Search Results (48,325)

Acidic wetting–drying cycles can progressively deteriorate sandstone used in rock engineering, but the link between macroscopic mechanical degradation and three-dimensional pore-crack evolution remains unclear. The objective of this work was to clarify the deterioration mechanism of sandstone under repeated acidic wetting–drying action. Sandstone specimens were subjected to 0, 5, 10, 20, or 30 wetting–drying cycles in a H₂SO₄ solution with an initial pH of 2.00. Triaxial shear testing, micro-CT reconstruction, scanning electron microscopy, and a correlation analysis were combined to characterize changes in the mechanical behavior and microstructure. With an increasing cycle number, the cohesion and internal friction angle decreased by 40.85% and 11.27%, respectively, while the pore network became progressively enlarged, connected, and structurally complex. The pore fractal dimension increased from 2.18 to 2.49. Scanning electron microscopy images showed enlarged pores and cracks, looser particle contacts, and local damage along particle boundaries. Increasing the confining pressure improved the peak resistance and restricted crack development. A correlation analysis indicated that mechanical degradation was closely associated with the evolution of the pore and crack structure. These results establish a multiscale relationship between the loss of mechanical performance and microstructural deterioration, providing a basis for evaluating sandstone stability in acidic environments. Full article

Cities are the main sites of production, employment, and capital accumulation in advanced economies, which places urban economic performance at the centre of economic policy and urban governance. This work develops an integrated grey-based multi-criteria approach for assessing that performance and applies it to the sixteen G7 cities covered by the Global Power City Index (GPCI). Criterion weights are obtained with Grey RANCOM (G-RANCOM), which converts the ordinal rankings of a five-member expert panel into interval weights, and the cities are ranked with Grey MUNRA (G-MUNRA), which aggregates linear, vector, and non-linear normalization. Each performance entry is an interval bounded by the minimum and the maximum annual score observed in the GPCI Economy function over 2021–2025. Market size, economic vitality, and business environment emerge as the most influential criteria, and New York, London, and Tokyo occupy the highest positions, while Osaka, Milan, and Fukuoka occupy the last three. Robustness is tested via scenario analyses on the model parameters and through a global analysis of 100,000 replications in which all of them vary jointly. New York holds the first position in 74% of the replications and the three lowest positions are unchanged in 87%, whereas cities in adjacent middle positions are not separated reliably. Rankings produced by five established grey approaches, by crisp and fuzzy counterparts, and by the published GPCI Economy rankings agree with the reported ordering, with Spearman correlations between 0.92 and 1.00. The framework offers urban policymakers a transparent benchmarking tool for evidence-based competitiveness strategies. Full article

Forecasting power consumption is essential for intelligent power management in IoT-enabled smart environments, where heterogeneous behaviors appear from diverse building usages. University campuses are considered environments that share similarities with smart cities, making them suitable for power dynamics analysis. We build upon an IoT-based Advanced Metering Infrastructure (AMI) we deployed at our Engineering School’s Campus (INPT, Morocco), and an optimized XGBoost pipeline enhanced via Genetic Algorithms. Limited modeling granularity is addressed in heterogeneous consumption patterns. We propose and justify a cluster-aware approach partitioning data $\left(D\right)$ into K regimes such that $D={\bigcup }_{c=1}^K{C}_c$. Each cluster is treated as a homogeneous behavioral profile and modeled using a GA-XGBoost model, enabling an intermediate granularity between global and meter-level learning. Experiments on real-world campus AMI data show that our proposed GA-XGBoost model consistently outperforms SVR and LSTM baselines across all clusters. In addition, cluster-specific models further improve performance compared to a single GA-XGBoost model trained without clustering, achieving a 48.42% improvement in MASE. Overall, beyond improving forecasting accuracy, cross-cluster generalization shows performance degradation and distributional shift when models are transferred across clusters, while residual diagnostics reveal differences in variance, temporal dependence, and non-Gaussianity. Full article

