Search Results (108,062)

This paper utilizes panel data of Chinese A-share listed manufacturing firms from 2006 to 2022 and measures regional digital infrastructure by the number of internet broadband access ports per capita. It systematically examines the moderating role of digital infrastructure in the relationship between labor protection policies and firms’ labor productivity. The findings are as follows: (1) Digital infrastructure exhibits a positive moderating effect on the relationship between the Labor Contract Law and firms’ labor productivity. This conclusion remains generally robust across multiple robustness tests and endogeneity treatments, and the direction of the results remains consistent after applying an instrumental variable approach to alleviate endogeneity concerns. (2) The digital transformation channel exhibits a negative relationship, indicating that compliance pressure associated with the institutional reform generates a short-term “crowding-out effect” on firms’ digital investment; the human capital channel shows a positive relationship, indicating that digital infrastructure strengthens the institutional effect by improving the level of urban human capital. (3) The moderating effect is particularly pronounced in cities with strong digital industry foundations, abundant fiscal resources, and firms that have not received government digital subsidies. These results provide empirical support for optimizing the supporting environment of labor protection policies, accelerating digital infrastructure development, and enhancing enterprise adaptability to institutional changes. Full article

Severe full-thickness corneal lacerations disrupt the tight cellular and extracellular matrix (ECM) organization required for corneal transparency. Following injury, an influx of transforming growth factor beta (TGFβ) into the corneal stroma signals the formation of haze-inducing myofibroblasts, resulting in excessive stromal remodeling and corneal haze. We hypothesized that MasR activation using NorLeu³Angiotensin (1-7) (NLE) engages the pro-resolving arm of the renin–angiotensin system (RAS) to minimize fibrotic corneal repair. In this study, 6 mm stellate-shaped, full-thickness corneal lacerations were induced in New Zealand Black (NZB) rabbits and treated with topical vehicle, or 0.1%, 0.3%, or 0.45% NLE. Corneal healing was evaluated using noninvasive corneal imaging, histology, and the gene expression of RAS- and fibrosis-related targets (MasR, AT1R, TGFβR1). Corneal imaging revealed significantly decreased corneal haze (p < 0.05) and increased keratocyte density with 0.1% NLE treatment (p < 0.05). Immunofluorescence showed significantly reduced α-smooth muscle actin (αSMA), indicating decreased myofibroblast formation (p < 0.05). Additionally, 0.1% NLE reduced stromal TGFβR1, suggesting that NLE mediates its activity by disrupting the TGFβ/TGFβR axis. MasR and AT1R gene expression were downregulated, which contributes to a reduction in fibrosis. Collectively, these findings suggest that the NLE activation of MasR modulates RAS and TGFβ/TGFβR signaling to reduce myofibroblast activity and fibrosis following severe corneal trauma. Full article

In this work, sodium alginate (NaAlg) membranes were enhanced with synthesized zinc oxide (ZnO) nanoplates to enable efficient pervaporation dehydration of isopropyl alcohol (IPA). A comprehensive suite of characterisation techniques—scanning electron (SEM) and atomic force (AFM) microscopy, Fourier-transform infrared (FTIR) spectroscopy, nuclear magnetic resonance (NMR), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), thermogravimetric analysis (TGA), contact angle and liquid uptake measurements—along with density functional theory (DFT) calculations, was employed to establish robust structure–property relationships and to elucidate filler–polymer interactions. Membranes with different ZnO contents were prepared, and membranes based on the optimal NaAlg-ZnO(5%) composite were cross-linked with CaCl₂ to improve stability in aqueous solutions, and supported membranes were developed for prospective applications by applying this composite onto the prepared porous cellulose acetate (CA) substrate. This developed cross-linked supported NaAlg-ZnO(5%)/CA membrane had a permeation flux increased by 2 times or more compared to a dense NaAlg membrane during dehydration of IPA (12–30 wt.% water) with a permeate water content above 99 wt.%. The integrated experimental–theoretical approach provides mechanistic insight into ZnO–NaAlg interactions and demonstrates the strong potential of these mixed matrix membranes for high-efficiency alcohol dehydration, offering a rational design paradigm for next-generation pervaporation membranes. Full article

We establish two definitions of the convolution of vector measures on locally compact groups by employing injective tensor integration. These two formulations are shown to be isomorphic. We further investigate fundamental properties of the convolution of vector measures, including a representation in terms of double integrals and its behavior under the Fourier transform. In particular, we demonstrate that the Fourier transform of the convolution admits a factorization analogous to the classical case, with an inherent asymmetry arising from the vector-valued setting. Full article

