Search Results (332,234)

Heavy metal (HM) contamination threatens environmental sustainability, food safety, and agricultural productivity worldwide. HM toxicity adversely affects plant growth, reducing germination rates by 20–50%, impairing seedling establishment, and inhibiting shoot and root development by 30–60% in various crops. HM disrupts key physiological processes, including photosynthesis, stomatal regulation, membrane integrity, nutrient uptake, and enzymatic and nonenzymatic antioxidant activities. These disruptions largely result from oxidative stress, caused by the excessive accumulation of reactive oxygen species, which damage cellular components. To counteract HM toxicity, plants deploy a complex defense network involving antioxidant enzymes, metal chelation by phytochelatins and metallothioneins, vacuolar sequestration, and symbiotic interactions with arbuscular mycorrhizal fungi, which can retain 40–70% of metals in roots and reduce translocation to shoots. At the molecular level, MAPK (Mitogen-Activated Protein Kinase) signaling pathways, transcription factors (e.g., WRKY, MYB, bZIP, and NAC), and phytohormonal crosstalk regulate the expression of stress-responsive genes expression to enhance HM stress tolerance. Advances in nanotechnology offer promising strategies for the remediation of HM-contaminated soils and water sources (HM remediation); engineered and biogenic nanoparticles (e.g., ZnO, Fe₃O₄) improve metal immobilization, reduce bioavailability, and enhance plant growth by 15–35% under HM stresses, although excessive doses may induce phytotoxicity. Future applications of nanotechnology in HM remediation should consider nanoparticle transformation (e.g., dissolution and agglomeration) and environmentally relevant concentrations to ensure efficacy and minimize phytotoxicity. Integrating phytoremediation with nanoparticle-enabled strategies provides a sustainable approach for HM remediation. This review emphasizes the need for a multidisciplinary framework linking plant science, biotechnology, and nanoscience to advance HM remediation and safeguard agricultural productivity. Full article

To date, it remains unclear how students position themselves within the tension between participation, achievement, and body norms in physical education (PE), as well as what role participatory structures play in this process. This paper, therefore, investigates the intersection of these dimensions by examining students’ experiences of participation in PE settings characterized by a high degree of diversity. Theoretically, the study is grounded in concepts of participatory and diversity-sensitive didactics, which serve as analytical frameworks for examining school practices. Semi-structured qualitative interviews were conducted with secondary school students across different grade levels. The data were analyzed using structured qualitative content analysis, yielding five main categories: moments of participation, self-positioning, understanding of the body, understanding of performance, and performance requirements. These categories are illustrated and interrelated through three exemplary student portraits. The findings indicate that participation in PE is a dynamic and negotiated process, shaped by teachers’ orientations and students’ agency, social dynamics, and prevailing body and performance norms. While some students benefit from inclusive practices, others encounter structural and symbolic barriers. The study highlights the potential of participatory, diversity-sensitive, and sensitizing teaching to foster agency, challenge exclusionary norms, and enable meaningful engagement for all students. These insights contribute to current debates on diversity, inclusion, and democratic education in PE. Full article

A central obstacle to the practical deployment of Reinforcement Learning (RL) is the prevalence of sparse rewards, which often necessitates task-specific dense signals crafted through costly trial-and-error. Automated reward decomposition and return–redistribution methods can reduce this burden, but they are largely semantically agnostic and may fail to capture the multifaceted nature of task performance, leading to reward hacking or stalled exploration. Recent work uses Large Language Models (LLMs) to generate reward functions from high-level task descriptions, but these specifications are typically static and may encode biases or inaccuracies from the pretrained model, resulting in a priori reward misspecification. To address this, we propose the Metacognitive Introspective Reward Architecture (MIRA), a closed-loop architecture that treats LLM-generated reward code as a dynamic object refined through empirical feedback. An LLM first produces a set of computable reward factors. A dual-loop design then decouples policy learning from reward revision: an inner loop jointly trains the agent’s policy and a reward-synthesis network to align with sparse ground-truth outcomes, while an outer loop monitors learning dynamics via diagnostic metrics and, upon detecting pathological signatures, invokes the LLM to perform targeted structural edits. Experiments on MuJoCo benchmarks show that MIRA corrects flawed initial specifications and improves asymptotic performance and sample efficiency over strong reward-design baselines. Full article

