Search Results (21,707)

The rapid growth of electric-vehicle charging demand has increased the need for reliable station-level congestion monitoring and early warning. Existing studies mainly predict charging demand, load, occupancy, or availability, whereas charging congestion pressure is usually shaped by multiple operational factors. This study proposes an interpretable station-level charging congestion pressure assessment and multi-horizon early-warning framework. A Charging Congestion Pressure Index (CCPI) is constructed by integrating occupancy, arrival pressure, charging or occupation duration, service volume, and price–time context into a unified station–hour pressure representation. Based on temporally aligned current, lagged, and rolling features, future high-pressure states are predicted at 1 h, 3 h, and 6 h horizons. Using 1423 charging stations and 6,181,512 station–hour observations from September 2022 to February 2023, this study evaluates whether the proposed station–hour pressure representation can support multi-horizon high-pressure warning under temporal and station-level validation settings. Results show that current pressure is a strong short-term persistence baseline, while learning-based models provide larger F1-score gains at longer horizons. Extreme Gradient Boosting (XGBoost) achieved F1 gains of +0.022, +0.040, and +0.068 over the persistence baseline at the 1 h, 3 h, and 6 h horizons, respectively. Ablation, temporal validation, station holdout validation, and block-bootstrap tests further support the stability of the proposed framework. These findings indicate that interpretable pressure-index construction and temporally consistent multi-horizon warning can provide an engineering decision-support basis for charging-infrastructure operation, station-level congestion monitoring, and proactive resource management. Full article

Global olive oil consumption continues to grow, and Spain, particularly Andalusia, holds the largest cultivation area within the EU, covering 1.68 million hectares, which expanded by 7.7% between 2013 and 2023. However, precise data on the planting systems used are lacking; this information is essential for assessing crop intensification and its environmental, economic, and social implications. This study applies Deep Learning (DL) techniques to classify the planting system of olive parcels previously identified in a national cadastral database, using high-resolution RGB imagery acquired from a nationwide public aerial orthophotography program. Five DL algorithms (VGG19, InceptionV3, MobileNet, ResNet50, and Xception) were compared across different planting-system classification schemes and spatial resolutions, using a dataset of 8000 images (2000 per class) divided into training (70%), validation (20%), and testing (10%) subsets. The best results were obtained with a three-class scheme (traditional, intensive, and super-intensive) at 0.5 m/pixel resolution, reaching precision values of 99.00% with MobileNet and 98.66% with VGG19. These results support extending the approach to larger areas for regional-scale monitoring of planting systems, including other Mediterranean olive-growing regions, and provide a basis for further research on the water use and labor demand associated with each planting system. Full article

Background: Crude oil refinery waste sludge (COWS) ranks among the most compositionally complex and ecotoxicologically hazardous industrial residues. Although bulk total petroleum hydrocarbon (TPH) and summed polycyclic aromatic hydrocarbon (PAH) removal are routinely reported, the metabolite-level biochemical fate of individual petrogenic compounds, spanning ring dihydroxylation, catechol cleavage, and entry into central carbon metabolism, remains largely unmapped under co-composting with diverse animal manures. Objectives: This study aimed to construct a metabolite-resolved, microbially anchored biochemical fate map of crude oil sludge during co-composting. Methods: Aerobic microcosms combining crude oil sludge, garden soil, and a wood-chip bulking agent were amended separately with poultry, horse, cow, or swine/pig manure alongside an unamended control and then incubated at 22 °C for 300 days. Analyses integrated untargeted gas chromatography-mass spectrometry (GC-MS) metabolomics, targeted PAH quantification (EPA Methods 3541/8270), 16S rRNA gene amplicon sequencing (Illumina MiSeq, V1–V3, paired-end 300 bp), physicochemical monitoring, and culture-dependent isolation, with National Institute of Standards and Technology (NIST) Mass Spectral library annotation. Results: GC-MS resolved 1169 metabolite features across 17 samples, comprising 538 annotated compounds within 11 chemical classes and 631 unknowns, of which 151 recurred in at least 10 samples. Petrogenic markers (n-alkanes C₁₄–C₃₆, hopanoids, steranes, and alkylated dibenzothiophenes) and ring-cleavage intermediates (2-hydroxyfluorene, 1,4-naphthoquinone, phenanthrene-methanol, benzenediols, butanedioic acid, fatty alcohols C₁₆–C₂₀) elucidated a four-stage degradation cascade consistent with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways map01220 and map00624. PAH mean-removal ranked swine/pig (88.0%) > horse (87.0%) > poultry (80.5%) > cow (79.1%) > control (68.2%). Sequencing recovered 2969 operational taxonomic units (OTUs) enriched in Pseudomonas, Achromobacter, Stutzerimonas, Dietzia, Gordonia, and Mycobacterium, with Pseudomonas dominating high-removal systems; respiration peaked at 18.7 mg CO₂-C g⁻¹ in poultry treatments. Conclusions: This work establishes a metabolite-resolved map linking hydrocarbonoclastic taxa to separate degradation steps. The co-occurrence of oxygenated PAH intermediates with decreasing parent PAH concentrations serves as an indicator of transformation processes and may assist in identifying potential residual-risk signals, thereby supporting remediation evaluation and process optimization. Full article

