Search Results (750)

Additive manufacturing has recently become a key enabling technology in industrial fields, ranging from customized products for everyday usage to aerospace applications and small-batch industrial tooling. The future prospects extend up to the biofabrication of human organs. Ensuring the quality and repeatability of this process requires a systematic and comprehensive investigation of the underlying physical phenomena. In particular, melt-pool evolution is a critical feature, since irregularities in its spatial profile can influence microstructural evolution and weaken the integrity of the manufactured part. Microscale defects arising from balling and keyhole phenomena, often associated with recoil pressure, can severely degrade the quality of the resulting scanned track. This paper reviews the current state of optical approaches for melt-pool characterization and feature monitoring relevant to industrial laser additive manufacturing for process control and quality improvement, with a special focus on pyrometry and high-speed imaging. A single high-speed camera was generally used in experiments for melt-pool feature extraction, but two cameras were used to bypass emissivity values, which are otherwise difficult to obtain. Mathematical models were introduced to provide complementary information about melt-pool features, while artificial intelligence algorithms were used in other cases to process optical information. New melt-pool imaging databases and classifiers are expected in the near future to enable fast selection of appropriate process parameter windows, eliminating costly trial-and-error experiments. Full article

Although the coupling coordination relationship (CCR) between ecological environment and economic development has received extensive scholarly attention, investigations into the underlying mechanisms of this coupling coordination remain insufficient. Taking the Chishui River Basin (CRB) in Southwest China as the study area, this study integrates remote sensing data and county-level statistical datasets. Firstly, the quality of the ecological environment and economic development level of the CRB are systematically evaluated. Secondly, an improved coupling coordination degree model (ICCDM) is adopted to quantify the CCR between the ecological environment and economic development, as well as its spatiotemporal evolution characteristics. Finally, an obstacle degree model and panel Tobit model are employed to explore the influencing factors of the CCR from both intrinsic and extrinsic perspectives. The results show that during the study period, both the ecological environment index (EEI) and the economic development index (EDI) in the CRB exhibited upward trends, with pronounced inter-county disparities. The CCR between ecological environment and economic development was continuously optimized, and the coupling coordination degree (CCD) displayed a distinct spatial gradient pattern of downstream regions > midstream regions > upstream regions. Obstacle degree analysis identifies significant heterogeneity in the obstacle factors for CCR improvement across the basin: Renhuai and Zunyi are dominated by ecological environment constraints, while 11 counties including Chishui and Xishui are mainly restricted by economic development constraints. Industrial structure, ecological endowment, industrialization level and government capacity are vital positive driving factors for the CCR in the CRB, whereas Terrain conditions act as a key negative restraining factor. This study indicates that the overall coupling coordination level between ecological environment and economic development in the CRB is still relatively low and requires further enhancement. Therefore, region-specific differentiated regulation strategies are urgently needed to achieve high-level coordinated development between the ecological environment and economy in the CRB. Full article

Given the constraints inherent to island tourism resources, optimizing their allocation and utilization scientifically and efficiently has emerged as a critical challenge for both academic inquiry and policy-making. This study investigates pathways to enhance island tourism sustainability through the development of mathematical models quantifying tourism intensity, efficiency, and resource abundance, utilizing multi-source heterogeneous data on tourism resources in Hainan from 2012 to 2022. The study reveals that: (1) The spatial structure of tourism development progressed from an initial “north–south dual-core driven, fragmented in the west” pattern, through an intermediate “north–south dual-core driven, fragmented in the east” phase, and ultimately evolved into a “north–south dual-core driven, east–west isolated” configuration. (2) Spatiotemporal evolution of Hainan Island’s tourism industry is driven by a combination of policy interventions, natural endowments, transport infrastructure, economic foundations and population size. (3) Tourism economic effects exhibit marked regional heterogeneity across Hainan. Eastern regions are strongly influenced by per capita tourism income and hotel density, whereas northern areas depend more on the tertiary industry share; significant spatial spillover effects are also observed. (4) Spatial econometric modeling further indicates that influential factors do not uniformly exert positive effects on the tourism sector and its subsystems, with indirect effects exceeding direct effects by approximately 22.41 times. Although this research underscores the importance of human–environment interactions, it does not quantify the specific ecological consequences of tourism development. Future policy should integrate an ecological footprint model within a coordinated “tourism–ecology–protection” framework to balance economic and ecological goals, while also accounting for external shocks affecting the tourism economy. Full article

