Search Results (13,000)

Regional water systems face growing pressure from climate variability, water scarcity, and increasingly complex wastewater pollution. These challenges require governance models that integrate institutional coordination with effective technological solutions. This review is based on a structured analysis of peer-reviewed literature indexed in Scopus, Web of Science, and ScienceDirect, covering publications from approximately 2014 to 2025. The findings show that clearly defined institutional roles, basin-level coordination, stable financing mechanisms, and active stakeholder participation significantly improve governance outcomes. Technological advances such as membrane filtration, advanced oxidation processes, nature-based treatment systems, and digital monitoring platforms enhance treatment efficiency, resilience, and opportunities for resource recovery. Regions differ widely in their ability to adopt these solutions, mainly due to variations in governance coherence, investment capacity, and climate-adaptation readiness. The review highlights the need for policy frameworks that align institutional reforms with technological modernization, including the adoption of basin-based planning, digital decision-support systems, and circular water-economy principles. These measures provide actionable guidance for policymakers and regional authorities seeking to strengthen long-term water security and wastewater management performance. Full article

This paper introduces AIDE, an active inference-driven evaluation framework designed to provide a unified and theoretically grounded approach for analyzing sequential textual data. AIDE formulates the evaluation problem as variational inference in a latent dynamical system, enabling joint treatment of representation, temporal structure, and predictive reasoning. The framework integrates (i) a representation and augmentation module based on variational learning and contrastive semantic encoding, (ii) a parametric state–space model that captures the evolution of latent states and supports probabilistic forecasting, and (iii) a policy-selection mechanism that minimizes the expected free energy, guiding a latent diffusion generator to produce coherent and interpretable evaluation outputs. This formulation yields a principled pipeline linking evidence accumulation, latent-state inference, and policy-driven generative reporting. Experimental studies demonstrate that AIDE provides stable inference, coherent predictions, and consistent evaluation behavior across heterogeneous textual sequences. The proposed framework offers a general probabilistic foundation for dynamic evaluation tasks and contributes a structured methodology for integrating representation learning, dynamical modeling, and generative mechanisms within a single variational paradigm. Full article

The automation of quality control in agricultural food processing, particularly the detection of incomplete peeling in taro, constitutes a critical frontier for ensuring food safety and optimizing production efficiency in the Industry 4.0 era. However, this domain is fraught with significant technical challenges, primarily stemming from the inherent visual characteristics of residual peel: extremely minute scales relative to the vegetable body, highly irregular morphological variations, and the dense occlusion of objects on industrial conveyor belts. To address these persistent impediments, this study introduces a comprehensive solution comprising a specialized dataset and a novel detection architecture. We established the Taro Peel Industrial Dataset (TPID), a rigorously annotated collection of 18,341 high-density instances reflecting real-world production conditions. Building upon this foundation, we propose MDB-YOLO, a lightweight, multi-dimensional bionic detection model evolved from the YOLOv8s architecture. The MDB-YOLO framework integrates a synergistic set of innovations designed to resolve specific detection bottlenecks. To mitigate the conflict between background texture interference and tiny target detection, we integrated the C2f_EMA module with a Wise-IoU (WIoU) loss function, a combination that significantly enhances feature response to low-contrast residues while reducing the penalty on low-quality anchor boxes through a dynamic non-monotonic focusing mechanism. To effectively manage irregular peel shapes, a dynamic feature processing chain was constructed utilizing DySample for morphology-aware upsampling, BiFPN_Concat2 for weighted multi-scale fusion, and ODConv2d for geometric preservation. Furthermore, to address the issue of missed detections caused by dense occlusion in industrial stacking scenarios, Soft-NMS was implemented to replace traditional greedy suppression mechanisms. Experimental validation demonstrates the superiority of the proposed framework. MDB-YOLO achieves a mean Average Precision (mAP50-95) of 69.7% and a Recall of 88.0%, significantly outperforming the baseline YOLOv8s and advanced transformer-based models like RT-DETR-L. Crucially, the model maintains high operational efficiency, achieving an inference speed of 1.1 ms on an NVIDIA A100 and reaching 27 FPS on an NVIDIA Jetson Xavier NX using INT8 quantization. These findings confirm that MDB-YOLO provides a robust, high-precision, and cost-effective solution for real-time quality control in agricultural food processing, marking a significant advancement in the application of computer vision to complex biological targets. Full article

