Search Results (154)

With the designation of the first cohort of national parks and the continued operation of remaining pilots, China’s national park reform has entered a critical stage requiring consolidation and adaptive improvement. A key challenge lies in the ambiguous status of five pilot zones, which lack a standardized evaluation mechanism to guide decisions on future inclusion or exit. This study develops a comprehensive indicator system specifically tailored to assess the construction and development of national park pilots, thereby supporting evidence-based governance beyond initial entry criteria. Drawing on relevant theories and China’s institutional context, the framework employs Analytic Hierarchy Process, expert consultation, and fuzzy scoring to determine indicator weights and evaluation standards. The resulting system integrates three dimensions—ecological protection system, management system, and public service system. Nanshan National Park was selected as a case study, scoring 87.77 in 2024 (Class II, “Proficient”), with strong overall performance but notable weaknesses in landscape connectivity, recreational product diversity, and regional integration. These findings suggest the need for targeted improvements in ecological corridors, service enrichment, and community benefit-sharing. Overall, the proposed framework provides a replicable tool for evaluating pilot zones, offering practical insights for refining China’s national park development and enhancing governance effectiveness. Full article

In pigeon farming, manure cleaning is predominantly manual, a method that is both slow and costly, and exposes workers to harsh conditions. Addressing these challenges, this paper introduces a cleaning robot for pigeon farms utilizing magnetic strip navigation combined with RFID signal recognition and derives the magnetic-navigation control model. This method can improve operational stability and accuracy. Given the farm’s unstable environment, a control algorithm based on fuzzy PID with Kalman filtering was developed. This algorithm mitigates input disturbances and measurement noise by integrating Kalman filtering into the fuzzy PID feedback loop, thereby refining signal accuracy. Numerical simulations conducted in Matlab/Simulink demonstrate that the inclusion of Kalman filtering reduces the time of target signal tracking by nearly 1 s compared to fuzzy PID and by almost 2 s relative to standard PID under identical disturbances. Experimental tests confirm that this algorithm significantly improves the robot’s operational stability and reduces magnetic-navigation deviation, underscoring its advancement and practicality over traditional PID and fuzzy PID methods. Full article

This study investigates the application of Bayesian probability models in the diagnostic assessment of learning disabilities. The objective of this study was to determine whether specific conditions identified in expert reports could predict subsequent diagnoses. The sample consisted of 201 expert reports on children diagnosed with learning disabilities, which were analysed using qualitative content analysis, fuzzy set qualitative comparative analysis (fsQCA), and Bayesian conditional probability models. Variables such as vocabulary, working memory index, processing speed, and visuomotor coordination were examined as potential predictors. The analysis demonstrated that Bayesian networks captured conditional links, such as the strong association between working memory and perceptual inference, as well as an unexpected negative link between vocabulary and verbal comprehension. The study concludes that Bayesian networks provide a transparent and data-driven framework for pre-screening and risk assessment in special education settings. The limitations of this study include the absence of a control group and exclusive reliance on SNI cases. Future research should explore the integration of abductive reasoning into automated diagnostic software to enhance inclusivity and support decision-making. Full article

Generative Artificial Intelligence (GAI) has become a topic of increasing societal and academic relevance, with its rapid diffusion reshaping public debate, policymaking, and scholarly inquiry across diverse disciplines. Building on this context, the present study explores the factors influencing GAI adoption among Spanish digital natives (Millennials and Zoomers), using data from a large national survey of 1533 participants (average age = 33.51 years). The theoretical foundation of this research is the Theory of Planned Behavior (TPB). Accordingly, the study examines how perceived usefulness (USEFUL), innovativeness (INNOV), privacy concerns (PRI), knowledge (KNOWL), perceived social performance (SPER), and perceived need for regulation (NREG), along with gender (FEM) and generational identity (GENZ), influence the frequency of GAI use. A mixed-methods design combines ordered logistic regression to assess average effects and fuzzy set qualitative comparative analysis (fsQCA) to uncover multiple causal paths. The results show that USEFUL, INNOV, KNOWL, and GENZ positively influence GAI use, whereas NREG discourages it. PRI and SPER show no statistically significant differences. The fsQCA reveals 17 configurations leading to GAI use and eight to non-use, confirming an asymmetric pattern in which all variables, including PRI, SPER, and FEM, are relevant in specific combinations. These insights highlight the multifaceted nature of GAI adoption and suggest tailored educational, communication, and policy strategies to promote responsible and inclusive use. Full article