Photovoltaic (PV) inverter fault prediction is critical for maintaining system reliability and minimizing energy loss. While recent studies have improved predictive accuracy using data-driven approaches, most evaluations remain focused on offline settings and do not address how probabilistic predictions are translated into operational decisions. This study investigates multi-horizon fault prediction for PV inverters under real-world constraints, with a particular focus on decision-level behavior. A modular prediction framework is implemented by combining transformer-based TimeXer embeddings with probabilistic classification using XGBoost. The model operates on sliding-window sensor data and produces fault probabilities across multiple future horizons. To support operational use, these probabilities are aggregated into a single risk score, and threshold-based alarm policies are evaluated through a systematic threshold sweep. The results show that predictive performance varies across horizons, with usable lead-time information concentrated in near-term predictions. Under severe class imbalance, imbalance-aware training significantly improves detection performance in precision–recall space, but performance remains sensitive to temporal variation. Most importantly, the threshold-sweep analysis reveals a structural trade-off between detection performance and alarm burden, where achieving moderate early-warning capability requires substantially increased alarm rates. These findings indicate that improving predictive accuracy alone is insufficient for practical deployment. Instead, decision-level behavior must be explicitly considered when designing predictive maintenance systems under operational constraints. Full article

With urbanization transforming from scale expansion to high-quality development and the increasing prominence of the ecological environment constraints of drainage basins, systematically identifying the mechanism of action of non-point source pollution from a high-quality development perspective is significant for coordinating urban development and environmental protection. Based on remote sensing data on atmospheric pollution and multi-source spatial big data such as nighttime light (NTL), LandScan population, point of interest (POI), and land use data from 2013 to 2025, this study applies methods including deposition flux analysis, deep learning fusion, bivariate spatial autocorrelation, and geographically weighted regression (GWR) to empirically analyze the spatiotemporal evolution characteristics, spatial correlation, and local impacts of high-quality urban development on non-point source pollution in the Chenghai drainage basin. We find that, firstly, non-point source pollution and high-quality urban development in the Chenghai drainage basin both present significant stage-specific and spatial heterogeneity. In other words, the two are not mutually independent spatial elements in space; instead, they are closely and significantly correlated, with their correlation types showing obvious spatial agglomeration characteristics. Secondly, the impact of high-quality urban development on non-point source pollution evolves in stages. It gradually shifts from a whole-region, homogeneous, strongly positive driving force to spatial differentiation. Specifically, from 2013 to 2017, the whole-region regression coefficients are generally greater than 0.5, meaning that urban development represents a strong, whole-region driving force promoting pollution. However, after 2017, this impact evolves into a stable spatial differentiation pattern. It mainly shows that the northern urban core area, where coefficients are greater than 0.5, maintains a continuous strong positive driving force. Meanwhile, the peripheral area, where coefficients are generally lower than 0, creates a negative inhibition effect. Based on the above rules, further analysis shows that the impact of high-quality urban development on non-point source pollution is absolutely not a simple linear relationship. Instead, it is a result of the coupling effect of multiple factors, including development stage, spatial location, and governance level. Therefore, to positively affect the ecological environment through high-quality development, model transformation and precise governance are essential. The findings of this study deepen our understanding of the transformation of urban development models and the response mechanism of non-point source pollution. They also provide a scientific basis and decision support for promoting the coordinated governance of high-quality urban development and non-point source pollution by region and stage in plateau lake drainage basins, as well as for improving the sustainable development of drainage basins. Full article

Online asynchronous learning offers considerable flexibility but frequently faces challenges in sustaining engagement, interactivity, and inclusivity across diverse learner populations. This study introduces the OPTIMAL framework—an Online, Pedagogy- and Technology-Integrated, Microcurricula Approach for interactive and inclusive Learning—synthesising universal design for learning, active learning, and constructive alignment with technology integration frameworks (TPACK and PICRAT), operationalised through a microcurricula-as-a-service architecture. A three-year longitudinal case study (2022/23 to 2024/25) examined the application of the framework to a data literacy and analytics module serving over 5000 students across more than 15 programs and five faculties at Dublin City University. The module design constructively aligned learning outcomes, content, and technology at three levels to support multiple learning pathways, formative assessment, and transdisciplinary engagement, deliberately fostering transformative uses of technology in a fully asynchronous environment. Mixed-methods evaluation—combining learning analytics, surveys (n = 1743), and qualitative feedback—demonstrated sustained positive outcomes across all three years, including 95–99% completion rates, consistently high satisfaction, and longitudinal gains in engagement and pass rates. These findings demonstrate how the deliberate integration of pedagogical theory, technological frameworks, and modular curriculum architecture can deliver scalable, inclusive, and high-engagement online education, offering both a transferable, evidence-based model for educators and curriculum designers and longitudinal empirical validation for researchers. Full article

