Search Results (1,372)

Intensive vegetable production can increase the transfer of persistent toxic trace elements from agricultural soils into the food chain, particularly where agrochemical use, irrigation inputs, and local geochemical conditions are insufficiently characterized. This study was undertaken to assess toxic trace-metal contamination levels in soils and vegetables from two renowned vegetable-producing subdistricts—Shibganj and Kahaloo—in the Bogra district, Bangladesh. The study also estimated potential human health risks by evaluating the dietary intake of these elements. It measured Pb, Ni, Cd, and Cr content in six vegetables and their respective farm soils using an atomic absorption spectrophotometer (AAS). The average concentrations of Pb, Ni, Cd, and Cr in farm soils of Shibganj and Kahaloo subdistricts were 158.3 ± 8.83, 31.5 ± 5.25, 0.43 ± 0.08, and 14.1 ± 2.16 µg g⁻¹ and 164.1 ± 4.60, 35.7 ± 6.91, 0.53 ± 0.14, and 9.37 ± 2.87 µg g⁻¹, respectively. Soils collected from all locations in both subdistricts of Bogra fall under ‘moderate’ ecological risk. Regarding the pollution load index (PLI), 66.7% of Shibganj and 75.0% of Kahaloo sampling sites had a PLI > 1.0, confirming that ‘metal pollution exists.’ Based on the calculated bioconcentration factors (BCFs), Cr and Cd show a high tendency to migrate from soil to various vegetables in the study area, though the mean Cd BCF for brinjal in Shibganj exceeded 1.0 due to a single high observation. The results demonstrated that the edible parts of potatoes, onions, and chilies accumulate significant amounts of toxic trace elements. The calculated mean daily intake of Pb and Cr in all vegetables ranged from 0.33 to 1.21 mg person⁻¹ day⁻¹ and from 0.10 to 0.64 mg person⁻¹ day⁻¹, respectively, exceeding the upper tolerable intake limits. Similarly, dietary intake of potatoes showed both non-carcinogenic and carcinogenic risks, while brinjal showed only carcinogenic risks for adults. Redundancy analysis (RDA) indicates that the measured soil parameters are strong predictors of the response variables (trace element content in various vegetables). Overall, the results identified Pb-dominated soil contamination and human exposure to Pb and Cr associated with vegetables as the principal concerns. To address these issues, priority actions should be given to source apportionment and testing of various agricultural inputs. Additionally, before implementing site-specific remediation or issuing consumption advisories, these risks should be validated through metal speciation and bioaccessibility analyses. Full article

Objectives: Dementia and osteoporosis are common and debilitating conditions that often coexist in older adults. We investigated mortality in patients with and without dementia as the primary outcome in a prospective memory clinic cohort. Falls and fractures were assessed as secondary outcomes, and associations with baseline acetylcholinesterase inhibitor (AChEI) use were explored. Methods: In a prospective observational cohort study, data were collected during routine clinical visits over four years. Data included demographics, dementia diagnosis, AChEI use, falls, fractures, bone mineral density (BMD) when clinically available, and mortality. Analysis included chi-square tests, Kaplan–Meier survival curves, Cox proportional hazards models, and recurrent-event models. Because AChEI analyses were exploratory and included several related endpoints, Benjamini–Hochberg false discovery rate (FDR) correction was applied to endpoint-level AChEI p-values. Results: 744 patients were enrolled; the mean age was 80.99 ± 6.8 years; 58.5% female; 55.8% with dementia. AChEI use was recorded in 113 patients (15.2%) at baseline. Over 4 years, 16.61% of participants experienced at least one fall with a cumulative fracture risk of 30.90%. Mortality was significantly higher in dementia patients (44.58% vs. 27.05%; p < 0.001). Dementia patients had double the mortality risk (OR: 1.956; 95% CI: 1.425–2.686). Annual and cumulative mortality rates increased progressively from 5.51% and in year 1 to 17.69% and 36.83% respectively by year 4. Baseline AChEI use was not significantly associated with mortality. Risk/100 for patient with dementia vs. without dementia for falls was 4.04 vs. 4.32 and fracture was 4.37 vs. 3.01. AChEI users had a trend of lower incidence/100 patient-years for falls of 3.01 vs. 4.37 and fractures 6.78 vs. 10.09. Conclusions: Dementia patients have higher mortality risk but not falls or fracture risk in this cohort. Although clinical cohort and animal studies suggest a benefit for AChEIs, this was not evident in this study possibly due to clinical cohort limitations. The trend of reduced falls and fracture in the AChEI cohort warrants further study. Full article

