Search Results (8,746)

As China has increased implementation of its opening-up strategy and the “Belt and Road” initiative, Chinese enterprises have encountered significant historical opportunities to expand their outward foreign direct investment (OFDI). However, international organizations and major nations are increasingly focusing on nonfinancial indicators for multinational corporations; as a result, enterprises frequently encounter social responsibility crises in cross-border investments. Consequently, Chinese firms must enhance their environmental, social, and governance (ESG) practices to bolster their comprehensive competitiveness, which is crucial for promoting successful international engagement and sustainability. This research explores the U-shaped relationship between ESG performance and OFDI, examining how different stages of the corporate lifecycle affect OFDI. The findings indicate that ESG investments compete with OFDI for internal resources during the introduction, growth, and decline phases, thereby inhibiting OFDI activities. In contrast, strong ESG performance in the maturity phase provides a competitive advantage in international markets, facilitating OFDI. The empirical analysis uses a fixed-effects model on a sample of Chinese A-share-listed companies from 2009 to 2022 and employs the PSM, 2SLS, and System GMM methods to test for endogeneity. The results reveal that (1) a positive U-shape relationship between ESG performance and corporate OFDI, and the inflection point occurs when the ESG score equals 69.04. Moreover, (2) the corporate lifecycle intensifies this nonlinear relationship, with growth-phase firms showing a significant inhibitory effect and mature-phase firms showing a pronounced promotional effect. Finally, (3) the U-shaped relationship between ESG performance and corporate OFDI is more pronounced in nonstate-owned enterprises. Based on these findings, this paper provides targeted policy recommendations for enterprises and governments. Full article

Insects exhibit a range of ecological relationships with plants, including pollination, seed dispersal, parasitism, predation, and herbivory. Interactions between insects and plants are shaped by internal daily timekeeping systems in both sets of organisms termed circadian clocks. This review describes the impact of the circadian clocks of insects and plants on herbivory, which is highly relevant not only to natural ecosystems, but also to agriculture and forest management. Following an introduction to the circadian clocks of plants and insects, we discuss the circadian organization of relevant aspects of plant metabolism and defense. Next, we describe how insect clocks govern herbivory-associated physiology and behavior before exploring how rhythmic processes in plants and insects interact to temporally control herbivory. Finally, we describe how insights from the clock control of herbivory may inform pest management strategies and what future research in this area may contribute. Full article

Groundwater, a critical resource for regional water security and public health, faces escalating threats from heavy metal contamination—a pressing environmental challenge worldwide. This study focuses on the central-south Hunan region of China, a mineral-rich, densely populated area characterized predominantly by non-point-source pollution, aiming to systematically unravel the spatial patterns, source contributions, and associated health risks of heavy metals in local groundwater. Based on 717 spring and well water samples collected in 2024, we determined pH and seven heavy metals (As, Cd, Pb, Zn, Fe, Mn, and Tl). By integrating hydrogeological zoning, lithology, topography, and river networks, the study area was divided into 11 assessment units, clearly revealing the spatial heterogeneity of heavy metals. The results demonstrate that exceedances of Cd, Pb, and Zn were sporadic and point-source-influenced, whereas As, Fe, Mn, and Tl showed regional exceedance patterns (e.g., Mn exceeded the standard in 9.76% of samples), identifying them as priority control elements. The spatial distribution of heavy metals was governed the synergistic effects of lithology, water–rock interactions, and hydrological structure, showing a distinct “acidic in the northeast, alkaline in the southwest” pH gradient. Combined application of the APCS-MLR and PMF models resolved five principal pollution sources: an acid-reducing-environment-driven release source (contributing 76.1% of Fe and 58.3% of Pb); a geogenic–anthropogenic composite source (contributing 81.0% of Tl and 62.4% of Cd); a human-perturbation-triggered natural Mn release source (contributing 94.8% of Mn); an agricultural-activity-related input source (contributing 60.1% of Zn); and a primary geological source (contributing 89.9% of As). Monte Carlo simulation-based health risk assessment indicated that the average hazard index (HI) and total carcinogenic risk (TCR) for all heavy metals were below acceptable thresholds, suggesting generally manageable risk. However, As was the dominant contributor to both non-carcinogenic and carcinogenic risks, with its carcinogenic risk exceeding the threshold in up to 3.84% of the simulated adult exposures under extreme scenarios. Sensitivity analysis identified exposure duration (ED) as the most influential parameter governing risk outcomes. In conclusion, we recommend implementing spatially differentiated management strategies: prioritizing As control in red-bed and granite–metamorphic zones; enhancing Tl monitoring in the northern and northeastern granite-rich areas, particularly downstream of the Mishui River; and regulating land use in brick-factory-dense riparian zones to mitigate disturbance-induced Mn release—for instance, through the enforcement of setback requirements and targeted groundwater monitoring programs. This study provides a scientific foundation for the sustainable management and safety assurance of groundwater resources in regions with similar geological and anthropogenic settings. Full article

