Search Results (1,492)

Carbon flow tracking and spatial pattern optimization at the scale of urban functional zones are key scientific challenges in achieving carbon neutrality. However, due to the complexity of carbon metabolism processes within urban functional zones, related studies remain limited. To address these scientific challenges, this study, based on the “source–sink–flow” ecosystem services framework, develops an integrated analytical approach at the scale of urban functional zones. The carbon balance is quantified using the CASA model in combination with multi-source data. A network model is employed to trace carbon flow pathways, identify critical nodes and interruption points, and optimize the urban spatial pattern through a low-carbon land use structure model. The research results indicate that the overall carbon balance in Hangzhou exhibits a spatial pattern of “deficit in the center and surplus in the periphery.” The main urban area shows a significant carbon deficit and relatively poor connectivity in the carbon flow network. Carbon sequestration services primarily flow from peripheral areas (such as Fuyang and Yuhang) with green spaces and agricultural functional zones toward high-emission residential–commercial and commercial–public functional zones in the central area. However, due to the interruption of multiple carbon flow paths, the overall carbon flow transmission capacity is significantly constrained. Through spatial optimization, some carbon deficit nodes were successfully converted into carbon surplus nodes, and disrupted carbon flow edges were repaired, particularly in the main urban area, where 369 carbon flow edges were restored, resulting in a significant improvement in the overall transmission efficiency of the carbon flow network. The carbon flow visualization and spatial optimization methods proposed in this paper provide a new perspective for urban carbon metabolism analysis and offer theoretical support for low-carbon city planning practices. Full article

In this study, a Z-scheme Ho₂InSbO₇/Ag₃PO₄ (HAO) heterojunction photocatalyst was successfully fabricated for the first time by ultrasound-assisted solvothermal method. The structural features, compositional components and morphological characteristics of the synthesized materials were thoroughly characterized by a series of techniques, including X-ray diffraction, Fourier transform infrared spectroscopy, Raman spectrum, X-ray photoelectron spectroscopy, transmission electron microscopy, scanning electron microscopy and energy-dispersive X-ray spectroscopy. A comprehensive array of analytical techniques, including ultraviolet-visible diffuse reflectance absorption spectra, photoluminescence spectroscopy, time-resolved photoluminescence spectroscopy, photocurrent testing, electrochemical impedance spectroscopy, electron paramagnetic resonance, and ultraviolet photoelectron spectroscopy, was employed to systematically investigate the optical, chemical, and photoelectronic properties of the materials. Using oxytetracycline (OTC), a representative tetracycline antibiotic, as the target substrate, the photocatalytic activity of the HAO composite was assessed under visible light irradiation. Comparative analyses demonstrated that the photocatalytic degradation capability of the HAO composite surpassed those of its individual components. Notably, during the degradation process, the application of the HAO composite resulted in an impressive removal efficiency of 99.89% for OTC within a span of 95 min, along with a total organic carbon mineralization rate of 98.35%. This outstanding photocatalytic performance could be ascribed to the efficient Z-scheme electron-hole separation system occurring between Ho₂InSbO₇ and Ag₃PO₄. Moreover, the adaptability and stability of the HAO heterojunction were thoroughly validated. Through experiments involving the capture of reactive species and electron paramagnetic resonance analysis, the active species generated by HAO were identified as hydroxyl radicals (•OH), superoxide anions (•O₂⁻), and holes (h⁺). This identification provides valuable insights into the mechanisms and pathways associated with the photodegradation of OTC. In conclusion, this research not only elucidates the potential of HAO as an efficient Z-scheme heterojunction photocatalyst but also marks a significant contribution to the advancement of sustainable remediation strategies for OTC contamination. Full article

Terahertz (THz) has attracted substantial attention owing to its unique advantages in high-speed communications. However, conventional THz waveguide systems are inherently constrained by high transmission losses, stringent fabrication precision requirements, and extreme sensitivity to structural defects. Topological edge states with topological protection have driven significant advancements in THz wave manipulation. Nevertheless, the width of the topological waveguide based on edge states remains restricted. In this work, we put forward a type of spin photonic crystal with three-layer heterostructures, where large-area topological waveguide states are demonstrated. The results show that these topological waveguide states are localized within the region of Dirac photonic crystals. They also display spin-momentum-locking characteristics and maintain strong robustness against defects and sharp bends. Furthermore, a THz beam splitter and a topological beam modulator are implemented. The designed heterostructures expand the applications of multi-functional topological devices and provide a prospective pathway for overcoming the waveguide bottleneck in THz applications. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article