Satellite-based precipitation products such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG, V07) provide global coverage but exhibit systematic biases in daily accumulations, particularly for extreme events. This study presents a hybrid bias-correction framework (LSEQM+DL) for daily satellite precipitation that sequentially integrates Linear Scaling (LS) for mean bias, Empirical Quantile Mapping (EQM) with a Generalized Pareto Distribution (GPD) tail adjustment for distributional alignment, and a Convolutional Neural Network (CNN) refinement that targets extreme-precipitation pixels. A station-density confidence mask scales the deep-learning influence with gauge density, so the CNN refinement is strongest where the reference, the CPC Unified Gauge-Based Analysis of Daily Precipitation (CPC-UNI), is best constrained. The framework targets the IMERG Late Run (IMERG-L), whose roughly 14 h latency suits near-real-time flood monitoring. It is applied over Indonesia (2001–2025) and evaluated against CPC-UNI and 171 independent stations of the Meteorological, Climatological, and Geophysical Agency (BMKG) through three pillars: adjusting values, aligning distributions, and preserving extremes. At independent stations, the correction brings the standard deviation ratio from 0.71 (LS) to 1.00, the relative bias from $-$11.4% to $-$0.6%, and the 99th-percentile ratio from 0.71 to 1.01, and reduces a 21% over-estimation of wet-day frequency to within 5% of that observed. These gains carry a designed cost: the probability of detection falls from 0.78 to 0.65, while pixel-level temporal metrics (correlation, root-mean-square error, Nash–Sutcliffe efficiency) remain largely unchanged, confirming that the framework improves statistical properties rather than day-to-day timing. Relying only on globally available satellite and gauge-analysis data, and degrading gracefully where gauges are sparse, the framework is portable in principle with regional recalibration of its three tuning parameters. The corrected near-real-time product, with its station-density mask as a spatially explicit quality indicator, is intended to support flood monitoring, water resource management, and climate risk assessment in Indonesia and other gauge-sparse tropical regions. Full article

In arid regions, potentially toxic elements (PTEs) can accumulate in oasis farmland soils, posing risks to both ecosystems and human health and threatening the long-term sustainability of agricultural production. However, we still lack a clear understanding of how multiple elements co-accumulate and what non-linear processes drive their buildup. Here, we investigated typical agricultural soils in the Aksu area of Xinjiang. We measured 12 elements (As, Cr, Cu, Ni, Zn, Co, V, Se, F, Ba, Sn, Mn) in 28 surface samples. To assess the pollution levels, we used three indices: the single-factor index (P_i), the geo-accumulation index (I_geo), and the Nemerow composite index. Source apportionment was performed with positive matrix factorization (PMF). We then built an XGBoost model to predict the Nemerow index, and applied SHAP (Shapley additive explanations) to quantify the marginal contribution and non-linear response of each element. Our results show that the average concentrations of Se, F, and As are 1.47, 1.27, and 1.35 times the national background values, respectively. The exceedance rate (P_i > 1) for these elements ranges from 78.6% to 92.9%. Nevertheless, the overall pollution is mild: only one out of 28 sampling sites (3.6%) falls into the moderately polluted category. PMF resolved three major sources: (1) parent material plus evaporation enrichment (F, Mn, and Ba, ~45% of the total contribution); (2) agricultural and anthropogenic activities (As, Cr, V, Zn, ~40%); and (3) local industrial or waste inputs (Sn and Ba, ~15%). The XGBoost model shows good predictive performance on the test set (R² = 0.864, RMSE = 0.104). SHAP analysis reveals that Se, F, and As are the main drivers of the composite pollution index. Se has a clear threshold: once its concentration goes above 0.3 mg·kg⁻¹, its positive contribution jumps sharply. Overall, the farmland soils in Aksu show mild enrichment of several elements, with Se and F as the main indicators. Evaporation enrichment and farming practices are the dominant processes behind this enrichment. The integrated framework—pollution indices, PMF source apportionment, XGBoost prediction, and SHAP interpretation—provides a scientifically sound way to manage soil environments in arid regions. Full article

