Search Results (3,172)

Nearly four decades after the 1986 reactor explosion, several hundred semi-feral dogs persist within the Chornobyl Exclusion Zone. Genomic work has characterised their population structure and shown that radiation-induced mutation does not, on current evidence, drive the differentiation between groups; the cause of that differentiation remains unresolved, and their epigenome has never been examined. These dogs are a potentially valuable model for chronic, low-dose, multi-stressor exposure, not because they are “radiation-adapted”, a contested claim, but because they are a human-adjacent, densely sampled, pedigreed mammal for which comparative canine–human tools already exist. Since 2017, a humane sterilisation programme, carried out at the request of and in cooperation with the Ukrainian authorities, has reached near-complete coverage of the established populations. That programme was justified: dogs are formally prohibited in the zone, they injure workers, free-roaming animals inside an operating nuclear facility raise security as well as veterinary concerns, and reproduction was outpacing what the environment could support. Our argument concerns timing rather than policy. The scientific value of this population was never evaluated while it was still breeding, and the archived material that does exist, roughly 400 dogs including parent–offspring trios and repeat-sampled individuals, was assembled for population genetics rather than for the regulatory questions now worth asking. That archive should be analysed for methylation and expression now, as this is the only access to the population as it was. The animals themselves have not gone: several hundred identified dogs are still alive there, so sampling at veterinary contact and the retention of tissue at natural death or clinically indicated euthanasia remain possible, provided they are planned and funded in advance rather than improvised. More generally, the scientific evaluation of a population should accompany the decision to manage it rather than follow it, because sterilisation is not reversible at the population scale and an archive cannot be assembled retrospectively. We set out what such an evaluation requires at the outset of a programme. Its most concrete element asks nothing of the animals: a sterilisation clinic is the only routine access to a free-roaming population under anaesthesia, and the reproductive tissue it removes—the one material capable of addressing germline questions—is discarded as surgical waste. Full article

Islatravir is a novel nucleoside reverse transcriptase translocation inhibitor recently approved for the treatment of HIV-1 infection in virologically suppressed PLWH. While we previously documented high-level phenotypic resistance to islatravir (ISL) in HIV-1 subtype C variants harboring combinations of type 2 thymidine analogue mutations (TAM2) along with the M184V resistance-associated mutation, the primary contributor to the observed phenotype remains unclear. This report dissects the relative contribution of M184V to ISL resistance in the context of TAM2-containing variants through systematic reversion analysis. We show that reverting mutant 184V to wildtype M184 resulted in substantial sensitization to ISL, with fold-change reductions ranging from 8.1 to 13.4-fold across three TAM2 genetic backgrounds. Contrary to the well-documented antagonism between M184V and TAMs in zidovudine resistance, these two resistance pathways exhibit cooperative rather than antagonistic interactions in the context of ISL resistance. These findings provide critical evidence for refinement of genotypic resistance interpretation algorithms for ISL and have direct implications for the future clinical management of ISL-based antiretroviral therapy in people living with HIV. Importantly, elucidating the mechanistic basis of M184V-TAM2 cooperation would establish a more predictive framework for understanding how complex resistance mutation patterns impact ISL susceptibility and guide treatment decisions in virologically experienced individuals. Considering the diminished use of TAM2-seletive antiretroviral drugs in contemporary ART regimens, islatravir is likely to be an effective treatment option. Full article