Video-based action recognition for neural rehabilitation—spanning stroke recovery, Parkinsonian gait assessment, and cerebral palsy monitoring—faces critical challenges, including temporal ambiguity, non-causal motion correlations, and the absence of causally grounded dynamics modeling. While transformer-based architectures achieve strong performance, they often exploit spurious temporal and environmental cues, limiting reliability in safety-critical clinical settings. We propose NeuroPrisma, a neuro-prismatic video framework that integrates frequency-domain spectral decomposition with causal intervention under Structural Causal Models (SCMs) via the backdoor criterion. NeuroPrisma introduces (i) a Prismatic Spectral Attention (PSA) module, which applies discrete Fourier transforms to decompose temporal features into multi-scale frequency bands, disentangling slow postural dynamics from rapid corrective movements, and (ii) a Causal Intervention Layer (CIL), which performs do-calculus-based backdoor adjustment to remove confounding influences and produce causally invariant representations. PSA preconditions representations prior to intervention, improving confounder estimation and causal robustness. Extensive evaluation against seven state-of-the-art models (I3D, SlowFast, TimeSformer, ViViT, Video Swin Transformer, UniFormerV2, and VideoMAE) demonstrates that NeuroPrisma achieves 98.7% Top-1 accuracy on UCF101, 82.4% on HMDB51, 71.2% on Something-Something V2, and 91.5%/95.8% on NTU RGB+D (Cross-Subject/Cross-View), consistently outperforming prior methods. It further reduces the Causal Confusion Score (CCS) by 42.3%, indicating substantially lower reliance on spurious correlations, while maintaining real-time performance with 23.4 ms latency per 16-frame clip on an NVIDIA A100 GPU. All improvements are statistically significant (p < 0.001, Cohen’s d = 0.72–1.24). Evaluation was conducted exclusively on benchmark datasets (UCF101, HMDB51, Something-Something V2, and NTU RGB+D) under controlled conditions, without direct clinical validation on neurological patient cohorts. Overfitting was mitigated using three random seeds (42, 123, 456), RandAugment, Mixup (α = 0.8), weight decay (0.05), and early stopping. Cross-dataset generalization from UCF101 to HMDB51 without fine-tuning achieved 76.2% Top-1 accuracy. Future work will focus on prospective clinical validation across stroke, Parkinson’s disease, and cerebral palsy populations, including correlation with standardized clinical assessment scales such as Fugl–Meyer, UPDRS, and GMFCS. These results establish NeuroPrisma as a causally grounded and computationally efficient framework for reliable, real-time movement assessment in clinical rehabilitation systems. Full article

The synergistic control of multiple pollutants is critically challenged by complex nonlinear interactions, strong spatiotemporal heterogeneity, and the difficulty of tracing causal drivers. Deep learning offers high predictive power but suffers from the “black-box” problem, limiting its acceptance in environmental decision-making. Explainable Deep Learning (XDL) integrates physical mechanisms with interpretable algorithms, achieving both prediction accuracy and explanatory transparency. This review systematically evaluates the effectiveness and limitations of XDL in analyzing multi-pollutant interactions, with a comparative focus on atmospheric and aquatic environments. Key techniques, including SHAP, attention mechanisms, and physics-informed neural networks, are examined for their roles in synergistic monitoring, source apportionment, and regulatory optimization. The main findings reveal that: (1) XDL, particularly the “tree model + SHAP” paradigm, has become a dominant tool for quantifying driving factors, yet most attributions remain correlational rather than causal; (2) physics-informed fusion (soft vs. hard constraints) improves physical consistency but faces unresolved conflicts between data and physical laws, with current models lacking a conflict detection mechanism; (3) cross-media comparison shows a unified technical logic of “physical mechanism guidance + post hoc feature attribution”, but atmospheric applications lead in embedding advection–diffusion constraints, while aquatic research excels in spatial topology modeling via graph neural networks; (4) critical bottlenecks include the lack of causal inference, uncertainty-unaware interpretations, and data scarcity. Future directions demand a shift from correlation-only to causal-aware attribution, from blind fusion to conflict-detecting systems, and from no evaluation standards to domain-specific validation benchmarks. XDL is poised to transform multi-pollutant governance from experience-driven to intelligence-driven approaches, provided that verifiable interpretability and physical consistency become core design principles. Full article