Ferroptosis is an iron-dependent form of regulated cell death (RCD) characterized by intracellular iron homeostasis disruption and lipid peroxide accumulation. It is involved in many pathological processes, including malignant tumors, cardiovascular diseases, inflammatory diseases, and mitochondrial disorders. Cysteine desulfurase (NFS1), a key enzyme in mitochondrial iron-sulfur (Fe-S) cluster biosynthesis, participates in regulating cellular ferroptosis by maintaining Fe-S cluster homeostasis and modulating the ACO1/IRP1 axis, the Xc⁻–glutathione (GSH)–glutathione peroxidase 4 (GPX4) axis, and the p53/STAT signaling pathway. When the function of NFS1 is abnormal, the intracellular free iron level is elevated, followed by reactive oxygen species (ROS) accumulation and lipid peroxidation. NFS1 expression exhibits significant variation across different tissues. Upregulation of NFS1 in tumors can enhance tumor cell resistance to ferroptosis; thus, it can promote tumor growth, drug resistance, and metastatic ability. Conversely, downregulation of NFS1 in cardiomyocytes and neurons exacerbates ferroptosis and causes functional impairment. Here, we systematically review recent advances in the molecular mechanisms of NFS1-mediated ferroptosis and its role in various disease models, intending to clarify key components in the upstream regulatory network of ferroptosis and explore the application value of NFS1 as a potential therapeutic target. The review shows that NFS1 plays an important role in cellular fate regulation, which has significant clinical application potential in the treatment of cancer and interventions for neurological and cardiovascular diseases. Therefore, it can provide a new theoretical basis and research direction for subsequent mechanism research and targeted therapeutic strategy development. Full article

Net ecosystem exchange (NEE) is a central metric for assessing carbon cycling, and its accurate quantification is critical for understanding terrestrial-atmosphere carbon exchange dynamics. However, in complex alpine regions, high-resolution NEE estimation remains challenging due to limited observations and heterogeneous surface processes. To address this, we developed a multimodal feature fusion model (Multimodal-CNN-Attention-RF, MMCA-RF) that integrates convolutional neural networks (CNN) and random forest (RF) for NEE estimation in the Babao River Basin on the northeastern Tibetan Plateau. The model incorporates a cross-modal attention mechanism to dynamically optimize feature interactions, thereby better capturing the spatially heterogeneous responses of vegetation to environmental drivers. Results demonstrate that MMCA-RF exhibits strong stability and generalization, with R² values of 0.89 (training) and 0.85 (testing). Based on model outputs, the Babao River Basin acted as a carbon sink during 2017–2023, with a mean annual NEE of −100.86 gC m⁻² yr⁻¹. Spatially, NEE showed pronounced heterogeneity, while seasonal variation followed a unimodal pattern. Among vegetation types, grasslands contributed the largest total carbon sink, whereas open woodlands showed the highest sequestration efficiency per unit area. Driver analysis identified temperature as the dominant control on NEE spatial variation, with interactions between temperature, precipitation, and topography further enhancing heterogeneity. This study provides a high-accuracy modeling approach for monitoring carbon cycling in alpine ecosystems and offers insights into the stability of regional carbon pools under climate change. Full article