Background: Early identification of fall and frailty risk is essential for preventing disability and maintaining functional independence in older adults. Although simple functional assessments are widely used in community settings, their combined application as a classification approach in large real-world populations remains limited. Methods: This cross-sectional study included 2979 community-dwelling adults (67.6 ± 8.3 years) enrolled in a municipal physical activity program. Participants underwent standardized assessments of gait speed, handgrip strength, and balance. A composite fall/frailty risk classification was defined using established functional cut-offs. Associations between functional variables and risk classification were examined using correlation analyses and group comparisons. Results: Overall, 45% of participants were classified as high risk. Women showed a higher prevalence compared to men (47% vs. 35%). Lower gait speed (r = −0.56), reduced handgrip strength (r = −0.32), and shorter balance time (r = −0.47) were significantly associated with higher risk classification (all p < 0.001). Conclusions: Functional performance measures are strongly associated with a composite classification of fall and frailty risk. These findings support the use of simple, scalable screening tools in community and primary care settings to identify vulnerable older adults and inform early intervention strategies. Full article

Soil pollution constitutes a critical global environmental challenge driven by industrialization, intensive agriculture, urban expansion, mining, and the application of synthetic agrochemicals. This article presents seven annotated Python-based Machine Learning (ML) workflows for soil pollution assessment, structured around five contaminant groups: heavy metals, pesticides, microplastics, per- and polyfluoroalkyl substances (PFAS), and excess macronutrients. The contribution has three distinct components. First, a literature synthesis drawing on more than 100 peer-reviewed studies contextualizes each contaminant group within current spectroscopic, geochemical, and ML-based detection frameworks. Second, a conceptual six-step workflow links field sampling, ML-based analysis, and scenario-based risk modelling to soil ecosystem service (SES) assessment. Third, seven executable Python scripts—implementing Random Forest regression, XGBoost with SHAP explainability, 1-D Convolutional Neural Networks, LSTM time-series forecasting, PCA-based dimensionality reduction, Monte Carlo uncertainty propagation, and GeoPandas geospatial mapping—serve as illustrative demonstrations using a benchmark dataset. All reported performance metrics are derived from synthetic data and represent workflow demonstrations, not validated field results. Radionuclides are acknowledged as an important contaminant class but fall outside the defined scope of this study. The scripts are provided as reproducible templates for adaptation to real contaminated-site datasets. Full article

Groundwater is vital to subsurface ecosystems and to maintaining water supplies for human and environmental needs. Heavy metal pollution of water bodies poses a significant threat to environmental health and human well-being. In this paper, a detailed spatiotemporal analysis of heavy metal pollution at a network of 35 sampling sites is presented for the summer, autumn, and winter seasons. The water samples were analyzed based on the concentration (μg/L) of eight priority metals, such as Lead (Pb), Mercury (Hg), Cadmium (Cd), Arsenic (As), Chromium VI (Cr-VI), Copper (Cu), Zinc (Zn), as well as Iron (Fe). Measuring contamination levels, identifying space hotspots, and explaining seasonal variations were the key tasks. The results show that Fe, Zn, and Cu concentrations are consistently high across seasons, with mean values ranging from 234.3 to 253.2 µg/L (Fe), 163.2 to 175.6 µg/L (Zn), and 107.0 to 109.2 µg/L (Cu), indicating a widespread geogenic or diffuse source. The most significant seasonal deviation was observed in the fall when there were unusually high levels of Cd and Hg, with mean concentrations reaching 0.476 µg/L and 0.312 µg/L, respectively, suggesting a strong seasonal contamination event or mobilization process. The spatial analysis showed that the locations (e.g., L6, L8, L10, L28, L29) exhibited common hotspots for different metals, with maximum concentrations reaching up to 793.3 µg/L (Fe), 602.3 µg/L (Zn), and 508 µg/L (Cu). Principal Component Analysis (PCA) effectively separated seasonal trends and classified metals into anthropogenic (Pb, Cd, Hg, Cr (VI)) and geogenic/diffuse (Fe, Zn, Cu) groups. The health risk assessment indicated no significant non-carcinogenic risk, although children are more vulnerable, while arsenic levels in winter approached the upper acceptable carcinogenic limit (up to 1.1 × 10⁻⁴). Overall, the study highlights the importance of multi-seasonal monitoring by capturing temporally abrupt contamination events and provides a novel integrated framework that combines seasonal analysis, spatial hotspot identification, and multivariate techniques, distinguishing it from conventional single-season or non-integrated studies. Full article

