consideration focuses on informed consent which is at the core of empowering producers of data. In this paper, we discuss some of the complexities associated with informed consent and consider studies of producers’ perceptions to inform research ethics guidelines and practice. The second consideration focuses on the researcher. Similarly, we explore studies that focus on researchers’ perceptions and experiences.

Keywords: big data, ethics, producers’ perceptions, researchers’ perceptions

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Authors: Avishkar Gawade, Omkar Bansode, Ketan Bhambure, Bhargav Deore

Abstract:

In recent year wide range of business intelligence technology have been applied to different area in order to support decision making process BI enables extraction of knowledge from data store. BI tools usually used in public health field for financial and administrative purposes.BI uses a dashboard in presentation stage to deliver information to information to end users.In this paper,we intend to analyze some open source BI tools on the market and their applicability in the clinical sphere taking into consideration the general characteristics of the clinical environment.A pervasive BI platform was developed using a real case in order to prove the tool viability.Analysis of various BI Tools in done with the help of several parameters such as data security,data integration,data quality reporting and anlaytics,performance,scalability and cost effectivesness.

Keywords: CDSS, EHR, business intelliegence, tools

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Authors: Kwaku Damoah

Abstract:

This study presents a comprehensive data-driven analysis to understand the evolution of adverse events (AEs) in pharmacovigilance. Utilizing data from the FDA Adverse Event Reporting System (FAERS), we employed three analytical methods: rank-based, frequency-based, and percentage change analyses. These methods assessed temporal trends and patterns in AE reporting, focusing on various drug-active ingredients and patient demographics. Our findings reveal significant trends in AE occurrences, with both increasing and decreasing patterns from 2000 to 2023. This research highlights the importance of continuous monitoring and advanced analysis in pharmacovigilance, offering valuable insights for healthcare professionals and policymakers to enhance drug safety.

Keywords: event analysis, FDA adverse event reporting system, pharmacovigilance, temporal trend analysis

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Authors: Som Pal Baliyan

Abstract:

The purpose of this quantitative study was to analyze farm management skills in broiler poultryproducers in Botswana. The study adopted a descriptive and correlation research design. The population of the study was the poultry farm operators who had been in broiler poultry farming at least for two years. Based on the information from literature, a questionnaire was constructed for data collection on seven areas of farm management skills namely; planning skills, accounting and financial management skills, production management skills, product procurement and marketing skills, decision making skills, risk management skills, and specific technical skills. The validity and reliability of the questionnaire were accomplished by a panel of experts and by calculating the Cronbach’s alpha coefficient, respectively. Data were collected through a survey of 60 randomly sampled poultry farm operators in Botswana. Data were analyzed through descriptive statistical tools whereby the level of farm management skills were determined by calculating means and standard deviations of the management skills among the broiler producers. The level of farm management skills in broilers producers was discussed. All the seven farm management skills were ranked based on their calculated means. The specific technical skills and risk management skills were the highest and the lowest ranked farm management skills, respectively.Findings revealed that the broiler producers had skills above the average level only in specific technical skills whereas the skill levels in the remaining six farm management skills under study were found below the average level. This prevailing low level of farm management skills can be justified asthe cause of failure or poor performance of the broiler poultry farms in Botswana. Therefore, in order to improve the efficiency and productivityin broiler production in the country, it was recommended that the broiler poultry producers should be adequately trained in areas of planning skills, financial management skills, production management skills, product procurement and marketing skills, decision making skills and risk management skills.

Keywords: poultry production, broiler production, management skills, levels of skills

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Authors: Vladimir Korovkin, Albert Park, Evgeny Kaganer

Abstract:

The paper studies the second-level digital divide (the one defined by the way how digital technology is used in everyday life) between regions of the Russian Federation. The paper offers a systemic review of literature on the measurement of the digital divide; based upon this it suggests a composite Digital Life Index, that captures the complex multi-dimensional character of the phenomenon. The model of the index studies separately the digital supply and demand across seven independent dimensions providing for 14 subindices. The Index is based on Internet-borne data, a distinction from traditional research approaches that rely on official statistics or surveys. Regression analysis is used to determine the relative importance of factors like income, human capital, and policy in determining the digital divide. The result of the analysis suggests that the digital divide is driven more by the differences in demand (defined by consumer competencies) than in supply; the role of income is insignificant, and the quality of human capital is the key determinant of the divide. The paper advances the existing methodological literature on the issue and can also inform practical decision-making regarding the strategies of national and regional digital development.