This study, based on an 11-year historical series (2014–2024) from a Brazilian municipality, evaluated the technical, environmental, and preliminary economic potential of energy recovery routes for circularity and decarbonization in municipal solid waste management within an emerging economy context. The mass flow was characterized, and three different scenarios were modeled for the treatment of rejects—Refuse-Derived Fuel (RDF), landfill biogas recovery, and Waste-to-Energy (WtE) incineration. Results indicate that, despite a 31.75% diversion rate of household solid waste from final landfill disposal through sorting, recycling, and composting, approximately 105,000 tons of high-calorific rejects (3332 kcal/kg) are landfilled annually. The comparative analysis revealed that the WtE route offers the highest electricity generation potential (62,982 MWh/year), gross revenue potential from electricity generation, and substantial mass reduction (75%). However, project-level feasibility remains dependent on investment, operating, financing, and regulatory conditions. Conversely, the RDF route faces economic barriers because, in the analyzed regional market, cement plants charge industrial acceptance fees for receiving RDF, rather than purchasing it as a fuel, while landfill disposal remains comparatively low-cost. The findings indicate that, in the analyzed municipal context, relying solely on landfill optimization is insufficient for decarbonization, supporting a hybrid model that integrates thermal treatment of rejects while preserving social inclusion through waste picker cooperatives, with potential relevance to municipalities in emerging economies characterized by comparable landfill dependence, waste composition, and social recycling structures. Full article

Agricultural cooperatives play a key role in improving the competitiveness and sustainability of the agri-food sector. This study evaluates the productivity dynamics and operational performance of twenty-two Greek fruit and vegetable cooperatives during 2019–2023, using Data Envelopment Analysis (DEA) and the Malmquist Productivity Index to assess technical efficiency, scale efficiency, and productivity change over time. Total assets, operating expenses, financial expenses, and equity capital were used as inputs, while sales, operating surplus, and return on assets (ROA) were used as outputs. Mean technical efficiency was 0.718 under Constant Returns to Scale (CRS) and 0.820 under Variable Returns to Scale (VRS), with sixteen of the twenty-two cooperatives operating under increasing returns to scale. Total factor productivity declined on average by approximately 6.4% per year (TFPCH = 0.936), a cumulative decline of approximately 23% over 2019–2023, driven by technological regress (TECHCH = 0.863) that more than offset gains in technical efficiency (EFFCH = 1.085), mainly attributable to pure technical efficiency (PECH = 1.109) rather than scale efficiency. These results provide useful evidence for managers and policymakers, highlighting the importance of organizational improvement, technological modernization, and economies of scale in strengthening the long-term competitiveness and economic sustainability of Greek fruit and vegetable cooperatives. Full article

Transferring unmanned surface vehicle (USV) algorithms from simulation to physical vessels is constrained by heterogeneous hardware interfaces, degraded wireless links, and ambiguous boundaries between human and autonomous control. This study designs and implements a ship–shore cooperative experimental platform comprising a shore control station, a portable control terminal, and an onboard system. The platform integrates multimodal sensing, private-radio and 4G/5G communication, an independent short-range remote-control (RC) path, and hardware arbitration. Small, safety-relevant commands are transmitted redundantly over the heterogeneous links using a shared application protocol with sequence-based deduplication. A two-dimensional control-authority model separates the authorized control source (shore, portable terminal, or short-range RC) from the active onboard behavior (path following, local collision avoidance, or safety protection) to organize fail-safe degradation and control transfer. Geometric light detection and ranging (LiDAR) obstacle detection and optimal reciprocal collision avoidance provide a representative onboard avoidance workflow. Full-scale vessel tests closed the loop from mission dispatch and command parsing to actuation and status feedback and demonstrated autonomous navigation, human takeover, and local collision avoidance. Across four water environments, the platform recorded 5.39 h of multimodal data over 18.26 km of valid trajectories. The results establish a physical testbed for ship–shore cooperative control, algorithm transfer, and multimodal data acquisition. Full article