This paper presents a structured strategy for integrating the Sustainable Development Goals (SDGs) into university courses by linking them to competencies and learning outcomes. The proposed methodology, based on fuzzy logic, evaluates the degree of alignment between teaching activities and selected SDGs through matrices that connect competencies with assessment activities and expected learning outcomes, improving the gap regarding the inclusion of the SDGs and their articulation in terms of competencies. The approach was applied to two subjects from the Master’s Degree in Renewable Energy and Energy Efficiency at the Distance University of Madrid: “Electricity Market” and “Wind Energy”. In both cases, the learning outcomes were redesigned, and the activities were adjusted to ensure meaningful incorporation of sustainability principles into the curriculum. The method enables quantification of each activity’s contribution to the SDGs and supports a critical review of curriculum design to ensure coherent integration. The results indicate that project-based activities show the highest alignment with the SDGs, particularly with Goals 7, and 12, which achieve an average rating of 0.7 (high). The developed tool provides a practical and replicable solution for sustainability-oriented curriculum planning and can be adapted to other disciplines and educational programs. Full article

This paper examines how shifts in sustainable capital markets influence societal well-being through the lens of a “Jazz Economy”, highlighting improvisation and adaptability in financial systems while grounding the analysis in empirical modelling. A panel of EUROSTAT indicators for 27 EU member states (2019–2022) was analyzed, including green bond issuance, market capitalization, environmental taxation, social spending, life expectancy, and subjective life satisfaction. Hierarchical clustering grouped these indicators into coherent patterns of “financial swings”, which were then linked to a composite quality-of-life index through an Adaptive Neuro-Fuzzy Inference System (ANFIS), with results benchmarked against linear regression and random forests. The inclusion of time lags between fiscal, financial, and social indicators strengthens the causal interpretation of the results, moving beyond simple correlations. Findings show that higher public environmental protection spending combined with a strong net international investment position consistently predicts greater life satisfaction, whereas income and longevity alone do not guarantee improvements in subjective well-being, reflecting nonlinear interactions among fiscal, financial, and social variables. Robustness checks, including the exclusion of pandemic years, confirm the stability of outcomes. The study concludes that cohesive fiscal–financial strategies, integrating environmental policy and macro-financial resilience, are essential for enhancing quality of life and that sustainable finance can deliver tangible social benefits beyond metaphorical framing. Full article

The rapid development of contemporary artificial intelligence and Internet of Things (IoT) technologies has given rise to the emerging paradigm of the AIoT (Artificial Intelligence of Things), which is profoundly impacting human life and driving the digital transformation of industries and society. The AIoT not only enhances product functionality and convenience but also accelerates the achievement of the United Nations Sustainable Development Goals (SDGs). However, the widespread adoption of these technologies still poses challenges related to social inclusivity, particularly regarding insufficient accessibility for elderly users, which may exacerbate the digital divide and social inequality, contradicting SDG 10 (reducing inequality). This study integrates AIoT product development processes based on sustainability and universal design principles using methods such as Quality Function Deployment, the Analytic Hierarchy Process, the Scenario Method, the Entropy Weight Method, and Fuzzy Comprehensive Evaluation. The features of this process include ease of operation and high flexibility, making it suitable for cross-departmental product development while prioritizing the needs of diverse age groups throughout the development process. The research findings indicate that the AIoT product concepts proposed can meet the needs of diverse users, contributing to the realization of age-friendly products. This study provides a reference point for future AIoT product development, promoting the inclusive and sustainable development of smart technology. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