In the context of the global dual-carbon goals and China’s DP strategy, strengthening the coupling between digital productivity (DP) and the high-quality development of the energy industry (HQDEI) is essential for resource-based regions. Doing so can help these regions overcome transition constraints and advance green, low-carbon development. Using panel data for nine prefecture-level cities in Guizhou Province from 2014 to 2023, we construct composite indices for DP and HQDEI with an improved entropy-weight TOPSIS approach. We then characterize their spatiotemporal evolution using a coupling coordination degree (CCD) model and kernel density estimation. Finally, we examine the determinants of coupling coordination through panel regression and threshold models. The results show that: (1) The CCD between DP and HQDEI efficiency continues to increase, with regional differences displaying a periodic convergence–divergence pattern and a spatial structure characterized by core agglomeration and outward diffusion. Gradient disparities in coordinated development are evident between central and peripheral areas. (2) Consumption upgrading and fiscal self-sufficiency significantly promote CC, whereas a traditional resource-dependent growth model significantly suppresses it. Constrained by short-term adaptation and integration costs, digital innovation currently exerts a negative effect, and its enabling potential has not yet been fully realized. (3) Nonlinear tests identify a single digital-infrastructure threshold: the enabling effect of digital innovation turns positive only once infrastructure surpasses a critical level, revealing pronounced interval heterogeneity. This study advances the theoretical understanding of the bidirectional coupling between DP and HQDEI, provides empirical guidance for energy digital transformation and high-quality development in resource-based regions of western China, and offers transferable insights for green, low-carbon transitions in traditional energy regions worldwide. Full article

Under the dual strategic imperatives of carbon neutrality and digital transformation, the power industry plays a pivotal role in advancing green and low-carbon development. Green Total Factor Productivity (GTFP) provides a comprehensive measure of efficiency in the power sector under energy and environmental constraints. Using panel data from 31 Chinese provinces over the period 2012–2023, this study employs a super-efficiency Slacks-Based Measure (SBM) model, kernel density estimation, standard deviation ellipse analysis, the Gini coefficient, and fuzzy-set Qualitative Comparative Analysis (fsQCA) to systematically examine the spatiotemporal evolution, regional disparities, and digital-driven improvement pathways of power industry GTFP. The results indicate that national power-sector GTFP exhibits a fluctuating upward trend, accompanied by pronounced regional heterogeneity. A distinct spatial pattern has emerged, characterized by rapid improvement in the western region, relative stability in the eastern region, contraction in the central region, and persistent lagging in the northeastern region. Spatially, the distribution has evolved from an initial east–west dual-core structure to a three-tier gradient pattern led by the west, stabilized in the east, and depressed in the central region. Kernel density estimation reveals a clear multi-peak polarization trend, while standard deviation ellipse analysis shows a relatively stable spatial center with continuously expanding dispersion along the northeast–southwest axis. Further analysis demonstrates that interregional differences remain the primary source of overall inequality, with rapidly widening intraregional disparities in the western region. Configurational analysis identifies five digital-economy-driven pathways to high GTFP, highlighting that no single optimal configuration exists. Instead, multiple combinations of technological, organizational, and environmental conditions jointly facilitate GTFP enhancement. These findings provide empirical evidence to support differentiated and precision-oriented policy design for promoting coordinated digital transformation and green development in China’s power industry. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

In the context of increasingly frequent and severe natural disasters, scientifically measuring and analyzing the efficiency of natural disaster emergency management in China is of great practical significance for enhancing the performance of the emergency management system and promoting its systematic and high-quality development. This study first applies a super-efficiency SBM-DEA model with undesirable outputs to systematically measure the efficiency of China’s natural disaster emergency management system during the period 2019–2023. Subsequently, the Dagum Gini coefficient and Kernel Density estimation are employed to examine regional disparities and dynamic evolution across eastern, central, western, and northeastern China. Finally, the coefficient of variation and spatial econometric models are applied to test the spatial convergence characteristics of emergency management efficiency. The results indicate that: (1) China’s overall disaster emergency management efficiency remains at a relatively low level and exhibits a fluctuating trend characterized by an initial increase followed by a decline. The regional distribution pattern of emergency efficiency is ranked as “Northeast > Central > West > East”. (2) The average annual contributions of intra-regional disparities, inter-regional disparities, and transvariation density to the overall variation in national emergency management efficiency are 27.58%, 39.90%, and 32.53%, respectively, indicating that inter-regional disparities and transvariation density are the dominant sources of systemic differences among regional subsystems. (3) The national distribution of emergency management efficiency displays a bimodal pattern, indicating polarization; however, the secondary peak is relatively flat, suggesting a weakening trend of provincial-level polarization and a gradual narrowing gap with high-efficiency regions. (4) σ-divergence is observed at the national level and in the central region, while both absolute and conditional β-convergence exist to varying degrees at the national level and across all four regions. Nevertheless, the enhancement of natural disaster emergency management efficiency has not yet realized a system-level transition from convergence in growth rates to convergence in efficiency gaps. In addition, economic development, technological progress, urbanization, and industrial structure exert significantly heterogeneous effects on disaster emergency management efficiency across different regions. Full article