With the shortage of natural aggregates and the massive accumulation of iron tailings (ITs) solid waste restricting the sustainable development of asphalt pavement engineering, replacing natural aggregates with ITs has become a promising low-carbon solution with prominent economic and social benefits. However, the poor interfacial adhesion between ITs and asphalt severely restricts the engineering application of tailings, and the micro-interaction mechanism at their interface still lacks systematic clarification, which is the key research gap addressed in this work. Different from conventional macro road performance tests, this study innovatively combined molecular dynamics (MD) simulation with microscopic characterization, including Fourier transform infrared spectroscopy (FT-IR) and atomic force microscopy (AFM), to comprehensively reveal the interfacial interaction mechanism between ITs and asphalt at the molecular and microscales. The results indicate that asphalt molecules exhibit higher aggregation concentration and diffusivity on Al₂O₃ and Fe₂O₃ surfaces than on SiO₂ surfaces, proving stronger interfacial interaction between asphalt and iron-rich oxide minerals. Moderate temperature optimizes the adhesion performance of asphalt with Al₂O₃ and Fe₂O₃, while the interfacial bonding of asphalt on CaCO₃ and SiO₂ weakens as temperature rises. The silane coupling agent KH-550 can effectively react with acidic minerals, SiO₂ minerals in ITs, which significantly increases the concentration, diffusion coefficient, and distribution uniformity of asphalt molecules at the interface. FT-IR results verify that the combination of ITs and asphalt mainly relies on physical adsorption without generating new chemical bonds. AFM tests further confirm that alkaline minerals improve the surface roughness of asphalt mastic, and KH-550 greatly enhances the micro-adhesion force of the interface. The novelty of this work lies in clarifying the mechanism of typical mineral components in ITs and revealing the modification enhancement law of silane coupling agent and alkali minerals at the micro level. This study provides a scientific theoretical support for the high-value engineering utilization of ITs in asphalt pavement, and offers a reference for optimizing the interfacial modification design of solid waste aggregate. Full article

The conversion of livestock manure, including poultry waste (PW), into biochar represents a sustainable strategy to recycle nutrients while reducing environmental risks. This study evaluated how pyrolysis temperature regulates physicochemical properties, carbon structure, and nutrient dynamics in biochars produced from PW. Raw PW and biochars generated at 300 and 600 °C were characterized through proximate and elemental analyses, Fourrier Transform Infrared spectroscopy (FTIR), soil nutrient assessment, and germination bioassays. A multivariate approach was used to analyze the experimental data sets. Increasing pyrolysis temperature significantly reduced biochar yield (83.62% to 64.36%), while promoting carbon condensation and mineral enrichment, as indicated by the decline in H/C ratio from 1.02 to 0.22 and the increase in ash content from 41.47% to 56.77%. FTIR analysis revealed a progressive attenuation of O–H and aliphatic C–H functional groups and a relative increase in aromatic structures with increasing temperature, indicating structural reorganization of the carbon matrix. Total concentrations of macro- and micronutrients generally increased with temperature; for example, total Cu increased from 78.62 to 114.17 mg kg⁻¹, while Zn increased from 557.03 to 819.66 mg kg⁻¹ between 300 and 600 °C. In contrast, the bioavailable fractions of Fe, Cu, and Zn determined using the chelating agent DTPA declined, although not significantly (p < 0.05), with increasing pyrolysis temperature. Principal component analysis clearly distinguished raw PW from pyrolyzed materials, confirming pyrolysis temperature as the main factor dictating biochar properties. PW exhibited severe phytotoxicity, which was partially mitigated with increasing pyrolysis temperature. Overall, pyrolysis enhanced carbon stabilization and micronutrient immobilization, highlighting PW-derived biochars as promising soil amendments for improving nutrient management and reducing the environmental risks associated with raw PW application. Full article

The people-oriented city serves as the value orientation of urban work in the new era, and age-friendliness is precisely its core practical standard for intergenerational equity and inclusive sharing. Currently, the renovation of old residential areas should transcend single-dimensional physical patching and shift towards an all-age-friendly model that meets the complex needs of multi-age groups. Taking Tuanjiehu Communities in Beijing as a case study, this research constructs an evaluation system covering three dimensions—place, atmosphere, and culture—and 22 third-level indicators, and adopts the Semantic Differential Method (SD) and Analytic Hierarchy Process (AHP) to quantitatively analyze residents’ perceptions. The study finds that old residential areas generally suffer from problems such as “insufficient place safety and functionality, lack of atmospheric vitality, and weak cultural cultivation”. Based on these findings, a progressive renewal strategy of “Consolidating Safety Foundation → Boosting Community Vitality → Cultivating Community Culture” is proposed, offering an empirical illustration for the all-age-friendly renovation of high-density urban old residential areas to transform from “survival-oriented” spaces to “life-oriented” homes, offering preliminary insights for the all-age-friendly renovation of similar high-density urban old residential areas. Full article

