Search Results (13,281)

This study presents the deployment of Machine Learning (ML) models on low-cost edge devices (ESP32) for real-time water quality prediction in tilapia aquaculture. A compact monitoring and control system was developed with low-cost sensors to capture key environmental parameters under field conditions in Northern Thailand. Three ML models—Multiple Linear Regression (MLR), Decision Tree Regression (DTR), and Random Forest Regression (RFR)—were evaluated. RFR achieved the highest accuracy (R² > 0.80), while MLR, with moderate performance (R² ≈ 0.65–0.72), was identified as the most practical choice for ESP32 deployment due to its computational efficiency and offline operability. The system integrates sensing, prediction, and actuation, enabling autonomous regulation of dissolved oxygen and pH without constant cloud connectivity. Field validation demonstrated the system’s ability to maintain DO within biologically safe ranges and stabilize pH within an hour, supporting fish health and reducing production risks. These findings underline the potential of Edge AIoT as a scalable solution for small-scale aquaculture in resource-limited contexts. Future work will expand seasonal data coverage, explore federated learning approaches, and include economic assessments to ensure long-term robustness and sustainability. Full article

This study investigates the atmospheric concentrations, spatiotemporal distribution, the associated health risks and the ozone formation potential of benzene, toluene, ethylbenzene and xylenes (BTEX) across 33 monitoring sites of Greece over a one-year period. Samples were collected using passive diffusive samplers and analyzed by gas chromatography–mass spectrometry (GC-MS). The highest BTEX concentrations were detected during winter and autumn, particularly in urban and industrial areas such as in the Attica and Thessaloniki regions, likely due to enhanced emissions from combustion-related activities and reduced atmospheric dispersion. Health risk assessment revealed that hazard quotient (HQ) values for all compounds were within the acceptable limits. However, lifetime cancer risk (LTCR) for benzene exceeded the recommended limits in multiple regions during the colder seasons, indicating notable public health concern. Source apportionment using diagnostic ratios suggested varying seasonal emission sources, with vehicular emissions prevailing in winter and marine or industrial emissions in summer. Xylenes and toluene exhibited the highest ozone formation potential (OFP), underscoring their role in secondary pollutant formation. These findings demonstrate the need for seasonally adaptive air quality strategies, especially in Mediterranean urban and semi-urban environments. Full article

Coastal ecosystems sustain biodiversity, food resources, and human livelihoods, yet are increasingly exposed to climate change, pollution, and anthropogenic stressors. These pressures affect not only ecosystem integrity but also human health, highlighting the urgency of adopting the One Health framework. While One Health has gained global prominence, its systematic application in coastal and marine governance remains limited. This study provides the first bibliometric review of One Health research in coastal and marine contexts, analyzing 154 publications from Scopus (2003–2025) using Bibliometrix under PRISMA-S guidelines. Scientific output was minimal until 2015 but accelerated after 2020, peaking at 37 publications in 2024. Less than 20% of studies explicitly integrated all three One Health dimensions. Research has largely centered on environmental monitoring and aquaculture health, with antimicrobial resistance, climate–health linkages, and integrated coastal indicators underexplored. Keyword mapping revealed two distinct yet connected clusters: a biomedical cluster emphasizing antibiotics, resistance, and microbiology, and an environmental cluster focusing on pollution, ecosystems, and zoonotic risks. Outputs are geographically concentrated in high-income countries, particularly the USA, Brazil, and the UK, while contributions from low- and middle-income coastal regions remain scarce. These findings confirm both the rapid growth and the fragmentation of One Health scholarship in coastal contexts. By identifying gaps, trends, and collaboration patterns, this study builds an evidence base for embedding One Health in coastal monitoring, climate adaptation, and governance, advancing multiple United Nations’s Sustainable Development Goals. Full article

