Search Results (20,642)

As Industry 4.0 transforms manufacturing operations, maintenance organizations face a group decision-making problem: how to consolidate diverse expert judgments into a defensible, transparent ranking of the competencies that maintenance personnel most need. This paper applies an integrated Delphi–AHP framework—with explicit notation, operators, and diagnostics—to prioritize maintenance competencies in advanced-manufacturing settings. The Delphi stage consolidates expert-generated items under median–interquartile-range consensus and round-to-round stability rules, while the Analytic Hierarchy Process (AHP) transforms validated pairwise comparisons into ratio-scale priority weights through geometric-mean Aggregation of Individual Judgments (AIJ) and eigenvector derivation. Consistency screening (CI/CR), inter-rater agreement (Kendall’s W), and perturbation-based sensitivity analysis accompany the resulting weight vector. A bounded AI-assisted consistency-check step supports terminology harmonization during Delphi statement consolidation, subject to explicit human-validation constraints. A panel of fifteen industry experts participated in the study; five competency dimensions and twenty-nine indicators were retained through three Delphi rounds. AHP weighting identified Basic Knowledge and Skills as the highest-priority dimension, followed by Safety and Regulation Awareness and Problem-Solving Ability. Aggregated pairwise comparison matrices, local and global weights, and sensitivity results are reported to support reproducibility. The study contributes a rigorously specified application of combined Delphi–AHP to a domain—Industry 4.0 maintenance asset management—where multi-criteria decision analysis has seen limited formal application, and closes common specification gaps in published Delphi–AHP implementations. Full article

In a global business environment marked by digital disruption, Small and Medium-sized Enterprises (SMEs) must integrate digital transformation with strategic agility and organizational resilience. This study addresses the fragmentation of the current management literature by developing an AI-driven meta-theory through a high-performance computational synthesis of 4811 academic publications from the OpenAlex database. Utilizing a theoretically grounded hybrid framework of lexical filtering (TF-IDF), semantic embedding (SciBERT), and a diverse ensemble of five Large Language Models (LLMs), we move beyond descriptive mapping to identify the ontological and integrative mechanisms of SME adaptation. The methodology is validated through a multi-stage expert audit of model reasoning traces to ensure theoretical alignment. Results reveal a clear dominance of Contingency Theory (20.5%) and Resource-Based View (14.1%), which are re-conceptualized here as Regulatory–Technical Brokerage and Internal Fortification. Through Social Network Analysis (SNA) and Aggregate Constraint metrics, the study identifies Innovation Frontiers that are operationally challenging to synthesize through traditional manual reviews at this scale. The research concludes by formulating four meta-theoretical propositions and an integrative synergetic mechanism, explaining how SME resilience emerges as an emergent property of cross-layer alignment between technical, cognitive, and structural logics. By providing this causal roadmap, the study establishes a robust, AI-augmented blueprint for SMEs to function as intelligent, self-regulating nodes within a Post-Normal digital ecosystem. Full article

BK and JC Polyomaviruses (BKPyV and JCPyV) are ubiquitous human pathogens capable of establishing lifelong, asymptomatic persistence in the majority of the global population. While decades of research have focused on their lytic replication cycles and the development of severe diseases, such as polyomavirus-associated nephropathy (PVAN) caused by BKPyV and progressive multifocal leukoencephalopathy (PML) caused by JCPyV, their primary evolutionary strategy is one of persistence rather than pathogenesis. This review shifts the perspective from a replication-centric framework towards an evolutionary persistence model, detailing the multi-layered host and viral determinants that maintain the homeostatic balance. At the cellular level, viral genomes are restricted by chromatinization into minichromosomes and host S-phase licensing. These constraints are reinforced by innate immune sensing and adaptive T-cell and antibody responses that curtail systemic dissemination while permitting periodic, low-level urinary shedding, which is essential for horizontal transmission. In addition to these host barriers, the viruses utilize intrinsic regulatory mechanisms to prevent excessive replication and immune detection, including the stable archetype non-coding control region (NCCR), viral microRNAs that downregulate early gene expression, and the small t antigen (STAg). Finally, we address unresolved questions regarding the full spectrum of cellular reservoirs, the molecular triggers of reactivation, and the ecological factors shaping their transmission routes. Understanding these maintenance mechanisms is crucial for refining clinical interventions and managing the rare, devastating transitions from silent persistence to lytic disease. Full article