A multi-dimensional quantitative assessment of high-quality development (HQD) in China’s livestock industry and the identification of its main constraints are essential to understanding its current stage and future direction. Guided by global sustainability targets and the United Nations’ Sustainable Development Goals (SDGs), an evaluation system was constructed by this study. This system integrates five key aspects: product safety, output efficiency, resource conservation, environmental friendliness, and regulatory effectiveness. Using provincial panel data from China for 2013–2022, this research applies the entropy-weighted TOPSIS method, kernel density estimation (KDE), and an obstacle degree model for analysis, the goal is to support food security and foster environmentally sustainable growth. The findings indicate the following: (1) Notable inter-provincial disparities exist in the HQD of China’s livestock industry, revealing a spatial pattern of “leading in the east, stable in the center, and lagging in the west.” (2) The nationwide evolution exhibits a “convergence followed by divergence” pattern: from 2013 to 2017, the primary peak of the KDE rose and its width narrowed; from 2018 to 2022, the primary peak declined and its width widened, indicating that inter-provincial disparities first narrowed and then expanded. At the regional level, the development pattern is characterized by eastern polarization, central stability, and western lock-in. (3) Obstacle factor analysis identifies product safety and environmental friendliness as the principal constraints on HQD in the livestock industry. Addressing these bottlenecks is crucial for ensuring the supply of livestock products (SDG 2: Zero Hunger), promoting resource conservation and green production (SDG 12: Responsible Consumption and Production), and alleviating the ecological and environmental pressures of the livestock industry (SDG 15: Protection of Terrestrial Ecosystems). The challenges related to resources, the environment, and quality safety confronting China’s livestock industry are common among developing countries. Consequently, the evaluation framework established in this study can offer methodological references for relevant nations. Full article

This paper proposes an innovative research algorithm “measurement—pattern—driving force—synergy” that determines the eco-efficiency of 83 Russian federal subjects (2000–2019) using the Slacks-Based Measure (SBM) model with non-desired outputs (incorporating comprehensive input indicators such as water resources and electricity input, and dual non-desired outputs of waste gas and wastewater). Combined with hot spot analysis, a gravity center model, and panel Tobit regression, we reveal the temporal-spatial evolution and driving mechanisms of eco-efficiency in resource-based economies. The research finds that the overall eco-efficiency of Russia is at a medium level and shows a dynamic correlation with the economic development stage. In the early stage of the period under review, there was a high degree of synergy, but the efficiency declined during the period of rapid economic growth. Later, it rebounded somewhat in tie with technological progress. Spatially, it presents a special pattern of low efficiency in the western European industrialized regions and high efficiency in the Arctic and Far East peripheral regions, reflecting the spatial heterogeneity of resource-dependent economies and the survival-constrained efficiency feature. The analysis of influencing factors indicates that per capita GDP has a significant positive driving effect on eco-efficiency, but the expansion of residents’ consumption, the improvement of education level and the dependence on foreign trade all have inhibitory effects, highlighting the path dependence of the current growth model on the structure of resource consumption. The research suggests that Russia should implement differentiated spatial governance in the future, promote the green transformation of consumption and trade structures, and strengthen the ecological orientation of the education and scientific research system to achieve a fundamental transformation of regional sustainable development from survival constraints to innovation-driven. Full article

