Search Results (68)

Distance learning has become essential to higher education, yet its application in military officer training presents unique academic, operational, and security challenges. For Lithuania’s future officers, remote education must foster not only knowledge acquisition but also decision-making, leadership, and operational readiness—competencies traditionally developed in immersive, in-person environments. This study addresses these challenges by integrating System Dynamics Modelling, Contemporary Risk Management Standards (ISO 31000:2022; Dynamic Risk Management Framework), and Learning Analytics to evaluate the interdependencies among twelve critical factors influencing the system resilience and effectiveness of distance military education. Data were collected from fifteen domain experts through structured pairwise influence assessments, applying the fuzzy DEMATEL method to map causal relationships between criteria. Results identified key causal drivers such as Feedback Loop Effectiveness, Scenario Simulation Capability, and Predictive Intervention Effectiveness, which most strongly influence downstream outcomes like learner engagement, risk identification, and instructional adaptability. These findings emphasize the strategic importance of upstream feedback, proactive risk planning, and advanced analytics in enhancing operational readiness. By bridging theoretical modelling, contemporary risk governance, and advanced learning analytics, this study offers a scalable framework for decision-making in complex, high-stakes education systems. The causal relationships revealed here provide a blueprint not only for optimizing military distance education but also for enhancing overall system resilience and adaptability in other critical domains. Full article

Smart Mobile Devices (SMD)—including hardware devices, such as smartphones, tablets, and wearables; the software systems that animate them; and the data-communication infrastructure that connects them—pose increasing sustainability challenges due to their short lifespans, high resource demands, and growing e-waste. While Sustainable Product–Service Systems (S.PSS) have been applied in various sectors to support environmental goals, limited research has addressed their application in the context of SMD. This study aims to explore how S.PSS can be tailored to support sustainability in the SMD sector. For that, it combines a literature review with a multiple-case analysis of seventeen commercial offerings to develop a conceptual framework refined through six expert interviews. Cases were coded using the classical PSS typology and other sector-specific criteria and subsequently clustered in a polarity diagram to identify designable patterns, underpinning the conceptual framework. The study contributes an S.PSS-SMD framework comprising a sector-tailored classification and sixteen archetypal models, operationalized in an archetypal map with potential opportunities. Theoretically, the study offers a sector-grounded operationalization that extends S.PSS design theory to digital product–service ecosystems. It provides a strategic decision aid for designing business models, service bundles, stakeholder roles, and lifecycle responsibilities to pursue win–win environmental and economic sustainability. Full article

This study explores the integration of Business Intelligence (BI) and Operations Research (OR) as a driver of sustainability within the evolving framework of Logistics 4.0. As logistics systems face pressures from environmental regulations, digital transformation, and stakeholder expectations, the intersection of data analytics and optimization emerges as a critical lever for sustainable operations. Grounded in a Delphi study conducted in a Portuguese logistics firm, this research captures expert consensus across five dimensions of BI implementation: data infrastructure, real-time decision-making, operational transparency, stakeholder coordination, and sustainability performance monitoring. Methodologically, this study employed two iterative Delphi rounds with 61 cross-functional professionals directly engaged with the organization’s BI systems, particularly Microsoft Power BI. Findings indicate that integrating BI with OR models enhances organizational capacity for proactive scenario planning, carbon footprint reduction, and ESG-aligned decision-making. The results also underscore the importance of cross-departmental collaboration, data governance maturity, and user training in fully leveraging BI for sustainable value creation. By providing both theoretical insights and practical guidance, this study advances the emerging discourse on data-driven sustainability in logistics. It offers actionable insights for logistics managers, sustainability strategists, and policymakers seeking to operationalize digital sustainability and embed intelligence-driven approaches into resilient, low-carbon supply chains. Full article

Healthcare systems worldwide face growing challenges related to staff shortages, excessive workload, and deteriorating working conditions, which compromise both staff well-being and care quality. Despite these issues, there is a lack of validated tools that capture healthcare professionals’ subjective perceptions of resource adequacy. This study presents the development and initial validation of the Staff Resource Adequacy Questionnaire for Healthcare Professionals (SRAQ-HP), a multidimensional tool designed to assess staffing adequacy and workload, quality of care, and working conditions and support. The development process followed a mixed-methods design, incorporating theoretical foundations from Kanter’s empowerment theory, role enactment models, and professional competence frameworks. The initial item pool of 32 statements was reduced to 26 through expert reviews, focus groups, and pilot testing (n = 35). Content validity index (CVI = 0.931) and face validity index (FVI = 0.976) demonstrated high content relevance and clarity. Cronbach’s alpha for the full scale was 0.841, confirming internal consistency. Expert re-review confirmed strong content (S-CVI/Ave = 0.931) and face validity (FVI = 0.976) for the final 26-item version. Three core dimensions were retained: Staffing Adequacy and Workload, Quality of Care, and Working Conditions and Support. The SRAQ-HP provides a novel, evidence-based approach to systematically assess workforce sufficiency and support structures in clinical settings. It can guide decision-making in healthcare institutions and inform national workforce policies. Further research with larger and more diverse samples is needed to confirm its factorial validity and practical applicability. Full article

