Search Results (524)

Cybersecurity is increasingly recognized as a key enabler of Sustainable Development Goals (SDGs) and especially SDG 3 (Good Health and Well-being) as healthcare systems become more digitized. This study prioritizes cybersecurity control families from the NIST 800-53r5 framework using a structured framework combining the Analytic Hierarchy Process (AHP) and the Impact–Feasibility Matrix. From the impact–feasibility perspective, expert judgment reveals that while impact is the primary driver in selecting controls, feasibility—particularly budget and cost constraints—plays a decisive role in real-world implementation. A group of fifteen experts, including cybersecurity officers, health IT professionals, and public health advisors, has participated in structured surveys as per the methodological framework of this paper. Financial and budgetary limitations emerged as the top feasibility barrier, often determining whether high-impact controls are deployed or delayed. This underscores the need for strategic investments and phased implementation approaches, particularly in resource-constrained health systems. The results provide a practical roadmap for policymakers and healthcare administrators to allocate cybersecurity resources effectively, balancing technical necessity with economic feasibility to support resilient digital health infrastructures. Full article

Professional certification of engineers plays a crucial role in verifying competencies and ensuring the safety and quality of engineering outputs. However, most existing certification systems assign fixed validity periods (e.g., 3–5 years) without considering individual engineer characteristics or the intensity of technological progress in specific fields. This study examines the key factors influencing the optimal validity period of engineering certifications and proposes it as a measurable indicator to support safety in engineering practice. A new model is introduced that integrates expert judgment, fuzzy set theory, and bibliometric analysis of Q1/Q2 Scopus-indexed publications. The model incorporates three main factors: competence level, professional experience, and the technological intensity of the discipline. A case study from the engineering certification system of Kazakhstan demonstrates the model’s practical applicability. Certification bodies, policymakers, and engineering organizations can use these findings to establish more flexible certification validity periods, thereby ensuring timely reassessment of competencies and reducing safety risks. For example, for mechanical engineers, the optimal validity period is 3 years rather than the statutory 5 years; in other words, the model recommends a 40% reduction in certification validity. This reduction reflects the combined effects of competency level, professional experience, and technology intensity on certification renewal schedules. Overall, the proposed factorial approach supports a more personalized and safety-oriented certification process and offers insights into improving national qualification systems. Full article

Tactical production–distribution planning in paper manufacturing involves hierarchical decision-making under hybrid uncertainty, where aleatory randomness (demand fluctuations, machine variations) and epistemic uncertainty (expert judgments, market trends) simultaneously affect operations. Existing approaches fail to address the bi-level nature under hybrid uncertainty, treating production and distribution decisions independently or using single-paradigm uncertainty models. This research develops a bi-level dependent-chance goal programming framework based on uncertain random theory, where the upper level optimizes distribution decisions while the lower level handles production decisions. The framework exploits structural symmetries through machine interchangeability, symmetric transportation routes, and temporal symmetry, incorporating symmetry-breaking constraints to eliminate redundant solutions. A hybrid intelligent algorithm (HIA) integrates uncertain random simulation with a Reinforcement-Learning-enhanced Arithmetic Optimization Algorithm (RL-AOA) for bi-level coordination, where Q-learning enables adaptive parameter tuning. The RL component utilizes symmetric state representations to maintain solution quality across symmetric transformations. Computational experiments demonstrate HIA’s superiority over standard metaheuristics, achieving 3.2–7.8% solution quality improvement and 18.5% computational time reduction. Symmetry exploitation reduces search space by approximately 35%. The framework provides probability-based performance metrics with optimal confidence levels (0.82–0.87), offering 2.8–4.5% annual cost savings potential. Full article

Dendrobium officinale is an important medicinal and edible plant in China, widely used in the dietary health industry and pharmaceutical field. Due to the different geographical origins and cultivation methods, the nutritional value, medicinal quality, and price of Dendrobium are significantly different, and accurate identification of the origin is crucial. Current origin identification relies on expert judgment or requires costly instruments, lacking an efficient solution. This study proposes a Variational Inference-enabled Data-Efficient Learning (VIDE) model for high-precision, non-destructive origin identification using a small number of image samples. VIDE integrates dual probabilistic networks: a prior network generating latent feature prototypes and a posterior network employing variational inference to model feature distributions via mean and variance estimators. This synergistic design enhances intra-class feature diversity while maximizing inter-class separability, achieving robust classification with limited samples. Experiments on a self-built dataset of Dendrobium officinale samples from six major Chinese regions show the VIDE model achieves 91.51% precision, 92.63% recall, and 92.07% F1-score, outperforming state-of-the-art models. The study offers a practical solution for geographical origin identification and advances intelligent quality assessment in Dendrobium officinale. Full article