Keywords: digital transformation, second-level digital divide, composite index, digital policy, regional development, Russia

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Authors: Zeenath Solih

Abstract:

The aim of the study is to analyze the factors that influence the decision to pursue a hospitality and tourism program for students of Maldives when pursuing higher education options. This research would further explore the implications and relationship between the universities and students. Quantitative research method will be used to demonstrate the hypothesis and achieve the objectives of this research, a questionnaire consisting of 30 closed questions will be used which will be analyzed based on SPSS18 software to handle and extract the data.10 public school and 3 private schools with secondary education and 3 universities with higher education facilities and a total of 500 students participated in the survey. The findings include selection criteria for decision making for higher studies being the university’s reputation, excellence and quality of educational program, the preference of pursuing further studies from a public over private universities and the academic, cultural and socio demographic factors that influence the students choice of program and university. Finally the study will provide valuable insight to how universities need to market their programs to attract the right students.

Keywords: choice criteria, higher education, hospitality and tourism studies, information sources

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Authors: Mui Joo Tang, Eang Teng Chan

Abstract:

Advertising is commonly used to foster sales and reputation of an institution. It is at first the growth of print advertising that has increased the population and number of periodicals of newspaper and its circulation. The rise of Internet and online media has somehow blurred the role of media and advertising though the intention is still to reach out to audience and to increase sales. The relationship between advertising and audience on a product purchase through persuasion has been developing from print media to online media. From the changing media environment and audience, it is the concern of this research to study the impact of online advertising to such a relationship cycle. The content of online advertisements is much of text, multimedia, photo, audio and video. The messages of such content format may indeed bring impacts to its audience and its credibility. This study is therefore reflecting the effectiveness of online advertisement and its influences on generation Y in their purchasing behavior. This study uses Media Dependency Theory to analyze the relationship between the impact of online advertisement and media usage pattern of generation Y. Hierarchy of Effectiveness Model is used as a marketing communication model to study the effectiveness of advertising and further to determine the impact of online advertisement on generation Y in their purchasing decision making. This research uses online survey to reach out the sample of generation Y. The results have shown that online advertisements do not affect much on purchase decision making even though generation Y relies much on the media content including online advertisement for its information and believing in its credibility. There are few other external factors that may interrupt the effectiveness of online advertising. The very obvious influence of purchasing behavior is actually derived from the peers.

Keywords: generation Y, purchase decision, print media, online advertising, persuasion

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Authors: Dana R. Thomson, Catherine Linard, Sabine Vanhuysse, Jessica E. Steele, Michal Shimoni, Jose Siri, Waleska Caiaffa, Megumi Rosenberg, Eleonore Wolff, Tais Grippa, Stefanos Georganos, Helen Elsey

Abstract:

The Sustainable Development Goals (SDGs) and Urban Health Equity Assessment and Response Tool (Urban HEART) identify dozens of key indicators to help local decision-makers prioritize and track inequalities in health outcomes. However, presentations and discussions at the International Conference on Urban Health (ICUH) 2017 suggested that additional indicators are needed to make decisions and policies. A local decision-maker may realize that malaria or road accidents are a top priority. However, s/he needs additional health determinant indicators, for example about standing water or traffic, to address the priority and reduce inequalities. Health determinants reflect the physical and social environments that influence health outcomes often at community- and societal-levels and include such indicators as access to quality health facilities, access to safe parks, traffic density, location of slum areas, air pollution, social exclusion, and social networks. Indicator identification and disaggregation are necessarily constrained by available datasets – typically collected about households and individuals in surveys, censuses, and administrative records. Continued advancements in earth observation, data storage, computing and mobile technologies mean that new sources of health determinants indicators derived from 'big data' are becoming available at fine geographic scale. Big data includes high-resolution satellite imagery and aggregated, anonymized mobile phone data. While big data are themselves not representative of the population (e.g., satellite images depict the physical environment), they can provide information about population density, wealth, mobility, and social environments with tremendous detail and accuracy when combined with population-representative survey, census, administrative and health system data. The aim of this paper is to (1) flag to data scientists important indicators needed by health decision-makers at the city and sub-city scale - ideally free and publicly available, and (2) summarize for local decision-makers new datasets that can be generated from big data, with layperson descriptions of difficulties in generating them. We include SDGs and Urban HEART indicators, as well as indicators mentioned by decision-makers attending ICUH 2017.

