Search Results (18,285)

High-voltage electric-vehicle (EV) fast charging requires accurate coordination between the off-board charger and the battery management system during voltage and current negotiation. This study evaluates a Hybrid Particle Swarm Optimization-Whale Optimization Algorithm (HPWOA) schedule for training a dual-output feedforward neural network that directly estimates ChargePower_kW and ChargeCurrent_A. Ten protocol-state and battery-condition variables were used as inputs. The 158-dimensional neural-weight vector was optimized using 75 Particle Swarm Optimization (PSO) iterations, followed by 75 Whale Optimization Algorithm (WOA) iterations. Using the supplied 500-record dataset, a reproducible 30-seed sample-level evaluation was conducted with a common 3775 fitness-function-evaluation budget for PSO, the WOA, the SFSA, and the HPWOA. The Stochastic Fractal Search Algorithm (SFSA), therefore, used 30 iterations because it evaluates five diffusion candidates per individual. The reported HPWOA mean ± standard deviation (SD) was RMSE = 4.658 ± 0.986 kW and R² = 0.843 ± 0.071 for power, and RMSE = 12.686 ± 2.687 A and R² = 0.858 ± 0.059 for current. The HPWOA outperformed the WOA and SFSA, but not standalone PSO. A conventional mini-batch Adam-trained dual-output neural network produced RMSE = 1.704 ± 0.121 kW and 4.946 ± 0.273 A, and R² = 0.980 ± 0.003 and 0.979 ± 0.002, respectively. Charger-grouped five-fold validation gave the HPWOA R² = 0.857 ± 0.045 (power) and 0.873 ± 0.048 (current). An analytical P = V × I reconstruction was physically consistent in construction and did not show a statistically significant power–RMSE difference from direct HPWOA outputs. The results, therefore, position the two-phase schedule as a reproducible comparative baseline rather than as a demonstrated replacement for gradient-based training or a physically constrained reconstruction. Full article

Background: Highly pathogenic avian influenza (HPAI) poses a critical global threat to both the poultry industry and public health. Although Morocco currently maintains HPAI-free status, the country faces substantial risk due to its location along major migratory flyways and its extensive commercial trade networks within the poultry sector. Available data indicate that the links between live bird markets, production farms, and related facilities in the poultry sector constitute a pivotal determinant of disease epidemiology. This study aimed to analyze these movements and determine how they can influence the spread of the disease and to guide policymakers in developing effective risk-based surveillance and control strategies tailored to the local context.: A questionnaire-based cross-sectional survey was conducted across the Casablanca-Settat region, including nine provinces and 138 municipalities, to investigate the movement patterns within the poultry sector in this region. Social network analysis (SNA) was employed to construct a movement network, and findings were spatially visualized using Geographic Information Systems (GIS) and analyzed through network analysis in R.: A total of 945 transport routes were recorded in 126 municipalities. Centrality measures identified three predominant network nodes exhibiting high degree and betweenness centrality values. Our findings indicate that although the network has a low density, the transmission of IAHP remains critical due to frequent bidirectional links and a heterogeneous network structure. The high concentration of movements by a few “hub” municipalities leads to an early emergence and rapid dissemination.: The identification of highly influential nodes allows veterinary authorities to prioritize and target surveillance activities toward municipalities with the greatest likelihood of disease introduction or persistence. The network’s structural connectivity suggests a theoretical potential for rapid HPAI spread, underscoring the importance of the frequent bidirectional links identified between municipalities. This protects both Morocco’s poultry industry and public health. Full article