FeAl intermetallic compounds exhibit high application potential in high-voltage transmission lines to withstand external forces such as powerlines’ own gravity and wind force. The ordered crystal structure in FeAl intermetallic compounds endows materials with high strength, but the remarkable brittleness at room temperature restricts engineering applications. This contradiction is essentially closely related to the deformation mechanism at the nanoscale. Here, we performed molecular dynamics simulations to reveal anomalous grain size effects and deformation mechanisms in nanocrystalline FeAl intermetallic material. Models with grain sizes ranging from 6.2 to 17.4 nm were systematically investigated under uniaxial tensile stress. The study uncovers a distinctive inverse Hall-Petch relationship governing flow stress within the nanoscale regime. This behavior stems from high-density grain boundaries promoting dislocation annihilation over pile-up. Crucially, the material exhibits anomalous ductility at ultra-high strain rates due to stress-induced phase transformation dominating the plastic deformation. The nascent FCC phase accommodates strain through enhanced slip systems and inherent low stacking fault energy with the increasing phase fraction paralleling the stress plateau. Nanoconfinement suppresses the propagation of macroscopic defects while simultaneously suppressing room-temperature brittle fracture and inhibiting the rapid phase transformation pathways at extreme strain rates. These findings provide new theoretical foundations for designing high-strength and high-toughness intermetallic nanocompounds. Full article

The ability of the neuropeptide oxytocin to affect glial cell function is receiving increasing attention. We previously reported that oxytocin at a low nanomolar concentration could inhibit both astrocytic Ca²⁺ signals and glutamate release. Here, we investigate the ability of oxytocin receptors to couple both inhibitory and stimulatory pathways in astrocytes, as already reported in neurons. We assessed the effects of oxytocin at concentrations ranging from low to high in the nanomolar range on intracellular Ca²⁺ signals and on the glutamate release in astrocyte processes freshly prepared from the striatum of adult rats. Our main findings are as follows: oxytocin could induce dual responses in astrocyte processes, namely the inhibition and facilitation of both Ca²⁺ signals and glutamate release; the inhibitory and the facilitatory response appeared dependent on activation of the G_i and the G_q pathway, respectively; both inhibitory and facilitatory responses were evoked at the same nanomolar oxytocin concentrations; and the biased agonists atosiban and carbetocin could duplicate oxytocin’s inhibitory and facilitatory response, respectively. In conclusion, due to the coupling of striatal astrocytic oxytocin receptors to different transduction pathways and the dual effects on Ca²⁺ signals and glutamate release, oxytocin could also play a crucial role in neuron–astrocyte bi-directional communication through a subtle regulation of striatal glutamatergic synapses. Therefore, astrocytic oxytocin receptors may offer pharmacological targets to regulate glutamatergic striatal transmission, which is potentially useful in neuropsychiatric disorders and in neurodegenerative diseases. Full article

This study investigates the strengthening mechanisms of char in silicon dioxide thermal reduction through systematic high-temperature experiments using three char types (YQ1, CW1, HY1) characterized by X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and scanning electron microscopy. HY1 char demonstrated superior reactivity due to its highly ordered microcrystalline structure, characterized by the largest aromatic cluster size (L_a) and lowest defect ratio (I_D/I_G = 0.37), which directly correlated with enhanced reaction completeness. The carbon–silicon reaction reactivity increased progressively with temperature, achieving optimal performance at 1550 °C. Addition of Fe and Fe₂O₃ significantly accelerated the reduction process, with Fe₂O₃ exhibiting superior catalytic performance by reducing activation energy and optimizing reaction kinetics. The ferrosilicon formation mechanism proceeds through a two-stage pathway: initial char-SiO₂ reaction producing SiC and CO, followed by SiC–iron interaction generating FeSi, which catalytically promotes further reduction. These findings establish critical structure–performance relationships for char selection in industrial silicon production, where microcrystalline ordering emerges as the primary performance determinant. The identification of optimal temperature and additive conditions provides practical pathways to enhance energy efficiency and product quality in silicon metallurgy, enabling informed raw material selection and process optimization to reduce energy consumption and improve operational stability. Full article