Global water scarcity constitutes a critical sustainability challenge, particularly in agricultural and rural contexts exposed to climate variability. Beyond technical and infrastructural solutions, collective and community-based water management practices have gained increasing relevance as sustainable alternatives grounded in local and ancestral knowledge. This study presents a systematic qualitative review of collective practices for alternative water management implemented worldwide between 2018 and 2023, following the PRISMA methodology, and based on a screening of the Scopus database, 31 peer-reviewed studies were selected and analysed through thematic synthesis. The systematic review identified five interconnected dimensions: (1) water management and governance, (2) conservation and storage, (3) hydrological restoration, (4) efficient water use, and (5) recognition of local knowledge. The results show that collective water management practices contribute to water security, ecological resilience, and adaptive capacity in rural territories, particularly when aligned with local socio-environmental conditions. The study highlights the importance of integrating scientific and community-based knowledge to advance context-specific and sustainable water management strategies, contributing to ongoing debates on sustainability, rural development, and adaptive water governance. Full article

Background: Type 2 diabetes mellitus (T2DM) represents a pressing global health challenge, underscoring the urgency of developing effective dietary interventions derived from natural resources. Zaojiaomi polysaccharide (ZJMP) from the endosperm of Gleditsia sinensis seeds (zaojiaomi), a traditional edible product, exhibits largely underexplored potential in T2DM management. Methods: In the present study, the antidiabetic effects and underlying mechanisms of ZJMP were investigated using a rat model of T2DM induced by a high-fat diet (HFD) combined with streptozotocin (STZ). Relevant biochemical indicators were detected, and histopathological examination was performed. The expression levels of key components of the TLR4/MyD88/NF-κB signaling pathway, as well as the inflammatory cytokines IL-6 and IL-1β in renal tissues, were further analyzed. Additionally, gut microbiota composition and the levels of short-chain fatty acids were determined. Results: ZJMP treatment significantly ameliorated hyperglycemia and dyslipidemia, elevated serum insulin levels, reduced intestinal mucosal permeability, and attenuated histopathological lesions in the heart, kidney, and pancreas of T2DM rats. Meanwhile, ZJMP notably alleviated renal inflammation by suppressing the production of IL-1β and IL-6, as well as inhibiting the TLR4/MyD88/NF-κB pathway. Furthermore, ZJMP administration effectively modulated gut microbiota composition and increased fecal concentrations of acetic acid and propionic acid. Conclusions: Collectively, these findings elucidate the novel bioactivity of ZJMP and highlight its potential as a promising functional food ingredient or dietary supplement for T2DM management. Full article

Periodic time-series forecasting is central to 6G-enabled urban digital twins, where both cellular traffic management and environmental sensing demand accurate predictions over recurring diurnal and weekly regimes. Contrastive self-supervised learning has emerged as a promising approach for learning temporal representations, yet when applied to such periodic data, it suffers from class collision: temporally distant but semantically similar recurrent patterns are pushed apart as false negatives. We propose Clustering-Enhanced Contrastive Learning (CECL), which couples temporal contrastive learning with a clustering regularizer that maintains a Gaussian mixture structure over the latent space, preserving global periodic structure while retaining local discriminability. We evaluate CECL on five datasets across three tracks spanning two domains: cellular traffic forecasting (Milan CDR, 20 cells), multi-site hourly air-quality forecasting (Beijing Multi-Site, 12 stations; QUANT, 3 cities), and daily air-quality forecasting (Haikou and Taizhou). Across all tracks, CECL consistently outperforms supervised and contrastive baselines, reducing RMSE by 3–$10\%$ relative to the strongest contrastive competitor (CoST). These results demonstrate that clustering-guided contrastive regularization yields robust gains for periodic forecasting in both 6G network management and environmental sensing scenarios. Full article