Municipal dog shelters are often grouped as outsourced facilities despite operator differences. We analyzed 33,875 dog admissions and 90 municipality-year observations from 2016 to 2025 across nine Korean municipalities, with three per operator category; operator type was fixed and nested within municipality. Among 32,838 known outcomes, live-release rates were 69.1%, 58.1%, and 40.0%, and euthanasia rates were 14.0%, 22.8%, and 42.5% in corporation-operated, veterinary hospital-operated, and individually operated shelters, respectively. The corporation live-release estimate ranged from 62.6% to 72.0% under alternative classifications of unresolved records. Small-sample cluster-robust comparisons were imprecise: the individually versus corporation-operated euthanasia odds ratio was 4.72 (95% confidence interval, 0.40–56.35), and every operator interval included 1.0. Contextual adjustment yielded odds ratios of 0.97 for both outcomes for veterinary hospital- versus corporation-operated shelters. Among 33,836 admissions with common 180-day follow-up, live-release rates were 59.9%, 53.0%, and 39.8%, and euthanasia rates were 9.1%, 16.7%, and 42.0%; annual odds ratios were 1.05 (0.93–1.19) and 0.90 (0.78–1.05), respectively. Results describe operator-linked municipal systems, not causal effects. Standardized follow-up, complete reporting, capacity monitoring, and larger prospective studies are needed. Full article

Accidents between vehicles and wildlife (WVCs) represent a complex management challenge, requiring integrated strategies that balance biodiversity conservation with public security and socio-ecological resilience. However, existing GIS hotspot analyses often identify spatial patterns without quantifying the structural landscape drivers that compel animal–road interactions. This study aims to identify “ecological traps” through an integrated landscape diagnostic framework combining Kernel Density Estimation (KDE) for statistical hotspot identification and landscape metrics (FRAGSTATS) for structural diagnosis, using the wild boar (Sus scrofa) as a focal species. An exploratory case analysis of high-collision locations was conducted, utilizing a high-quality dataset of 161 precisely georeferenced incidents recorded between 2015 and 2020 within the most critical municipalities of the Province of Avellino (Southern Italy). Results highlight two primary hotspots: the Guardia Lombardi-Conza corridor and the Avellino Nord-Pratola Serra axis. Quantitative analysis reveals that 39.1% of incidents occurred in non-irrigated arable lands and 19.9% in broad-leaved forests, with 52.8% of events situated within 500 m of river systems, which function as primary ecological movement corridors. Furthermore, fragmentation indices (Patch Density, Edge Density) were significantly higher in these focus areas, confirming that habitat isolation and the loss of core patches force animals to traverse infrastructure. These findings underscore the urgency of evidence-based spatial planning, offering a methodological framework with potential applicability for prioritizing mitigation actions, such as ecological corridors and intelligent signaling, to enhance the resilience of socio-ecological systems. This framework provides a scalable model for sustainable land management, ensuring that biodiversity conservation is integrated into long-term infrastructure governance. Full article

Automatic classification of livestock behaviors, including feeding, rumination, standing, lying, walking, and drinking, using wearable accelerometers has become an important tool in precision livestock farming (PLF). However, the influence of preprocessing strategies on classification performance and computational efficiency remains poorly understood. This study investigated the effects of filtering, normalization, data representation, and machine learning algorithms using five accelerometer datasets comprising 3,533,974 records collected from different livestock species and sampling frequencies. Four filtering strategies, three normalization methods, two data representations, and four machine learning algorithms were evaluated using a standardized pipeline. Model performance was assessed using weighted F1-scores together with statistical and computational analyses. The absence of filtering achieved the highest average performance, reaching a weighted F1-score of 0.773 with Random Forest, whereas high-pass filtering consistently reduced performance (minimum average F1 = 0.605 across sampling frequencies) while increasing computational cost. Z-score standardization improved the performance of scale-sensitive algorithms, increasing SVM performance by up to 8.6% compared with no normalization. Feature-based representations provided greater stability for conventional machine learning models, whereas raw signals generally benefited the 1D-CNN. These findings demonstrate that preprocessing strategies should be selected according to the learning algorithm, signal characteristics, and computational constraints rather than applied as universal procedures, providing methodological guidance for the development of efficient livestock behavior monitoring systems. Full article