The swift growth of the low-altitude economic ecosystem and Unmanned Aerial Vehicle (UAV) swarm applications across diverse sectors presents significant challenges for engineering managers in terms of effective performance evaluation and operational optimization. Traditional evaluation methods often struggle with the inherent complexities, dynamic nature, and multi-faceted performance criteria of UAV swarms. This study introduces a novel Bayesian Network (BN)-based multicriteria decision-making framework that systematically integrates expert intuition with real-time data. By employing variance decomposition, the framework establishes theoretically grounded, bidirectional mapping between expert-assigned weights and the network’s probabilistic parameters, creating a unified model of subjective expertise and objective data. Comprehensive validation demonstrates the framework’s efficacy in identifying critical performance drivers, including environmental awareness, communication ability, and a collaborative decision. Ultimately, our work provides engineering managers with a transparent and adaptive tool, offering actionable insights to inform resource allocation, guide technology adoption, and enhance the overall operational effectiveness of complex UAV swarm systems. Full article

The reliability of decision-making algorithms within soft set theory is fundamentally constrained by their underlying membership functions. Traditional binary approaches overlook the implicit connections between the attributes a candidate possesses and those it lacks—connections that can be inferred from the wider candidate pool. To address this core challenge, this paper puts forward the Relational Membership Value Calculation (RMVC), an algorithmic framework whose core is a fine-grained relational membership function. Our approach moves beyond binary logic to capture these nuanced interrelationships. We provide a rigorous theoretical analysis of the proposed algorithm, including its computational complexity and robustness, which is validated through a comprehensive sensitivity analysis. Crucially, a comparative analysis using the Gini Index quantitatively demonstrates that our method provides significantly higher granularity and discriminatory power on a representative case study. The RMVC is implemented as an open-source Python program, providing a foundational tool to enhance the reasoning capabilities of AI-driven decision support and expert systems. Full article

Existing risk assessment approaches for soft rock tunnels in fault-fractured zones typically employ single weighting schemes, inadequately integrate subjective and objective weights, and fail to define clear risk. This study proposes a risk-grading methodology that integrates an enhanced game theoretic weight-balancing algorithm with an optimized cloud entropy extension cloud model. Initially, a comprehensive indicator system encompassing geological (surrounding rock grade, groundwater conditions, fault thickness, dip, and strike), design (excavation cross-section shape, excavation span, and tunnel cross-sectional area), and support (support stiffness, support installation timing, and construction step length) parameters is established. Subjective weights obtained via the analytic hierarchy process (AHP) are combined with objective weights calculated using the entropy, coefficient of variation, and CRITIC methods and subsequently balanced through a game theoretic approach to mitigate bias and reconcile expert judgment with data objectivity. Subsequently, the optimized cloud entropy extension cloud algorithm quantifies the fuzzy relationships between indicators and risk levels, yielding a cloud association evaluation matrix for precise classification. A case study of a representative soft rock tunnel in a fault-fractured zone validates this method’s enhanced accuracy, stability, and rationality, offering a robust tool for risk management and design decision making in complex geological settings. Full article

A public transportation system consists of complex processes and requires comprehensive planning activities for a city when dealing with the travel behavior decisions of passengers. Travel behavior decisions are important in selecting suitable transportation alternatives for passengers. In the literature, little attention has been paid to prioritizing the criteria and ranking the alternatives for assessing sustainable and traditional public transportation modes when considering the travel behavior decisions of passengers. In this paper, a five-phased novel decision analysis framework, including Analytic Hierarchy Process (AHP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), and VIekriterijumsko KOmpromisno Rangiranje (VIKOR) techniques, is proposed to evaluate the alternatives. In addition, to the best of our knowledge, the novel decision-making framework in this paper has not been employed before to assess sustainable transportation alternatives dealing with the travel behavior decisions of passengers. Next, the thirteen criteria are specified, including economics, safety, travel quality, and environmental and health aspects, to analyze the travel behavior decisions of passengers with regard to the experts’ notions, published reports, and papers. Then, the seven public transportation alternatives are determined, including sustainable and traditional transportation modes. A case study was carried out in Istanbul, Türkiye. Based on the results, service frequency, the vehicle type and its mechanism, and ease of accessibility were found to be the top three significant criteria that affect travel behavior decisions. Furthermore, metro, Marmaray, and metrobus are the top three public transportation alternatives. In addition, the results were verified. Moreover, managerial and theoretical recommendations are provided to policymakers. Lastly, sustainable development goals 11.2 and 11.b can be achieved by designing an accessible, affordable, environmentally friendly, safe, and sustainable public transportation system when analyzing the travel behavior decisions of passengers. Full article