Immune checkpoint inhibitors (ICIs) improve lung cancer prognosis but are associated with immune-related adverse events, most commonly thyroid dysfunction. While prior studies and guidelines have focused on thyroid dysfunction during ICI therapy, data on hypothyroidism and its monitoring after ICI therapy remain limited. We aimed to investigate hypothyroidism incidence and implementation of thyroid function monitoring after ICI therapy completion in patients with lung cancer. We conducted a retrospective observational study using the DeSC claims database of approximately 12 million individuals in Japan. Patients with lung cancer who received ICI therapy between April 2014 and August 2023 were included; those with a history of thyroid hormone replacement or insufficient follow-up were excluded. Among 6883 eligible patients, 277 (4.0%) developed hypothyroidism requiring hormone replacement post-ICI therapy completion (median onset, 67.0 d). Risk factors included ICI plus bevacizumab therapy and a history of myasthenia gravis, while steroid use for ≥28 d during ICI therapy lowered the risk. Post-ICI therapy completion thyroid monitoring was performed in 73.7% of patients, with test date distribution showing a median of 126.0 d and mode of 21.0 d. Hypothyroidism was frequently found to develop within 2 months post-ICI therapy completion, highlighting the need for continued thyroid monitoring and prospective studies to establish optimal surveillance strategies. Full article

Understanding the dose of ambient PM_2.5 inhaled by middle school students during active commuting between home and school is essential for optimizing their travel routes and reducing associated health risks. However, accurately modeling this remains challenging due to the difficulty of measuring ambient PM_2.5 concentrations along commuting routes at a population scale. In this study, we overcome this limitation by employing spatiotemporally dense observations of on-road ambient PM_2.5 concentrations collected through a massive mobile monitoring fleet consisting of around 200 continuously operating taxis installed with air quality monitoring instruments. Leveraging these rich on-road PM_2.5 observations combined with a GIS-terrain-based PM_2.5 dosage modeling approach, we (1) assess middle school students’ PM_2.5 dosages during morning (7:00 am–8:00 am) home–school walking commuting along the shortest-distance route; (2) examine the feasibility of identifying an alternative route for each student that minimizes PM_2.5 dosages during commuting; (3) investigate the trade-off between the relative reduction in PM_2.5 dosage and the relative increase in route length when opting for the alternative lowest-dosage route; and (4) examine whether exposure inequalities exist among students of different family socioeconomic statuses (SES) during their home–school commutes. The results show that (1) 18.8–57.6% of the students can reduce the dose of PM_2.5 by walking along an alternative lowest-dose route; (2) an alternative lowest-dose route could be found by walking along a parallel, less-polluted local road or walking on the less-trafficked side of the street; (3) seeking an alternative lowest-dose route offers a favorable trade-off between effectiveness and cost; and (4) exposure inequities do exist in a portion of students’ walking commutes and those students from higher-SES are more likely to experience higher exposure risks. The findings in our study could offer valuable insights into commuter exposure and inspire future research. Full article

Malignant cerebral infarction (MCI) is rare but often fatal. Early identification helps guide monitoring and decompressive surgery. This study evaluated whether serum biomarkers add predictive value beyond clinical and imaging data in severe stroke patients with anterior circulation large vessel occlusion (LVO). In this prospective study, 73 acute severe LVO stroke patients underwent whole-brain CT perfusion (CTP) with rCBV-based core measurement at admission and follow-up MRI at 24 ± 12 h for infarct and edema volume assessment. Serum biomarkers (s100b, NSE, VEGF, ICAM1) were sampled a median of 20.5 h after baseline imaging. Logistic regression models predicted MCI using baseline variables (NIHSS, ASPECTS, rCBV < 30%), adding treatment data (rtPA, mTICI, NIHSS posttreatment), and adding serum biomarkers. Performance was assessed by AUC, accuracy, F1, and cross-validated R². MCI occurred in 18/73 (24%) patients. Baseline models showed an AUC of 0.72; adding treatment improved the AUC to 0.88. Biomarkers slightly increased the AUC (0.90) but did not improve F1. Higher s100b was associated with more severe injury but did not enhance the prediction of MCI. Models with baseline imaging and treatment best explained infarct (R² ≈ 0.27) and edema (R² ≈ 0.58). In conclusion, admission severity, CTP, and early treatment response are the main predictors of MCI and aid early risk stratification of patients. Despite their pathophysiologic relevance, serum biomarkers do not add substantial predictive value. Full article