Decades-old enterprise-resource-planning (ERP) systems lock operational data inside unstructured, human-readable reports, forcing slow, costly, error-prone manual re-keying. Because multimodal large language model (MLLM) capability is uneven, deploying MLLMs for extraction means trusting outputs without a labeled reference. We test this with a within-document controlled experiment on 400 controlled-substance stock-ledger documents (2951 records, 11 fields, predominantly Thai) from a Thai pharmaceutical factory, comparing trained human double-entry against four open-weight MLLMs (2 × 2 design: vendor × architecture) via OpenRouter. Human double-entry left 14 discrepancies against the adjudicated gold standard, none common to both operators. The strongest model, Qwen3-VL-32B-Instruct (Dense), reached 93.95% cell accuracy; among these four models, field accuracy varied more across vendors, whereas structural completeness differed consistently between dense models (0 missing records) and Mixture-of-Experts models (up to 51 of 2951 dropped). Deterministic accounting invariants flagged 0.61% of its records, leaving the unflagged majority 94.1% accurate across all 11 fields; adding calendar rules flagged 4.61% and raised residual date accuracy from 92.1% to 95.9%. We report both operating points and recommend the extended level where date fidelity is regulatory-critical. The pipeline is 13.5–29.4× faster in wall-clock terms and 97.5–99.5% cheaper. Gold-free, rule-based verification thus locates where MLLM reliability holds, giving human–AI collaboration quantified, disclosed residual risk rather than an implied guarantee. Even at the more conservative operating point, unflagged records average 94.5% accuracy across all 11 fields but only 43.7% on the free-text Remarks field, which the triage cannot check; the results support risk reduction and the localization of review effort, not unrestricted regulatory reliability across all fields. Full article

Periodic manual oversight is difficult to scale for large portfolios of funded research projects. Progress delays, budget irregularities, and superficial reporting often go undetected until final acceptance. Many conventional detection methods also generate false positives when contextually supported schedule adjustments resemble anomalous patterns. We present the Intelligent Project Monitoring System (IPMS), which couples a feature-decoupled multi-task LSTM-Attention network with an Adaptive Isolation Forest. The LSTM-Attention component models project workflows through finite state machines and predicts milestone deviations. The Adaptive Isolation Forest then flags records after a context gate screens cases meeting the study’s legacy legitimate-deviation criteria before final alerting. A multi-head attention module tracks how execution performance evolves over the project lifecycle, and the system includes a loss-ratio signal for candidate-shift review and feedback-gated controlled recalibration; its response was evaluated only under one researcher-designed synthetic global policy-change injection. On a real-world dataset from a provincial management platform, in which approximately 7% of legacy-labeled records carried an anomalous reference label, IPMS achieved an AUC of 0.924 and a false-positive rate of 4.2% against the available legacy binary reference labels, a 76.9% relative reduction in observed FPR compared with standard Isolation Forest. Milestone deviation prediction reached an MAE of 1.85 days, 34.2% lower than standard LSTM. Execution profiling achieved an MAE of 0.082. Removing the deviation filter alone degraded F1 by 12.3%, and removing multi-scale fusion increased the miss rate for long-duration stalls by 23%. Full article