Achieving the Sustainable Development Goals (SDGs) hinges critically on extensive international cooperation. However, the extent and evolution of such cooperation at the global level, along with the relative synergy contributions of different country groups, remain insufficiently studied and inadequately quantified. This study aims to assess the level of global cooperation in advancing the SDGs from 2001 to 2023. It further examines the synergy contributions of countries across different income groups and geographic regions. To this end, a synergy-based statistical framework is employed for the analysis. Our results indicate that global cooperation has shown a steady upward trend during this period, yet substantial disparities persist across different goals and indicators. High-income countries contributed most to economic SDGs, whereas low-income countries contributed most to environmental and social SDGs. Regionally, North America and Europe contributed most to economic synergy. Asia, Africa, Latin America, and the Caribbean made more substantial contributions to social and environmental progress. This study enhances the understanding of global cooperation dynamics related to the SDGs. It also provides evidence-based insights to support the design and timely adjustment of more effective international cooperation strategies. Full article

Background: Accurate deformity classification and fusion-level planning are essential in adolescent idiopathic scoliosis (AIS) surgery and are traditionally guided by Cobb angle measurement and the Lenke system. Multimodal large language models (LLMs) (e.g., ChatGPT-4.0; Claude 3.7 Sonnet, Gemini 2.5 Pro, DeepSeek-R1-0528 Chat) are increasingly used for image interpretation despite limited validation for radiographic decision-making. This study evaluated the agreement and reproducibility of contemporary multimodal LLMs for AIS assessment compared with expert spine surgeons. Methods: This single-center retrospective study included 125 AIS patients (94 females, 31 males; mean age 14.8 ± 1.9 years) who underwent posterior instrumentation (2020–2024). Two experienced spine surgeons independently performed Lenke classification (including lumbar and sagittal modifiers) and selected fusion levels (UIV–LIV) on standing AP, lateral, and side-bending radiographs; discrepancies were resolved by consensus to establish the reference standard. The same radiographs were analyzed by four paid multimodal LLMs using standardized zero-shot prompts. Because LLMs showed inconsistent end-vertebra selection, LLM-derived Cobb angles lacked a common anatomical reference frame and were excluded from quantitative analysis. Agreement with expert consensus and test–retest reproducibility (repeat analyses one week apart) were assessed using Cohen’s κ. Evaluation times were recorded. Results: Surgeon agreement was high for Lenke classification (92.0%, κ = 0.913) and fusion-level selection (88.8%, κ = 0.879). All LLMs demonstrated chance-level test–retest reproducibility and very low agreement with expert consensus (Lenke: 1.6–10.2%, κ = 0.001–0.036; fusion: 0.8–12.0%, κ = 0.003–0.053). Claude produced missing outputs in 17 Lenke and 29 fusion-level cases. Although LLMs completed assessments far faster than surgeons (seconds vs. ~11–12 min), speed did not translate into clinically acceptable reliability. Conclusions: Current general-purpose multimodal LLMs do not provide reliable Lenke classification or fusion-level planning in AIS. Their poor agreement with expert surgeons and marked internal inconsistency indicate that LLM-generated interpretations should not be used for surgical decision-making or patient self-assessment without task-specific validation. Full article

The influence of lactic acid bacteria (LAB) strains of various species isolated from Chinese traditional sourdough on the properties of rice sourdough and the textural and flavor qualities of steamed rice bread (SRB) was investigated. Lactiplantibacillus plantarum-fermented rice sourdough had a higher total titratable acidity (13.10 mL) than the other groups. Strains Lacticaseibacillus paracasei PC1 (LPC), Lactobacillus helveticus H1 (LH), Lactobacillus crustorum C1 (LC), Lactobacillus paralimentarius PA1 (LPA), and Lactiplantibacillus plantarum P1 (LP) showed marked protein hydrolysis during rice sourdough fermentation and increased free amino acid levels in rice sourdoughs relative to the control. The Fourier Transform Infrared Spectroscopy results indicated that LAB fermentation could promote the strengthening of inter-intramolecular hydrogen bonds and cause modifications in protein structures; however, these effects varied among the different strains. The LC and LPC strains had the most significant effect on improving the specific volume and textural properties of SRBs. Gas chromatography-mass spectrometry (GC-MS) and GC-ion mobility spectrometry (IMS) identified 33 and 35 volatile compounds, respectively, in the LAB-fermented SRBs, and differentiation was observed in the volatile profiles of SRBs made using different LAB strains. The differential impacts of LAB strains during rice sourdough fermentation can assist in the selection of candidate microorganisms for the production of high-quality gluten-free rice products. Full article