The global expansion of Korean language education has created an urgent need for scalable, objective, and consistent methods for assessing the writing skills of non-native (L2) learners. Traditional manual grading is resource-intensive and prone to subjectivity, while existing Automated Essay Scoring (AES) systems often struggle with the linguistic nuances of Korean and the specific error patterns of L2 writers. This paper introduces a novel hybrid AES system designed specifically for Korean L2 writing. The system integrates two complementary feature sets: (1) a comprehensive suite of conventional linguistic features capturing lexical diversity, syntactic complexity, and readability to assess writing form and (2) a novel semantic relevance feature that evaluates writing content. This semantic feature is derived by calculating the cosine similarity between a student’s essay and an ideal, high-proficiency reference answer generated by a Large Language Model (LLM). Various machine learning models are trained on the Korean Language Learner Corpus from the National Institute of the Korean Language to predict a holistic score on the 6-level Test of Proficiency in Korean (TOPIK) scale. The proposed hybrid system demonstrates superior performance compared to baseline models that rely on either linguistic or semantic features alone. The integration of the LLM-based semantic feature provides a significant improvement in scoring accuracy, more closely aligning the automated assessment with human expert judgments. By systematically combining measures of linguistic form and semantic content, this hybrid approach provides a more holistic and accurate assessment of Korean L2 writing proficiency. The system represents a practical and effective tool for supporting large-scale language education and assessment, aligning with the need for advanced AI-driven educational technology systems. Full article

Background: Cities worldwide face continuous challenges in sustainable development, particularly in transportation systems where decisions have long-lasting impacts on urban infrastructure and quality of life. The evaluation of sustainable transportation alternatives requires careful consideration of multiple indicators, making it a complex multi-criteria decision-making process. Existing multi-criteria decision-making (MCDM) frameworks often overlook the dual uncertainties introduced by both fuzzy expert judgments and probabilistic performance measures, hindering robust evaluation of transportation alternatives in developing countries. Methods: In response, this study introduces a novel hybrid methodology combining fuzzy set theory and Monte Carlo simulation to evaluate transportation alternatives through 14 comprehensive sustainability indicators. Addressing the critical need for sustainable public transportation assessment in rapidly urbanizing developing countries, where existing assessment frameworks frequently prove inadequate, we present a case study from Amman, Jordan. Results: The results reveal that a Bus Rapid Transit (BRT) system outperforms both conventional automobiles and small buses in 87.06% of simulation scenarios, underscoring its robust sustainability profile. The sensitivity analysis highlights that a BRT system is highly robust, with minimal sensitivity to changes in most criteria and strong responsiveness to critical factors such as land usage. Conclusions: This research provides decision-makers with a comprehensive, evidence-based tool for evaluating public transport investment under uncertainty. The methodology’s ability to account for multiple stakeholder perspectives while handling uncertainty makes it particularly valuable for urban planners and policymakers facing complex transportation infrastructure decisions in rapidly evolving urban environments. Full article

Multicomponent concrete is a widely used industrial material, yet its performance evaluation still relies heavily on expert judgment and long-term monitoring. With the rapid development of artificial intelligence (AI), machine learning has emerged as a promising tool in building science for analyzing complex datasets and reducing uncertainties associated with human factors. This study applies a variety of machine learning techniques—including linear and polynomial regressions, tree-based algorithms (Decision Tree, Random Forest, ExtraTrees, AdaBoost, CatBoost, and XGBoost), and the TabPFN model—to investigate the key factors influencing concrete compressive strength. To enhance interpretability, SHAP analysis was employed to uncover feature importance and interactions, offering new insights into the underlying mechanisms of multicomponent concrete. The findings provide a data-driven approach to support engineering design, facilitate decision-making in construction practice, and contribute to the development of more efficient and sustainable building materials. Full article