Keywords: health determinant, health outcome, mobile phone, remote sensing, satellite imagery, SDG, urban HEART

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Authors: Dimitrios J. Dimitriou

Abstract:

The decades after the end of the second War provide evidence that infrastructures investments contibute to economic development, on terms of productivity and income growth. In order to force productivity and increase competitiveness the financing of large transport infrastructure projects are on the top of the agenda in strategic planning process. Such a decision may take form some days to some decades and stakeholders as well as decision makers need tools in order to estimate the economic impact on natioanl economy of such an investment. The key question in such decisions is if the effects caused by the new infrastructure could be able to boost economic development on one hand, and create new jobs and activities on the other. This paper deals with the review of estimation of the mega transport infrastructure projects economic effects in economy.

Keywords: economic impact, transport infrastructure, strategic planning, decision making

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Authors: Mehran Asadi, Afrand Agah

Abstract:

In data mining, partitioning is used as a fundamental tool for classification. With the help of partitioning, we study the structure of data, which allows us to envision decision rules, which can be applied to classification trees. In this research, we used online social network dataset and all of its attributes (e.g., Node features, labels, etc.) to determine what constitutes an above average chance of being a divergent node. We used the R statistical computing language to conduct the analyses in this report. The data were found on the UC Irvine Machine Learning Repository. This research introduces the basic concepts of classification in online social networks. In this work, we utilize overfitting and describe different approaches for evaluation and performance comparison of different classification methods. In classification, the main objective is to categorize different items and assign them into different groups based on their properties and similarities. In data mining, recursive partitioning is being utilized to probe the structure of a data set, which allow us to envision decision rules and apply them to classify data into several groups. Estimating densities is hard, especially in high dimensions, with limited data. Of course, we do not know the densities, but we could estimate them using classical techniques. First, we calculated the correlation matrix of the dataset to see if any predictors are highly correlated with one another. By calculating the correlation coefficients for the predictor variables, we see that density is strongly correlated with transitivity. We initialized a data frame to easily compare the quality of the result classification methods and utilized decision trees (with k-fold cross validation to prune the tree). The method performed on this dataset is decision trees. Decision tree is a non-parametric classification method, which uses a set of rules to predict that each observation belongs to the most commonly occurring class label of the training data. Our method aggregates many decision trees to create an optimized model that is not susceptible to overfitting. When using a decision tree, however, it is important to use cross-validation to prune the tree in order to narrow it down to the most important variables.

Keywords: online social networks, data mining, social cloud computing, interaction and collaboration

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Authors: Hyun-Woo Cho

Abstract:

Detection of incipient abnormal events in production processes is important to improve safety and reliability of manufacturing operations and reduce losses caused by failures. The construction of calibration models for predicting faulty conditions is quite essential in making decisions on when to perform preventive maintenance. This paper presents a multivariate calibration monitoring approach based on the statistical analysis of process measurement data. The calibration model is used to predict faulty conditions from historical reference data. This approach utilizes variable selection techniques, and the predictive performance of several prediction methods are evaluated using real data. The results shows that the calibration model based on supervised probabilistic model yielded best performance in this work. By adopting a proper variable selection scheme in calibration models, the prediction performance can be improved by excluding non-informative variables from their model building steps.

Keywords: calibration model, monitoring, quality improvement, feature selection

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Authors: S. Valarmathi, P. B. Harathi, R. Sridhar, S. Balasubramanian

Abstract:

Machine learning tools in medical diagnosis is increasing due to the improved effectiveness of classification and recognition systems to help medical experts in diagnosing breast cancer. In this study, ID3 chooses the splitting attribute with the highest gain in information, where gain is defined as the difference between before the split versus after the split. It is applied for age, location, taluk, stage, year, period, martial status, treatment, heredity, sex, and habitat against Very Serious (VS), Very Serious Moderate (VSM), Serious (S) and Not Serious (NS) to calculate the gain of information. The ranked histogram gives the gain of each field for the breast cancer data. The doctors use TNM staging which will decide the risk level of the breast cancer and play an important decision making field in fuzzy logic for perception based measurement. Spatial risk area (taluk) of the breast cancer is calculated. Result clearly states that Coimbatore (North and South) was found to be risk region to the breast cancer than other areas at 20% criteria. Weighted value of taluk was compared with criterion value and integrated with Map Object to visualize the results. ID3 algorithm shows the high breast cancer risk regions in the study area. The study has outlined, discussed and resolved the algorithms, techniques / methods adopted through soft computing methodology like ID3 algorithm for prognostic decision making in the seriousness of the breast cancer.