Background/Objectives: This study aimed to investigate the clinical characteristics associated with psychiatric hospitalizations in children with ASD and to evaluate changes in blood-based Inflammatory Indices following treatment. Methods: A retrospective study of 269 children and adolescents with ASD (≤18 years) admitted to Nanjing Brain Hospital between 2012 and 2023 was conducted. Electronic medical records were reviewed for demographic characteristics, primary reasons for hospitalization, ASD-specific clinical scale scores, and routine laboratory parameters. The systemic immune–inflammation index (SII), neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), platelet-to-lymphocyte ratio (PLR), and non-enzymatic antioxidant indicators (uric acid, total bilirubin, direct bilirubin, prealbumin) were calculated. Results: The mean age at admission was 12.13 years, and 80.3% of admitted patients were male. The leading reasons for hospitalization were aggression (76.21%), psychosocial stressors (48.32%), and ADHD-related symptoms (46.09%), with distinct patterns observed by intellectual disability status, sex, and developmental stage. Multiple regression analyses revealed that age, sex, and specific admission indications were significantly associated with baseline inflammatory indices (SII, NLR, PLR, WBC, NEU, PLT; all p < 0.05) and antioxidant indices (UA, TBIL, DBIL, PA; all p < 0.05). Among 196 patients with 4-week follow-up data, significant post-treatment reductions were observed in SII, WBC, NEU, UA, TBIL, and DBIL, alongside increased prealbumin (all p < 0.05). Conclusions: Psychiatric hospitalizations of Chinese children with ASD are driven by distinct behavioral and neurodevelopmental profiles. Concurrently, accessible blood-based inflammatory and antioxidant indices show significant baseline clinical associations and post-treatment alterations, suggesting their potential as adjunctive biological markers in acute psychiatric settings. These findings are limited by the retrospective single-center design, absence of a control group, and lack of post-treatment clinical severity assessments. Full article

Coupling-coordination degree (CCD) indices are often interpreted as evidence that urban systems are developing in a mutually supportive manner. The standard formula, however, combines similarity between subsystem scores with their average level. This article evaluates those two components against the following external outcome: municipal environmental services. A city panel for China in 2002–2024 combines broadband and mobile adoption, urban road provision, licensed internet data-center records, and seven indicators of water, gas, drainage, sewage, waste, and urban greening. The preferred sample contains 6094 observations for 275 cities in 28 provinces. We estimate city fixed-effect models with province-by-year fixed effects and report province-clustered, two-way-clustered, and 9999-repetition wild-cluster-bootstrap inference. In the baseline rank-based construction, a one-standard-deviation increase in relative digital-transport alignment is associated with a 0.046-standard-deviation increase in the environmental-service index (wild-bootstrap p = 0.042), whereas the portfolio-level coefficient is 0.191 (p < 0.001). The alignment estimate is concentrated in water and gas access and is not robust to complete-component outcomes, PCA or entropy weighting, logarithmic standardization, road density, or lags beyond one year. Portfolio level remains positive across these tests, although lead and reverse-direction estimates preclude causal interpretation. The findings show why infrastructure scale, relative configuration, and realized service outcomes should be monitored separately. For SDGs 9 and 11, city governments should target documented service bottlenecks and operating capacity rather than maximize a composite coordination score. Full article

This study presents a screening-level multitemporal assessment of surface water quality in the Atrato River (Chocó, Colombia), a tropical river system heavily influenced by artisanal and illegal gold mining, elevated sediment loads, and untreated domestic wastewater. Using records from 20 monitoring stations (2020–2025) operated by the regional environmental authority (CODECHOCÓ), six water quality indices (ICA, ICOMO, ICOMI, ICOSUS, ICOMINERÍA, ICOTRO) were analyzed through a framework combining Theil–Sen trend estimation, Kendall’s tau correlation, inter-period median comparison, and an index orientation normalization procedure. Results suggest spatially heterogeneous patterns: organic contamination improved in 11 of 17 evaluable stations, while mining contamination (ICOMINERÍA) showed positive directional slopes in 14 of 17 evaluable stations. Of these, four middle-reach stations reached statistical significance (p < 0.05; Kendall’s τ = 0.618–0.667). At the network level, the mean ICOMINERÍA value increased from 0.191 in 2020 to 0.502 in 2025 (+163%), representing a directional signal that should be interpreted as screening-level evidence requiring confirmation through denser temporal sampling. The proposed framework provides support for potential applicability for detecting environmental change in data-scarce monitoring networks and provide screening-level evidence relevant to the enforcement monitoring of environmental rights granted under Colombia’s landmark Sentencia T-622 (2016). Full article

