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GeoAI in Temperature Correction for Rice Heat Stress Monitoring with Geostationary Meteorological Satellites
No full textTo address the challenge of obtaining high-spatiotemporal-resolution and high-precision temperature grids for agricultural meteorological monitoring, this research focuses on rice heat stress monitoring with the China Meteorological Administration Land Data Assimilation System (CLDAS) and develops a temperature correction model that synergizes physical mechanisms with a data-driven strategy by introducing a GeoAI framework. Ensemble learning methods (XGBoost, LightGBM, and Random Forest) were utilized to process a comprehensive set of predictors, integrating dynamic surface features derived from FY-4 satellite’s high-frequency observation data. The data comprised surface thermal regime metrics, specifically the daily maximum land surface temperature (LSTmax) and its diurnal range (LSTmax_min), along with vegetation indices including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). Further, topographic attributes derived from a digital elevation model (DEM) were incorporated, such as slope, aspect, the terrain ruggedness index (TRI), and the topographic position index (TPI). The approach uniquely capitalized on the temporal resolution of geostationary data to capture the diurnal land surface dynamics crucial for bias correction. The proposed models not only enhanced temperature data quality but also achieved impressive accuracy. Across China, the root mean square error (RMSE) was reduced to 1.04 °C, mean absolute error (MAE) to 0.53 °C, and accuracy (ACC) to 0.97. Additionally, the most notable improvement was that the RMSE decreased by nearly 50% (from 2.17 °C to 1.11 °C), MAE dropped from 1.48 °C to 0.80 °C, and ACC increased from 0.72 to 0.96 in the southwestern region of China. The corrected rice heat stress data (2020–2023) indicated that significant negative correlations exist between yield loss and various heat stress metrics in the severely affected middle and lower Yangtze River region. The research confirms that embedding geostationary meteorological satellites within a GeoAI framework can effectively enhance the precision of agricultural weather monitoring and related impact assessments
Antipsychotic Treatment and Longitudinal Body Mass Index Trajectories in Youth with and Without Autism Spectrum Disorder
No full textBackground: Children and adolescents with autism spectrum disorder (ASD) frequently receive antipsychotics and are considered at increased risk for weight gain. Few studies have compared longitudinal weight trajectories between youth with ASD and those with other psychiatric disorders. Methods: This naturalistic, registry-based study used data from the SENTIA cohort, which prospectively monitors antipsychotic safety in individuals under 18 years at a university hospital in Spain. Clinical characteristics were compared between participants with and without ASD. Longitudinal body mass index (BMI) z-score trajectories were analysed using linear mixed-effects models. Results: The sample included 266 participants, of whom 113 (42.5%) had ASD. Individuals with ASD were more often male and initiated antipsychotic treatment at a younger age. Of the 26 participants prescribed an antipsychotic before age 6, 88.5% had ASD. Comorbidity profiles were similar across groups. Risperidone and aripiprazole were the most frequently prescribed antipsychotics. BMI z-scores increased over time (β = 0.130, p = 0.017), and baseline BMI z-score was the strongest predictor. ASD diagnosis did not modify the average linear rate of BMI z-score change (time × ASD: p = 0.251); however, a significant quadratic time × ASD interaction (β = −0.016, p = 0.041) was consistent with a more pronounced early increase followed by earlier attenuation of BMI z-scores in the ASD group. Conclusions: Although antipsychotic treatment was initiated earlier in youth with ASD, no clear difference was observed in the rate of BMI z-score change. Differences in weight trajectories underscore the need for metabolic monitoring in antipsychotic-treated youth
The Effect of Sport-Specific Brain Endurance Training on Performance in Elite Orienteering Athletes