Wireless sensor networks (WSNs) play a critical role in cyber-physical applications such as industrial monitoring, environmental sensing, and critical infrastructure management. In these environments, security mechanisms must not only detect malicious activities but also explain how compromised measurements propagate through sensing, aggregation, and decision processes while operating under stringent resource constraints. Existing approaches typically address intrusion detection, trust management, provenance analysis, or blockchain-based integrity independently, providing limited support for integrated and explainable security. This paper presents PhyProvTrust-WSN, a physics-aware framework that combines physics-consistency validation, dynamic provenance graphs, evidence-based trust propagation, multi-factor risk fusion, and selective evidence anchoring to improve the transparency and auditability of secure sensor data aggregation. The framework models sensing, forwarding, aggregation, validation, and response events as a bounded provenance directed acyclic graph (DAG), enabling causal tracing of suspicious activities while maintaining low memory and communication overhead. A weighted risk fusion mechanism integrates anomaly evidence, domain-consistency assessment, trust evolution, and inherited provenance risk to support explainable security decisions. Rather than continuously recording all events, only high-risk or decision-relevant evidence hashes are anchored to a permissioned audit layer, reducing storage and communication costs. To avoid overclaiming, the proposed framework is evaluated using a hybrid methodology that combines attack-labeled WSN datasets, real sensor measurements for physics-consistency validation, and simulation-based overhead analysis. The results demonstrate that the integrated framework provides strong detection capability while improving explainability, supporting root-cause analysis, and maintaining bounded communication and storage overhead suitable for resource-constrained WSN deployments. Full article

Peanut (Arachis hypogaea L.) is a globally vital oil and cash crop. However, frequent cold stress severely compromises its yield stability. To address this challenge, we conducted quantitative trait locus (QTL) mapping for two cold tolerance-associated traits, namely relative emergence rate (RER) and relative emergence index (REI), across four distinct environments using a recombinant inbred line (RIL) population derived from the cold-tolerant landrace Silihong and cold-sensitive cultivar Jinonghei 3. Two core QTLs associated with cold tolerance were detected, including qRER6 stable across all environments with PVE of 4.91–5.15% and a co-localized QTL qRER18.1/qREI18.2 on chromosome 18 that governs both target traits with PVE of 14.56–14.71% and 10.34%. Through integrated analysis of QTL mapping and Weighted gene co-expression network analysis (WGCNA), Arahy.657RUG was identified as a candidate cold tolerance gene and designated Ahcold18. Differential expression analysis and heterologous overexpression in Arabidopsis thaliana under freezing stress (−9 °C) showed that Ahcold18 enhances plant survival under low-temperature stress, suggesting a general role in cold tolerance that warrants further investigation in the context of peanut chilling tolerance. A gene-based KASP marker (KASP-2374669), developed from variant sites within Ahcold18, showed preliminary association with RER and REI in the RIL population; however, further validation in diverse germplasm is required to confirm its utility for marker-assisted selection. This study provides a critical genetic resource and a precise technical tool for marker-assisted breeding of cultivated peanut with cold tolerance. Full article

This research presents a comparative performance evaluation of two advanced maximum power point tracking (MPPT) methodologies, namely sliding mode control (SMC) and the Kalman filter (KF), specifically applied to a standalone photovoltaic water pumping system (PVWPS). To achieve economic viability, the system is designed for storage-less operation, driving a three-phase induction motor (IM) via a high-dynamic direct torque control (DTC) scheme and a three-level inverter. The core technical contribution addresses the critical challenge of maximizing energy yield under partial shading conditions (PSCs). PSCs result in a complex, non-convex power–voltage (P−V) characteristic, containing multiple peaks, where conventional MPPT algorithms fail to consistently locate the global maximum power point (GMPP). To overcome this deficiency, we implemented the SMC-based MPPT algorithm to exploit its inherent robustness and rapid dynamic response, and compared it with the Kalman filter MPPT, which relies on stochastic state estimation to achieve accurate tracking and effective disturbance rejection. MATLAB/Simulink analysis compares the proposed techniques with the perturb and observe (P&O) MPPT method. The comparison considers tracking efficiency, convergence speed, and steady-state ripple under various shading conditions to identify the most effective control strategy for improving PVWPS performances. The reported performance evaluations are based on numerical simulations conducted within the MATLAB/Simulink environment, using a validated system model. Full article