This article presents an integrated assessment of pasture ecosystem degradation under conditions of extreme aridity in the Atyrau Region, where high livestock density, limited grazing capacity, and institutional fragmentation of land tenure exacerbate degradation risks. The study aimed to conduct a spatio-temporal analysis of pasture conditions and identify critical load zones to support sustainable management strategies. The methodology was based on a multi-factor Anthropogenic Load (AL) model integrating (1) calculation of pasture load (PL) using 2023 agricultural statistics with livestock numbers converted into livestock units; (2) spatial analysis of grazing concentration through Kernel Density Estimation in ArcGIS 10.8; (3) assessment of infrastructural accessibility (Accessibility Index, A_i); and (4) quantitative evaluation of institutional land use organization (Institutional Index, I_i). This integrative approach enabled the identification of stable, transitional, and critically overloaded zones and provided a cartographic basis for sustainable management. Results revealed persistent degradation hotspots within 3–5 km of water sources and settlements, while up to 40% of productive pastures remain excluded from use. The proposed AL model demonstrated high reproducibility and applicability for environmental monitoring and regional land use planning in arid regions of Central Asia. Full article

Smart grid operators face escalating cyber threats and tight resource constraints, demanding the transparent, defensible prioritization of security controls. This paper asks how to select cybersecurity controls for smart grids while retaining picture fuzzy evidence throughout and supporting policy-sensitive “what-if” analyses. We propose a hybrid Picture Fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) and Combinative Distance-based Assessment (CODAS) framework that carries picture fuzzy evidence end-to-end over a domain-specific cost/benefit criteria system and a relative-assessment matrix, complemented by multi-scenario sensitivity analysis. Applied to ten prominent solutions across twenty-nine sub-criteria in four dimensions, the model highlights Performance as the most influential main criterion; at the sub-criterion level, the decisive factors are updating against new threats, threat-detection capability, and policy-customization flexibility; and Zero Trust Architecture emerges as the best overall alternative, with rankings stable under varied weighting scenarios. A managerial takeaway is that foundation controls (e.g., OT-integrated monitoring and ICS-aware detection) consistently remain near the top, while purely deceptive or access-centric options rank lower in this context. The framework contributes an end-to-end picture fuzzy risk-assessment model for smart grid cybersecurity and suggests future work on larger expert panels, cross-utility datasets, and dynamic, periodically refreshed assessments. Full article

Wound healing is impaired under diabetic conditions due to reduced angiogenesis, thereby increasing the risk of wound-healing complications. Studies have shown that inhibition of α- and β-adrenoceptors delays wound healing. This study investigates the effects of topical salbutamol (TS) on STZ-induced diabetic wound healing in rats. The rats were divided into two initial groups: non-diabetic and diabetic. Diabetes mellitus was induced in the second group with STZ (65 mg/kg). Excision wounds were inflicted on the dorsal thoracic region, 1–1.5 cm away from the vertebral column on either side, following anesthesia on all groups. Group 2 was subdivided into untreated diabetic wounds, low-dose-TS-treated diabetic wounds (6.25 mg/mL), medium-dose-TS-treated diabetic wounds (12.5 mg/mL), and high-dose-TS-treated diabetic wounds (25 mg/mL), and were monitored for 14 days. Percentage wound contraction and the time required for complete wound closure were observed and recorded. In addition, oxidative stress and inflammatory markers such as NO, CRP, MPO, TGF-β1, TNF-α, IL-6, IL-1β, NO, and hexosamine were estimated in wound exudates and tissue over 14 days. TS treatment resulted in 100% wound contraction in all treated wounds within 14 days compared to untreated non-diabetic and diabetic wounds. Increased NO, TGF-β1, and hexosamine activity was observed in TS-treated wounds when compared to untreated diabetic wounds. In addition, TS treatment decreased the activity of IL-1β, TNF-α, IL-6, CRP, and MPO, all of which were elevated in the untreated diabetic wounds. The current study shows that the application of TS significantly improved diabetic wound contraction and aided the healing process. Angiogenic markers, such as TGF-β1 and NO, were prominently increased, supporting the role of sympathetic nerve stimulation in angiogenesis. Full article