This scoping review investigates the use of fuzzy logic in serious games. Articles were searched in nine databases: ACM Digital Library, IEEE Xplore, IOPscience, MDPI, PubMed, ScienceDirect, Springer, Wiley, and Web of Science. The search retrieved 494 articles published between January 2020 and February 2025, of which 28 met the inclusion criteria. Specifically, four research questions were addressed, focusing on the taxonomy of serious games that use fuzzy logic, the characteristics of game design, the purpose and implementation of the fuzzy logic system within the game, and the experiments conducted in the studies. Results reported that 80% of the studies focused on educational serious games, while 20% addressed health applications. Mouse, keyboard, and smartphone touch screen were the most widely used interaction methods. The adventure genre was the most widely implemented in the studies (35.71%). Fuzzy logic was mainly used for adjusting game difficulty, followed by providing tailored feedback in the game. Mamdani inference was the most widely used inference method in the studies. Although 79% of the studies involved human participants in their experiments, 57% did not perform any statistical analysis of their results. Full article

We present a unified framework for fuzzy statistical convergence of Grünwald–Letnikov (GL) fractional differences in Bag–Samanta fuzzy normed linear spaces, addressing memory effects and nonlocality inherent to fractional-order models. Theoretically, we establish the uniqueness, linearity, and invariance of fuzzy statistical limits and prove a Cauchy characterization: fuzzy statistical convergence implies fuzzy statistical Cauchyness, while the converse holds in fuzzy-complete spaces (and in the completion, otherwise). We further develop an inclusion theory linking fuzzy strong Cesàro summability—including weighted means—to fuzzy statistical convergence. Via the discrete Q-operator, all statements transfer verbatim between nabla-left and delta-right GL forms, clarifying the binomial GL↔discrete Riemann–Liouville correspondence. Beyond structure, we propose density-based residual diagnostics for GL discretizations of fractional initial-value problems: when GL residuals are fuzzy statistically negligible, trajectories exhibit Ulam–Hyers-type robustness in the fuzzy topology. We also formulate a fuzzy Korovkin-type approximation principle under GL smoothing: Cesàro control on the test set $\{1,x,x^2\}$ propagates to arbitrary targets, yielding fuzzy statistical convergence for positive-operator sequences. Worked examples and an engineering-style case study (thermal balance with memory and bursty disturbances) illustrate how the diagnostics certify robustness of GL numerical schemes under sparse spikes and imprecise data. Full article

Background: The growing complexity of last-mile logistics (LML) in urban environments has created an urgent need for sustainable, efficient, and stakeholder-inclusive solutions. This study addresses these challenges by exploring a holistic, human-centered approach to evaluating LML strategies, recognizing the diverse expectations of logistics service providers, delivery personnel, customers, and local authorities. Methods: To capture both subjective and objective factors influencing decision-making, the study employs a Multi-Criteria Decision-Making (MCDM) framework that integrates the Fuzzy Analytic Hierarchy Process (FAHP) and Evaluation based on Distance from Average Solution (EDAS). Evaluation criteria encompass operational efficiency, environmental impact, social acceptance, and technological feasibility. Results: Six LML solutions were assessed and ranked using this approach. The results indicate that the cargo bike (A₂) emerged as the most favorable alternative, while electric freight vehicles (A₅) ranked lowest. These findings reflect significant trade-offs between stakeholder priorities and the varying performance of different delivery strategies. Conclusions: The proposed methodology offers practical guidance for designing balanced and socially responsible urban logistics systems. By emphasizing inclusivity in decision-making, this approach supports the development of LML solutions that are not only operationally effective but also environmentally sustainable and broadly accepted by stakeholders. Full article