In order to reduce the risk of collapse disasters during tunnel construction in mountainous areas and to make full use of the available data, a collapse risk assessment model for highway tunnel construction was established based on the WOA–XGBOOST algorithm. Three major categories of tunnel construction risk, namely engineering geological factors, survey and design factors, and construction management factors, were selected as the first-level indicators, and 14 secondary indicators were further specified as the input variables of the collapse risk assessment model for tunnel construction. The confusion matrix and accuracy metrics were employed to evaluate the training and prediction performance of the risk assessment model on both the training set and the test set. The results show that subjective weights derived from the G1 method were integrated with objective weights generated by the WOA–XGBOOST algorithm. A game-theory-based weight integration strategy was then applied to optimize the combined weights, effectively mitigating the biases inherent in single-method weighting approaches. Risk quantification was systematically conducted using a cloud model, while spatial risk distribution patterns were visualized through graphical cloud-mapping techniques. After completion of model training, the proposed model achieved a high accuracy of over 99% on the training set and around 95% on the held-out test set based on an available dataset of 100 collapse-prone tunnel construction sections. Case-based verification further suggests that, in the studied collapse scenarios, the predicted risk levels are generally consistent with the actual engineering risks, indicating that the model is a promising tool for assisting tunnel construction risk assessment under similar conditions. The research outcomes provide an efficient and reliable approach for assessing risks in tunnel construction, thereby offering a scientific basis for engineering decision-making processes. Full article

The aim of the study was to evaluate how controlled laboratory addition with Pb, Cd, and Cu affects the fatty acid profile of milk and acid-coagulated cheese from three geographical regions (R1, R2, R3), considering the influence of regional characteristics and the March–April 2025 harvesting period. Comparative analysis of the lipid profile (SFA and UFA) and the ratios between fatty acids showed that region R2 displayed the most balanced nutritional structure, followed by regions R1 and R3. The lipid indices (IA 2.5–4, IT 3–4.4, HH 0.4–0.6, HPI 0.2–0.4) confirmed this pattern across all regions, indicating that R2 is characterized by a favorable, antiatherogenic, and antithrombotic lipid profile, whereas R1 exhibits an intermediate profile and R3 a markedly unbalanced profile. The same trend was observed for the lipid composition of the blank cheese samples. Heavy metal fortification produced major shifts in fatty acid composition and lipid indices. At the maximum level permitted by legislation, the changes were moderate, with SFA increasing from 71% to 77% and essential ω-3 and ω-6 PUFA decreasing, resulting in increased IA and IT and reduced HH and HPI. At 10× the maximum limit, the lipid profile became severely unbalanced: SFA increased to 81%, UFA dropped to 17%, ω-3 fatty acids were nearly absent, and ω-6 levels declined sharply, amplifying their imbalance. These changes were accompanied by a substantial deterioration in all lipid indices. These findings demonstrate that fatty acid composition (SFA, MUFA, PUFA) and lipid parameters (IA, IT, HH, HPI) serve as highly sensitive markers of heavy metal-induced oxidative stress in dairy products. Overall, the study shows that while the fatty acid profiles of milk from different regions reliably indicate both geographical origin and nutritional quality, exposure to heavy metal addition profoundly disrupts these profiles, together with their lipid indices, producing changes significant enough to signal compromised safety and diminished functional value of the resulting cheese. Full article