This study aimed to develop, implement, and evaluate an integrated leadership and entrepreneurship training program for family farmers participating in rural associations and cooperatives. Grounded in competency-based management and experiential learning theory, the study adopted a quantitative quasi-experimental pretest–posttest design with a non-equivalent control group. Eighty family farmers participated, comprising an experimental group (n = 40) and a control group (n = 40). The intervention consisted of a 24-h competency-based training program delivered over three months through face-to-face workshops, problem-based learning activities, and AI-supported tutoring. Entrepreneurial competencies related to achievement, planning, and power were assessed before and after the intervention using a structured questionnaire based on McClelland’s Entrepreneurial Behavioral Characteristics framework. Data were analyzed using mixed-design ANOVA following assessments of instrument reliability, normality, and homogeneity of variances. Compared with the control group, participants in the experimental group exhibited larger increases in self-reported entrepreneurial competency scores over time across the overall score and the three competency dimensions. These observed differences were consistent with participation in the educational intervention; however, because the study employed a non-randomized quasi-experimental design and relied exclusively on self-reported measures, the findings should be interpreted as evidence of associations rather than definitive causal effects. Overall, the study provides preliminary evidence that competency-based educational interventions integrating entrepreneurship and leadership-related competencies may help strengthen managerial competencies among family farmers in the context under study. Nevertheless, the absence of participant-level data for additional robustness analyses, together with the limited psychometric evidence and the lack of long-term follow-up, suggests that the findings should be interpreted with caution and confirmed in future studies employing longitudinal designs, stronger measurement procedures, and independent replication. Full article

Laser-induced graphene (LIG) has great potential for multiple applications because of its large specific surface area, facile fabrication process, and tunable properties. However, abundant lattice defects severely degrade its conductivity. Herein, from an innovative perspective of precursor design, the poly-phenylamines (P-PAs) with improved solubility and strong light absorption were synthesized, which act as an intercalated polymer for graphene oxide (GO) nanosheets. On this basis, the composite precursors show remarkably enhanced photothermal conversion capability and a compact stacked structure. These bring a 60% reduction in I_D/I_G in LIG after laser irradiation. To explain the above phenomenon, an isolation effect induced by the compact stacking precursor is proposed based on experimental results. Furthermore, the cooperative effect between P-PAs and Fe²⁺ is introduced, and a fluffy LIG aerogel with the lowest I_D/I_G ratio of 0.17 is prepared, which is barely achievable in conventional LIGs. When the obtained graphene aerogel is compounded with PDMS, the as-prepared thermal-conductive composite gel reaches a thermal conductivity of 1.05 W·m⁻¹·K⁻¹ and an ultralow interfacial thermal resistance of 37.2 mm²·K·W⁻¹ under a low graphene loading of 3.3 wt%. This intercalation strategy in GO precursor supplies a new route for preparing high-quality LIGs and thermal-conductive gels, which show great application prospects in thermal management devices. Full article

Timely vehicular sensing is important for traffic monitoring, cooperative driving, and road-safety management. High mobility, time-varying wireless conditions, and limited edge resources nevertheless make information freshness difficult to maintain. This paper studies age of information (AoI) minimization in a three-layer UAV-assisted edge network comprising vehicle devices (VDs), unmanned aerial vehicles (UAVs), and a cloud center (CC). VDs periodically generate sensor-data packets, UAVs provide mobile edge processing and data-relaying services, and the CC coordinates system-wide resource allocation. The joint optimization of sensor-data transmission, UAV movement, packet processing, computation offloading, and bandwidth allocation is formulated within a cooperative multi-agent framework. To solve this problem, we propose a collaborative heterogeneous federated actor–critic (CHFAC) framework. Its significance-aware federated learning mechanism evaluates local model updates according to update significance, alignment with the global learning direction, and training stability and uses the resulting contribution scores for non-uniform agent selection and contribution-weighted aggregation. In the considered simulation setting, evaluation over 1000 test episodes yields an average AoI of $7.45\pm 1.65$ and a worst-case AoI of $38.72\pm 24.06$. The average AoI is $79.0\%$, $63.9\%$, and $29.2\%$ lower than that obtained by the implemented HF-MARL, H-MAAC, and non-federated baselines, respectively. These results demonstrate the effectiveness of CHFAC for freshness-aware vehicular sensing in dynamic UAV-assisted edge environments. Full article