Integrating artificial intelligence (AI) and cause-related marketing has reshaped corporate social responsibility practices while triggering a conflict between technological instrumental rationality and moral value transmission. Building on the Persuasion Knowledge Model (PKM) and AI aversion literature, this research employs two experiments to reveal that AI disclosure exerts a unique inhibitory effect on consumers’ purchase intentions in cause-related marketing contexts compared to non-cause-related marketing scenarios. Further analysis uncovers a chain mediation pathway through consumer skepticism and advertisement attitudes, explaining the psychological mechanism underlying AI disclosure’s impact on purchase intentions. The study also identifies the moderating role of AI aversion within this chain model. The findings provide a new theoretical perspective for integrating AI disclosure, consumer psychological responses, and marketing effectiveness while exposing the “value-instrumentality” conflict inherent in AI applications for cause-related marketing. This research advances the evolution of the PKM in the digital era and offers practical insights for cause-related marketing enterprises to balance AI technology application with optimized disclosure strategies. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

This study endeavors to elucidate the mechanisms and pathways through which the digital economy shapes agricultural green development, providing theoretical underpinnings and practical guidance for the green transformation of regional agriculture. (1) Using panel data from 18 prefecture-level cities in Sichuan Province (2013–2022), a comprehensive evaluation index system for agricultural green development was formulated. Fixed-effects, mediating-effects, and threshold-effects models were employed to systematically analyze the direct effects, transmission pathways, and nonlinear characteristics of the digital economy on agricultural green development. (2) The fixed-effects model shows that the digital economy markedly propels agricultural green development in Sichuan Province. The mediating-effects model verifies two transmission pathways: “digital economy → technological progression → agricultural green development” and “digital economy → industrial structure upgrading → agricultural green development”. The threshold-effects model suggests that when the digital economy is in the low-threshold interval, it exerts a suppressive impact on agricultural green development; however, once the threshold is surpassed, its promoting effect strengthens significantly. (3) The results demonstrate the following findings: First, the digital economy exerts a significant positive effect on agricultural green development. Second, this promoting effect exhibits significant nonlinear characteristics that vary with the level of digital economy development. Third, the impact manifests remarkable regional heterogeneity, necessitating context-specific development strategies. (4) Five optimization recommendations are proposed: promote the categorized development of agricultural digital technologies and industrial upgrading; advance digital infrastructure and technology adaptation in phases; design differentiated regional policies; establish a hierarchical and classified long-term guarantee mechanism; and strengthen the “industry-university-research-application” collaborative innovation and dynamic monitoring system. Full article

Northern Thailand frequently suffers from severe PM2.5 air pollution, especially during the dry season, due to agricultural burning, local emissions, and transboundary haze. Understanding how pollution moves across regions and identifying source–receptor relationships are critical for effective air quality management. This study investigated the spatial and temporal dynamics of PM2.5 in northern Thailand. Specifically, it explored how pollution at one monitoring station influenced concentrations at others and revealed the seasonal structure of PM2.5 transmission using network-based analysis. We developed a Python-based framework to analyze daily PM2.5 data from 2022 to 2023, selecting nine representative stations across eight provinces based on spatial clustering and shape-based criteria. Delaunay triangulation was used to define spatial connections among stations, capturing the region’s irregular geography. Cross-correlation and Granger causality were applied to identify time-lagged relationships between stations for each season. Trophic coherence analysis was used to evaluate the hierarchical structure and seasonal stability of the resulting networks. The analysis revealed seasonal patterns of PM2.5 transmission, with certain stations, particularly in Chiang Mai and Lampang, consistently acting as source nodes. Provinces such as Phayao and Phrae were frequently identified as receptors, especially during the winter and rainy seasons. Trophic coherence varied by season, with the winter network showing the highest coherence, indicating a more hierarchical but less stable structure. The rainy season exhibited the lowest coherence, reflecting greater structural stability. PM2.5 spreads through structured, seasonal pathways in northern Thailand. Network patterns vary significantly across seasons, highlighting the need for adaptive air quality strategies. This framework can help identify influential monitoring stations for early warning and support more targeted, season-specific air quality management strategies in northern Thailand. Full article

The global elimination of hepatitis C virus (HCV) has been prioritized by the World Health Organization (WHO) as a key public health target, with a deadline set for 2030. This initiative aims to significantly reduce both new infection rates and HCV-associated mortality. A major breakthrough in achieving this goal has been the development of direct-acting antiviral agents (DAAs), which offer cure rates exceeding 95%, along with excellent safety and tolerability. Nevertheless, transmission via parenteral routes continues to be the dominant pathway, particularly among high-risk groups, such as individuals who inject drugs, incarcerated populations, those exposed to unsafe medical practices, and healthcare professionals. Identifying, monitoring, and delivering tailored interventions to these groups is crucial to interrupt ongoing transmission and to reduce the burden of chronic liver disease. On a global scale, several nations have demonstrated measurable progress toward HCV elimination, with some nearing the targets set by WHO. These achievements have largely resulted from context-adapted policies that enhanced diagnostic and therapeutic access while emphasizing outreach to vulnerable communities. This review synthesizes current advancements in HCV prevention and control and proposes strategic frameworks to expedite global elimination efforts. Full article