No full textAim: To investigate the effect of a 6-week sport-specific BET intervention on cognitive and physical performance in elite orienteering athletes. Methods: A single-arm cross-over study with an initial 6-week control period (CON) followed by a 6-week brain endurance training (BET). Thirteen Danish national team orienteering athletes participated in the study. CON athletes adhered to planned physical, cognitive, and technical training. BET athletes added 20 min of route choice assessment (RCA) training after each weekly aerobic training session. The 30 min Stroop color-word task and a sport-specific RCA task evaluated general and sport-specific cognitive performance. A submaximal (1000 m) and a maximal (5000 m) running test were also conducted. Endpoints were assessed pre and post CON and post BET. Results: Average time used per RCA task was 1.4 ± 0.4 s lower following BET (27%) (p = 0.009) compared with no change after CON. Similarly, the total number of correct Stroop answers increased by 13.8 ± 5.21 points (2%) after BET with no change after CON. RCA time use declined steeply from session 1–7, whereafter average time use plateaued. Running performance did not differ significantly between periods. Conclusion: BET improved sport-specific performance and aspects of general cognitive performance, and may effectively improve cognitive parts important for elite orienteering performance
Straw Biochar Optimizes 15N Distribution and Nitrogen Use Efficiency in Dryland Foxtail Millet
No full textThe combined application of straw biochar and nitrogen fertilizer is an increasingly studied strategy to enhance soil fertility and crop yield. Optimizing the biochar-nitrogen interaction could be a choice for increasing nitrogen use efficiency (NUE) and reducing nitrogen loss in dryland agriculture. However, the mechanisms by which it regulates nitrogen allocation and absorption in foxtail millet (Setaria italica) are still limited in terms of mechanical understanding. Based on preliminary experiments, the optimal biochar-nitrogen interaction for soil nutrient absorption was identified. A field experiment was conducted with six treatments in an arid region of northwestern China: N1C1 (N1: 130 kg ha−1 + C1: 100 kg ha−1, control group), N2C4 (N2: 195 kg ha−1 + C4: 250 kg ha−1), N3C1 (N3: 260 kg ha−1 + C1: 100 kg ha−1), N3C2 (N3: 260 kg ha−1 + C2: 150 kg ha−1), N3C3 (N3: 260 kg ha−1 + C3: 200 kg ha−1), and N3C4 (N3: 260 kg ha−1 + C4: 250 kg ha−1). The results demonstrated that the biochar–nitrogen ratio significantly influenced topsoil total nitrogen, microbial biomass carbon (SMBC), and microbial biomass nitrogen (SMBN). All biochar-to-nitrogen combinations sharply increased soil total nitrogen by 133.11–151.52% compared to pre-sowing levels, providing a fundamental base for microbial-driven nitrogen transformation. Low nitrogen addition is more conducive to biomass accumulation, with N2C4 significantly increasing by 62.82%. Although a high biochar-to-nitrogen ratio reduced leaf relative chlorophyll content (SPAD) by 5.72–16.18% and net photosynthetic rate (Pn) by 16.09–52.65% at the heading stage, these did not compromise final yield. Importantly, N2C4, N3C1, and N3C4 significantly increased spike 15N abundance by 71.45%, 13.21%, and 19.43%, respectively. N2C4 grain production increases by 53.77–110.57% in two years and was positively correlated with spike 15N abundance, reflecting high nitrogen partial factor productivity. In conclusion, a reasonable biochar-nitrogen interaction enhances nitrogen allocation and grain yield by stimulating microbial activity and strengthening soil–plant synergy, the certified strategy effectively supports sustainable dryland agriculture by simultaneously increasing productivity and improving soil health
GMF-Net: A Gaussian-Matched Fusion Network for Weak Small Object Detection in Satellite Laser Ranging Imagery
No full textDetecting small objects in Satellite Laser Ranging (SLR) CCD images is critical yet challenging due to low signal-to-noise ratios and complex backgrounds. Existing frameworks often suffer from high computational costs and insufficient feature extraction capabilities for such tiny targets. To address these issues, we propose the Gaussian-Matched Fusion Network (GMF-Net), a lightweight and high-precision detector tailored for SLR scenarios. The core scientific innovation lies in the Gaussian-Matched Convolution (GMConv) module. Unlike standard convolutions, GMConv is theoretically grounded in the physical Gaussian energy distribution of SLR targets. It employs multi-directional heterogeneous sampling to precisely match target energy decay, enhancing central feature response while suppressing background noise. Additionally, we incorporate a Cross-Stage Partial Pyramidal Convolution (CSPPC) to reduce parameter redundancy and a Cross-Feature Attention (CFA) module to bridge multi-scale features. To validate the method, we constructed the first dedicated SLR-CCD dataset. Experimental results show that GMF-Net achieves an mAP@50 of 93.1% and mAP@50–95 of 52.4%. Compared to baseline models, parameters are reduced by 26.6% (to 2.2 M) with a 27.4% reduction in computational load, demonstrating a superior balance between accuracy and efficiency for automated SLR systems
Correction: Ahmadi, B.; Shirazi, E. A Heuristic-Driven Charging Strategy of Electric Vehicle for Grids with High EV Penetration. Energies 2023, 16, 6959
No full textIn the original publication [...