Background: Hypertension requires lifelong antihypertensive medication to reduce cardiovascular risk. While most studies have focused on individual determinants of adherence, the role of perceived environmental and social characteristics remains unclear. This study examined the associations of depressive symptoms, satisfaction with the community environment, social participation, and social contact with antihypertensive medication non-adherence among older adults with hypertension in South Korea. Methods: A cross-sectional secondary analysis was conducted using the 2023 Korea Community Health Survey, including 44,822 adults aged 65 years and older with physician-diagnosed hypertension who were currently taking antihypertensive medication. Medication non-adherence was defined as taking medication on fewer than 24 of the previous 30 days, and associated factors were identified using complex sample multivariable logistic regression. Results: The prevalence of medication non-adherence was 0.4%. Higher satisfaction with the community environment was associated with lower odds of medication non-adherence (OR = 0.83, 95% CI = 0.72–0.95), whereas greater social participation was associated with higher odds (OR = 1.39, 95% CI = 1.09–1.78). Social contact, depressive symptoms, and the remaining characteristics were not significantly associated with medication non-adherence. Conclusions: Antihypertensive medication non-adherence among older adults is associated with perceived environmental characteristics as well as individual factors. Whether community-based approaches addressing these conditions can improve medication adherence requires evaluation in longitudinal or interventional studies. Full article

Background/Objectives: Medical secretaries, who serve as patients’ first point of contact and are frequently exposed to stress arising from illness, waiting, and the hospital environment, remain under-examined in health-workforce research. Previous studies have positioned emotional intelligence in different roles within job-attitude and performance models, including as an antecedent of job satisfaction. Because cross-sectional self-report data cannot establish temporal ordering or causal mediation, the present study examined whether emotional intelligence statistically accounts for part of the cross-sectional association between medical secretaries’ job satisfaction and self-reported job performance. Methods: A cross-sectional study was conducted with medical secretaries from two public hospitals in Bitlis Province, Türkiye (April–May 2025). Of 176 eligible employees invited, 134 complete questionnaires were analyzed (response rate: 76.1%). Emotional intelligence was assessed with the Wong and Law Emotional Intelligence Scale, job satisfaction with the five-item short form of the Brayfield–Rothe scale, and self-reported job performance with a four-item scale. Group comparisons, Pearson correlations, linear regression, a 10,000-resample bootstrap indirect-association analysis, a reverse-model sensitivity analysis, and a Harman single-factor diagnostic were performed. Results: Job satisfaction was positively associated with self-reported job performance (r = 0.275; p = 0.001) and emotional intelligence (r = 0.341; p < 0.001), while emotional intelligence showed a stronger association with performance (r = 0.539; p < 0.001). The conditional direct association of job satisfaction with performance was not statistically significant after emotional intelligence entered the model (β = 0.103; p = 0.187); the cross-sectional indirect association through emotional intelligence was 0.114 (95% bootstrap CI: 0.042–0.204). Explained variance increased from 7.6% to 30.0%, leaving 70.0% unexplained. In the reverse specification (emotional intelligence → job satisfaction → performance), the indirect association was not statistically supported (ab = 0.009; 95% bootstrap CI: −0.005 to 0.025). Harman’s first unrotated component accounted for 38.7% of item variance. Conclusions: Emotional intelligence accounted statistically for a meaningful but incomplete portion of the job satisfaction–performance association in this sample. The results are compatible with the proposed indirect pathway but do not establish temporal order or causality. Replication using longitudinal designs, multiple data sources, objective performance indicators, and additional occupational factors such as emotional labor, burnout, workload, and organizational support is warranted. Full article

Efficient operation of interconnected production systems requires accurate modeling of upstream–downstream interactions to support informed operational decision-making. This study proposes an interconnected Adaptive Neuro-Fuzzy Inference System (ANFIS) framework consisting of two sequentially linked ANFIS models representing the upstream manufacturing stage and the downstream packaging stage, where the output of the first model is incorporated as an input to the second model. The framework uses operational Key Performance Indicators (KPIs), including mean time between failures (MTBF), mean time to repair (MTTR), uptime, reject rate, short stops, and long stops, to capture the nonlinear relationships between manufacturing and packaging operations. Unlike conventional single-model approaches, the proposed methodology represents the manufacturing and packaging stages as interconnected neuro-fuzzy subsystems, explicitly modeling their operational dependencies while maintaining model interpretability. Following data preprocessing and conditioning, two ANFIS models were developed using real industrial data and integrated into a MATLAB/Simulink environment for scenario-based analysis. The developed ANFIS models achieved low testing errors and satisfactory predictive performance on real industrial data. Simulation-based analyses were subsequently conducted to evaluate the effects of stoppage behavior and maintenance-related improvements on production-line uptime. The results indicate that the proposed framework captures upstream–downstream production dependencies and provides an interpretable decision-support tool for evaluating operational improvement scenarios in manufacturing environments. Full article