Industrial digitalization reshapes production processes and can potentially improve carbon productivity by optimizing factor allocation and energy efficiency. Using panel data for 30 Chinese provinces from 2012 to 2022, this study constructs a comprehensive industrial digitalization index with four dimensions and 13 indicators using the entropy method and examines its impact on carbon productivity (GDP per unit of CO₂ emissions). We employ the Dagum Gini coefficient and kernel density estimation to describe regional disparities and their evolution, a dynamic panel threshold model to test the nonlinear role of industrial transformation and upgrading, and a spatial Durbin model to identify spatial spillover effects. The results indicate that industrial digitalization has risen nationwide but remains uneven; industrial digitalization significantly enhances carbon productivity, with stronger effects in the eastern and western regions and in plain areas; the effect exhibits a double-threshold pattern with respect to industrial transformation and upgrading, implying a U-shaped relationship; and industrial digitalization generates positive spatial spillovers. These findings suggest that policy should coordinate digital infrastructure investment with industrial upgrading and regional collaboration to accelerate low-carbon, high-efficiency growth. Full article

The cloud spatial uniformity in the test section is crucial for icing wind tunnels in aircraft icing research and airworthiness certification. To achieve uniform supercooled large droplet (SLD) icing conditions, both the spatial variation in droplet size distribution and the concentration should be considered. In this study, the spatial distribution of droplets under three SLD conditions is explored in the Aviation Industry Corporation of China Aerodynamics Research Institute (AVICARI)’s FL-61 icing wind tunnel. Measurements are conducted at 12 test points in vertical and horizontal directions using the holographic airborne cloud particle imager (HACPI) in conjunction with a two-axis traversing system. The droplet images obtained at specific test points below the test section centerline show deformation phenomena for droplets larger than 400 $\mathsf{\mu }$m. Additionally, the aspect ratio of deformed droplets increases with droplet size. The spatial evolution of the median volume diameter (MVD) and liquid water content (LWC) is examined. For two spray arrangements where the activated nozzles are positioned close, the test point where the LWC peak in the vertical direction occurs is higher than that of the MVD peak. Further analysis focuses on the size distribution of droplets in the vertical direction. The results show that the settling effect of the droplets larger than 50 $\mathsf{\mu }$m is evident under a flow velocity of 78 m/s. Meanwhile, the position where large droplets tend to appear lowers as the droplet size increases. Finally, the spatial uniformity of droplet size distributions at the same radial distance is discussed. Full article

In smart logistics and intelligent manufacturing scenarios, the deployment of Autonomous Guided Vehicles (AGVs) necessitates vision systems that balance stringent real-time constraints with high detection accuracy. However, contemporary lightweight models often struggle with multi-scale feature representation and precision degradation. To address these challenges, this study presents LSOD-YOLO, a tailored evolution of YOLO11n designed for embedded AGV systems. Our methodology focuses on three architectural innovations: (1) we propose a Lightweight Shared Convolution Detection (LSCD) head integrated with Group Normalization (GN) and a scale-adaptive mechanism to harmonize multi-scale feature responses; (2) we re-engineer the backbone using a Star-Net architecture enhanced by Gated MLPs and Depthwise Attention to refine local spatial modeling; and (3) we integrate multi-branch residuals and Channel Attention (CAA) into the C3k2-Star-CAA module to enhance robustness against occlusions and complex backgrounds. The experimental validation on a self-built AGV industrial dataset and COCO128 reveals a compelling performance leap: a 30 FPS increase in throughput and a 1.5% gain in precision, all achieved with 32.8% fewer parameters. These findings confirm that LSOD-YOLO achieves a superior trade-off between computational efficiency and reliability, showing great potential for seamless deployment in resource-constrained AGV visual tasks. Full article