Vessel arrival priority determination is a critical factor affecting port safety and efficiency in maritime traffic management, yet existing approaches relying on First Come, First Served (FCFS) principles or empirical judgment have limitations in systematic decision-making. This study aims to develop a systematic decision-making framework that overcomes these limitations by creating an automated, expert knowledge-based priority determination system for vessel traffic services. A dynamic score-based vessel arrival priority determination model was developed integrating the Delphi technique and Fuzzy Analytic Hierarchy Process (Fuzzy AHP). Basic score evaluation factors were derived through Delphi surveys conducted with 50 field experts, and weights were calculated by differentially applying Fuzzy AHP and conventional AHP according to hierarchical complexity. The proposed model consists of a dynamic scoring system integrating basic scores reflecting vessel characteristics and operational conditions, special situation scores considering emergency situations, and risk scores quantifying safety intervals between vessels. To validate the model performance, simulation-based evaluation with eight scenarios was conducted targeting experienced VTS (Vessel Traffic Services) officers, demonstrating strong agreement with expert judgment across diverse operational conditions. The developed algorithm processes real-time maritime traffic data to dynamically calculate priorities, providing port managers and maritime authorities with an automated decision support tool that enhances VTS management and coastal traffic operations. Full article

This study addresses a key challenge in Additive Manufacturing (AM): while it promises sustainable production, manufacturing defects often lead to significant material and energy waste. The purpose of this research is to apply the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method to identify and map the cause-and-effect relationships among common AM defects. By doing this, the goal is to pinpoint the most influential ‘root’ causes, allowing for more targeted and effective quality improvements. The methodology is based on a qualitative approach using the expert judgment of a panel of six professionals. The DEMATEL analysis successfully sorted the defects into two categories: those that are primary causes and those that are symptoms or effects. The main findings show that contamination is the most significant causal factor, meaning that it strongly influences other defects. In contrast, dimensional inaccuracy is the most affected factor, acting as a symptom of other underlying issues. In conclusion, the study finds that focusing on mitigating root causes like contamination, warping, and porosity is crucial for achieving improvements across the process chain. This framework allows engineers to prioritize quality control efforts on the fundamental problems, rather than on superficial defects, thereby maximizing efficiency and waste reduction. Ultimately, this research provides a clear, actionable framework for improving quality control and promoting more sustainable manufacturing practices. Full article

Background: Improving operational efficiency in the mining industry increasingly de-pends on a mature asset management framework and the careful selection of reliable, sustainable suppliers for systems, personnel, equipment, and services. Given the complexity of mining operations and the growing use of digital tools, choosing the right maintenance management system requires a robust decision-making process that considers economic, environmental, and social sustainability factors. Methods: This study develops and compares two multi-criteria decision-making approaches, a ranking method and a fuzzy logic-based model to evaluate four maintenance management systems against fifteen sustainability-related criteria. Expert opinions from executives and operational managers in the South African mining sector were gathered, focusing on factors such as cost, integration, reliability, ease of use, inventory control, and predictive capabilities. Results: The ranking method produced a clear, quantitative order of preference, while the fuzzy model addressed uncertainty and subjectivity in expert judgments. Both methods identified the same top choice: UPKEEP, followed by SAP, FIIX, and LIMBLE. Conclusions: This comparison shows that combining fuzzy logic with sustainability-focused evaluation can improve the flexibility and reliability of supplier selection in asset management. The proposed approach offers practical guidance for aligning maintenance system choices with broader sustainability goals in mining operations. Full article

Boat collisions pose severe threats to maritime safety, economic activity, and environmental sustainability. Conventional risk assessment methods—such as Failure Mode and Effects Analysis, and Fault Tree Analysis—are widely applied but remain inadequate for addressing the uncertainty, subjectivity, and interdependency of risk factors in complex maritime environments. This study proposes a fuzzy Multi-Criteria Decision-Making framework for the risk assessment of boat collisions. The model integrates fuzzy logic with Analytic Hierarchy Process for criterion weighting and the Technique for Order Preference by Similarity to the Ideal Solution for risk ranking. Fuzzy logic is employed to capture linguistic expert judgments and to manage vague or incomplete data, which are common challenges in marine operations. Key collision risk factors—human error, boat engine system failure, environmental conditions, and intentional threats—are identified through literature review, incident data analysis, and expert consultation. A comparative analysis with a baseline non-fuzzy model demonstrates the added value of the fuzzy-integrated framework, showing improved capacity to handle imprecision and uncertainty. The model outputs not only prioritise risk rankings but also support the identification of critical control actions and effective safety measures. A case study of Nigerian waters illustrates the practicality of the framework in guiding risk mitigation strategies and informing policy decisions under uncertainty. Full article