Sustainability is increasingly recognised as a key priority in tourism development. Based on stakeholder theory, sustainable tourism implicates the involvement of diverse destination stakeholders. However, the roles of local residents and tourism providers as stakeholders who play an important role in achieving sustainability have largely been examined separately. This study addressed the dual role of local resident tourism providers operating in rural areas by exploring their perceptions, implementation, and recommendations related to sustainable tourism practices (STPs). By integrating stakeholder theory with systems theory, the study considered local resident tourism providers as embedded within a complex destination system. Data were collected through an online and offline survey of local resident tourism providers in the rural area of Istria County, Croatia, with 82 valid responses obtained. A qualitative approach based on an interpretive research paradigm was applied, and reflexive thematic analysis (RTA) was used to analyse the data. The findings indicate that respondents have an understanding of the concept of STPs and implement them, mainly in an environmental context, in their tourism activities. A focus on the quality of tourism services, presentation of local identity and heritage, and relationships and communication with guests emerged as important to respondents, while stakeholder cooperation between providers was limited in their responses. These findings suggest that resident tourism providers may be unaware of the importance of stakeholder collaboration, including their own role, in achieving sustainable tourism development. However, the findings also highlight the importance of adopting a systems approach: respondents’ identification of STPs and recommendations for improving their implementation point to the need for appropriate enabling conditions at destination system level. The findings are intended to be of interest to researchers, destination management organisations, local authorities, and tourism policymakers working in the field of sustainable tourism. Full article

Oxidative stress and persistent inflammation can compromise tissue repair and the performance of locally delivered biomaterials. Here, we developed an aqueous alginate–lysozyme–tannic acid biohybrid hydrogel designed to combine injectability, antioxidant functionality, and extrusion-based printability. A composition screen varying alginate and lysozyme at a fixed tannic acid concentration identified a clear balance between flowability and structural integrity. An intermediate formulation provided shear-thinning and elastic-dominant rheological behavior, controlled hydration, cytocompatibility, antioxidant activity, and practical syringe/nozzle extrusion. The selected formulation was transferable to an approximately 80 mL batch and could be deposited into a multilayer structure. The findings are consistent with the formation of a cooperative polysaccharide–protein–polyphenol network and demonstrate the feasibility of integrating redox functionality with material processability. Future studies should focus on quantitative print fidelity validation, molecular interaction analysis, lysozyme activity retention, longer term stability, and application-relevant in vivo evaluation. Such development may support the future translation of injectable and printable antioxidant biomaterials for chronic wound management and tissue regeneration applications. Full article

Accurate cross-dataset state-of-health prediction for lithium-ion batteries remains challenging due to distribution shifts arising from diverse cathode chemistries, operating temperatures, and charge–discharge protocols across heterogeneous battery fleets. Drawing upon established machine learning paradigms, this study tailors a Domain-Adaptive Mixture-of-Experts (DA-MoE) framework to the battery prognostic context, automatically selecting the optimal domain adaptation strategy for each target domain through a physics-aware, lightweight linear gating network comprising merely 32 learnable parameters. The framework integrates a shared Transformer-based backbone with four adaptation strategies spanning the full spectrum of target-domain information utilization, namely zero-shot transfer, Test-Time Adaptation, Fine-Tuning, and Model-Agnostic Meta-Learning. A comprehensive evaluation on 564 battery cells from seven publicly available datasets under Leave-One-Domain-Out Cross-Validation protocol demonstrates that the proposed framework achieves an average coefficient of determination of 0.864 with perfect oracle strategy alignment under full domain training and maintains competitive generalization at an average $R^2$ of 0.795 when each target domain is held out during gating network training. Hard argmax selection consistently outperforms weighted fusion across all seven domains with an average margin of +0.027 in $R^2$, confirming that the four adaptation strategies compete rather than cooperate in this application context. A feature ablation analysis identifies sample count as the dominant determinant of strategy selection with performance degradation of $\Delta R^2=-0.182$ upon removal, followed by the early-cycle degradation slope and early-cycle nonlinearity index as secondary signals, all of which are computable at deployment time without future ground-truth SOH information. The proposed framework provides a practically deployable solution for battery management systems operating across heterogeneous fleets with minimal computational overhead and strong cross-dataset generalization capability. Full article