Nerve injuries are frequent complications of complex oral and maxillofacial surgical procedures, particularly following extensive tumor resections. Secretome-based, cell-free therapies derived from mesenchymal stromal cells have emerged as promising regenerative approaches; however, robust manufacturing requires the identification of critical quality attributes (CQAs) that ensure product potency and consistency. The influence of replicative senescence during in vitro expansion on the quality of jaw periosteum-derived mesenchymal stromal cell (JPC) secretomes has not yet been established. This study investigated whether the expansion state of JPCs affects the composition and neuro-supportive potency of their secretomes. Secretomes from four independent JPC donors were collected separately at early and late passages, pooled within each passage-specific preparation, and applied to human induced pluripotent stem cell-derived neurons. Neuronal survival, neurite outgrowth, and neuronal marker expression were assessed as functional readouts. Secretome composition was characterized by quantitative proteomics and enzyme-linked immunosorbent assay (ELISA) of selected senescence-associated secretory phenotype (SASP) factors. Secretomes derived from early-passage JPCs significantly enhanced neuronal survival and neurite outgrowth, whereas late-passage secretomes displayed reduced neuro-supportive activity. Proteomic profiling identified a pronounced shift toward inflammatory and stress-associated signaling, whereas performed ELISAs confirmed senescence-associated remodeling of the secretome, including increased abundance of SASP-associated factors in late-passage preparations. These findings demonstrate that prolonged in vitro expansion profoundly influences both the composition and biological potency of JPC-derived secretomes. Collectively, this study identifies the passage-associated senescence-like phenotype of JPCs as a key determinant of secretome quality and supports its consideration as a critical quality attribute for the manufacturing and standardization of JPC-derived secretome products. Monitoring and controlling the expansion state of JPCs may therefore be essential to ensure the consistency, potency, and clinical translation of secretome-based regenerative therapies. Full article

Evidence linking annual wildfire burden to administrative wildlife monitoring counts remains limited, particularly in fire-prone Mediterranean settings. This study examined whether wildfire activity was associated with province-level administrative target-species monitoring counts in Türkiye, focusing on delayed rather than contemporaneous relationships. A retrospective ecological panel design (2005–2024) harmonized official wildfire and wildlife inventory data across 22 Turkish provinces (440 observations). Two-way fixed effects panel models estimated same-year, lagged, and supplementary robustness associations between wildfire exposure (fire count, burned area, and damage per fire) and administrative target-species counts. Same-year models showed no clear associations. Burned area showed its clearest negative association at a two-year lag, while paired models indicated negative one- and two-year associations for burned area and damage per fire. Province-specific trend models preserved the main negative directions, and false-discovery-rate adjustment retained the lag-1 burned-area and damage-per-fire terms in the paired model families. The lag-2 burned-area association remained visible after excluding 2021 and after winsorizing extreme burned-area values. Restricted target-frame sensitivity analyses supported a narrower interpretation of the administrative outcome; deer/roe-deer frames showed negative cumulative burned-area associations, whereas wild-goat frames yielded less precise estimates. Burned area and damage per fire were interpreted as spatial fire-footprint indicators rather than direct burn-severity measures. The highest composite risk scores were observed in Antalya, Muğla, and İzmir. Distinguishing wildfire frequency from areal fire burden remains central for interpreting delayed administrative monitoring patterns. Full article