Antimicrobial resistance (AMR) has emerged as a planetary health emergency, driven not only by the clinical misuse of antibiotics but also by diverse environmental dissemination pathways. This review critically examines the role of environmental compartments—water, soil, and air—as dynamic reservoirs and transmission routes for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs). Recent metagenomic, epidemiological, and mechanistic evidence demonstrates that anthropogenic pressures—including pharmaceutical effluents, agricultural runoff, untreated sewage, and airborne emissions—amplify resistance evolution and interspecies gene transfer via horizontal gene transfer mechanisms, biofilms, and mobile genetic elements. Importantly, it is not only highly polluted rivers such as the Ganges that contribute to the spread of AMR; even low concentrations of antibiotics and their metabolites, formed during or after treatment, can significantly promote the selection and dissemination of resistance. Environmental hotspots such as European agricultural soils and airborne particulate zones near wastewater treatment plants further illustrate the complexity and global scope of pollution-driven AMR. The synergistic roles of co-selective agents, including heavy metals, disinfectants, and microplastics, are highlighted for their impact in exacerbating resistance gene propagation across ecological and geographical boundaries. The efficacy and limitations of current mitigation strategies, including advanced wastewater treatments, thermophilic composting, biosensor-based surveillance, and emerging regulatory frameworks, are evaluated. By integrating a One Health perspective, this review underscores the imperative of including environmental considerations in global AMR containment policies and proposes a multidisciplinary roadmap to mitigate resistance spread across interconnected human, animal, and environmental domains. Full article

Water systems serve as multifaceted environmental pools for antibiotic-resistant bacteria (ARB) and resistance genes (ARGs), influencing human, animal, and ecosystem health. This review synthesizes current understanding of how antibiotics, ARB, and ARGs enter surface, ground, and drinking waters via wastewater discharge, agricultural runoff, hospital effluents, and urban stormwater. We highlight key mechanisms of biofilm formation, horizontal gene transfer, and co-selection by chemical stressors that facilitate persistence and spread. Case studies illustrate widespread detection of clinically meaningful ARB (e.g., Escherichia coli, Pseudomonas aeruginosa, Klebsiella pneumoniae) and mobile ARGs (e.g., sul1/2, tet, bla variants) in treated effluents, recycled water, and irrigation return flows. The interplay between treatment inefficiencies and environmental processes underscores the need for advanced treatment technologies, integrated monitoring, and policy interventions. Addressing these challenges is critical to curbing the environmental dissemination of resistance and protecting human and ecosystem health. Full article

A deterministic Mixed-Integer Nonlinear Programming (MINLP) model for the Integrated Generation and Transmission Expansion Planning (IGTEP) problem is presented. The proposed framework is distinguished by its foundation on the complete AC power flow formulation, which is solved to global optimality using BARON, a deterministic MINLP solver, which ensures the identification of truly optimal expansion strategies, overcoming the limitations of heuristic approaches that may converge to local optima. This approach is employed to establish a definitive, high-fidelity economic and technical benchmark, addressing the limitations of commonly used DC approximations and metaheuristic methods that often fail to capture the nonlinearities and interdependencies inherent in power system planning. The co-optimization model is formulated to simultaneously minimize the total annualized costs, which include investment in new generation and transmission assets, the operating costs of the entire generator fleet, and the cost of unsupplied energy. The model’s effectiveness is demonstrated on the IEEE 14-bus system under various dynamic load growth scenarios and planning horizons. A key finding is the model’s ability to identify the most economic expansion pathway; for shorter horizons, the optimal solution prioritizes strategic transmission reinforcements to unlock existing generation capacity, thereby deferring capital-intensive generation investments. However, over longer horizons with higher demand growth, the model correctly identifies the necessity for combined investments in both significant new generation capacity and further network expansion. These results underscore the value of an integrated, AC-based approach, demonstrating its capacity to reveal non-intuitive, economically superior expansion strategies that would be missed by decoupled or simplified models. The framework thus provides a crucial, high-fidelity benchmark for the validation of more scalable planning tools. Full article