With the advent of Big Data, data mining techniques have become crucial for improving decision-making across diverse sectors, yet their employment demands significant resources and time. Time is critical in industrial contexts, as delays can lead to increased costs, missed opportunities, and reduced competitive advantage. To address this, systems for analyzing data can help prototype data mining pipelines, mitigating the risks of failure and resource wastage, especially when experimenting with novel techniques. Moreover, business experts often lack deep technical expertise and need robust support to validate their pipeline designs quickly. This paper presents Rainfall, a novel framework for rapidly prototyping data mining pipelines, developed through collaborative projects with industry. The framework’s requirements stem from a combination of literature review findings, iterative industry engagement, and analysis of existing tools. Rainfall enables the visual programming, execution, monitoring, and management of data mining pipelines, lowering the barrier for non-technical users. Pipelines are composed of configurable nodes that encapsulate functionalities from popular libraries or custom user-defined code, fostering experimentation. The framework is evaluated through a case study and SWOT analysis with INGKA, a large-scale industry partner, alongside usability testing with real users and validation against scenarios from the literature. The paper then underscores the value of industry–academia collaboration in bridging theoretical innovation with practical application. Full article

Despite advancements in divergence and distance measures across fuzzy set extensions, the development of such measures for Globular T-Spherical Fuzzy Sets (G-TSFSs) remains significantly unexplored. Existing approaches often fall short in capturing the rich semantics and high-dimensional uncertainty that G-TSFSs represent, limiting their utility in complex decision environments. This study is motivated by the need to fill this critical gap and advance decision science through more expressive and structurally aligned tools. This paper introduces a suite of novel divergence measures (Div-Ms) specifically formulated for G-TSFSs, a powerful tool for capturing uncertainty in multi-criteria group decision-making (MCGDM) under complex conditions. These Div-Ms serve as the foundation for developing new distance measures (Dis-Ms) and similarity measures (SMs), where both Dis-Ms and SMs are symmetry-based and their essential mathematical properties and supporting theorems are rigorously established. Leveraging these constructs, we propose a robust G-TSF-TOPSIS framework and apply it to a real-world problem, selecting optimal solar energy systems (SESs) for a university context. The model integrates expert evaluations, assuming equal importance due to their pivotal and complementary roles. A sensitivity analysis over the tunable parameter (ranging from 4.0 to 5.0 with an increment of 0.2) confirms the robustness and stability of the decision outcomes, with no changes observed in the final rankings. Comparative analysis with existing models shows superiority and soundness of the proposed methods. These results underscore the practical significance and theoretical soundness of the proposed approach. The study concludes by acknowledging its limitations and suggesting directions for future research, particularly in exploring adaptive expert weighting strategies for broader applicability. Full article

Financial time series in volatile markets often exhibit non-stationary behavior and signatures of stochastic chaos, challenging traditional forecasting methods based on stationarity assumptions. In this paper, we introduce a novel multi-expert forecasting system (MES) that leverages ensemble machine learning techniques—including bagging, boosting, and stacking—to enhance prediction accuracy and support robust risk management decisions. The proposed framework integrates diverse “weak learner” models, ranging from linear extrapolation and multidimensional regression to sentiment-based text analytics, into a unified decision-making architecture. Each expert is designed to capture distinct aspects of the underlying market dynamics, while the supervisory module aggregates their outputs using adaptive weighting schemes that account for evolving error characteristics. Empirical evaluations using high-frequency currency data, notably for the EUR/USD pair, demonstrate that the ensemble approach significantly improves forecast reliability, as evidenced by higher winning probabilities and better net trading results compared to individual forecasting models. These findings contribute both to the theoretical understanding of ensemble forecasting under chaotic market conditions and to its practical application in financial risk management, offering a reproducible methodology for managing uncertainty in highly dynamic environments. Full article

This study developed an integrated framework to enhance agility, resilience, sustainability, and inclusiveness in Emirati public transport organizations. Using a mixed-methods approach, the research combined semi-structured interviews with 19 experts and a structured questionnaire administered to 38 specialists. The DEMATEL method was applied to analyze and visualize the interdependencies among key factors influencing transport system performance. Results indicate that operational efficiency, demand–supply forecasting, and ridership estimation are central to agility; green migration strategies, governance, and service design drive resilience; service diversity, technology, and infrastructure adequacy underpin sustainability; and service level types and seamless transfers are critical to inclusiveness. These dimensions were synthesized into a cohesive model that captures both strategic alignment and system adaptability. The study contributes a validated, multi-dimensional decision-making tool for policymakers and transport authorities, offering practical guidance for aligning transport strategies with national goals and the UN Sustainable Development Goals. While tailored to the UAE context, the framework is adaptable to other urban environments undergoing rapid transformation. While tailored to the UAE context, the framework is adaptable to other urban environments undergoing rapid transformation. The study’s empirical rigor is established through a validated questionnaire and expert-based DEMATEL analysis, ensuring theoretical robustness and real-world applicability. Full article