Wastewater treatment plants (WWTPs) are essential infrastructure for urban water management. Their stable operation is critical to effluent quality and public safety. However, wastewater treatment involves complex biochemical processes and extensive electromechanical equipment. Hazards such as sludge flotation in secondary clarifiers, fire, electric shock, and toxic gas poisoning may occur. These hazards can threaten worker safety and reduce treatment efficiency. Traditional inspection mainly relies on manual patrols, fixed-camera monitoring, and experience-based judgment. These methods often have low efficiency, limited coverage, and delayed responses. To address these limitations, this paper investigates robot-based hazard detection for WWTPs. A multisource hazard detection dataset is constructed for secondary clarifiers and confined spaces, including images collected by an inspection robot. Object detection models are then applied to identify typical hazards. Comparative experiments are conducted using Faster R-CNN and several YOLO-series models. YOLOv12 achieves mAP@0.5 values of 0.917 and 0.819 for sludge flotation detection and confined space hazard detection, respectively. It also provides a good balance between detection performance and inference efficiency. The results demonstrate that robot vision combined with object detection can support intelligent inspection in WWTPs. Full article

Smart lighting is becoming a strategic component of intelligent and low-carbon urban infrastructure because it combines efficient illumination with connectivity, remote management, and continuous operational monitoring. This study proposes a reproducible unit-level methodological framework that transforms field records from a functional smart-lighting installation into traceable indicators of electrical performance, energy efficiency, avoided emissions, preliminary economic benefit, sensitivity, and conditional scalability. The approach treats the luminaire not only as an electrical load, but as a monitored urban energy node whose operation can be validated, characterized, and compared under planning-oriented control scenarios. The methodology integrates data preprocessing, electrical consistency assessment, representative baseline definition, scenario-based energy modeling, explicit environmental conversion, and conditional scaling to homogeneous lighting assets. The results reveal a stable electrical operating regime and show that managed operating conditions can generate sustained reductions in energy use and associated environmental impacts while preserving analytical transparency between measured variables and scenario-derived indicators. Sensitivity and multivariable analyses further support the robustness of the unit-level interpretation and highlight the value of monitored lighting data for comparative decision-making. The framework therefore provides a technically grounded reference for smart-city lighting management, energy planning, and scalable infrastructure assessment, with relevance to the objectives of SDG 7, SDG 11, and SDG 13. Overall, the study contributes an original data-driven perspective for integrating IoT-enabled lighting, remote supervision, and sustainability-oriented urban management within a common analytical structure. Full article

Childhood obesity is an important global health issue closely related to metabolic syndrome and metabolic dysfunction-associated fatty liver disease (MASLD), currently the most common chronic liver disease in children. Oxidative stress and insulin resistance are the driving forces in hepatocyte injury and disease progression. There is growing interest in new biomarkers for detecting and monitoring metabolic and hepatic changes. We aimed to synthesize current evidence regarding biomarkers of oxidative stress and MASLD, and the potential modulatory role of selenium in pediatric populations. A literature search of PubMed, Scopus, Web of Science, and the Cochrane Library identified studies in children and adolescents (0–18 years) published between January 2000 and December 2025. Associative diagnostic biomarkers included indicators of oxidative damage, antioxidant-defense markers, and integrated oxidative stress indices, which appear promising but lack pediatric reference ranges, while prognostic biomarkers capture the inflammatory consequences of redox imbalance but with limited liver specificity. Metabolomics profiling and multiomics approaches remain experimental. Selenium, an essential trace element with antioxidant properties, plays a role in maintaining redox balance through selenoproteins, but pediatric evidence is limited and at times contradictory. Few biomarkers are clinically ready. Further longitudinal studies in pediatric populations are needed to better clarify the role of selenium, as well as to support the development of age-appropriate diagnostic strategies utilizing multiomics for MASLD, empowering clinicians to implement timely interventions in childhood obesity. Full article

Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which was further used to illustrate its potential for monitoring the temporal dynamics of different forest habitat types. Very high-resolution multispectral data from the WorldView-2/3 satellites were used, complemented by multispectral and LiDAR data acquired by an unmanned aerial vehicle (UAV). Four target forest vegetation types were mapped within a six-class classification scheme that also included “Other vegetation” and “Soil/Others” as non-target/background classes. The performance of ten supervised classification algorithms was evaluated, including Minimum Distance, Mahalanobis Distance, Parallelepiped, Spectral Angle Mapper, Maximum Likelihood, Naïve Bayes, K-Nearest Neighbors, Random Forest, Support Vector Machine, and the transformer-based deep learning model SegFormer. The results indicate that Random Forest achieved the highest overall accuracy, while Support Vector Machine and SegFormer also showed competitive performance, particularly when spectral information was integrated with vegetation indices and topographic variables. The study provides practical evidence on the selection of input data and classifiers for detailed forest vegetation mapping in a large and topographically complex island. Full article