Background: Achieving deep decarbonization in global supply chains is essential for advancing net-zero objectives; however, the integrative role of artificial intelligence (AI) and robotics in this transition remains insufficiently explored. This study examines how these technologies support carbon-emission reduction across supply chain operations. Methods: A curated corpus of 83 Scopus-indexed peer-reviewed articles published between 2013 and 2025 is analyzed and organized into six domains covering supply chain and logistics, warehousing operations, AI methodologies, robotic systems, emission-mitigation strategies, and implementation barriers. Results: AI-driven optimization consistently reduces transport emissions by enhancing routing efficiency, load consolidation, and multimodal coordination. Robotic systems simultaneously improve energy efficiency and precision in warehousing, yielding substantial indirect emission reductions. Major barriers include the high energy consumption of certain AI models, limited data interoperability, and poor scalability of current applications. Conclusions: AI and robotics hold substantial transformative potential for advancing supply chain decarbonization; nevertheless, their net environmental impact depends on improving the energy efficiency of digital infrastructures and strengthening cross-organizational data governance mechanisms. The proposed framework delineates technological and organizational pathways that can guide future research and industrial implementation, providing novel insights and actionable guidance for researchers and practitioners aiming to accelerate the low-carbon transition. Full article

Leading agribusiness firms are pivotal to modernizing agricultural supply chains, yet evidence on how digital transformation translates into high-quality development remains fragmented. Using a 2024 sample of 30 Chinese national agribusiness leaders and the technology–organization–environment (TOE) framework, we integrate grey relational analysis with DEMATEL to quantify interdependencies among conditions, and combine fuzzy-set QCA with necessary condition analysis to identify both configurational pathways and binding constraints. The results of the analysis indicate that high-quality development rarely stems from a single driver; it emerges from complementary bundles linking digital technologies and R&D investment with organizational readiness (e.g., talent and governance) under supportive external conditions (e.g., policy incentives and market pressure). The findings provide a configurational explanation of digital upgrading in agribusiness and inform differentiated digital strategies for managers and policymakers. Full article

This study proposes a circular economy strategy to recover phenolic compounds by valorizing shrimp shell waste into a chitosan biosorbent (CH-B). Its adsorption efficiency was evaluated compared to commercial activated carbon (AC) and synthetic XAD-4 resin. Kinetic analysis revealed that while both pseudo-first-order (PFO) and pseudo-second-order (PSO) models exhibited high correlations (R²$\ge$ 0.96), both CH-B and XAD-4 resin were best described by the PFO model. This aligns with diffusion-controlled processes consistent with the porous and physical nature of these adsorbents. In contrast, AC followed the PSO model. Isotherm modeling indicated that CH-B and AC fit the Temkin model, reflecting heterogeneous surfaces, whereas XAD-4 followed the Langmuir model (monolayer adsorption). Notably, CH-B exhibited a maximum adsorption capacity (q_m) of 229.2 mg/g, significantly outperforming XAD-4 (104.8 mg/g) and AC (90.2 mg/g). Thermodynamic and kinetic modeling confirmed that the adsorption mechanism was governed by a combination of electrostatic interactions, π–π stacking, and hydrogen bonding between the hydroxyl/amine groups of chitosan and phenolic compounds. Optimization using Box–Behnken design for CH-B showed optimal acidic pH and moderate temperature but non-significant effect of CH-B dose in the experimental domain. Optimisation results showed unexpected high removal efficiency at low CH-B dosages. A tentative explanation may be adsorbent aggre-gation, which needs to be confirmed by further experimental evidence. Full article