Background/Objectives: Falls are a major threat to healthy ageing and the preservation of independence, yet the risk of falls in older adults commonly arises from multiple interacting domains rather than a single factor. This longitudinal analysis assessed whether frailty, functional limitation, and balance performance were associated with future falls among older adults. Methods: This longitudinal analysis assessed older adults aged 65 years and older over a three-year follow-up interval, using baseline data collected in 2015 and follow-up falls data collected in 2018. Baseline health, functional, and physical performance measures were evaluated in relation to self-reported falls at follow-up. Candidate predictors comprised sociodemographic, health, functional, and physical performance variables. Univariable logistic regression, adjusted screening models, and a final complete-case multivariable logistic regression model were used. Results: The eligible analytic sample comprised 1932 participants, of whom 480 (24.8%) reported falls at follow-up. In adjusted analyses, greater frailty index, greater ADL limitation, female sex, shorter full-tandem balance time, depressive symptoms, and greater self-rated mobility severity were associated with higher odds of future falls. However, only frailty index, ADL limitation, full-tandem balance time, and female sex were retained in the final multivariable model. In the final multivariable model of 1451 participants, frailty index per 0.1-unit increase (OR 1.26, 95% CI 1.07–1.48), ADL limitation score per 1-point increase (OR 1.30, 95% CI 1.16–1.45), female sex (OR 1.62, 95% CI 1.17–2.25) and longer full-tandem balance time per 10 s increase (OR 0.89, 95% CI 0.82–0.96) remained associated with falls. The final model showed modest discrimination and acceptable apparent calibration. Conclusions: Higher frailty, greater ADL limitation, poorer tandem balance and female sex were associated with subsequent falls. These results reinforce the value of a multidomain approach to fall-risk assessment. Full article

This article examines the Integrevise^TM platform through a repeated cross-sectional, multi-cycle pilot case study of viva-based verification in AI-mediated assessment environments. Integrevise^TM pairs a submitted written artifact with a short adaptive viva in which students explain their work, reasoning, and application in their own words. Rather than functioning as an AI detector or automated grading system, the platform operates as a diagnostic assessment layer intended to surface comprehension, authorship confidence, and disengagement risk before final grades become the only available signal. The pilot was conducted across Fall 2025 and Spring 2026 at a private liberal arts college in the Midwest; these phases involved different student groups and are therefore treated as iterative implementation cycles rather than a longitudinal cohort. Results should be interpreted as preliminary pilot evidence. In Spring 2026, 52 vivas were completed, but formal student survey data were limited to seven respondents and showed mixed perceptions: only 14.3% agreed that the oral assessment helped them think more deeply about the assignment, whereas 57.1% disagreed or strongly disagreed. Platform feedback was also incomplete, with 20 of 52 vivas (38.5%) producing no student feedback record. Qualitative feedback, tutor observations, and implementation notes nevertheless suggest that viva-based verification may help identify some comprehension gaps and implementation barriers that written artifacts alone may not reveal. The findings, therefore, support continued investigation of Integrevise^TM as a process-rich assessment intervention, but not broad claims of efficacy or scalability without larger, more systematic validation. Full article

Background: Stroke represents a major contributor to long-term disability and is commonly associated with impaired gait, balance, and mobility, which reduce independence and increase fall risk. Gait biofeedback training provides real-time performance-related feedback and may facilitate motor relearning. This study aimed to synthesize the available evidence of gait biofeedback training on spatiotemporal gait parameters in stroke survivors. Methods: PubMed, Embase, Web of Science, and the Cochrane Library were searched up to 7 April 2026. RCTs involving stroke survivors with gait impairment that compared gait biofeedback training with non-biofeedback rehabilitation and reported spatiotemporal gait outcomes were included. Risk of bias and certainty of evidence were assessed using RoB-1 and GRADE, respectively. Meta-analyses were conducted using mean difference (MD) with 95% confidence intervals (CIs). Heterogeneity was assessed using I² and τ², and 95% prediction intervals (PI) were calculated where possible. Results: 10 RCTs involving 304 participants were included. Compared with control interventions, gait biofeedback training may improve gait velocity (MD = 9.78 cm/s, 95% CI 6.06 to 13.50, p < 0.001, 95% PI 2.14 to 17.41) and step length (MD = 5.88 cm, 95% CI 1.14 to 10.61, p = 0.01, 95% PI −10.18 to 21.94). However, the certainty of evidence was stronger for gait velocity than for step length. A significant effect on cadence was observed in the primary analysis, but this finding was unstable in the sensitivity analysis. No significant pooled effects were found for stride length or stance time. The wide PI for step length, stride length, and stance time indicates that the expected effects may vary across future clinical settings. Conclusions: Gait biofeedback training may improve gait velocity after stroke. Evidence for step length improvement is more tentative, while evidence for cadence, stride length, and stance time remains insufficient or unstable. Additional well-designed high-quality RCTs are needed to confirm these findings and determine optimal feedback modes and training protocols. The review was registered in PROSPERO (CRD420261354683). Full article