Background: Cervical cancer remains a major public health problem, causing approximately 660,000 new cases and 348,000 deaths annually, with more than 90% occurring in low- and middle-income countries. The WHO African Region accounts for approximately 20% of global cases and 30% of deaths. In November 2020, the World Health Assembly adopted the Global Strategy for Cervical Cancer Elimination with 90–70–90 targets for 2030. Multi-age cohort (MAC) campaigns have emerged as a high-impact strategy for achieving population-level HPV vaccination coverage. This study documents the implementation and outcomes of HPV MAC campaigns across Côte d’Ivoire, Ghana, Liberia, and Sierra Leone in 2025. Methods: This multi-country mixed-methods analysis integrated quantitative administrative campaign data with qualitative programmatic assessments across eight operational readiness domains from the WHO Immunization Readiness Assessment Tool. Data were collected between January and December 2025 from national reports, WHO supervision missions, and partner reports. Coverage analysis used administrative data triangulated with independent monitoring. Equity analysis used school enrolment data and geographic accessibility indices. Results: The campaigns targeted 7,376,096 girls aged 9–18 years and reached approximately 6,306,294, achieving 85.5% overall coverage. Côte d’Ivoire, Ghana, Liberia and Sierra Leone reported administrative coverage of 88.0%, 84.5%, 60.0% and 100%, respectively. All four countries achieved the minimum operational readiness threshold of 80% across all assessed domains. A total of 35,860 health workers were trained, with post-training competency scores increasing from 58.8% to 89.5%. Cold chain functionality exceeded 98% across all campaigns. A total of 314 cases of Adverse Events Following Immunization (AEFIs) were reported, all classified as mild, giving an approximate rate of 5.0 per 100,000 doses. Coverage gaps between in-school and out-of-school girls ranged from 18 to 35 percentage points. Conclusions: The findings suggest that large-scale adolescent HPV immunization can be successfully implemented in resource-limited settings when supported by political commitment, multisectoral coordination and adequate operational resources. The single-dose schedule transition under Gavi 6.0 offers opportunities to simplify delivery. Priority actions include integrating HPV into routine immunization, scaling equity strategies for out-of-school girls and ensuring sustainable financing. Full article

Introduction: Cognitive dysfunction (“brain fog”) is a common manifestation of post-acute COVID-19 syndrome (PACS) and may persist long after the acute infection. While cross-sectional studies have described cognitive deficits, longitudinal evidence on recovery trajectories remains limited. Methods: We conducted a longitudinal observational study of neurocognitive performance and neuropsychiatric symptoms in patients with PACS. Participants underwent assessment with 20 standardized tests covering five cognitive domains (memory, attention, language, executive functions, psychomotor processing speed); anxiety, depression, and sleep quality were assessed at three time points. Changes were analysed using the Friedman test. Results: Forty-two patients were included (median age 57 years; 35.7% female) from a predominantly hospitalized cohort (81% hospitalised; 66.7% requiring respiratory support). Patients who completed all three assessments (completers, n = 42) were compared with those who attended the first evaluation but did not complete follow-up (non-completers, n = 544); completers were more severely ill during the acute phase rather than healthier or more motivated. At the group level, statistically significant improvements over time were observed across the whole sample in verbal short-term learning, visuospatial memory, working memory, constructional praxis, phonological verbal fluency, and psychomotor processing speed (all p ≤ 0.05); after Benjamini–Hochberg adjustment across the twenty cognitive outcomes, visuospatial span forward and backward and psychomotor processing speed remained significant (all FDR-adjusted p ≤ 0.013), with the change confined to the first six months. Sleep quality also improved (p < 0.0001). Conclusion: In this cohort, group-level performance improved in six of the twenty tests administered, of which three remained significant after correction for multiple comparisons, while 28 of 42 patients (66.7%) still scored in the impaired range on at least one test at 12 months, and 17 (40.5%) on two or more. These findings highlight the importance of long-term neuropsychological monitoring and integrated cognitive-psychiatric evaluation in post-COVID care. Given the small, predominantly hospitalized sample, improvements should be interpreted cautiously and confirmed in larger controlled studies, although the use of alternate forms for part of the battery makes task-specific learning an incomplete explanation. Full article