Glioblastoma multiforme (GBM) is characterized by aggressive growth, extensive vascularization, high metabolic malleability, and a striking capacity for therapy resistance. Current treatments involve surgical resection and concomitant radiation therapy and chemotherapy, prolonging survival times marginally due to the therapy resistance that is built up by the tumor cells. A growing body of research has identified exosomes as critical enablers of therapy resistance. These nanoscale vesicles enable GBM cells to disseminate oncogenic proteins, nucleic acids, and lipids that collectively promote angiogenesis, maintain autophagy under metabolic pressure, and suppress apoptosis. As interest grows in targeting tumor communication networks, exosome-based therapeutic strategies have emerged as promising avenues for improving therapeutic outcomes in GBM. This review integrates current insights into two complementary therapeutic strategies: inhibiting exosome biogenesis and secretion, and engineering exosomes as precision vehicles for the delivery of anti-tumor molecular cargo. Key molecular regulators of exosome formation—including the endosomal sorting complex required for transport (ESCRT) machinery, tumor susceptibility gene 101 (TSG101) protein, ceramide-driven pathways, and Rab GTPases—govern the sorting and release of factors that enhance GBM survival. Targeting these pathways through pharmacological or genetic means has shown promise in suppressing angiogenic signaling, disrupting autophagic flux via modulation of autophagy-related gene (ATG) proteins, and sensitizing tumor cells to apoptosis by destabilizing mitochondria and associated survival networks. In parallel, advances in exosome engineering—encompassing siRNA loading, miRNA enrichment, and small-molecule drug packaging—offer new routes for delivering therapeutic agents across the blood–brain barrier with high cellular specificity. Engineered exosomes carrying anti-angiogenic, autophagy-inhibiting, or pro-apoptotic molecules can reprogram the tumor microenvironment and activate both the intrinsic mitochondrial and extrinsic ligand-mediated apoptotic pathways. Collectively, current evidence underscores the potential of strategically modulating endogenous exosome biogenesis and harnessing exogenous engineered therapeutic exosomes to interrupt the angiogenic and autophagic circuits that underpin therapy resistance, ultimately leading to the induction of apoptotic cell death in GBM. Full article

Glycosaminoglycans (GAGs) are highly negatively charged polysaccharides that play essential roles in numerous physiological and pathological processes through their interactions with proteins. These interactions govern cellular signaling, inflammation, coagulation, and recognition. Surface Plasmon Resonance (SPR) has emerged as a key biophysical technique for label-free, real-time characterization of biomolecular interactions, offering insights into binding kinetics, affinity, and specificity. SPR-based approaches to glycosaminoglycan–protein interaction studies offer powerful tools for elucidating the roles of GAGs in a wide range of physiological and pathological processes. In this review, we systematically discuss experimental strategies, data analysis methods, and representative applications of SPR-based glycosaminoglycan–protein interactions. Special attention is given to the challenges associated with GAG heterogeneity and immobilization, as well as recent technological advances that enhance sensitivity and throughput. To our knowledge, this review represents one of the first systematic and up-to-date summaries specifically focused on recent advances in applying SPR to the study of glycosaminoglycan–protein interactions. Full article

Exposure to Particulate Matter (PM) is linked to respiratory and cardiovascular diseases, certain types of cancer, and accounts for approximately seven million premature deaths globally. While governments and organizations have implemented various strategies for Air Quality (AQ) such as the deployment of Air Quality Monitoring Networks (AQMN), these networks often suffer from limited spatial coverage and involve high installation and maintenance costs. Consequently, the implementation of networks based on Low-Cost Sensors (LCS) has emerged as a viable alternative. Nevertheless, LCS systems have certain drawbacks, such as lower reading precision, which can be mitigated through specific calibration models and methods. This paper presents the results and conclusions derived from simultaneous PM₁₀ and PM_2.5 monitoring comparisons between LCS nodes and a T640X reference sensor. Additionally, Relative Humidity (RH), temperature, and absorption flow measurements were collected via an Automet meteorological station. The monitoring equipment was installed at the Faculty of Environment of the Universidad Distrital in Bogotá. The LCS calibration process began with data preprocessing, which involved filtering, segmentation, and the application of FastDTW. Subsequently, calibration was performed using a variety of models, including two statistical approaches, three Machine Learning algorithms, and one Deep Learning model. The findings highlight the critical importance of applying FastDTW during preprocessing and the necessity of incorporating RH, temperature, and absorption flow factors to enhance accuracy. Furthermore, the study concludes that Random Forest and XGBoost offered the highest performance among the methods evaluated. While satellites map city-wide patterns and MAX-DOAS enables hourly source attribution, our calibrated LCS network supplies continuous, street-scale data at low CAPEX/OPEX—forming a practical backbone for sustained micro-scale monitoring in Bogotá. Full article