Background: Health assessment in custodial settings represents a core medico-legal function, particularly when clinical changes may influence detention compatibility and judicial decisions. Objective documentation of disease progression during incarceration is essential to ensure respect for the principle of healthcare equivalence and the protection of fundamental rights. Methods: A retrospective descriptive medico-legal analysis was conducted on 37 male inmates referred for forensic evaluation. Standardized assessments included anamnesis, physical and psychiatric examination, evaluation of medical records, and comparative evaluation of pre-detention and detention-period health status. Results: Pre-existing conditions accounted for 73% of pathologies, while 27% developed during imprisonment. Clinical deterioration occurred in 35.1% of inmates (13 cases). Psychiatric disorders were frequent (27%, 10 inmates), predominantly depressive. Conclusions: The findings indicate that imprisonment may constitute a setting that contributes to the progression of medical and psychiatric conditions, highlighting structural and organizational challenges within penitentiary healthcare. Strengthening standardized medico-legal assessment protocols, improving multidisciplinary collaboration, and ensuring continuity of care between prison and community health services are crucial steps toward protecting inmates’ health. This study provides an exploratory assessment of health conditions in a cohort of inmates within the Italian correctional system, emphasizing the central role of healthcare in custodial settings. Since many Italian correctional facilities are equipped with well-developed healthcare services and appropriate diagnostic and therapeutic resources, numerous acute and chronic medical conditions can be effectively managed within the prison setting, ensuring continuity of care while limiting the need for transfer to external healthcare facilities. Full article

Background: Clinical pharmacists play a crucial role in identifying and resolving drug-related problems (DRPs) in hospitals. However, there is limited empirical evidence concerning their effect on service organizations in smaller regional facilities in Saudi Arabia. Methods: A retrospective serial cross-sectional study was conducted using three years of administrative data (2023–2025) from two public hospitals in the Al-Baha region. A total of 1812 different database records were identified after full-scale data cleaning, validation, and deduplication. The statistical framework included chi-square test of independence, pairwise two-proportions Z-test, three multivariate binary logistic regression models, Cramer’s V association analysis, and Ward’s hierarchical cluster modeling. Results: Active intervention rates significantly increased from 48.9% in 2023 to 72.4% in 2024 (z = −8.58, p < 0.001), stabilizing at 69.6% throughout 2025 (p < 0.001 versus the 2023 baseline), because 2023 data came from a different source system than 2024 to 2025 data, this pattern is reported descriptively rather as a programmatic integration. The most frequent clinical actions addressed therapy optimization (28.4%), inappropriate dosage regimens (23.6%), and missing drug orders (18.8%). Most entries recorded were for adult intensive care units (ICUs) (74.1%), and most involved patients aged 65 years or older (61.1% of records). ICU setting was independently associated with the intervention being classified as therapy optimization (adjusted odds ratio [aOR] 2.64, p < 0.001), but it was an inverse predictor of dosage-error tracking (OR 0.30, p < 0.001). Conclusions: This is among the first serial cross-sectional studies to build an evidence-based framework describing clinical pharmacy practices in the Al-Baha region, showing pharmacists’ roles and prioritizing advanced clinical training programs, reinforcing the need for critical analysis of health cluster policies across peripheral Saudi networks. Full article