Keywords: ID3 algorithm, breast cancer, fuzzy logic, MATLAB

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Authors: Sakapas Saengchai

Abstract:

This paper studied the participation of juvenile with driven of tobacco control in education institute: case study of Suan Sunandha Rajabhat University is qualitative research has objective to study participation of juvenile with driven of tobacco control in University, as guidance of development participation of juvenile with driven of tobacco control in education institute the university is also free-cigarette university. There are qualitative researches on collection data of participation observation, in-depth interview of group conversation and agent of student in each faculty and college and exchange opinion of student. Result of study found that participation in tobacco control has 3 parts; 1) Participation in campaign of tobacco control, 2) Academic training and activity of free-cigarette of university and 3) As model of juvenile in tobacco control. For guidelines on youth involvement in driven tobacco control is universities should promote tobacco control activities. Reduce smoking campaign continues include a specific area for smokers has living room as sign clearly, staying in the faculty / college and developing network of model students who are non-smoking. This is a key role in the coordination of university students driving to the free cigarette university. Including the strengthening of community in the area and outside the area as good social and quality of country.

Keywords: participation, juvenile, tobacco control, institute

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Authors: Asmae Chraibi, Rachid Lghoul, Nabil Rhiati

Abstract:

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

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

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Authors: Anna Guitart Atienza, Ana Fernández del Río, Madhav Nekkar, Jelena Ljubicic, África Periáñez, Eura Shin, Lauren Bellhouse

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The use of digital devices in healthcare or mobile health (mHealth) has increased in recent years due to the advances in digital technology, making it possible to nudge healthy behaviors through individual interventions. In addition, mHealth is becoming essential in poor-resource settings due to the widespread use of smartphones in areas where access to professional healthcare is limited. In this work, we evaluate mHealth interventions in low-income countries with a focus on causal inference. Counterfactuals estimation and other causal computations are key to determining intervention success and assisting in empirical decision-making. Our main purpose is to personalize treatment recommendations and triage patients at the individual level in order to maximize the entire intervention's impact on the desired outcome. For this study, collected data includes mHealth individual logs from front-line healthcare workers, electronic health records (EHR), and external variables data such as environmental, demographic, and geolocation information.

Keywords: causal inference, mHealth, intervention, personalization

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Authors: Zahra Bakhtiari

Abstract:

This article explores the development and evolution of the concept of environmental justice, which has shifted from being dominated by white and middle-class individuals to a civil struggle by marginalized communities against environmental injustices. Environmental justice aims to achieve equity in decision-making and policy-making related to the environment. The concept of justice in this context includes four fundamental aspects: distribution, procedure, recognition, and capabilities. Recent scholars have attempted to broaden the concept of justice to include dimensions of participation, recognition, and capabilities. Focusing on all four dimensions of environmental justice is crucial for effective planning and policy-making to address environmental issues. Ignoring any of these aspects can lead to the failure of efforts and the waste of resources.

Keywords: environmental justice, distribution, procedure, recognition, capabilities

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Authors: Chiara Mansanta, Daniela Sani

Abstract:

This paper studies the concept of intrapreneurship discovery for innovation and technology development related to the manufacturing industries set up in the center of Italy, in Marche Region. The study underlined the key drivers of the innovation process and the main factors that influence innovation. Starting from a literature study on open innovation, this paper examines the role of human capital to support company’s development. The empirical part of the study is based on a survey to 151 manufacturing companies that represent the 34% of that universe at the regional level. The survey underlined the main KPI’s that influence companies in their decision processes; then tools for these decision processes are presented.