Correction: Dhiman et al. Rare Earth Doped ZnO Nanoparticles as Spintronics and Photo Catalyst for Degradation of Pollutants. Molecules 2023, 28, 2838
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Incorporating ESG to Create a Low-Volatility S&P 500 Index Fund
No full textThe integration of environmental, social, and governance (ESG) principles into investment strategies represents a potential pathway for advancing financial sustainability and long-term market resilience. The usage of ESG screening techniques in portfolio construction is currently a subject of debate among practitioners and policymakers. This paper introduces a methodology that incorporates ESG scores into a low-volatility, Standard & Poor’s 500 index-based strategy without relying on traditional exclusionary screening. Rather than removing firms based solely on low ESG scores, we treat ESG as a predictive sustainability factor in identifying firms likely to experience extreme return volatility in the subsequent year, using a probit model and Fama–Macbeth estimation techniques. Firms with high ESG scores are found to be less likely to exhibit such behavior, suggesting an inverse relationship between ESG and risk. Our results show that portfolios constructed using this approach achieve higher average ESG scores, maintain returns equivalent to the benchmark, and reduce annualized return volatility by approximately 1.0%, a statistically significant reduction. By reframing ESG from a moral filter into a measurable risk mitigation mechanism, this study demonstrates how sustainability integration can enhance portfolio stability while supporting both financial and societal objectives. The proposed framework offers practical alternative for investors seeking exposure to sustainability-focused strategies while preserving traditional performance objectives
Methodologies for Assessing the Dimensional Accuracy of Computer-Guided Static Implant Surgery in Clinical Settings: A Scoping Review
No full textBackground: Computer-guided static implant surgery (CGSIS) is widely adopted to enhance the precision of dental implant placement. However, significant heterogeneity in reported accuracy values complicates evidence-based clinical decision-making. This variance is likely attributable to a fundamental lack of standardization in the methodologies used to assess dimensional accuracy. Objective: This scoping review aimed to systematically map, synthesize, and analyze the clinical methodologies used to quantify the dimensional accuracy of CGSIS. Methods: The review was conducted in accordance with the PRISMA-ScR guidelines. A systematic search of PubMed/MEDLINE, Scopus, and Embase was performed from inception to October 2025. Clinical studies quantitatively comparing planned versus achieved implant positions in human patients were included. Data were charted on study design, guide support type, data acquisition methods, reference systems for superimposition, measurement software, and accuracy metrics. Results: The analysis of 21 included studies revealed extensive methodological heterogeneity. Key findings included the predominant use of two distinct reference systems: post-operative CBCT (n = 12) and intraoral scanning with scan bodies (n = 6). A variety of proprietary and third-party software packages (e.g., coDiagnostiX, Geomagic, Mimics) were employed for superimposition, utilizing different alignment algorithms. Critically, this heterogeneity in measurement approach directly manifests in widely varying reported values for core accuracy metrics. In addition, the definitions and reporting of core accuracy metrics—specifically global coronal deviation (range of reported means: 0.55–1.70 mm), global apical deviation (0.76–2.50 mm), and angular deviation (2.11–7.14°)—were inconsistent. For example, these metrics were also reported using different statistical summaries (e.g., means with standard deviations or medians with interquartile ranges). Conclusions: The comparability and synthesis of evidence on CGSIS accuracy are significantly limited by non-standardized measurement approaches. The reported ranges of deviation values are a direct consequence of this methodological heterogeneity, not a comparison of implant system performance. Our findings highlight an urgent need for a consensus-based minimum reporting standard for future clinical research in this field to ensure reliable and translatable evidence
Mpox Clade IIb Virus Introduction into Kinshasa, Democratic Republic of the Congo, July 2025
No full textClade I mpox virus (MPXV) is endemic in the Democratic Republic of the Congo (DRC). Recent studies have described the changing epidemiology of mpox in the country, which has been mainly characterized by the emergence of new MPXV lineages, Clade Ib/sh2023 and Ia/sh2024, associated with sustained human-to-human transmission. Both Clade Ib/sh2023 and Ia/sh2024 are co-circulating in Kinshasa, the capital city of the DRC. Here, we report the first two cases of Clade IIb/sh2017 identified in Kinshasa, DRC, imported from West Africa and locally transmitted. Clinical specimens were collected and tested by PCR. We performed whole genome sequencing using a tiled-amplicon sequencing approach with Clade IIb MPXV-specific primers. The phylogenetic tree shows that Kinshasa Clade IIb MPXV is assigned to Clade IIb/sh2017 within the newly designated lineage G.1, as identified in January 2025 in Sierra-Leone