Search Results (21,200)

This study investigates upper-reservoir dam-axis siting in pumped-storage hydropower projects, where cut–fill balance and construction cost are critical under complex terrain conditions. Existing approaches still rely heavily on manual interpretation or static GIS-based analysis and therefore do not adequately optimize dam-axis geometry or earthwork balance. To address this limitation, we propose an Improved Adaptive Variable Neighborhood Search (IAVNS) algorithm that integrates high-resolution digital elevation model (DEM) data within a two-layer adaptive framework. The inner layer performs staged planar and elevation adjustments through adaptive neighborhood operators, whereas the outer layer conducts fitness-guided subregion migration to strengthen global exploration. Experiments on the Qiannan pumped-storage project show that IAVNS obtains layouts with improved cut–fill balance. In the 30-run benchmark comparison, IAVNS achieved a mean CFR of 1.31, which is close to, although slightly above, the upper bound of the adopted earthwork-balance reference interval. In the separate 20-run case-study analysis, the average storage-volume deviation was 0.13%, with run-level deviations ranging from $-1.39\%$ to $1.16\%$. In benchmark comparisons, IAVNS improves solution quality by 22.8% relative to the Genetic Algorithm (GA) and by 16.5% relative to classical Variable Neighborhood Search (VNS), while reducing convergence time by 49.5% and 27.4%, respectively. Sensitivity analysis further suggests that the framework remains locally robust under practically reasonable parameter perturbations, and the module-level ablation study indicates that the observed performance gains arise mainly from the problem-tailored search mechanisms for dam-axis siting rather than from a generic combination of metaheuristic components. Taken together, the case-study results, repeated-run comparison, sensitivity analysis, and ablation study support the use of IAVNS as a geometry-oriented decision-support framework for preliminary dam-axis design in terrain-sensitive hydraulic engineering applications. Full article

We address the growing urgency of climate action and China’s pivotal role in advancing carbon capture, utilization, and storage (CCUS) toward its “dual carbon” goals. This study examines factors influencing CCUS development in China using structural equation modeling (SEM), identifying five critical determinants: resources, environment, market, technology, and government–society dimensions. Empirical data from expert surveys underscore the need for integrated policy measures, including fiscal incentives, standardized evaluation, international cooperation, and energy infrastructure upgrades. The findings enable effective decarbonization and provide a transferable framework for emerging economies. Full article

To overcome the inherently low temperature sensitivity of fiber Bragg gratings (FBGs) in engineering applications under low-temperature conditions, a sensitivity-enhanced FBG temperature sensor based on a polytetrafluoroethylene (PTFE) encapsulation sleeve was developed. Four adhesive materials—silicone thermal grease, polydimethylsiloxane (PDMS), epoxy resin, and modified acrylic ester—were employed to package the FBG within the PTFE sleeve to improve its temperature sensitivity. Thermal stress simulations of the proposed sensor structure were carried out using COMSOL Multiphysics^® 6.2, and the simulation results showed good agreement with the experimental data. Based on the experimental results, the sensitivity-enhancement effects of PTFE combined with different adhesives, as well as the influences of the PTFE sleeve length and wall thickness, were systematically investigated. The results indicate that, within the temperature range of −35 °C to 15 °C, increasing both the length and thickness of the PTFE sleeve can effectively improve the temperature sensitivity of the sensor. When epoxy resin was used as the encapsulating adhesive, the sensor achieved a maximum sensitivity of 117.4 pm/°C, corresponding to a 13.19-fold increase compared with that of a bare FBG sensor. This sensitivity-enhancing packaging structure significantly improves both the temperature sensitivity and linearity of FBG temperature sensors, while also substantially reducing fabrication costs. Full article

Against the backdrop of environmental governance systems transitioning from command-and-control to multi-stakeholder collaboration, elucidating the mechanisms and pathways through which voluntary environmental regulations influence green technological innovation in heavily polluting enterprises holds significant implications for advancing green innovation and high-quality development. This paper systematically examines the synergistic mechanisms of command-and-control versus voluntary environmental regulations on green technological innovation in heavily polluting enterprises, utilising data from listed companies in China’s high-pollution industries between 2008 and 2024. Unlike previous studies predominantly focused on the impact of a single regulatory type, this study reveals an interactive effect between the two: moderate command-and-control regulation provides essential institutional support for voluntary environmental regulation, such as ISO 14001 certification, thereby generating a complementary enhancement effect. However, overly stringent command-and-control regulation diverts innovation resources from enterprises, thereby suppressing the incentive effect of voluntary regulation. This conclusion transcends the traditional analytical paradigm within environmental regulation theory that treats command-and-control and voluntary regulations as mutually exclusive opposites, revealing instead a dynamic relationship where both synergistic and constraining effects coexist. This discovery provides crucial theoretical underpinnings and empirical evidence for constructing an environmental governance system that combines command-and-control constraints with flexible incentives, ensuring compatibility between policy objectives and corporate behaviour. Full article