Background/Objectives: Thyroid dysfunction and emotion-regulation difficulties are each linked to antenatal distress but are rarely measured in the same women. We examined whether emotion regulation accounts for the cross-sectional association between thyroid biochemistry and antenatal distress in hypothyroid pregnancy. Methods: In this single-center cross-sectional study, 360 pregnant women with hypothyroidism completed measures of emotion-regulation difficulties and skills and four distress measures at one antenatal visit, alongside thyroid testing. Exposures were standardized log thyroid-stimulating hormone (TSH) and free thyroxine (free T4); the outcome was a composite distress score. Parallel two-mediator models with covariate adjustment were fitted, and bias-corrected bootstrapping was used to estimate the indirect associations. Results: Higher log TSH (total association 0.189, 95% confidence interval [CI] 0.108 to 0.272) and lower free T4 (−0.200, 95% CI −0.278 to −0.120) were associated with greater distress. Most of each association was statistically accounted for by emotion-regulation difficulties and skills; direct associations included zero. Apparent route-specific moderation by gestational week and by thyroid autoimmunity was not supported, because the two indices did not differ. Conclusions: Measurement at a single visit leaves temporal ordering unidentified, so these findings decompose a cross-sectional association rather than demonstrate causal mediation; within that constraint they support assessing emotion regulation alongside thyroid monitoring and depression screening. 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

Yeast infections are becoming an increasing public health concern, mainly due to the spread of opportunistic species and the emergence of strains resistant to commonly used antifungal drugs. Marine resources are a promising source of bioactive compounds, including polysaccharides and other biopolymers with potential antifungal applications. In this study, a sulphated polysaccharide-rich extract was obtained from the red alga Jania pedunculata var. adhaerens and chemically characterized. Its antifungal activity was compared with that of a commercial chitosan formulation against clinically relevant yeasts, including species of Candida, Cryptococcus, Clavispora, Naganishia and Trichosporon. Growth kinetics were monitored in liquid medium over 24 h, and antifungal activity was evaluated through growth rate analysis, growth inhibition at 20 h and susceptibility clustering. The sulphated polysaccharide-rich extract showed moderate but consistent growth inhibition, with the strongest effects observed at 5 mg mL⁻¹. Maximum growth inhibition reached 60.9% in Cryptococcus deuterogattii and 59.8% in Candida albicans, although no complete inhibition was observed within the tested concentration range. Chitosan showed a stronger antifungal effect, with minimal inhibitory concentration (MIC) values between 10 and 20 µg mL⁻¹ in several species and maximum inhibition values above 80% in the most susceptible yeasts. However, C. albicans showed marked resistance to chitosan, with inhibition below 12%. K-means clustering confirmed distinct susceptibility profiles between treatments, supporting a species-dependent response. Overall, these results highlight marine-derived biopolymers as promising antifungal candidates and show that chitosan and algal sulphated polysaccharides produce distinct, species-dependent antifungal profiles. Full article

Expansive mudstone tunnels often suffer long-term convergence and support damage because excavation-induced unloading is coupled with water-induced swelling. This study proposes a particle flow modeling framework for expansive mudstone tunnels by linking tunnel-wall displacement, swelling pressure, and the equivalent particle radius expansion coefficient. Constant-volume swelling pressure tests were first conducted to determine the representative swelling pressure of the mudstone. An independent confined particle model was then established to calibrate the relationship between macroscopic swelling pressure and microscopic particle expansion. The results show that a stable swelling pressure of 300 kPa corresponds to an equivalent particle radius expansion coefficient of 3.11%. Incorporating this calibrated swelling mechanism into the tunnel model indicates that swelling intensifies excavation-induced damage, increasing the final crack number from 1566 to 1852 and enlarging the equivalent damage-zone diameter from 21.6 m to 22.8 m. Under the original support scheme, the damage depth reaches 5.45 m, and the final crown settlement reaches 177.6 mm. After reinforcement, these values decrease to 3.40 m and 81.1 mm, respectively. Field monitoring confirms the predicted deformation-control trend. The proposed framework provides a practical approach for simulating swelling-induced damage evolution and optimizing support design in expansive mudstone tunnels. Full article