Keywords: business model, decision making, intrapreneurship discovery, standard methodology

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Authors: Mohan Bikram Thapa

Abstract:

Despite the successful implementation of community forestry program, a number of pros and cons have been raised on Terai community forestry in the case of lowland locally called Terai region of Nepal, which climatically belongs to tropical humid and possessed high-quality forests in terms of ecology and economy. The study aims to investigate the local pricing strategy of forest products and its impacts on equitable forest benefits sharing, the collection of community fund and carrying out livelihood improvement activities. The study was carried out on six community forests revealed that local people have substantially benefited from the community forests. However, being the region is heterogeneous by socio-economic conditions and forest resources have higher economic potential, the decision of low pricing strategy made by the local people have created inequality problems while sharing the forest benefits, and poorly contributed to community fund collection and consequently carrying out limited activities of livelihood improvement. The paper argued that the decision of low pricing strategy of forest products is counterproductive to promote the equitable benefit-sharing in the areas of heterogeneous socio-economic conditions with high-value forests. The low pricing strategy has been increasing accessibility of better off households at a higher rate than poor, as such households always have the higher affording capacity. It is also defective to increase the community fund and carry out activities of livelihood improvement effectively. The study concluded that unilateral decentralized forest policy and decision-making autonomy to the local people seems questionable unless their decision-making capacities are enriched sufficiently. Therefore, it is recommended that empowerments of decision-making capacity of local people and their respective institutions together with policy and program formulation are prerequisite for efficient and equitable community forest management and its long-term sustainability.

Keywords: benefit sharing, community forest, livelihood, pricing mechanism, Nepal

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Authors: Roman Graf, Sergiu Gordea, Heather M. Ryan

Abstract:

This paper presents an approach for the easy creation of an institutional risk profile for endangerment analysis of file formats. The main contribution of this work is the employment of data mining techniques to support risk factors set up with just the most important values that are important for a particular organisation. Subsequently, the risk profile employs fuzzy models and associated configurations for the file format metadata aggregator to support digital preservation experts with a semi-automatic estimation of endangerment level for file formats. Our goal is to make use of a domain expert knowledge base aggregated from a digital preservation survey in order to detect preservation risks for a particular institution. Another contribution is support for visualisation and analysis of risk factors for a requried dimension. The proposed methods improve the visibility of risk factor information and the quality of a digital preservation process. The presented approach is meant to facilitate decision making for the preservation of digital content in libraries and archives using domain expert knowledge and automatically aggregated file format metadata from linked open data sources. To facilitate decision-making, the aggregated information about the risk factors is presented as a multidimensional vector. The goal is to visualise particular dimensions of this vector for analysis by an expert. The sample risk profile calculation and the visualisation of some risk factor dimensions is presented in the evaluation section.

Keywords: digital information management, file format, endangerment analysis, fuzzy models

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Authors: Robin C. Ladwig

Abstract:

The future of work becomes less predictable, which requires increasing the adaptability of organizations to social and work changes. Society is transforming regarding gender identity in the sense that more people come forward to identify as trans and gender diverse (TGD). Organizations are ill-equipped to provide a safe and encouraging work environment by lacking inclusive organizational structures. The qualitative multi-method research about TGD inclusivity in the workplace explores the enablers and barriers for TGD individuals to satisfactory engage in the work environment and organizational culture. Furthermore, these TGD insights are analyzed about their organizational implications and awareness from a leadership and management perspective. The semi-structured online interviews with TGD individuals and the photo-elicit open-ended questionnaire addressed to leadership and management in diversity, career development, and human resources have been analyzed with a critical grounded theory approach. Findings demonstrated the significance of TGD voices, the support of leadership and management, as well as the synergy between voices and leadership. Hence, it indicates practical implications such as the revision of exclusive language used in policies, data collection, or communication and reconsideration of organizational decision-making by leaders to include TGD voices.

Keywords: future of work, occupational identity, organisational decision-making, trans and gender diverse identity

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Authors: Sulemana Ibrahim

Abstract:

Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Mojtaba Saeedinezhad, Sarah Yousefi

Abstract:

In this paper, we present a hybrid subspace approach for Time Delay Estimation (TDE) in multivariable systems. While several methods have been proposed for time delay estimation in SISO systems, delay estimation in MIMO systems were always a big challenge. In these systems the existing TDE methods have significant limitations because most of procedures are just based on system response estimation or correlation analysis. We introduce a new hybrid method for TDE in MIMO systems based on subspace identification and explicit output error method; and compare its performance with previously introduced procedures in presence of different noise levels and in a statistical manner. Then the best method is selected with multi objective decision making technique. It is shown that the performance of new approach is much better than the existing methods, even in low signal-to-noise conditions.