In the digital era, sustainable finance is increasingly expected not only to expand financial access, but also to strengthen ESG transparency, accountability, and the alignment between corporate disclosure and actual practice. Against this backdrop, this study examines whether digital finance enhances corporate ESG disclosure–practice consistency by mitigating corporate ESG decoupling. Using Chinese A-share listed firms from 2011 to 2024 as the sample, we further investigate the moderating roles of corporate digitalization and executives’ digital background. The results show that digital finance significantly reduces corporate ESG decoupling, and this finding remains robust after alternative variable specifications, sample adjustments, stricter fixed-effects settings, and instrumental-variable estimation. Across the environmental, social, and governance dimensions, digital finance exhibits a stronger mitigating effect on social and governance decoupling. Corporate digitalization and executives’ digital background, acting as key micro-level enabling mechanisms through which regional digital finance translates into firm-level governance improvement, both significantly strengthen the mitigating effect of digital finance on corporate ESG decoupling. Further analysis shows that this effect mainly operates through easing financing constraints and reducing information asymmetry. This study contributes to the literature on sustainable finance, digital governance, and corporate sustainability by providing new evidence on how digital finance can narrow the ESG disclosure–practice gap and improve the consistency between corporate ESG disclosure and actual performance. It also offers practical implications for advancing the high-quality development of digital finance, strengthening firms’ digital capabilities, and enhancing the digital literacy of corporate executives. Full article

To address the power optimization problem of LCC-HVDC systems in multi-infeed receiving-end grids under system strength constraints, this paper systematically analyzes the influence mechanism of AC system strength on conventional DC transmission power, clarifying the quantitative relationship between the critical short circuit ratio and the system’s power transmission limit. A novel day-ahead power optimization method for multiple DC links is proposed, incorporating operational constraints such as frequency stability and voltage stiffness. Empirical simulation analysis of the Chinese Zhejiang Power Grid under a low-voltage typical operation mode in the summer of 2025 demonstrates that the optimized DC power transmission scheme significantly improves the system’s frequency response and voltage recovery characteristics under fault conditions, enhancing the overall security and stability level of the multi-infeed HVDC receiving-end grid. This research holds significant reference value for practical engineering applications. Full article

This study evaluated olive leaf extract (OLE) as a multifunctional dermocosmetic candidate for sebum-related and inflammatory responses relevant to oily and acne-prone skin using an axis-aligned in vitro panel: (i) sebocyte lipogenesis, (ii) inflammatory mediator production in keratinocytes, and (iii) fibroblast-mediated collagen gel contraction. In addition, supportive mechanistic evidence for the sebum-related effects of OLE was obtained by examining signaling proteins associated with sebocyte lipogenesis, including PPAR-γ and SREBP-1. As a result, OLE significantly inhibited linoleic acid-induced lipid accumulation in SEB-1 sebocytes without cytotoxicity. In HaCaT keratinocytes, OLE significantly reduced the production of pro-inflammatory cytokines, including IL-8, TNF-α, and PGE₂, induced by Cutibacterium acnes or UVB. In dermal fibroblast-containing collagen gels, OLE enhanced fibroblast-mediated gel contraction. Additionally, analysis of the main mechanisms of lipid inhibition using SEB-1 sebocytes revealed that OLE exerts a dual regulatory role in lipid synthesis and inflammation by downregulating AKT and ERK phosphorylation and inhibiting PPAR-γ and SREBP-1 expression. Furthermore, among the tested extracts, the 70% ethanol extract (OLE70) exhibited the strongest antioxidant activity, the greatest gel contraction response, and the highest content of oleuropein, a major bioactive phenolic compound derived from olive. Like OLE, oleuropein also showed sebum-regulatory activity by reducing lipid accumulation in SEB-1 sebocytes, an inhibitory effect on IL-8 expression in HaCaT keratinocytes, and an inhibitory effect on the expression of PPAR-γ and SREBP-1, which are involved in sebum secretion. Taken together, these findings suggest that OLE and its major phenolic constituent, oleuropein, may modulate sebum-related, inflammatory, oxidative, and dermal remodeling-associated responses in skin cell models. These results should be interpreted as exploratory and provide a basis for further mechanistic and translational investigation. Full article