Rural landscapes are changing rapidly, yet many assessments remain descriptive and weakly connected to planning instruments. This study connects rural landscape analysis with planning and management by examining post-earthquake transformations in Topaktaş (Tuşba, Van), a village redesigned and relocated after the 2011 events. Using ArcGIS 10.8 and the Analytic Hierarchy Process (AHP), we integrate DEM, slope, aspect, CORINE land cover Plus, surface-water presence/seasonality, and proximity to hazards (active and surface-rupture faults) and infrastructure (Karasu Stream, highways, village roads). A risk overlay is treated as a hard constraint. We produce suitability maps for settlement, agriculture, recreation, and industry; derive a composite optimum land-use surface; and translate outputs into decision rules (e.g., a 0–100 m fault no-build setback, riparian buffers, and slope thresholds) with an outline for implementation and monitoring. Key findings show legacy footprints at lower elevations, while new footprints cluster near the upper elevation band (DEM range 1642–1735 m). Most of the area exhibits 0–3% slopes, supporting low-impact access where hazards are manageable; however, several newly designated settlement tracts conflict with risk and water-service conditions. Although limited to a single case and available data resolutions, the workflow is transferable: it moves beyond mapping to actionable planning instruments—zoning overlays, buffers, thresholds, and phased management—supporting sustainable, culturally informed post-earthquake reconstruction. Full article

Water pumping stations are essential components of national water infrastructure, yet their construction involves complex, high-risk processes, and traditional risk management approaches often show significant limitations in practice. To address this challenge, this study proposes a Building Information Modeling (BIM)-based approach that integrates structured risk information into an interactive nD BIM environment. We first developed an extended Risk Breakdown Matrix (eRBM), which systematically organizes risk factors, assessment levels, and causal relationships. This is linked to the BIM model through a customized BIM–risk integration framework. Subsequently, the framework is further implemented and quantitatively validated via a Navisworks plug-in. The system incorporates three core components: (1) a structured risk information model, (2) a visualization mechanism for dynamic, spatiotemporal risk representation and (3) risk influence path analysis using the Decision-Making Trial and Evaluation Laboratory–Interpretive Structural Modeling (DEMATEL–ISM) method. The plug-in allows users to access risk information on demand and monitor its evolution over time and space during the construction process. This study makes contributions by innovatively integrating risk information with BIM and developing a data-driven visualization tool for decision support, thereby enhancing project managers’ ability to anticipate, prioritize, and mitigate risks throughout the construction lifecycle of water pumping station projects. Full article

Maintaining the integrity of cryogenically preserved biological materials is critical, as even brief, undetected temperature excursions in storage can compromise sample viability. Existing monitoring systems may miss transient thaw–refreeze events, posing serious quality risks. To address this, we developed and validated frozen indicator tubes that visually signal deviations from the frozen state, serving as a cost-effective backup to electronic monitors. Our first method uses an aqueous dye solution that immobilizes the dye when frozen; any thawing causes the dye to disperse, providing a clear, external visual cue of a partial or complete thaw. For ultra-low-temperature storage (−80 °C), we introduced a second method using an ethanol-based solution calibrated to indicate thaw events. This system detects temperature rises of 10 °C or more sustained for at least fifteen minutes—conditions that may jeopardize sample stability. When paired with standard monitoring systems, these indicator tubes offer an added layer of protection by providing simple, reliable, and immediate visual confirmation of critical temperature breaches. This innovation enhances confidence in cryogenic storage protocols and supports the long-term preservation of sensitive biological materials. Full article