High-quality hole machining of Ti-6Al-4V is critical for precision aerospace components but remains challenging due to the alloy’s poor machinability. In this study, the influence of cutting parameters on milling force, burr formation and the hole quality of Ti-6Al-4V was investigated. The mechanical properties and microstructure of the milled holes were analyzed. The research results show that milling depth is the primary factor governing variations in milling force and burr formation. The minimum milling force of 3.61 N is achieved at a milling depth of 60 μm, a feed per tooth of 2 μm/z and a cutting speed of 31 m/min. Compared to pre-optimization parameters, the milling force is decreased by 91.74%. Correspondingly, entrance burr width and hole-axis deviation were substantially reduced, indicating marked improvement in hole quality and geometrical accuracy. Microstructural observations show no deleterious phase transformations or excessive work-hardening under the optimized regime. The results deliver quantitative guidelines for parameter selection and tool application in micro-hole milling of Ti-6Al-4V and provide a foundation for further process modelling and optimization for aerospace manufacturing. Full article

The pursuit of low-carbon economic development represents an inherent requirement for implementing the Sustainable Development Goals (SDGs) and serves as a vital support for advancing SDG 7, SDG 9, and SDG 13. Drawing on provincial data from China (2006–2023), this research investigates how digital-real convergence influences low-carbon economic development. The results demonstrate a positive contribution of this convergence to growth in the low-carbon economy, and it proves to be superior to models reliant solely on either digital-digital or real-real convergence. A notable finding is the considerable regional variation in the effect. It is strong in both eastern and western parts of the country, which stands in sharp contrast to central China, where the effect is statistically insignificant or negative. Identified as underlying mechanisms are the agglomeration of innovative talent and the accumulation of innovative capital. Additionally, a single-threshold effect of urbanization level is identified, indicating that the positive impact strengthens only after urbanization surpasses a critical value. Furthermore, digital-real convergence not only enhances local low-carbon development but also generates positive spillover effects on neighboring regions. Thus, to fully advance the SDGs, policy formulation and implementation must account for regional heterogeneity, prioritize the elevation of urbanization levels, enhance cross-regional collaboration, and amplify the enabling role of digital-real integration. Full article

Enhancing photosynthetic efficiency represents a key approach for improving crop biomass, with its translation into higher grain yield being contingent upon the efficiency of photosynthate partitioning toward harvestable organs. The Rubisco small subunit (RbcS) gene family plays an essential role in this process by stabilizing and regulating Rubisco assembly and activity during photosynthesis. In this study, we identified 61 RbcS genes across B. napus, B. juncea, and B. carinata, and their diploid progenitors B. rapa, B. nigra, and B. oleracea by genome-wide screening and bioinformatic approaches. Phylogenetic relationships, gene structures, conserved domains, collinearity, cis-regulatory elements, expression profiles, and haplotype variations were systematically investigated, revealing the potential functional role significance and regulatory complexity of RbcS genes in photosynthesis. The results imply that the promoter type of this gene family may belong to light-inducible promoters. Furthermore, while a haplotype analysis provided valuable insights for selecting germplasm with potentially high photosynthetic efficiency, definitive confirmation of their effects requires functional validation. Collectively, our results establish a theoretical foundation for understanding the molecular mechanisms of BnRbcS genes and propose candidate genetic targets for further exploration to enhance photosynthetic performance in rapeseed breeding. Full article

Financial markets evolve through recurring phases of stability, turbulence, and structural transformation. Standard Hidden Markov Models (HMMs) assume fixed transition probabilities, which limits their ability to capture such higher-order changes in market behavior. This study introduces an Adaptive Hierarchical Hidden Markov Model (AH-HMM), where regime transitions depend on an unobserved meta-regime that reflects the broader macro-financial environment. Each meta-regime defines its own transition matrix across market states such as bull, bear, and turbulent phases. In this way, the model adapts dynamically to structural changes arising from crises, policy shifts, or variations in investor sentiment. Using weekly data for major equity indices, aggregated from daily prices, together with macro-uncertainty indicators, we show that the AH-HMM identifies key turning points including the Global Financial Crisis, the COVID-19 shock, and the post-2022 tightening cycle. In our empirical application, where we approximate the latent structural layer by low- and high-uncertainty environments defined from the VIX, the adaptive model attains a higher in-sample likelihood and delivers competitive out-of-sample forecasts and Value-at-Risk coverage relative to conventional HMMs and time-varying transition alternatives. Overall, the results highlight a mechanism of structural learning within market regimes and offer tools for risk management and policy analysis under uncertainty. Full article