The environmental quality of conference and exhibition places in small- and medium-sized cities plays a crucial role in attracting exhibitors, fostering the growth of the conference and exhibition industry and enhancing the market competitiveness of these places. However, past decision makers have often adopted planning models from large cities, neglecting the interaction between conference and exhibition places in smaller cities and local lifestyles as well as urban environments. From an “environment-behavior” perspective, this study reveals the unique interaction mechanisms between exhibitors and the built environment within such venues. Moving beyond the limitations of traditional research that focused solely on physical indicators, we place particular emphasis on exhibitors’ behavioral adaptations and their overall exhibition experience in the convention environment. To address this gap, this study employs a mixed-method approach that integrates field surveys, interviews, and questionnaires to systematically collect data from 10 representative cases. First, a preliminary study was conducted to establish an evaluation index system for place environmental quality. Through regression analysis, six key indicators—such as promotional atmosphere, site accessibility, and surrounding urban development conditions—were identified as significant factors influencing place quality. Second, subjective evaluations were conducted based on users’ actual experiences and experts’ professional insights, leading to the development of an importance–performance analysis model to assess value expectations and place environmental performance. The results indicated that users had high expectations for elements such as parking availability, transportation facilities, and the surrounding commercial atmosphere. In contrast, experts emphasized the significance of proximity to urban transportation hubs, site accessibility, and the spatial orientation of public spaces in determining environmental quality. Moreover, differences in evaluations among experts from various fields revealed notable variations in focus and priority considerations. Finally, based on a statistical analysis of the survey results, this study proposes three design recommendations—“adaptation, attraction, and quality enhancement”—to optimize the environmental quality of conference and exhibition places in small- and medium-sized cities, offering both theoretical and practical guidance for future planning, design, and evaluation. Full article

To address the challenges of assessing information security risks in smart energy systems, this study proposes a multi-attribute decision support method based on interval type-2 fuzzy numbers (IT2TrFN). First, expert questionnaires were designed to gather insights from eight specialists in the fields of smart energy and safety engineering. Linguistic terms associated with IT2TrFN were employed to evaluate indicators, converting expert judgments into fuzzy numerical values while ensuring data reliability through consistency measurements. Subsequently, a decision hierarchy structure and an expert weight allocation model were developed. By utilizing the score and accuracy functions of IT2TrFN, the study determined positive and negative ideal solutions to rank and prioritize the evaluation criteria. Key influencing factors identified include the rate of excessive initial investment, regulatory stringency, information security standards, environmental pollution pressure, and incident response timeliness. The overall risk index was calculated as 0.5839, indicating a moderate level of information security risk in the evaluated region. To validate the robustness of the model, sensitivity analyses were conducted by varying IT2FWA (Weighted aggregated operator) and IT2FGA (Weighted geometric operator) operator selections and adjusting weight coefficients. The results reveal that key indicators exhibit high risk under different scenarios. This method provides an innovative tool for the scientific evaluation of information security risks in smart energy systems, laying a solid theoretical foundation for broader regional applications and the expansion of assessment criteria. Full article

Rural domestic sewage management is a crucial pathway for achieving Sustainable Development Goal (SDG) 6 targets. Addressing the crucial challenge of prioritizing administrative villages for rural domestic sewage treatment at the county scale requires dedicated planning. However, county-level comprehensive evaluation models designed specifically for this purpose are currently limited. To address this gap, we developed a model based on 13 evaluation indicators encompassing village distribution characteristics, villager demographics, rural economic levels, and sanitation facility conditions. To gauge the varying emphasis on these factors by different groups, a questionnaire survey was conducted among experts, enterprises, and government departments involved in the rural sewage sector in China. Two counties from distinct regions were then chosen to validate these models. The Analytic Hierarchy Process (AHP) coupled with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method was employed to rank the importance of the factors and determine the prioritization of rural domestic sewage management in each area. The model results indicated that priority should be given to the county government, township government, ecologically sensitive areas, and administrative villages near tourist attractions in the two selected empirical counties for governance. A sensitivity analysis showed that altitude consistently exhibited high sensitivity in influencing the ranking results across all scenarios (0.4–0.6). In addition, the empirical results obtained were largely consistent with the priorities of local governments. The proposed framework offers a practical application for decision-making systems in rural domestic sewage management at the county level, providing theoretical support and scientific strategies. This holds great significance for achieving SDG 6. Full article