Automated guided vehicles (AGVs) and autonomous mobile robots (AMRs) are among the key enabling technologies driving intelligent logistics and industrial automation. Despite their widespread adoption and rapid technological evolution, the literature often addresses AGV and AMR systems in a fragmented manner, lacking a structured methodological perspective that highlights their architectural foundations, levels of autonomy, and technological maturity. This paper presents a methodological survey of AGV and AMR technologies, focusing on system-level architectures and core functional components rather than isolated algorithms. The survey systematically analyzes key technological dimensions, including sensing and perception, localization and positioning strategies, navigation and path-planning approaches, communication infrastructures, and multi-robot coordination mechanisms. A clear distinction is drawn between classical AGV systems, which rely on fixed infrastructure and predefined routes, and AMR systems, which exhibit adaptive, perception-driven, and self-configuring behaviors enabled by artificial intelligence techniques. Rather than proposing new algorithms, this paper organizes existing approaches into a coherent framework that highlights technological transitions from infrastructure-dependent guidance to autonomous, data-driven navigation. Recent trends such as cloud–edge integration, learning-based navigation, scalable fleet management architectures, and cooperative multi-robot systems are reviewed and discussed from a methodological standpoint, emphasizing their role in increasing flexibility, robustness, and operational efficiency in industrial and logistics environments. The survey also addresses cross-cutting challenges, including system transparency, safety and certification, interoperability, and sustainability. Finally, this paper outlines research directions aligned with the principles of Industry 5.0, highlighting the need for human-centered, resilient, and scalable AMR and AGV systems capable of safe and explainable operation in complex industrial contexts. Full article

Credit default prediction is a standard risk-management task, and large language models (LLMs) have been proposed as prompt-based alternatives, without task-specific parameter updating, for institutions that cannot deploy full machine learning (ML) pipelines. This study evaluates the Informed GPT on Colombian solidarity-sector cooperative lending data, benchmarking gpt-4o-mini against five tuned tree ensemble and gradient boosting classifiers on native imbalanced data (17.3% default rate, 12,861 loans). Six additions relative to the seminal reference are reported: (i) a new empirical domain (Colombian solidarity-sector cooperatives regulated by the SES); (ii) a multi-model benchmark rather than a logistic-regression-only baseline; (iii) a leakage-mitigation prompt design that excludes supervised-analysis-derived hints, causal directions, and target-distribution disclosures; (iv) a calibration analysis using Brier score, log loss, expected calibration error (ECE), reliability diagrams, and calibration slope and intercept; (v) bootstrap 95% confidence intervals, DeLong tests, and McNemar tests for paired significance; and (vi) matched label-budget learning curves for logistic regression, XGBoost, and LightGBM. Tuned ML models attain AUC $\approx 0.96$ (bootstrap CI $[0.94,0.98]$), while the LLM operates in the AUC 0.67–0.74 range across few-shot sizes $N\in \{0,10,20,40,80\}$. Under matched budgets, the LLM outperforms logistic regression at every N but is surpassed by gradient boosting once training samples reach approximately 40–80 observations. LLM probabilities are miscalibrated (ECE 0.11–0.21 vs. ≈0.03 for ML) and over-predict default (mean predicted probability 0.28–0.38 vs. observed base rate 0.17); threshold optimisation and post hoc calibration (Platt scaling, isotonic regression) are required for operational use. The findings qualify earlier claims about LLM auditability and position the approach as an assessment tool for cooperatives with fewer than ≈100 labelled defaults, rather than as a substitute for a well-resourced ML pipeline. Full article