Keywords: system identification, time delay estimation, ARX, OE, merit ratio, multi variable decision making

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Authors: Cristian Matti, Valerie Fowles, Eva Enyedi, Piotr Pogorzelski

Abstract:

The system mapping process can be defined as a knowledge service where a team of facilitators, experts and practitioners facilitate a guided conversation, enable the exchange of information and support an iterative curation process. System mapping processes rely on science-based tools to introduce and simplify a variety of components and concepts of socio-technical systems through metaphors while facilitating an interactive dialogue process to enable the design of co-created maps. System maps work then as “artifacts” to provide information and focus the conversation into specific areas around the defined challenge and related decision-making process. Knowledge management facilitates the curation of that data gathered during the system mapping sessions through practices of documentation and subsequent knowledge co-production for which common practices from data science are applied to identify new patterns, hidden insights, recurrent loops and unexpected elements. This study presents empirical evidence on the application of these techniques to explore mechanisms by which visual tools provide guiding principles to portray system components, key variables and types of data through the lens of climate change. In addition, data science facilitates the structuring of elements that allow the analysis of layers of information through affinity and clustering analysis and, therefore, develop simple indicators for supporting the decision-making process. This paper addresses methodological and empirical elements on the horizontal learning process that integrate system mapping through visual tools, interpretation, cognitive transformation and analysis. The process is designed to introduce practitioners to simple iterative and inclusive processes that create actionable knowledge and enable a shared understanding of the system in which they are embedded.

Keywords: indicators, knowledge management, system mapping, visual tools

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Authors: Sonia Anand Knowlton

Abstract:

In many ways, judges can express their personality within and beyond their role as a judge. Judges can use their unique backgrounds and life experiences to inform their legal reasons and can also participate in certain extrajudicial activities outside of their role on the bench. For many judges, the line between the expression of this judicial personality, on the one hand, and the consequence of jeopardizing the public’s perception of their impartiality, on the other, is ambiguous if not wholly unclear. In the famous Canadian decision R v RDS, for instance, a Black judge who was hearing a case about police violence against a Black person was accused of being biased after she acknowledged that her community’s racial dynamics may have impacted the police’s conduct. Many within the legal community might find comfort in the belief that judges do not need to bring their ‘personality’ to the bench in order to uncover the law’s truths and impartially apply it. Indeed, and for a good reason, judges are often discouraged from allowing their personality to shine through in their role as a judge – because the expression of judicial personality can compromise the public perception of the impartiality of the administration of justice. This paper evaluates the theoretical constraints on the expression of judicial personality as a tool for legal decision-making and argues that judges from minority groups are held to a higher level of impartiality. Specifically, minority judges are disproportionately constrained from 1) using life experience to apply the law and 2) engaging in certain extrajudicial activities.

Keywords: judging, legal decision making, judicial personality, extrajudicial activities

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Authors: Anna Sikharulidze, Gia Sirbiladze

Abstract:

For the evaluation of unreliability levels of movement on the closed routes in the vehicle routing problem, the fuzzy operators family is constructed. The interactions between routing factors in extreme conditions on the roads are considered. A multi-criteria decision-making model (MCDM) is constructed. Constructed aggregations are based on the Choquet integral and the associated probability class of a fuzzy measure. Propositions on the correctness of the extension are proved. Connections between the operators and the compositions of dual triangular norms are described. The conjugate connections between the constructed operators are shown. Operators reflect interactions among all the combinations of the factors in the fuzzy MCDM process. Several variants of constructed operators are used in the decision-making problem regarding the assessment of unreliability and possibility levels of movement on closed routes.

Keywords: vehicle routing problem, associated probabilities of a fuzzy measure, choquet integral, fuzzy aggregation operator

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Authors: Zahra Abdolkarimi, Naser Zouri

Abstract:

Nowadays, there is unbelievable growing of Robots created a collection of complex and motivate subject in robotic and intellectual ornate, also it made a mechatronics style base of theoretical and technical way in Robocop. Additionally, robotics system recommended RoboCup factor as a provider of some standardization and testing method in case of computer discussion widely. The actual purpose of RoboCup is creating independent team of robots in 2050 based of FiFa roles to bring the victory in compare of world star team. In addition, decision making of robots depends to environment reaction, self-player and rival player with using inductive Fuzzy system valuation subsidiary to solve issue of robots in land game. The measure of selection in compare with other methods depends to amount of victories percentage in the same team that plays accidently. Consequences, shows method of our discussion is the best way for Particle Swarm Optimization and Fuzzy system compare to other decision of robotics algorithmic.

Keywords: PSO algorithm, inference fuzzy system, chaos theory, soccer robot league

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