Groundwater storage is vital for managing water resources, especially as global water scarcity intensifies. Estimating groundwater levels regionally is challenging due to natural heterogeneity. We employed a large groundwater observation sample, along with Global Land Data Assimilation System (GLDAS) and Gravity Recovery and Climate Experiments (GRACE) datasets, to develop a random forest model for predicting groundwater levels in China’s Yellow River Basin. The model showed robustness, achieving an R² of 0.95 in calibration and an R² of 0.91 ± 0.009 in 10-fold cross-validation with 100 repetitions. Temporal predictability was lower, with an R² of 0.72 for April–May 2023; however, the temporal prediction is preliminary and limited by the short validation period (April–May 2023), which should be interpreted with caution. Spatial maps revealed significant seasonal declines in fall and winter, particularly in the middle and lower reaches. This study highlights the potential of machine learning with extensive observations to estimate regional groundwater levels and supports groundwater analysis with robust data. Full article

Background/Objectives: Although intravesical Bacillus Calmette–Guérin (BCG) is an established adjuvant therapy for non-muscle-invasive bladder cancer (NMIBC), chronic global shortages and adverse events (AEs) can occur. Thus, intravesical gemcitabine has been used as an alternative. We compared the long-term oncological outcomes and safety profiles of BCG and gemcitabine in treatment-naïve patients with intermediate- and high-risk NMIBC. Methods: Patients with intermediate- and high-risk NMIBC (n = 477) received adjuvant intravesical induction and maintenance therapy with intravesical BCG (n = 361) or gemcitabine (n = 116) and their data were collected retrospectively. Results: Compared with the gemcitabine group, the BCG group had significantly higher proportions of patients with T1 stage, high-grade tumors, high-risk tumors, and longer median follow-up duration. Over a median 36-month observation period, the BCG group exhibited significantly better recurrence-free survival (RFS) and high-grade RFS (HG-RFS) than the gemcitabine group. In the propensity score–matched high-risk population, BCG also outperformed gemcitabine in RFS and HG-RFS. BCG therapy was identified as a potent protective predictor, reducing the risk of recurrence and high-grade recurrence by 65% and 66%, respectively, in the total cohort, and by 69% and 71%, respectively, in the propensity score-matched high-risk subgroup. No significant differences were observed in the frequency of grade ≥3 AEs between BCG and gemcitabine. Conclusions: Intravesical BCG is strongly associated with superior oncological outcomes over gemcitabine in intermediate- and high-risk NMIBC. The results of this study offer pivotal practice-based insights to guide clinical strategies for managing NMIBC. Full article

This study provides a comprehensive overview of emerging contaminants in water resources. It includes a global perspective with specific insights from Portugal. Following PRISMA 2020 guidelines, peer-reviewed studies published between 2020 and 2025 were critically assessed to identify patterns of contamination, monitoring gaps and technological readiness levels. Results indicate frequently detected emerging contaminants including pesticides, antibiotics and antidepressants in surface water, groundwater and wastewater systems. Advanced analytical methods, particularly liquid chromatography coupled with high-resolution mass spectrometry, stands out as the main detection technique, allowing the identification of trace levels of contaminants. These techniques also support the identification of pollution patterns associated with agriculture, urban and industrial effluents. However, significant asymmetries persist between international and Portuguese research. Particularly evident in systematic monitoring networks and integrated risk assessment approaches. Conventional water/wastewater treatment plants show limited removal efficiency, while advanced oxidation processes, adsorption technologies and microalgae-based systems demonstrate promising but variable performance depending on scale and operational maturity. The findings highlight gaps between scientific advances and regulatory implementation, emphasizing the need for strengthened monitoring frameworks and technology scale-up strategies. They also call for improved integration between science, governance, and sustainability policies to ensure resilient water resource management in line with the Sustainable Development Goals. Full article