Linear alkylbenzene (LAB) is the main raw material used to make biodegradable detergents, and its production process is based on aromatic alkylation. HY zeolites that have undergone controlled dealumination and desilication have led industrial standards amongst solid acid catalysts because of their controllable acidity and hierarchical pore structure. Coke formation in such systems can assume a dual role, which is dependent on its condition. Though the over-deposition is known to cause deactivation by blocking the micropores, Bronsted acid-site masking, and diffusion collapse, the low-level deposition could also be done to increase the monoalkylate selectivity by the pore mouth catalysis, steric modulation, and selective suppression of secondary alkylation pathways. The critical review is done on the structural-kinetic interaction that determines the coke evolution in HY-based catalysts. In order to moderate the acid-site density and enhance hydrothermal stability, dealumination (Si/Al optimization of about 2.5 to 30–100) occurs, but to reduce deep-pore coke formation, desilication (interconnected mesopores) is created. The bimodal porosity and regulated acidity are found to be synergistic, as hierarchical HY zeolites produced through successive cycles of steam and alkaline treatments not only show LAB selectivity in excess of 90% but also exhibit much longer catalyst lifetimes. Quantitative research on the beneficial coke regime revealed that it was composed of about 36 wt% hydrogen-rich species, which were localized at the pore mouths, hence enhancing monoalkylation selectivity by 15–40%. Beyond a critical transition window (e.g., 8–12 wt.%), coke formation to condensed polyaromatic and graphitic products leads to fast deactivated coke formation, which is due to percolation limits and transport-controlled kinetics. More advanced techniques of characterization of the coke, e.g., temperature-programmed oxidation (TPO), ²⁷Al MAAS NMR, and UV-Raman spectroscopy, indicate how the coke is changed to highly structured graphitic deposits of high oxidation activation energy. Activity recovery of 85–98% is obtained in regeneration processes, including controlled oxidative calcination, microwave-based and plasma-based processes, and thermal management protocols, and it would be determined by the chemistry of the coke, its spatial distribution, and the regeneration protocols. This paper has developed a mechanistic coke control system by cross-tuning the acidity and development of an effective pore network, which led to a sustainable aromatic alkylation reaction with minimal activity loss, high selectivity, and long life. Full article

Under the dual pressures of global warming and high-density urbanization, extreme heatwaves have emerged as a critical ecological risk constraining the sustainable development of Chinese urban agglomerations. Based on multi-source remote sensing, meteorological, and economic data for 19 major urban agglomerations from 2014 to 2023, this study develops an emerging industrial agglomeration–energy activity–thermal environment response framework. Using XGBoost-SHAP interpretable machine learning and GeoSHAPLEY spatial decomposition, the nonlinear and spatially heterogeneous impacts of industrial agglomeration on heatwave characteristics are systematically quantified. Results indicate that the heatwave index increased from 0.619 to 0.637, with the model explaining 80.7 percent and 74.7 percent of variance in duration and frequency, respectively. Moreover, emerging industrial agglomeration ranks among the top contributors to both duration and frequency, explaining over 20 percent of duration variability and surpassing traditional industrial and socioeconomic factors. Heatwave duration and frequency exhibit nonlinear relationships. During early agglomeration, energy efficiency improvements generated marginal cooling of five to eight percent, whereas intensified agglomeration amplifies duration by over ten percent through energy-intensive activities and infrastructure heat islands. Meanwhile, green innovation at high agglomeration levels mitigates six to nine percent of the warming effect. In addition, spatial differentiation of industrial agglomeration, reflected by a Gini increase from 0.685 to 0.728 and inter-regional contribution around 62 percent, underpins heat risk heterogeneity. Furthermore, natural endowments, socioeconomic development, infrastructure, environmental regulation, and technological innovation significantly moderate these effects, with high-tech innovation attenuating heatwave amplification. Consequently, the thermal effects of industrial agglomeration follow a three-stage spatial evolution of warming, stabilization, and counter-regulation. These findings highlight that coordinated optimization of industrial spatial layout and green technological innovation is crucial for enhancing climate resilience and promoting low-carbon transformation in urban agglomerations. Full article

Drawing on the ecological economic system theory, this paper constructs a theoretical model to analyze the impact of enterprise digital transformation on carbon emissions and the critical mechanism of green innovation therein. Empirical evidence based on data from China’s A-share listed companies from 2007 to 2024 indicates the following: First, enterprise digital transformation significantly reduces corporate carbon emissions. This conclusion remains robust after a series of robustness checks and endogeneity treatments. Second, digital transformation promotes carbon reduction primarily by increasing the quantity of green innovation, whereas the mediating role of green innovation quality has not yet manifested. Third, heterogeneity analysis confirms our theoretical deductions, revealing that this carbon reduction effect is significantly stronger in regions with high environmental regulation intensity and is predominantly manifested in highly polluting industries. Fourth, descriptive spatial analysis indicates that digital transformation is associated with reduced carbon emissions in surrounding areas, exhibiting broader regional environmental correlations as the geographic range expands. Finally, the implementation of policy instruments, represented by carbon trading and low-carbon city construction, reinforces the carbon reduction effect of digital transformation. By integrating internal technological processes, contextual heterogeneities, descriptive spatial observations, and macro-policy environments, this paper provides holistic insights into the synergistic evolution of enterprise digitalization and green transition. Full article