Traditional biomechanical analysis is constrained by high capital costs and the physical limitations imposed by markers, posing significant barriers to clinical adoption. This review evaluates the emergence of artificial intelligence (AI)-based markerless motion capture (MMC) as a transformative approach for democratizing movement science in clinical rehabilitation. The discussion outlines the progression from legacy geometric visual hulls to advanced deep learning architectures, with particular focus on YOLO-based two-dimensional detection and spatio-temporal transformer models for three-dimensional pose estimation. Evidence indicates that multi-camera MMC frameworks achieve research-grade positional accuracy (16–34 mm Mean Per-Joint Position Error—MPJPE), while monocular systems provide sufficient sensitivity (82–88%) for longitudinal monitoring of geriatric fall risk and stroke recovery. While challenges persist in achieving precise axial rotation measurement, integrating real-time signal refinement enables objective and ecologically valid assessments in community-based healthcare settings. This technological advancement redefines movement analysis, shifting it from a laboratory-bound procedure to a widely accessible and interoperable diagnostic tool. Full article

Nickel has become a strategic mineral for the energy transition, yet its supply chain is increasingly shaped by a compound risk regime involving resource nationalism, processing concentration, geopolitical compliance rules, carbon-footprint requirements, and commodity-market volatility. This study develops NiChain-LLM-ABM, a large-language-model-empowered agent-based model for simulating nickel supply chain resilience under semantically rich policy shocks. The framework uses a policy semantic parsing module to transform official policy texts into structured shock parameters, a multi-agent strategy generation module to represent adaptive decisions by seven agent classes, a calibrated supply chain network module to simulate material, financial, and information flows, and a four-dimensional resilience assessment module. The model is anchored in observed nickel production, price, trade, and technology data from USGS, IEA, UN Comtrade, LME, and official legal sources, and its scenario outputs are generated through 100 Monte Carlo replications over 2025–2035. Results show that the baseline Comprehensive Resilience Index (CRI) declines from 0.620 in 2025 to 0.547 in 2035. Indonesian policy tightening causes the sharpest near-term deterioration, with CRI falling to 0.445 in 2028 and the simulated supply deficit reaching 24.5 kt Ni equivalent. A geopolitical compliance shock produces the lowest terminal resilience (CRI = 0.472 in 2035). A green-compliance scenario is disruptive in the short run but exceeds the baseline by 2035, while a coordinated policy portfolio raises the terminal CRI to 0.744, a 36.0% improvement over the baseline. Compared with a conventional rule-based ABM, the LLM-ABM reduces extreme-event backcasting error by 57%, improves policy-response fidelity by 53%, and more than doubles agent heterogeneity differentiation. The results support portfolio-based critical-mineral governance combining strategic reserves, overseas equity investment, recycling, technology substitution, and international cooperation. Full article

Background: In-hospital falls are common adverse events associated with injuries, functional decline, prolonged length of stay, and increased healthcare costs, which require effective and sustained nursing interventions. Objective: To identify, through a Systematic Literature Review, which nursing care interventions are effective in reducing the incidence/rate of falls among inpatients in hospital settings. Methods: A systematic literature review was conducted using the JBI methodology. The review was guided by the PICO framework (P: inpatients; I: nursing care interventions; C: usual care; O: incidence of accidental falls). A comprehensive search was performed in the MEDLINE, CINAHL, and Scopus databases. Studies were included if they evaluated nursing-led or nursing-related interventions aimed at fall prevention and reported fall-related results. Eligible study designs included randomized controlled trials, quasi-experimental studies, observational studies, and quality improvement initiatives. Study selection, data extraction, and critical appraisal were conducted according to JBI recommendations. Results: Six studies were included (quasi-experimental, cohort, prospective/observational, and quality improvement projects). Two main themes emerged: (1) structured multifactorial and educational interventions and (2) technology-based interventions. Multifactorial approaches that combine risk assessment, education, communication, and environmental measures have been shown to improve adherence and reduce falls. Technology-based interventions, especially video monitoring, showed the most consistent reductions in fall rates, including fewer nighttime falls and decreased need for one-to-one observation. The included studies were methodologically heterogeneous in design, clinical setting, and outcome definitions, which precluded statistical pooling and warrants caution in the interpretation of the findings. Conclusions: Structured, standardized, multifactorial, and nursing-led approaches can contribute to reducing inpatient falls. However, more robust and comparable studies are required to consolidate practice-relevant recommendations. Full article