Aquatic vegetation (AV) forms the structural and functional basis of lake ecosystems, providing irreplaceable ecological functions such as water self-purification and the sustenance of biodiversity. Under the “Yangtze River’s Great Protection Strategy”, the action of returning nets to the lake has significantly improved water-quality in the middle and lower reaches of the Yangtze River (MLRYR) basin. However, its ecological benefits for key biotic components, particularly AV communities, remain unclear. To address this knowledge gap, this study utilized Landsat and Sentinel-1 satellite imagery to analyze the dynamic evolution of enclosure aquaculture (EA) and AV in 25 lakes (>10 km²) within the MLRYR basin from 1989 to 2023. A U-Net deep learning model was employed to extract EA data (2016–2023), and a vegetation and bloom extraction algorithm was applied to map different AV groups (1989–2023). Results indicate that by 2023, 88% (22/25) of the lakes had completed EA removal. Over the 34-year period, floating/emergent aquatic vegetation (FEAV) exhibited fluctuating trends, while submerged aquatic vegetation (SAV) demonstrated a significant decline, particularly during the EA demolition phase (2016–2023), when its area sharply decreased from 804.8 km² to 247.3 km²—a reduction of 69.3%. Spatial comparative analysis further confirmed that SAV degradation was substantially more severe in EA removal areas than in EA retention areas. This study demonstrates that EA demolition, while beneficial for improving water quality, exerts significant short-term negative impacts on AV. These findings highlight the urgent need for lake governance policies to shift from single-objective management toward integrated strategies that equally prioritize water-quality improvement and ecological restoration. Future efforts should enhance targeted restoration in EA removal areas through active vegetation recovery and habitat reconstruction, thereby preventing catastrophic regime shifts to phytoplankton-dominated turbid-water states in lake ecosystems. Full article

Chinese local governments have long financed public services through land-sale revenues. The shift from selling undeveloped land to redeveloping existing urban areas has disrupted this traditional financing model, exposing a critical tension between the pursuit of immediate revenue and the assurance of long-term fiscal health. The continued dependence on land-based finance has led many local governments to overlook long-term public service obligations and the long-term operating deficits associated with intensive urban development. Thus, by examining the relationship between the development rights allocation and the sustainable fiscal capacity of the government, the study evaluates both short-term revenue generation and long-term expenditure commitments in urban redevelopment contexts. However, existing research has yet to provide actionable tools to reconcile this structural mismatch between short-term revenues and long-term liabilities. We employ a comprehensive analytical framework that integrates fiscal impact modeling with the optimization of development rights allocation. Based on this framework, we construct a quantitative, dual-period fiscal balance model using mathematical programming to analyze various combinations of land development rights supply strategies for achieving fiscal equilibrium. Our results identify multiple feasible supply combinations that can maintain fiscal balance while supporting sustainable urban development. The findings demonstrate that strategic development rights allocation functions as an effective tool for balancing short-term revenue needs with long-term obligations in local land finance systems. Our study contributes to establishing a sustainable land finance framework, particularly for jurisdictions lacking comprehensive land value capture mechanisms. The proposed approach offers an alternative to traditional land rights transfer models and provides guidance for avoiding long-term fiscal distress caused by excessive land transfer. The framework supports more sustainable urban redevelopment financing while maintaining fiscal responsibility across temporal horizons. Full article

The mitigation of traffic noise is essential for the development of sustainable and livable urban environments, a goal that is directly contingent on addressing tire-pavement interaction noise (TPIN) as the dominant acoustic pollutant at medium to high vehicle speeds. This comprehensive review addresses a critical gap in the literature by systematically analyzing the damping properties of pavement systems through a unified, multi-scale framework—from the molecular-scale viscoelasticity of asphalt binders to the composite performance of asphalt mixtures. The analysis begins by synthesizing state-of-the-art testing and characterization methodologies, which establish a clear connection between macroscopic damping performance and the underlying viscoelastic mechanisms coupled with the microscopic morphology of the binders. Subsequently, the review critically assesses the influence of critical factors, such as polymer modifiers including rubber and Styrene-Butadiene-Styrene (SBS), temperature, and loading frequency. This examination elucidates how these variables govern molecular mobility and relaxation processes to ultimately determine damping efficacy. A central and synthesizing conclusion emphasizes the paramount importance of the asphalt binder’s properties, which serve as the primary determinant of the composite mixture’s overall acoustic performance. By delineating this structure-property-performance relationship across different scales, the review consolidates a foundational scientific framework to guide the rational design and informed material selection for next-generation asphalt pavements. The insights presented not only advance the fundamental understanding of damping mechanisms in pavement materials but also provide actionable strategies for creating quieter and more sustainable transportation infrastructures. Full article