Violence against women and girls (VAWG) remains a critical problem within Brazilian higher education institutions, where deep-rooted cultural norms and institutional shortcomings continue to foster unsafe environments for female students. Although national and international bodies have raised concerns, few studies have thoroughly examined the layered causes of VAWG in academic settings using comprehensive analytical methods. This study aims to explore the causal factors of VAWG within Brazilian universities by applying a structured survey and analyzing the responses using the Fuzzy Synthetic Evaluation (FSE) approach. This method allows for a nuanced interpretation of the collected data by assigning weighted values to various contributing factors. The research assessed five major dimensions—individual, interpersonal, institutional, community and societal causal factors. The findings reveal that societal and institutional causes significantly contribute to VAWG, while individual factors play a comparatively minor role. These insights point to the structural and systemic nature of VAWG in academic settings, emphasizing the need for broad reforms. Based on the results, practical recommendations, including cultural reorientation, stricter institutional policies, and gender-sensitive training are recommended. By applying FSE in this context, the study offers a novel approach to evaluating and addressing gender-based violence (GBV) in higher education, contributing to a valuable model for future research and institutional policymaking. The results offer critical insights that can guide interventions to foster safer and more inclusive university environments in Brazil. Full article

In this study, we develop new Hermite–Hadamard and Hermite–Hadamard–Fejér type inequalities for fuzzy interval-valued functions (FIVFs) that exhibit h-Godunova–Levin convexity, using the framework of the Riemann–Liouville fractional (RLF) q-integral. We introduce novel fuzzy extensions of classical inequalities and establish corresponding inclusion relations by utilizing the properties of fuzzy RLF q-integrals. Furthermore, we validate the theoretical results through illustrative numerical examples and graphical representations, demonstrating the applicability and effectiveness of the derived inequalities in the context of fuzzy and interval analysis. Full article

Background: Greek islands face mounting pressures on their marine water resources due to tourism growth, agricultural runoff, climate change, and emerging pollutants. Safeguarding seawater quality is critical for ecosystem integrity, public health, and the sustainability of tourism-based economies. Objectives: This scoping review synthesizes and evaluates the existing research on seawater quality in the Greek islands, with emphasis on pollution sources, monitoring methodologies, and socio-environmental impacts, while highlighting the gaps in addressing emerging contaminants and aligning with sustainable development goals. Methods: A systematic literature search was conducted in Scopus, Google Scholar, ResearchGate, Web of Science, and PubMed for English- and Greek-language studies published over the last two to three decades. The search terms covered physical, chemical, and biological aspects of seawater quality, as well as emerging pollutants. The PRISMA-ScR guidelines were followed, resulting in the inclusion of 178 studies. The data were categorized by pollutant type, location, water quality indicators, monitoring methods, and environmental, health, and tourism implications. Results: This review identifies agricultural runoff, untreated wastewater, maritime traffic emissions, and microplastics as key pollution sources. Emerging contaminants such as pharmaceuticals, PFASs, and nanomaterials have been insufficiently studied. While monitoring technologies such as remote sensing, fuzzy logic, and Artificial Neural Networks (ANNs) are increasingly applied, these efforts remain fragmented and geographically uneven. Notable gaps exist in the quantification of socio-economic impact, source apportionment, and epidemiological assessments. Conclusions: The current monitoring and management strategies in the Greek islands have produced high bathing water quality in many areas, as reflected in the Blue Flag program, yet they do not fully address the spatial, temporal, and technological challenges posed by climate change and emerging pollutants. Achieving long-term sustainability requires integrated, region-specific water governance linked to the UN SDGs, with stronger emphasis on preventive measures, advanced monitoring, and cross-sector collaboration. Full article

This study aimed to investigate the future trajectories of last-mile delivery (LMD), and their implications for sustainable urban logistics and smart city planning. Through a Delphi-based scenario analysis targeting the year 2030, this research draws on inputs from a two-round Delphi study with 52 experts representing logistics, academia, and government. Four key thematic areas were explored: consumer demand and behavior, emerging delivery technologies, innovative delivery services, and regulatory frameworks. The projections were structured using fuzzy c-means clustering, and analyzed through the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), supporting a systemic understanding of innovation adoption in urban logistics systems. The findings offer strategic insights for municipal planners, policymakers, logistics service providers, and e-commerce stakeholders, helping align infrastructure development and regulatory planning with the evolving needs of last-mile logistics. This approach contributes to advancing resilient, low-emission, and inclusive smart city ecosystems that align with global sustainability goals, particularly those outlined in the UN 2030 Agenda for Sustainable Development. Full article