Purpose: Accurate assessment of nursing workload is essential for staffing allocation and operational management in emergency infusion rooms. However, workload generation is influenced by complex and potentially nonlinear interactions among patient volume, treatment duration, and care activities, which may not be adequately captured by conventional statistical approaches. This study aimed to identify key predictors associated with nursing workload intensity and develop an interpretable workload stratification framework using a Classification and Regression Tree (CRT) model. Methods: Daily operational data were collected from an emergency infusion room between July 2023 and August 2025. Daily chair utilization rate was used as a proxy indicator of workload intensity. Candidate predictors included total infusion duration, direct care encounters, number of patients receiving infusions, medication dispensing time, severe dependency, fall-risk patients, and triage-level patient volume. A CRT model was developed to identify hierarchical predictor relationships and threshold-based workload classification rules. Model robustness was evaluated using 10-fold cross-validation, comparative analyses with multiple linear regression, random forest, and gradient boosting models, and sensitivity analyses excluding total infusion duration. Results: The analysis included 761 valid observation days. Total infusion duration emerged as the most influential predictor, followed by direct care encounters and the number of patients receiving infusions. The CRT model identified clinically interpretable workload thresholds and generated a parsimonious decision structure for workload stratification. Re-substitution and cross-validation risk estimates were 0.045 (SE = 0.005) and 0.046 (SE = 0.005), respectively, indicating stable model performance. Although random forest and gradient boosting achieved higher predictive accuracy, the CRT model provided greater interpretability through transparent decision rules. Sensitivity analyses demonstrated that the overall workload stratification pattern remained largely unchanged after excluding total infusion duration. Conclusions: The CRT model identified total infusion duration, direct care encounters, and patient volume as key predictors associated with workload intensity in emergency infusion rooms. Although alternative models achieved higher predictive performance, the CRT approach provided interpretable workload stratification rules that may support staffing allocation and operational decision-making. The findings offer a practical data-driven framework for workload assessment in infusion care settings. Full article

By integrating ecological spatial data, offshore wind energy development zones, and the marine spatial planning (MSP) framework, it is possible to assess the relationship among Taiwan’s offshore renewable energy development, risks to marine biodiversity, and the effectiveness of marine spatial planning. The study adopts a mixed-method spatial–quantitative research design that integrates geospatial modelling, ecological risk assessment, spatial conflict analysis, and governance evaluation for quantification of biodiversity exposure to offshore wind infrastructure. Spatial overlay analysis is employed in the identification of geographic areas where offshore wind development intersects with high biodiversity vulnerability zones. Quantitative spatial indicators are used to assess the extent to which MSP reduces biodiversity exposure to offshore renewable energy infrastructure. The analytical framework integrates two parallel modelling domains including the ecological risk modelling domain and the spatial governance effectiveness domain. The spatial analysis of biodiversity vulnerability across Taiwan’s analyzed offshore areas revealed a BVI range of 0.12 to 0.88. The mean BVI value was 0.51 (S.D. = 0.18). The results further show that over 47% of the analyzed EEZ falls into high and very high vulnerability classes. The total offshore wind area located within high-risk and very high-risk zones accounted for 38% of the wind farm footprint. Smaller proportions occupy very low and low-risk zones, accounting for 7.1% and 21.4%, respectively, while 32.1% of wind infrastructure is in moderate-risk areas. Overlaying MSP boundaries with biodiversity risk zones showed that 62% of high-risk biodiversity areas are encompassed within MSP-designated protection, leaving 38% of high-risk zones unprotected. The findings show that biodiversity preservation and offshore wind development are not mutually exclusive but are rather dependent on efficient spatial planning, integrated governance, and flexible management to maintain sustainability. Full article