Nowadays, one of the key areas of sustainable agriculture is increasing animal welfare. However, in the absence of generally accepted measurement criteria and systems, measuring animal welfare can be considered a subjective area that makes measuring animal welfare complex. As a result, both increasing welfare and making intervention decisions are not clear for farm management. In our research, we develop a fuzzy logic-based decision support system that is able to handle the subjectivity arising from determining animal welfare. During focus group interviews, experts pointed out that animal welfare assessment systems do not provide adequate support in decision-making. However, the integration of different benchmarks (past, best values and competitors) and the triangular membership functions assigned to them in the assessment significantly supports decision-making. The models were tested with data collected with the Welfare Quality Assessment System of three dairy farms (Austrian, Hungarian, and Slovak). In our result the models show different assessment results; therefore, an aggregate assessment model was created by aggregating the results of the models. The aggregate model incorporates the value judgments and importance of the different models by applying the Choquet integral, thereby providing a more accurate assessment according to the criteria that meet the expectations of decision-makers. Our research shows that animal welfare assessment systems should be based on fuzzy logic and the application of multi-criteria benchmarks until standards reduce the uncertainty in measuring animal welfare levels. Full article

Urban areas increasingly face heat-related climate risks, necessitating targeted, nature-based interventions such as tree planting to improve resilience, livability, and public health. This study presents a data-driven workflow to identify urban tree planting potential (TPP) in the city of Dortmund, Germany. The approach integrates high-resolution spatial datasets capturing land cover, shading, thermal comfort, population density, and critical infrastructure. All variables were harmonized within a 50 m hexagonal grid, normalized, and combined into a composite TPP score using weighting schemes informed by expert judgment and sensitivity testing. Spatial and non-spatial clustering were applied to group urban areas by shared characteristics, and a connectivity analysis evaluated the spatial coherence of high-potential cells and their relationship to existing green infrastructure. The findings demonstrate the potential to strengthen urban green infrastructure and guide coordinated planting strategies while addressing both ecological and social priorities. The presented workflow offers a flexible, transferable tool to support municipalities in prioritizing effective greening interventions and integrating climate adaptation objectives into urban development planning. Full article

Sea anchors for fishing operations are essential equipment to enhance catch efficiency and ensure operational stability at sea. However, previous studies have mainly focused on theoretical modeling or experiments under restricted conditions, which have not sufficiently reflected the complex operating environments and practical needs of real-world fisheries. To address this gap, this study derived key factors to improve the design and operation of sea anchors and quantitatively analyze the relative importance and rank of these factors. An expert panel was formed from 25 participants, including jigging vessel captains, recreational fishing boat captains, sea anchor manufacturers, and research institute workers. Using a three-round Delphi process followed by Analytic Hierarchy Process (AHP) analysis, we distilled an initial list of 52 improvement suggestions into 15 prioritized items, quantitatively ranked by relative importance based on expert consensus. The highest-ranked factor was ‘Enhancement of fabric drying performance’, followed by ‘Application of low-cost, high-efficiency materials’, ‘Improvement of recovery’, ‘Enhancement of UV resistance’, and ‘Product quality certification’. The highest-weighted metric was ‘Improvement of usability’, followed by ‘Enhanced durability’ and ‘Improvement of functionality’. The consistency ratio (CR) of the pairwise-comparison matrix was 0.0014 (AHP acceptability criterion: CR ≤ 0.1), confirming the reliability and consistency of the analysis. By reflecting real-world priorities through a robust and systematic analytical process, this study offers a foundation for evidence-based improvements in sea anchor design and operation, overcoming the limitations of earlier approaches rooted in subjective judgment or trial-and-error experience. Full article

The growing demand for child-friendly, growth-adaptive furniture necessitates the establishment of an evaluation framework that integrates user perception and modular design. This study has proposed a model framework that encompasses eight dimensions, including Growth adaptability, Safety, Organization, Warmth, Convenience, Emotionality, Learning support, and Modularity (GSOWCELM)—aimed at evaluating modular children’s cabinets from a user-perception-oriented, sustainability-focused perspective. The study uses a hybrid weighting method combining the Analytic Hierarchy Process (AHP) and Entropy Weighting Method (EWM) for evaluation based on expert judgment and Likert scale feedback from 20 parents of children aged 1 to 12. The results show that Emotionality, Learning support, Safety, and Growth adaptability are the issues of greatest concern to users. This marks a shift in design focus from traditional practical functionality to emotional resonance and developmental support. Based on priority indicators, the study further proposes a modular configuration strategy tailored to children’s ages. This research provides a replicable, perception-centered framework for evaluating and optimizing children’s furniture systems, contributing to the development of sustainable home environments and offering insights for designers, educators, and policymakers. Full article