In this study, the influence of post-processing heat treatment on microstructure and mechanical properties of Ti-6Al-2Sn-4Zr-2Mo (Ti-6242) alloy fabricated by laser powder bed fusion (L-PBF) was investigated. The mechanical properties of the as-built and heat-treated samples with various temperatures (600–850 °C) were evaluated using a tensile test at room temperature. After heat treatments, both yield strength (YS) and ultimate tensile strength (UTS) gradually decreased, while the tensile elongation tended to increase as the heat treatment temperature increased. These variations were closely related to the microstructural evolution caused by heat treatment. Specifically, the decomposition of α′ martensite into the α + β lamellar structure and subsequent coarsening were promoted with increasing temperature, leading to stress relief and improved dislocation storage capability, which resulted in the variation in mechanical properties. Notably, although the mechanical strength was reduced after heat treatment with increasing temperatures, the lowest yield strength and ultimate tensile strength were measured as 1086.4 ± 16.5 and 1135.0 ± 15.0 MPa, respectively, which are comparable to or higher than those of conventionally processed Ti-6242. As a result, the post-processing heat treatment could be an effective approach to achieve desirable performance for targeted applications. Full article

The built environment, serving as the core spatial vehicle for human production and daily activities, constitutes a vital foundation for achieving sustainable urban development and high-quality renewal. However, amidst rapid urbanisation, certain areas continue to grapple with issues such as ageing infrastructure, inefficient land use, and imbalanced spatial structures, hindering the establishment of sustainable urban forms. Consequently, identifying the evolutionary characteristics and influencing mechanisms of the built environment from the perspective of spatial heterogeneity holds critical significance for advancing refined governance and sustainable planning. Taking Kwun Tong District in Hong Kong as a case study, this research constructs an Adaptive-Entropy Multi-Scale Geographically Weighted Regression (MGWR) analytical framework. This systematically reveals the spatial distribution patterns of built environment elements and their multi-scale spatial heterogeneity characteristics. The findings indicate the following: (1) The built environment exhibits significant spatial differentiation and clustering structures across different scales, reflecting complex spatial processes driven by multiple interacting factors (2) Compared with the OLS model at a 1000 m scale and the GWR model at a 500 m scale, the Adaptive-Entropy MGWR model at a 100 m scale demonstrated superior fitting accuracy and explanatory power. It more effectively captured local structural variations and scale effects, thereby offering greater guidance value for sustainable planning. Building upon these findings, this study further proposes pathway recommendations for urban renewal and built environment optimisation in Kwun Tong District, offering an analytical approach and technical framework that may serve as a reference for sustainable development in high-density cities. Full article

The time-varying magnetic field characteristics during the demagnetization process of complex ferromagnetic cavity structures were studied based on computational electromagnetic simulation. By establishing a simulation model of the complex ferromagnetic cavity structures and the magnetic field generation coil, the main factors affecting the time-varying magnetic field characteristics were analyzed and explained, including eddy current effects, hysteresis effects, material properties of the complex ferromagnetic cavity structure, and structural gap connections. The magnetic field amplitude at typical locations was investigated, and the temporal variation of the internal magnetic field was analyzed. Additionally, the evolution motion of eddy currents during the dynamic demagnetization process was simulated. It was found that the aforementioned factors significantly affect the internal magnetic field of the complex ferromagnetic cavity structures during the demagnetization process, and their influences intertwine, resulting in complex time-varying characteristics. Through theoretical analysis and numerical simulation, the mechanisms and rules of these influences were revealed. The research findings provide important references for optimizing the demagnetization process, improving demagnetization effectiveness, and developing equipment-level magnetic field protection criteria and design. Full article