With the rapid growth of the digital economy, the application of artificial intelligence (AI) technology has injected new momentum into persistent green innovation. Using data on Chinese A-share listed companies from 2010 to 2023, this article aims to investigate whether senior executives’ adoption of AI technology influences companies’ persistent green innovation and to identify the specific mechanisms underlying this relationship. To improve measurement accuracy, this paper employs the BERT model to conduct an in-depth analysis of corporate annual report texts to construct an executive AI adoption metric. The findings reveal that executive AI adoption significantly promotes corporate persistent green innovation, and this effect is primarily achieved through enhanced data factor allocation capabilities. Moreover, strategic agility positively moderates the relationship between executive AI adoption and corporate persistent green innovation. Specifically, the higher the level of strategic agility, the stronger the mediating role of data factor allocation in the relationship between executive AI adoption and corporate persistent green innovation. In particular, executive AI adoption plays a more significant role in fostering persistent green innovation among firms with higher total factor productivity and those facing intense market competition. Full article

The rapid development of the aquaculture industry has led to increasing discharges of hypersaline and nutrient-enriched maricultural wastewater. Functional ceramic membranes have garnered significant advantages due to their exceptional chemical stability and high tailorability through surface and interface engineering. This research reviewed recent advances including the functionalization of ceramic membranes and hybrid systems coupled with advanced oxidation processes (AOPs) for enhancing degradations of nutrients and organics in maricultural wastewater treatment. Catalytic ceramic membranes enhanced removal of micropollutants including antibiotics and heavy metals. This review further systematically classified categorization of established functional ceramic membranes and synthesizes cutting-edge modification approaches for membrane fouling mitigation. Finally, this review evaluated the application prospects, challenges for scaled implementation, and proposed future research directions of functional ceramic membranes in the treatment of maricultural wastewater. Full article

Medical workers are frequently exposed to high-stress environments, highlighting the need for non-invasive stress monitoring strategies based on autonomic nervous system activity. Ammonia emitted from the human skin surface has been reported to increase under physical and psychological stress; however, its relationship with autonomic nervous system (ANS) dynamics remains unclear. In this study, we performed simultaneous, time-resolved measurements of dermal ammonia emission and heart rate variability (HRV) in 11 medical workers during 3 h of routine work. Dermal ammonia emission flux was continuously monitored using a passive flux sampler (PFS) coupled with ion chromatography, while autonomic nervous system activity was assessed by Holter electrocardiography. The temporal profiles of ammonia emission were analyzed in relation to HRV indices, including high frequency (HF) and the low-frequency-to-high-frequency ratio (LF/HF). Dermal ammonia emission increased under conditions characterized by lower HF and/or higher LF/HF, whereas elevated HF was associated with reduced ammonia emission (r = −0.47, p < 0.001). Furthermore, temporal fluctuations in ammonia emission were associated with sympathetic–parasympathetic switching. These findings suggest that dermal ammonia emission may be associated with HRV-related physiological responses under real-world working conditions and may have potential as a non-invasive indicator for stress-related physiological monitoring. Full article

To realize the real-time structural health and operational safety monitoring of military and industrial devices, such as hypersonic vehicles, aero-engine blades, and thermal power plant boilers, at operating temperatures up to and beyond 1400 °C, this study presents a miniaturised, integrated, high-thermal-stability wireless sensor device. This study investigated the influence of temperature on the interdigital electrodes (IDEs) of surface acoustic wave (SAW) temperature sensors for three configurations: bare electrode, single-layer protective film, and multilayer composite film. While the exposed electrode exhibited thermal stability at 1000 °C, it underwent structural failure at 1250 °C. To achieve health monitoring at temperatures exceeding 1400 °C, an Al₂O₃/AlN/Al₂O₃ multilayer protective architecture was developed. The device demonstrated functionality up to 1400 °C with a temperature coefficient of frequency (TCF) of −40.03 ppm/°C, yielding a sensitivity of 12.0 kHz/°C at a center frequency of ~300 MHz. The electrode protection structure elevated the maximum operating temperature. A hybrid heterogeneous integration of high-temperature co-fired ceramic (HTCC) inverted-F antenna and a Langasite (LGS) SAW device with a multilayer composite film was realised. The wireless device maintained functionality from room temperature to 1400 °C and withstood 1400 °C for 2 h, exhibiting a maximum repeatability error of 12.67% (corresponding to a temperature measurement error of ~177.4 °C at 1400 °C). This integrated design enables the miniaturization of high-temperature wireless sensors, making them suitable for harsh environments. Full article