Driven by global sustainable development and intelligent technological innovation, the geotechnical engineering industry is transforming toward the direction of “Intelligent technology + Low-carbon circulation + Ecological friendliness”, creating an urgent demand for interdisciplinary talents with corresponding professional capabilities and sustainable awareness. To address the deficiencies in traditional postgraduate education (e.g., disjointed knowledge systems, inadequate practice oriented to Sustainable Geotechnical Engineering (SGE), and superficial integration of Education for Sustainable Development (ESD), this study constructs a “Four-Dimensional and Four-Stage” integrated talent training model based on the Sustainable Development Goals (SDGs) of the United Nations. Taking “Intelligent Technology—Geotechnical Theory—SGE Scenarios—ESD Literacy” as its core framework and adopting a progressive path of “Basic Cognition—Collaborative Application—Innovative Development—Sustainable Transformation”, this model was piloted among 23 postgraduate students through the course titled “Intelligent Design and Construction of Geotechnical Engineering”. The results show that all the students obtained officially granted software copyrights, their core professional capabilities were significantly improved, 100% of them applied their research achievements to SGE-related practices, and their ESD literacy was notably enhanced. Breaking through the traditional “knowledge-practice” dualistic framework of engineering education, this model achieves the in-depth integration of professional training and sustainable awareness cultivation and thus provides a replicable paradigm for the ESD education of interdisciplinary postgraduate students in the intelligent age. Full article

Small-object detection in remote sensing imagery faces two specific challenges that existing lightweight detectors fail to address jointly: the irreversible loss of high-frequency boundary cues during repeated downsampling, and feature smearing between neighboring instances caused by uniform multi-scale fusion. This paper presents SFD-Net, a spatial–frequency adaptive network designed to explicitly address these two limitations for aerial imagery. A backbone network and a spatial–frequency adaptive neck are used in the proposed model. Wavelet-based downsampling is applied in the backbone to reduce aliasing while preserving high-frequency information. The direction-sensitive aggregation is incorporated to better capture oriented structural patterns. In the neck, asymmetric and scale-dependent feature routing is introduced to enhance shallow boundary cues, improve instance separation in crowded regions, and limit interference from deep semantic features. Experiments on the VisDrone-DET2019, UAVDT, SIMD, and NWPU VHR-10 datasets demonstrate that SFD-Net achieves a favorable balance between detection accuracy and computational cost. In particular, on the SIMD dataset, SFD-Net achieves 82.2% mAP@0.5 and 66.7% mAP@0.5:0.95 with only 3.4 M parameters and 8.3 GFLOPs. These results indicate that the proposed method is an effective and parameter-efficient solution for remote sensing small-object detection, especially in resource-constrained deployment scenarios. Full article

Digital economy and agricultural technological innovation are key drivers of agricultural economic resilience and sustainable development. However, existing research has yet to clarify how they jointly affect agricultural economic resilience, particularly through potential nonlinear patterns and spatial spillover effects. Using panel data from 30 Chinese provinces, this study measures digital economy development and agricultural economic resilience via the entropy weight method. It systematically examines the direct impact, transmission mechanisms, threshold effects, and spatial spillover effects using two-way fixed effects, mediation, threshold regression, and spatial Durbin models. The findings are as follows. First, the digital economy significantly improves agricultural economic resilience, a result robust to various tests and endogeneity treatments. Second, agricultural technological innovation plays a partial mediating role, accounting for 19.37% of the total effect. Third, the resilience-enhancing effect of agricultural technological innovation exhibits a double-threshold pattern: its positive impact gradually strengthens as the digital economy develops to a higher level. Fourth, the digital economy generates a positive spatial spillover effect on agricultural economic resilience. Fifth, although the digital economy and agricultural technological innovation show synergistic development, their coupling coordination degree remains relatively low, indicating substantial untapped potential for synergy. From a sustainable development perspective, this study reveals the mechanisms through which the digital economy and agricultural technological innovation enhance agricultural economic resilience, providing empirical evidence and policy insights for strengthening agricultural risk resistance and achieving agricultural sustainability via digital transformation and technological progress. Full article