This paper presents the development of a high-resolution composite index to monitor and quantify climate-related risks across Italy. The country’s complex climatic variability, extensive coastline, and low insurance penetration highlight the urgent need for robust, locally calibrated tools to bridge the climate protection gap. Building on the methodological framework of existing actuarial climate indices, previously adapted for France and the Iberian Peninsula, the index integrates six standardised indicators capturing warm and cool temperature extremes, heavy precipitation intensity, dry spell duration, high wind frequency, and sea level change. It leverages hourly ERA5-Land reanalysis data and monthly sea level observations from tide gauges. Results show a clear upward trend in climate anomalies, with regional and seasonal differentiation. Among all components, sea level is most strongly correlated with the composite index, underscoring Italy’s vulnerability to marine-related risks. Comparative analysis with European indices confirms both the robustness and specificity of the Italian exposure profile, reinforcing the need for tailored risk metrics. The index can support innovative risk transfer mechanisms, including climate-related insurance, regulatory stress testing, and resilience planning. Combining scientific rigour with operational relevance, it offers a consistent, transparent, and policy-relevant tool for managing climate risk in Italy and contributing to harmonised European frameworks. Full article

β-thalassemia is a chronic genetic blood disorder characterized by defective β-globin synthesis, requiring frequent transfusions and resulting in iron overload, immune dysfunction, and increased susceptibility to infections. In these immunocompromised patients, altered immune responses lead to significant changes in the human virome, promoting viral persistence, reactivation, and expansion of pathogenic viral communities. This review explores the intricate relationship between β-thalassemia and the human virome, focusing on how clinical interventions and immune abnormalities reshape viral dynamics, persistence, and pathogenicity. Patients with β-thalassemia exhibit profound innate and adaptive immune dysregulation, including neutrophil dysfunction, T cell senescence, impaired B cell and NK cell activity, and expansion of myeloid-derived suppressor cells. These alterations create an immunological niche that favors viral reactivation and virome expansion. Iron overload enhances viral replication, while chronic transfusions introduce transfusion-transmitted viruses. Splenectomy and allo-HSCT further compromise viral surveillance. Additionally, disruptions in the gut virome, particularly bacteriophage-driven dysbiosis, may exacerbate inflammation and impair host–virus homeostasis. The human virome is not a passive bystander but a dynamic player in the pathophysiology of β-thalassemia. Understanding virome–immune interactions may offer novel insights for infection monitoring, risk stratification, and precision therapies in thalassemic patients. Full article

This paper introduces the Gesture-Based Physical Stability Classification and Rehabilitation System (GPSCRS), a low-cost, non-invasive solution for evaluating physical stability using an Arduino microcontroller and the DFRobot Gesture and Touch sensor. The system quantifies movement smoothness, consistency, and speed by analyzing “up” and “down” hand gestures over a fixed period, generating a Physical Stability Index (PSI) as a single metric to represent an individual’s stability. The system focuses on a temporal analysis of gesture patterns while incorporating placeholders for speed scores to demonstrate its potential for a comprehensive stability assessment. The performance of various machine learning and deep learning models for gesture-based classification is evaluated, with neural network architectures such as Transformer, CNN, and KAN achieving perfect scores in recall, accuracy, precision, and F1-score. Traditional machine learning models such as XGBoost show strong results, offering a balance between computational efficiency and accuracy. The choice of model depends on specific application requirements, including real-time constraints and available resources. The preliminary experimental results indicate that the proposed GPSCRS can effectively detect changes in stability under real-time conditions, highlighting its potential for use in remote health monitoring, fall prevention, and rehabilitation scenarios. By providing a quantitative measure of stability, the system enables early risk identification and supports tailored interventions for improved mobility and quality of life. Full article