Global urbanization is shifting from incremental expansion to stock optimization, and old residential communities have become important spatial units for low-carbon transition. However, in existing built environments, traditional process-based inventory methods face practical constraints, including missing original drawings, complex site conditions, and severe vegetation obstruction. As a result, systematic accounting of buildings, landscapes, and natural carbon sinks remains difficult. This study integrates life cycle assessment (LCA), BIM reverse modeling, 3D point clouds, DesignBuilder simulation, inventory-based accounting, and i-Tree Eco to construct a life cycle carbon emission accounting framework for old residential communities. The framework links current-condition data reconstruction, quantity take-off, operational energy simulation, landscape inventory accounting, and vegetation carbon sequestration assessment. It is applied to Nanyuan Xincun in Hefei to quantify the community-scale carbon source–sink structure. The results show that Nanyuan Xincun presents a clear operation-led emission pattern, with the operation and maintenance phase accounting for 82.52% of total positive emissions. Within architectural engineering, operation and maintenance accounts for 82.91%, while material production accounts for 13.28%. Landscape engineering shows a more mixed structure, with operation and maintenance accounting for 52.95% and material production accounting for 36.49%. Vegetation carbon sequestration analysis shows that mature trees and shrubs are the main ecological carbon assets. Annual sequestration reaches 16.95 t-CO₂e/a, and trees and shrubs contribute 92.85% of total vegetation carbon storage. Under current vegetation conditions, annual sequestration is equivalent to 32.99% of annual landscape operation emissions, indicating considerable ecological compensation potential. Based on these findings, this study proposes four optimization pathways: operational energy reduction, low-carbon material substitution, construction and demolition waste recycling, and mature tree protection. These pathways provide data support for refined carbon management and low-carbon renewal in existing communities. Full article

Handover optimization is essential for seamless connectivity in 5G and beyond networks. Existing approaches present fundamental challenges of centralized solutions achieving coordination and accuracy but creating privacy risks under the General Data Protection Regulation (GDPR), while distributed privacy-preserving approaches protect user data but lack the network-wide visibility necessary for optimal mobility decisions. This systematic review synthesizes 49 peer-reviewed studies published between 2010 and 2025, identified through a PRISMA-compliant search across IEEE Xplore, ScienceDirect, SpringerLink, MDPI, ACM Digital Library, and Google Scholar. Eligible studies addressed cellular handover or mobility management using traditional signal-based, Machine Learning, Federated Learning, Software-Defined Networking strategies, and reported quantitative performance metrics. A structured quality assessment evaluated methodological rigor, dataset validation, benchmarking practices, handover-specific metrics, and scalability. Synthesis evidence shows that existing approaches do not simultaneously satisfy critical requirements for next-generation mobility management of accuracy, privacy, scalability, and real-time network-wide coordination. Machine learning achieves high accuracy (up to 97%) but depends on centralized data; Reinforcement Learning supports real-time adaptation but incurs high computational costs; federated learning preserve privacy but suffers from limited global coordination; and software-defined networking enables centralized control but requires continuous transmission of raw data. Evidence quality is further limited to simulation-based assessments and limited real-world datasets. Overall, the reviews identify a clear evolution from reactive threshold-based methods towards proactive prediction and highlights the need for unified, privacy-preserving and globally coordinated handover frameworks. The findings point toward integrating federated learning with Software-Defined Mobile Networking as promising architectural direction for 6G mobility management. Full article