This study introduces a novel, automated image-to-BIM (Building Information Modeling) workflow designed to generate semantically rich and geometrically useful BIM models directly from RGB images. Conventional scan-to-BIM often relies on specialized, costly, and time-intensive equipment, specifically if LiDAR is used to generate point clouds (PCs). Typical workflows are followed by a separate post-processing step for semantic segmentation recently performed by deep learning models on the generated PCs. Instead, the proposed method integrates vision language object detection (YOLOv8x-World v2) and vision based segmentation (SAM 2.1) with Neural Radiance Fields (NeRF) 3D reconstruction to generate segmented, color-labeled PCs directly from images. The key novelty lies in bypassing post-processing on PCs by embedding semantic information at the pixel level in images, preserving it through reconstruction, and encoding it into the resulting color labeled PC, which allows building elements to be directly identified and geometrically extracted based on color labels. Extracted geometry is serialized into a JSON format and imported into Revit to automate BIM creation for walls, windows, and doors. Experimental validation on BIM models generated from Unmanned Aerial Vehicle (UAV)-based exterior datasets and standard camera-based interior datasets demonstrated high accuracy in detecting windows and doors. Spatial evaluations yielded up to 0.994 precision and 0.992 Intersection over Union (IoU). NeRF and Gaussian Splatting models, Nerfacto, Instant-NGP, and Splatfacto, were assessed. Nerfacto produced the most structured PCs suitable for geometry extraction and Splatfacto achieved the highest image reconstruction quality. The proposed method removes dependency on terrestrial surveying tools and separate segmentation processes on PCs. It provides a low-cost and scalable solution for generating BIM models in aging or undocumented buildings and supports practical applications such as renovation, digital twin, and facility management. Full article

The number of rubber plantations has increased significantly since 2000, especially in Southeast Asia and China, and their ecological impacts are becoming more evident. A robust rubber supply monitoring system is currently required at both the production and ecological levels. This study used Sentinel-2 multi-rule remote sensing images and a deep learning method to construct a deep learning model that could generate a distribution map of rubber plantations in Danzhou City, Hainan Province, from 2019 to 2024. For biomass modeling, 52 sample plots (27 of which were historical plots) were integrated, and the canopy structure was extracted as an auxiliary variable from the point cloud data generated by an unmanned aerial vehicle survey. Five algorithms, namely Random Forest (RF), Gradient Boosting Decision Tree, Convolutional Neural Network, Back Propagation Neural Network, and Extreme Gradient Boosting, were used to characterize the spatiotemporal changes in rubber plantation biomass and analyze the driving mechanisms. The developed deep learning model was exceptional at identifying rubber plantations (overall accuracy = 91.63%, Kappa = 0.83). The RF model performed the best in terms of biomass prediction (R² = 0.72, RRMSE = 21.48 Mg/ha). Research shows that canopy height as a characteristic factor enhances the explanatory power and stability of the biomass model. However, due to limitations such as sample plot size, image differences, canopy closure degree, and point cloud density, uncertainties in its generalization across years and regions remain. In summary, the proposed framework effectively captures the spatial and temporal dynamics of rubber plantations and estimates their biomass with high accuracy. This study provides a crucial reference for the refined management and ongoing monitoring of rubber plantations. Full article

Inflammatory bowel disease is characterised by chronic mucosal inflammation, oxidative stress, and impaired epithelial barrier function. Current therapies primarily suppress inflammation but do not effectively restore epithelial integrity. In this study, we established in vitro cell cultures of Vitis labrusca var. Isabella to obtain juices that were chemically characterised and assessed for antioxidant and anti-inflammatory activities in human intestinal epithelial cell lines (i.e., Caco-2). Chemical analysis revealed variable levels of stilbenoids, including trans-resveratrol and resveratrol diglucosides depending on culture conditions. The suspension-derived juice grown in darkness (SVMD) significantly reduced lipopolysaccharide-induced IL-1β and TNF-α release and mitigated oxidative stress in Caco-2 cells by lowering levels of intracellular reactive oxygen species. In Caco-2 monolayers infected with Salmonella enterica, SVMD preserved transepithelial electrical resistance, indicating protection of epithelial barrier integrity, without exerting direct antibacterial effects. These findings demonstrate that V. labrusca cell-culture juices exert potent antioxidant and anti-inflammatory actions and promote epithelial protection through modulation of redox balance. Overall, this study highlights the potential of sustainable cell-culture-derived materials as promising natural products for supporting intestinal homeostasis and managing gut inflammatory disorders. Full article