This study presents a systematic literature review of spatial planning frameworks for coastal hazard mitigation, conducted in accordance with the PRISMA 2020 guidelines. A structured search of two academic databases, Scopus and Web of Science, covering the period 2016 to 2026 identified 368 records, of which 27 peer-reviewed studies were included in the final synthesis. The review pursues four interrelated objectives: analyzing global publication trends in research on spatial planning for coastal hazard mitigation; identifying and describing coastal hazard typologies and their associated mitigation approaches; examining how spatial planning frameworks are integrated with hazard mitigation strategies; and synthesizing cross-cutting constructs that structure an integrative analytical model. Findings reveal a marked intensification of scholarly output over the final third of the review period, with Asia–Pacific and Europe as the most represented regions, while sub-Saharan Africa and small island developing states remain critically underrepresented. Coastal hazards are classified into four categories: slow-onset climate and hydro-geological processes, acute hydro-meteorological events, multi-risk systemic hazard interactions, and ecological and environmental degradation. Three analytically distinct integration families are identified: geospatial and technical modeling, NbS and ecosystem-planning integration, and participatory governance and institutional integration. From these, four cross-cutting constructs emerge inductively across the evidence base, namely spatial risk assessment and geospatial modeling, land-use governance and climate-proof planning, community-based resilience and participatory governance, and ecosystem-based adaptation and nature-based solutions, which together constitute an integrative analytical framework. The discussion demonstrates that governance capacity, rather than technical sophistication, is the primary moderator of implementation effectiveness, and that socio-spatial equity in hazard planning represents a cross-regional challenge irrespective of governance capacity level. Limitations include the geographic concentration of the evidence based in high-capacity planning contexts and the predominantly projected rather than implemented nature of effectiveness evidence. Full article

Rule-defined rankings often transform continuous marks, discrete judgments, trimming rules, caps, truncation, and tie-breaking variables into a single official order. When ranking margins are small, a formally valid outcome may nevertheless be sensitive to marginal changes in the recorded decision state. This article presents Score Cloud Analysis as a rule-aware statistical sensitivity-reporting method for such systems. The method represents the official score as a deterministic function of recorded inputs and recomputes scores and ranks under finite perturbations, rather than relying on local linear approximations. It defines deterministic diagnostics, including directional Group A/Group C (A/C) decision exposures and their aggregate contested-point exposure, total sensitivity exposure, fragility-to-margin ratios, Score Cloud overlap, and single-call rank sensitivity, and separates these from scenario-conditional Monte Carlo Rank Cloud frequencies. The method is illustrated using a synthetic Wushu Taolu case study because that setting contains majority decisions, trimmed rater marks, discrete difficulty values, Head Judge adjustments, and tie-break rules. The synthetic experiment is an internal-consistency stress test, not an empirical validation and not an estimate of judging-error rates. A small sensitivity study varies perturbation scale and intra-athlete dependence to show which conclusions are scenario-specific. The method separates procedural validity from local rank robustness and is transferable to other reconstructable, rule-based, rater-mediated ranking systems. Full article

Engineering schematics, such as electrical, mechanical, piping and instrumentation diagrams, record how industrial plants are built and operated, but most of them survive only as images or scanned sheets that software cannot read. Digitizing them by hand is slow and error-prone: an expert must find and classify hundreds of symbols, read dense technical text, and work out which label belongs to which component. Progress with learning-based methods has been held back on two fronts at once. There are almost no annotations that connect a text label to its symbol, and the drawings themselves are usually confidential, so even unlabeled sheets rarely reach the public domain. We address this with a system that turns a drawing into a structured, queryable graph: it detects and classifies the graphical components with an object detector, recovers the technical text, and then resolves which label belongs to which component. Our contributions are threefold: (i) the first at-scale dataset of manually annotated text-to-symbol links for industrial schematics; (ii) a complete, deployable digitization system combining tiled detection with sliced inference, off-the-shelf OCR, and a text-to-symbol association stage; and (iii) a rigorous, leakage-free benchmark of association methods. Under an observable-only candidate protocol, we find that on logic circuits association is dominated by geometry: a simple pairwise model reaches about 99% top-1 and a graph neural network matches but does not exceed it, whereas the denser P&IDs still benefit from a geometric rule-based chain. Detection reaches an mAP@50 of 0.995 on logic circuits and about 0.91 across the 107-class P&ID taxonomy. The system produces a partial semantic graph; connecting lines and flow direction are not extracted. Full article