The rapid expansion of clean energy markets, coupled with the growing importance of non-ferrous metals and technological innovation, has created a highly interconnected financial and economic system. Understanding the dynamics of these interdependencies is essential for assessing market resilience, investment diversification, and the sustainability of the global energy transition. This paper investigates the dynamic dependence and connectedness between clean energy, non-ferrous metals, and technological innovation indices, with particular attention to the impact of the COVID-19 pandemic as a global stress event. Using daily data from December 2004 to July 2020, we employ a comprehensive empirical framework that combines copula-based dependence modeling with a dynamic connectedness approach. This methodology allows us to capture nonlinear relationships, tail dependencies, and volatility spillovers across markets. The results reveal that the dependence structure between clean energy and the other sectors is symmetric and time-varying, with stronger linkages observed between clean energy and technological innovation than with non-ferrous metals. The connectedness analysis indicates a moderate level of total spillovers, with clean energy acting as the main transmitter of shocks and technological innovation as the primary receiver. Focusing on the COVID-19 period, we find a significant increase in both dependence and connectedness, suggesting that these markets become more severely integrated during periods of extreme uncertainty. These findings support the presence of contagion effects and highlight the reduced effectiveness of diversification strategies during crisis episodes. The results offer forward-looking implications for investors and policymakers regarding risk transmission, portfolio management, and the resilience of markets supporting the global transition toward sustainable energy. Full article

Integrated water resources management uses decision-making and planning techniques in developing long-term strategies to ensure the sustainability of water resources and the resulting water security of future generations. Policy formulation through such integrated planning interlinks with indicators serving as an information channel to decision-makers. The present effort aims to develop a specific methodology using technical, environmental, and social indicators, formulating composite indices to identify vulnerability to changing water conditions. Thus, a set of indices developed through a multiyear research effort in Latin America, namely Drought Vulnerability Index (DVI), Water Stress Vulnerability Index (WSTVI), Water Scarcity Vulnerability Index (WSCVI), and Water Changing Conditions Vulnerability Index (WCCVI). Time series analysis covered the years 1991–2020, whereas the reference period was 1961–2020. Climate and water resources information is mainly obtained from ERA5-Land reanalysis; social, economic, infrastructure, and institutional data derived from harmonized sources (COROADO Project-EU, FAO, The World Bank, WHO/UNICEF JMP). Statistical tests and Principal Component Analysis (PCA) identified the indicators included in the equations for each index. Expert knowledge played an important role in the development as data were collected according to known local specificities and global trends, as well as scientific criteria and methodological rigor regarding the proposed new indices. Finally, application of such a framework for spatially explicit analysis indicated higher levels of vulnerability to changing water conditions in the northern part of Mexico, the Andes, Bolivia, Paraguay, and Central America, and lower levels in Chile, Brazil, Uruguay, and Argentina. This application demonstrates that the produced composite indices may be implemented with matching success all over Latin America and, therefore, in diversified natural, technical, environmental, social and economic conditions. Full article

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions. Full article

Machine learning (ML) has become a key enabling technology for optimizing renewable energy systems and supporting global sustainability objectives. This paper presents a comprehensive review of recent advances in ML-based optimization techniques applied to clean and renewable energy systems, with particular emphasis on wind energy, hybrid energy systems, energy storage, and intelligent energy management. A systematic literature review covering peer-reviewed publications from 2021 to 2025 was conducted, resulting in the analysis of 138 high-quality journal and conference studies. The reviewed studies were categorized according to evolutionary algorithm-based hybrid models, classical neural networks, and deep learning architectures, including Convolutional Neural Network (CNN), LSTMs, GRUs, and attention-based models. The analysis demonstrates that hybrid ML–metaheuristic frameworks significantly enhance forecasting accuracy, system reliability, fault diagnosis, and multi-objective optimization compared to traditional methods. These intelligent approaches directly contribute to Sustainable Development Goals SDG-7 (Affordable and Clean Energy), SDG-9 (Industry, Innovation, and Infrastructure), and SDG-13 (Climate Action). Key challenges and future research directions are discussed, highlighting the need for scalable, explainable, and real-time ML solutions to enable resilient, low-carbon, and sustainable energy systems. Full article