Genetically Modified Foods (GMFs) have become one of the most controversial innovations in food production and biotechnology. Public concerns regarding the safety, ethical considerations, and health impacts of GMFs have fueled widespread debate. This study explores the impact of social media exposure on individuals’ knowledge of and attitudes toward Genetically Modified Foods and how these factors influence their purchase intention. The findings from an online survey of 467 participants in Saudi Arabia show that higher levels of social media exposure are associated with increased knowledge and stronger perceptions of both benefits and risks of GMFs. Purchase intentions, however, are driven primarily by perceived benefits (positively) and perceived risks (negatively), while knowledge exerts an indirect effect through these attitudinal components. Full article

Seabed topography is a key factor affecting the layout of underwater production systems. Developing a more scientific, intelligent, and integrated layout optimization method is the key to optimizing the layout of underwater production systems. To address the challenge of acquiring a more scientific, intelligent, and integrated optimization method, this paper proposes a multi-level integrated optimization model that incorporates three-dimensional seabed topography, obstacle areas, target locations, pipeline paths, and manifold connection relationships, with the primary objective of minimizing total investment cost. A hybrid algorithm combining H-MOPSO (Hierarchical Multi-Objective Particle Swarm Optimization) with K-means-ILP clustering, dynamic programming, and TEWA* pathfinding is raised to collaboratively solve for the global optimal layout, achieving a coupled “target grouping-manifold connection-path optimization” design. Based on the actual oilfield seabed topography and target data, this paper carries out case analysis and algorithm comparison experiments. The results show that the optimization method in this paper can significantly improve the layout economy and cost accuracy under the premise of meeting the engineering constraints. Among them, the PLEM parallel connection method reduces the pipeline laying cost by 25.72% and the overall layout investment cost by 5.39% compared with the traditional manifold series scheme. Full article

Pumped storage power stations (PSPSs) are crucial regulators for accelerating the global energy structure transformation and developing a renewable energy-dominated power system. The sediment entering the reservoirs leads to capacity loss, while the fine-grained sediment carried by water during pumping and power generation can cause cavitation in penstocks and abrasion of turbine blades, which may lead to frequent shutdowns for overhaul. Taking a pumped storage power station as an example, whose lower reservoir is on a sediment-laden river, this study simulates the sediment concentration and its particle size through turbines under different sediment regulation measures. The unit abrasion rate and overhaul cycle are further predicted. The results indicate that the sediment concentration through turbines (SCT) and the suspended sediment transport rate entering the lower reservoir are positively correlated. The higher the SCT, the coarser the sediment particle size through turbines. For the lower reservoir with delta or conical sedimentation patterns, lowering the water level and shutting down pumping during sediment peak processes can free up the effective storage capacity, reduce the SCT by approximately 26%, and extend the overhaul cycle to 1.5 times. The study also systematically introduces a practical and feasible method for predicting SCT and turbine blade abrasion, servicing for the sustainability of PSPSs. Full article

Local pier scour remains one of the leading causes of bridge failure, calling for predictions that are both accurate and uncertainty-aware. This study develops an interpretable data-driven framework that couples CatBoost (Categorial Gradient Boosting) for deterministic point prediction with NGBoost (Natural Gradient Boosting) for probabilistic prediction. Both models are trained on a laboratory dataset of 552 measurements of local scour at bridge piers using non-dimensional inputs (y/b, V/V_c, b/d₅₀, Fr). Model performance was quantitatively evaluated using standard regression metrics, and interpretability was provided through SHAP (Shapley Additive Explanations) analysis. Monte Carlo–based reliability analysis linked the predicted scour depths to a reliability index β and exceedance probability through a simple multiplicative correction factor. On the held-out test set, CatBoost offers slightly higher point-prediction accuracy, while NGBoost yields well-calibrated prediction intervals with empirical coverages close to the nominal 68% and 95% levels. This framework delivers accurate, interpretable, and uncertainty-aware scour estimates for target-reliability, risk-informed bridge design. Full article