The advent of generative AI (GenAI) and its growing use in education has sparked a renewed wave of school digital transformation. School principals are pivotal in advancing and shaping school digital transformation, yet little is known about how they understand and lead digital transformation in the age of GenAI, particularly within China’s complex educational system. This study employed Q methodology to identify the perceptions and leadership styles of Chinese K–12 school principals toward school digital transformation in the age of GenAI. An analysis of a 30-item Q set with a P sample of 23 principals revealed four leadership types: Cautious Observation–Technological Gatekeeping Leadership, Moderate Ambition–Culturally Transformative Leadership, Moderate Ambition–Emotionally Empowering Leadership, and High Aspiration–Strategy-Driven Leadership. Overall, principals’ stances on GenAI formed a continuum, ranging from cautious observation and skeptical optimism to active embrace. These perceptions and leadership styles were shaped by Confucian cultural values, a flexible central–local governance arrangement, and parents’ high expectations for students’ academic achievement. Furthermore, structural constraints in resource provision further heightened principals’ reliance on maintaining guanxi-based relationships. This study enhances the understanding of the diversity of principals’ leadership practices worldwide and offers actionable insights for governments and principals to more effectively advance AI-enabled school digital transformation. Full article

Pain is an unpleasant but essential sensory experience that serves as a protective mechanism, yet it can also manifest maladaptively in a wide range of pathological conditions. Current analgesic strategies rely heavily on opioid medications and non-steroidal anti-inflammatory drugs (NSAIDs); however, concerns regarding addiction, tolerance, and dose-limiting adverse effects highlight the urgent need for safer and more effective therapeutics. Voltage-gated sodium (Na_v) channels, which govern the initiation and propagation of action potentials, have emerged as promising targets for mechanism-based analgesic development. In particular, the Na_v1.7–Na_v1.9 subtypes have attracted substantial interest owing to their enrichment in the peripheral nervous system—despite broader expression elsewhere—and their central roles in nociception, offering the potential for non-addictive, subtype-selective pain modulation. This review summarizes the physiological roles of these channels in nociception, examines how disease-associated mutations shape pain phenotypes, and highlights recent advances in drug discovery targeting Na_v1.7 and Na_v1.8. The recent FDA approval of VX-548 (suzetrigine), a first-in-class and highly selective Na_v1.8 inhibitor, marks a major milestone that validates peripheral Na_v channels as clinically actionable targets for analgesia. We also discuss the remaining challenges and emerging opportunities in the pursuit of next-generation, mechanism-informed analgesics. Full article

Reproductive performance in livestock and poultry is a core determinant of economic efficiency in the animal industry. While traditional research has primarily focused on genetics, endocrinology, and immune regulation, emerging microbiome studies reveal that commensal microbiota within the gut and reproductive tracts play an underestimated yet pivotal role in host reproductive health. This review systematically synthesizes recent advances regarding the relationship between the microbiome and reproductive functions in major livestock species (cattle, pigs, sheep, and chickens). We first delineate the theoretical basis and mechanisms of the “gut-reproductive axis,” highlighting cross-system communication mediated by microbial metabolites, including short-chain fatty acids (SCFAs), indoles, and bile acids. Subsequently, we provide an in-depth comparative analysis of the microecological features of both female (vagina/uterus) and male (semen/epididymis) reproductive systems, examining their impacts on fertility, sperm quality, and pregnancy outcomes. Furthermore, we explore the molecular and systemic mechanisms governing microbial regulation of reproduction, encompassing the modulation of the hypothalamic-pituitary-gonadal (HPG) axis, the balance of local mucosal immunity and inflammation, and epigenetic regulation. Finally, we address current challenges—such as causal validation and the scarcity of multi-species databases—and propose future directions, including spatial multi-omics, AI-integrated analysis, and microbial intervention strategies. Ultimately, this review aims to offer a theoretical foundation and translational insights for elucidating reproductive regulatory networks and developing microbiome-driven precision strategies to enhance reproductive performance. Full article