Post-disaster reconstruction often prioritises the rapid delivery of housing and infrastructure, yet the long-term everyday performance of commercial environments embedded in recovery plans remains poorly understood. This study investigates why broadly similar post-disaster commercial blocks in Yenikent (Sakarya, Türkiye) have developed markedly different levels of spatial vitality and long-term everyday use. Yenikent is a state-led satellite city developed after the 1999 Marmara Earthquake, where residential, administrative and service functions were relocated to higher ground as part of a planned secondary urban centre. Twenty-five years later, these purpose-built commercial centres display markedly different levels and forms of everyday use, ranging from active neighbourhood service hubs to abandoned urban voids. Using a multiple-case, mixed-methods design, the study analyses all twelve post-disaster commercial clusters (eighteen buildings) through sectional and morphological analysis, a four-indicator Spatial Vitality Matrix (stationary activity, pedestrian flow, physical permeability, and active occupancy), and 88 semi-structured interviews with businesses, users, and neighbourhood headmen. The findings identify three dominant trajectories: (i) relatively robust service hubs sustained by institutional and neighbourhood anchors, (ii) fragile clusters where vitality is concentrated along accessible ground-level edges while upper or internal spaces shift towards storage, institutional, or ancillary uses, and (iii) functionally obsolete complexes associated with peripheral siting, poor topographic adaptation, and weak accessibility. The results further demonstrate that formal occupancy is not necessarily equivalent to spatial vitality: sectional relationships with the terrain, entrance legibility, façade permeability, and the integration of anchors into shared circulation are closely associated with whether commercial spaces sustain everyday activity. Interview evidence additionally reveals case-specific differences in perceived safety and gendered use of internal corridors and courtyards. The study supports a section-sensitive approach to post-disaster commercial development that prioritises topographic adaptation, legible access, active edges, and the integration of everyday anchors into shared spatial networks. Full article

Páramo ecosystems are among the most biodiverse and hydrologically critical landscapes on Earth, yet their long-term sustainability is increasingly threatened by climate-driven wildfires. Vegetation Cover Fires (VCFs) in these high-altitude environments degrade carbon stocks, disrupt freshwater regulation, and undermine biodiversity conservation goals central to the UN Sustainable Development Goals (SDGs 13, 15, and 6). Between 2001 and 2023, 128 fire events consumed approximately 815 ha in the Sumapaz locality (the world’s largest continuous páramo), representing 64.9% of all fires recorded across Bogotá’s 20 localities. Despite this disproportionate ecological and social impact, no spatially explicit, operational risk management framework has been available for the region, representing a critical sustainability governance gap. This study addresses that gap by proposing an integrated climate-adaptive risk assessment and management strategy based on (i) the Canadian Forest Fire Danger Rating System Fire Weather Index (FWI), derived from ERA5 reanalysis climate data; (ii) spatial and temporal hotspot analysis of MODIS FIRMS active fire detections; and (iii) IDEAM’s multi-component vulnerability and threat scoring protocol. Spatial data were processed using ArcGIS, and FWI sub-indices were computed for each month of the 2001–2023 period. The FWI averaged 0.78 (low danger) across the study period yet peaked at 13.7 in February 2010 (moderate-to-high danger), consistent with the year of highest recorded fire activity (19 events). High- and very high-risk areas (3.70% combined) coincide with slopes >25%, the presence of the invasive and pyrogenic Ulex europaeus, and proximity to populated and agricultural lands. This study concludes with a three-pillar risk management framework—risk knowledge, risk reduction, and disaster management—providing spatially targeted, operationally viable strategies for local and institutional actors that directly support the sustainable conservation of páramo ecosystem services (water supply, carbon sequestration, biodiversity). The framework is designed to be updatable on a monthly basis using freely available ERA5 data, enabling continuous adaptive governance of wildfire risk as a contribution to long-term territorial sustainability. Limitations regarding MODIS detection uncertainty, ERA5 spatial resolution in complex terrain, and the need for probabilistic modeling are explicitly acknowledged. Full article