This study examines lexical and syntactic convergence between Dai Lue and Chinese in the multilingual environment of Sipsongpanna, employing an apparent-time approach across three generational cohorts (N = 90, balanced gender). Through mixed-methods analysis (structured questionnaires and semi-structured interviews), significant diachronic variation was observed. Younger speakers exhibited pronounced convergence, adopting Chinese-derived syntactic patterns (e.g., prenominal quantifiers and preverbal adjunct phrases) and borrowing Chinese lexical elements (e.g., an adverb sɛn⁵⁵ ‘first’ ← Chinese 先 xiān, and a superlative marker tsui³⁵ ‘most/best’ ← Chinese 最 zuì). Middle-aged speakers use transitional hybrid structures, while older speakers more consistently maintain native Dai Lue features. The results conform with Labov’s age-grading model in contact linguistics and refine Thomason’s borrowing hierarchy by revealing two factors: First, the prestige of the Chinese language drives convergence among youth. Second, syntactic compatibility with Chinese is mediated not merely by language structure, but by discourse-pragmatic needs, functional load redistribution, and the social indexicality of borrowed structures. This underscores the interplay between sociolinguistic motivations and structural-adaptive constraints in language change. The findings provide critical insights into language contact mechanisms among ethnic minorities of China, with implications for sociolinguistic theory, language revitalization efforts, and bilingual education policy implementation in linguistically diverse communities. Full article

The performance of true random number generators (TRNGs) fundamentally depends on the quality of their entropy sources (ESs). However, many FPGA-friendly designs still rely on a single mechanism and struggle to achieve both high throughput and low resource cost. To address this challenge, we propose the exclusive OR (XOR) Self-Feedback Ring Oscillator (XORSFRO), an XORNOT-style TRNG that integrates two cross-connected XOR gates with a short inverter delay chain and clocked sampling. A unified timing model is developed to describe how arrival-time skew and gate inertial delay lead to cancellation, narrow-pulse generation, and inversion events, thereby enabling effective entropy extraction. Experimental results on Xilinx Spartan-6 and Artix-7 FPGAs demonstrate that XORSFRO maintains stable operation across standard process–voltage–temperature (PVT) variations, while achieving higher throughput and lower hardware overhead compared with recent FPGA-based TRNGs. The generated bitstreams pass both the NIST SP 800-22 and NIST SP 800-90B test suites without post-processing. Full article

Fused deposition modeling of polyether ether ketone offers distinct advantages for fabricating complex and lightweight structures. Although three principal build orientations theoretically exist for practical 3D engineering components, research on their effects remains limited, especially regarding the influence of the interaction between build orientation and printing path on mechanical performance. This study investigated the tensile and shear properties, as well as the failure mechanisms, of FDM-fabricated PEEK under the coupled effects of build orientation and printing path through mechanical testing, fracture morphology analysis, and statistical methods. The results indicate that the printing path exerts a dominant influence on anisotropic behavior, while the interaction between printing path and build orientation jointly governs the shear failure modes. Under identical printing paths, the elongation at break varied by up to twofold across different build orientations, reaching a maximum of 96%, whereas samples printed with W or T paths exhibited elongations at break below 5%. Although shear and tensile moduli remained largely consistent across build orientations, other mechanical properties demonstrated significant differences. Variations in cross-sectional dimensions induced by build orientation markedly affected tensile performance: the coupled effect of build orientation and printing path was found to render the path repetition frequency a critical factor in determining temperature uniformity within the printed region and the quality of interlayer interfaces, thereby constituting the core mechanism underlying anisotropic behavior. Furthermore, larger cross-sections re-duced tensile modulus but enhanced yield strength and elongation at break, highlight-ing the regulatory role of cross-sectional geometry on mechanical response. Based on these findings, a synergistic optimization strategy integrating printing path, build orientation, and tensile–shear performance is proposed to achieve tailored mechanical properties in FDM-fabricated PEEK components. This approach enables controlled enhancement of structural performance to meet diverse application requirements. Full article