Sustainable Development Goal 6 (SDG 6) seeks to ensure the availability and sustainable management of clean water and sanitation for all by 2030. Despite notable policy and institutional reforms, many developing countries, including South Africa, continue to face complex and interconnected challenges related to water quality, governance, infrastructure, financing, and institutional capacity that impede progress towards achieving SDG 6. A systematic literature review methodology was applied using predefined eligibility criteria and data sources to identify evidence related to drinking water quality and SDG 6 implementation. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, 241 records were initially identified. After screening and eligibility assessment, 115 records were retained for detailed analysis, while studies not focused on South Africa, not directly related to drinking water quality, or lacking sufficient empirical evidence were excluded. The review reveals that drinking water quality in South Africa is increasingly threatened by various challenges that may hinder the achievement of SDG 6. The findings highlight the importance of integrated interventions that combine infrastructure renewal, technological innovation, strengthened regulatory compliance, improved wastewater management, ecosystem protection, institutional reform, and stakeholder participation. To address these interconnected challenges, the study proposes the Governance, Technology, Finance, Capacity and Institutions (GTFCI) Framework as an integrated implementation pathway for SDG 6. The framework positions governance, institutional capacity, technology, and financing as foundational enablers of water service delivery, while integrated water resources management (IWRM) serves as the coordinating mechanism linking water services, ecosystem sustainability, and long-term water security. International cooperation and community participation function as cross-cutting support mechanisms that reinforce implementation across all SDG 6 targets. The proposed GTFCI Framework provides a practical and strategic model for strengthening drinking water quality, enhancing water security, and accelerating progress towards the sustainable achievement of SDG 6 by 2030. Full article

Background: The 2026 outbreak of Bundibugyo Ebola virus disease (BDBV) in eastern Democratic Republic of the Congo (DRC), centered in Ituri Province, represents the largest documented outbreak caused by Bundibugyo ebolavirus since its discovery in Uganda in 2007. The outbreak evolved within a complex humanitarian setting characterized by armed conflict, population displacement, mining-related migration, weak health systems, extensive population mobility, and an infodemic environment marked by misinformation and reduced public trust. We conducted a field investigation to assess epidemiological, operational, laboratory, infection prevention and control (IPC), community engagement, risk communication, and infodemic management challenges and identify priority interventions to strengthen outbreak control. Methods: A rapid field assessment was conducted between 12–15 June 2026 in Bunia, Rwampara Health Zone, and the Ituri Provincial Public Health Laboratory. Data were collected through direct observation, review of surveillance and laboratory reports, health facility assessments, stakeholder interviews, and analysis of outbreak response indicators. Epidemiological trends, surveillance performance, laboratory capacity, clinical care, IPC activities, logistics, risk communication, community engagement, and infodemic management approaches were evaluated. Results: As of 12 July 2026, the outbreak had resulted in 1926 laboratory-confirmed cases and 702 deaths, corresponding to an overall case fatality rate (CFR) of 36.4% across affected provinces. Ituri Province remained the epicenter, accounting for 90.8% of confirmed cases (1705/1877) and 85.5% of reported deaths (577/675). During the preceding 24 h, 53 new confirmed cases and 30 deaths were reported, including 20 community deaths (66.7%), highlighting persistent delays in detection, referral, and access to care. Surveillance systems identified 766 alerts, of which 678 (88.5%) were investigated, resulting in 235 suspected cases. Contact tracing remained a major challenge, with only 64.4% (4171/6475) of registered contacts successfully followed, below the recommended ≥95% target. Laboratory activities included testing of 137 specimens, with 29 positive results and an overall positivity rate of 21.2%. Decentralized molecular diagnostic platforms improved access to testing; however, data inconsistencies, delayed investigations, and gaps in outcome classification affected response monitoring. Major operational challenges included limited treatment capacity, high occupancy of Ebola treatment centres, shortages of trained personnel and IPC supplies, insecurity affecting response teams, and insufficient preparedness in newly affected areas. Community resistance, attacks on burial teams, detention of frontline responders, misinformation, and rumors contributed to delayed care-seeking, reduced acceptance of public health measures, and incomplete cooperation with contact tracing. Risk communication and community engagement efforts were constrained by limited outreach capacity, language barriers, low trust, and inadequate systems for rumor detection and infodemic response. Conclusions: The ongoing BDBV outbreak in eastern DRC demonstrates the difficulty of controlling Ebola transmission in conflict-affected and socially complex settings. Sustained transmission, community deaths, geographic expansion, and operational constraints highlight the urgent need to strengthen surveillance, contact tracing, laboratory systems, IPC capacity, clinical care, and integrated risk communication and infodemic management strategies. Building trust through community-centered approaches, proactive misinformation management, and engagement of trusted local actors will be essential to accelerate outbreak containment and strengthen preparedness across the Great Lakes region. Full article