Edge-enabled Internet of Things (IoT) systems are rapidly becoming the operational substrate of mission-critical infrastructure spanning industrial automation, smart healthcare, vehicular ecosystems, and cyber–physical environments. The distributed, resource-constrained, and physically exposed nature of these systems makes them persistent targets for a diverse and evolving spectrum of cyber attacks. Critically, cyber attacks on edge-enabled IoT systems do not merely threaten data confidentiality; they simultaneously erode two interdependent operational objectives: sustainability, the ability of the system to maintain continuous, energy-efficient operation within its resource envelope and resilience, the ability to absorb adversarial disruptions, recover operational continuity, and adapt to prevent recurrence. The structural conflict between defending sustainability and maintaining resilience under active cyberattack conditions constitutes a research gap that prior surveys have not systematically addressed. This survey introduces a cyber attack-driven Sustainability–Resilience (S-R) framework that positions cyber threats as the primary stressor forcing a bilateral trade-off between operational efficiency and continuity in edge-enabled IoT systems. A five-layer, attack-centric taxonomy is developed spanning: network-layer attacks (DDoS, MitM, routing manipulation, jamming); device and firmware attacks (malware injection, firmware compromise, sensor spoofing); data and AI/ML attacks (adversarial inputs, data poisoning, model inversion); federated and Byzantine attacks (gradient poisoning, backdoor injection, free-riding); and advanced persistent threats (APT-class intrusions, ransomware, LLM prompt injection, zero-day exploitation). For each attack class, the survey systematically analyses the impact on sustainability and resilience objectives, the resulting S-R conflict, and the state-of-the-art defensive strategies. The framework is formalised as a maximin optimisation over the joint S-R objective surface, incorporating the adaptive, goal-directed nature of the adversary through a game-theoretic formulation. Cross-domain analysis spanning Industrial IoT, smart healthcare, Internet of Vehicles, UAV-assisted IoT, smart grids, and tactical edge networks establishes domain-specific S-R operating constraints under representative attack scenarios. The survey concludes with a structured characterisation of open research challenges and forward-looking directions, providing a prioritised research agenda for advancing simultaneously sustainable and adversarially resilient edge-enabled IoT ecosystems. 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

Ecological speciation via divergent selection despite gene flow remains poorly documented in fungi. We integrated niche modeling and comparative genomics to examine ecological and genomic divergence among three ectomycorrhizal Thelephora species. T. ganbajun is restricted to mid-elevation Yunnan with climatically stable environments; T. aurantiotincta occupies lower elevations with pronounced temperature seasonality; and T. terrestris displays the broadest distribution, tolerating extreme temperature fluctuations and low precipitation. Phylogenomic analysis recovered the three species as distinct lineages, with T. terrestris diverging at 21.16 Mya and T. ganbajun and T. aurantiotincta splitting at 14.97 Mya. Gene flow analysis revealed unidirectional introgression: T. terrestris donated to both other species, and T. aurantiotincta donated to T. ganbajun, with negligible reverse migration. T. terrestris possesses the largest genome (40.73 Mb) with recent LTR retrotransposon expansion and amplified cytochrome P450 and lignocellulose-active CAZymes. T. ganbajun shows expanded copper transport and cell wall remodeling genes, while T. aurantiotincta exhibits expansion of fatty acid metabolism genes. These results document pronounced niche differentiation, genomic divergence, and directional historical gene flow among closely related species, providing an ecological and genomic basis for examining ecological speciation in fungi. Full article

Decisions made at the early stage of the design process have a substantial influence on the environmental, socio-economic, and technical performance of a building throughout its life cycle. Although an early-stage sustainability assessment is essential to achieving climate-neutral and nearly zero-energy buildings, its implementation remains challenging due to the need to integrate and evaluate large volumes of heterogeneous data originating from multiple sources and disciplines. In view of this challenge, this study presents a hybrid multi-level approach in which data fusion is combined with Building Information Modeling (BIM), web-based technologies, and multi-criteria decision-making (MCDM) methods to support the assessment of sustainable alternative design solutions for buildings. The proposed approach is implemented in the BIM4NZEB-DS web-based decision-support system and validated through a case study. In the data fusion model, BIM-derived information is combined with data from external sources within a unified environment, enabling designers to define sustainability indicators, assign relative level of importance, and evaluate design alternatives across key sustainability dimensions. Automated multi-criteria analysis enables the ranking and comparison of design solutions. The results of the case study demonstrate the capability of the proposed approach in terms of efficiently combining heterogeneous datasets and automating and facilitating systematic comparison of early-stage design alternatives. The findings indicate that a combination of BIM-based information management, web-enabled data fusion, and automated MCDM analysis enhances the transparency, consistency, and robustness of sustainability-oriented decision making. The approach described here contributes to the advancement of digital decision-support systems for sustainable building design and represents a practical tool for supporting climate-neutral building development during the most influential stages of the design process. Full article