With growing global concern over climate risk, high-carbon enterprises are assuming an increasingly critical role in strengthening climate resilience and fostering low-carbon development. However, how climate risk disclosure shapes their carbon performance—specifically through what mechanisms and pathways—remains a pivotal yet underexplored question. To address this gap, this study constructs a panel dataset comprising Chinese listed high-carbon companies over the period 2006–2022 and employs a two-way fixed-effects econometric model to assess how climate risk disclosure affects carbon performance while investigating the underlying mediating channel. The empirical results provide robust evidence that enhanced climate risk disclosure improves the carbon performance of high-carbon enterprises. Mechanism analysis indicates that this beneficial outcome is mainly achieved through promoting green technological innovation and easing corporate financial constraints. Heterogeneity analysis further shows that the effect is stronger among smaller companies, firms operating in less concentrated industries, and those headquartered in China’s eastern region. The policy implications derived from these findings include establishing and strengthening a mandatory climate risk disclosure framework, introducing targeted incentives for green innovation and transition finance and tailoring climate risk management strategies according to firm-specific characteristics. Overall, this study underscores climate risk disclosure as a crucial factor in supporting the shift toward low-carbon operations among high-carbon enterprises. Full article

With the deepening integration of 5G-Advanced (5G-A) technology into smart manufacturing, the large-scale deployment of dynamic terminals—such as mobile robots and automated guided vehicles (AGVs)—within industrial private networks introduces complex, time-varying penetration and path losses. This significantly degrades the accuracy of conventional signal quality and capacity estimation methods, which were primarily designed for static terminal scenarios, thereby posing substantial challenges to coverage and deployment planning of industrial 5G access points, with downstream implications for power capacity dimensioning. To address this problem, this paper proposes a coverage-driven base station deployment optimization method formulated as a combinatorial optimization problem. The study constructs a signal strength assessment and network throughput calculation model tailored for dynamic industrial environments. This model captures the joint impact of terminal mobility and environmental obstacles on signal propagation, thereby enabling more reliable estimation of coverage performance and power consumption. Furthermore, by formulating the base station placement optimization as a combinatorial optimization problem, and by introducing mechanisms for equivalent transformation of the objective function and data preprocessing, the proposed method substantially reduces redundant computations during heuristic iterations. Simulation results verify that, compared with conventional static planning approaches, the proposed scheme enhances both the accuracy and computational efficiency of deployment planning while maintaining coverage quality. This work provides a theoretical foundation and a practical methodology for deploying reliable and energy-efficient industrial 5G-A private networks. Full article

In Low Earth Orbit Beam-Hopping Satellite Systems (L-BHSS), co-channel interference among beams severely degrades communication quality. To address the inter-beam co-channel interference avoidance problem, this paper proposes a Parameter-Sharing Multi-Agent Deep Deterministic Policy Gradient-Based Beam Geometry Multi-Parameter Optimization Algorithm (PS-MADDPG-BGMPOA) for the joint optimization of satellite beam geometric parameters. The effects of free-space path loss, atmospheric impairments, and Rician fading are comprehensively considered, and a beam geometric multi-parameter optimization model is formulated with the objective of maximizing the long-term Signal-to-Interference-plus-Noise Ratio (SINR), incorporating beamwidth, beam center offset from the satellite nadir direction angle, inter-beam separation angle, and beam activation states. To tackle the resulting high-dimensional mixed action space, the proposed algorithm employs parameter sharing and grouped decision-making, which alleviates the dimensionality explosion problem and decouples the network scale from the number of beams, enabling efficient cooperative optimization with reduced training complexity. Simulation results show that, under various channel conditions and beam configurations, the proposed method effectively enhances communication quality and spectral efficiency while exhibiting superior real-time performance and stability. Full article

Rockfall is one of the most dangerous and unpredictable natural disasters that can seriously damage infrastructure. In traditional protection systems, sand is commonly used as a buffer material; however, the use of large sandbags as temporary protective structures has still not been investigated, and there are no established design guidelines available. This study aims to reveal the effect of rockfall impact on the dynamic response of a sandbag protection system for temporary restoration work in the event of a natural disaster. Initially, a numerical model based on finite element calculation was adopted to simulate the large sandbags under rockfall impact, which was verified by the full-scale experimental test data. The parameters identified were impactor velocity, acceleration, penetration depth, and sandbag displacement. After validation, the model was used for prediction analysis to examine the dynamic response and energy absorption characteristics of sandbags under different conditions, such as the influence of sand density, impactor velocity, impact height and the number of sandbags in the impact direction. The results propose an analytical basis for the establishment of performance-based guidelines for the design of sandbag walls as a temporary rockfall protection system. Full article