Search Results (4,662)

This study assesses regional science and technology (S&T) innovation efficiency across 30 Chinese provinces from 2011 to 2022, incorporating a sustainable development perspective. Employing a non-oriented global frontier super-slack-based measure (SBM) model that accounts for undesirable outputs, along with kernel density estimation, cluster analysis, and Moran’s I, the research investigates the spatiotemporal evolution of innovation dynamics. The findings demonstrate a marked upward trend, with the national average efficiency score rising from 0.260 to 0.703. Temporally, efficiency advanced through three stages: an initial period of universally low efficiency, a phase of widening disparities, and a final stage of overall improvement and stabilization. Spatial analysis reveals a persistent “strong in the east, weak in the west” disequilibrium; however, absolute β-convergence tests indicate a significant reduction in regional disparities (p < 0.05). Kernel density estimation reveals a shift from a polarized “pyramid” shape to a more balanced “spindle-shaped” distribution. This is evidenced by a decrease in kurtosis and a rightward shift in the median. Spatial autocorrelation, as measured by the Global Moran’s I, evolved from a statistically insignificant distribution in 2011 to a strong positive correlation (0.223, p < 0.05) by 2022. This progression reflects a transition from isolated “unipolar” hubs to integrated “multi-center block linkages.” The results suggest that, although polarization is diminishing and the national innovation baseline is improving, policy efforts should prioritize the development of emerging innovation corridors to address the remaining east–west divide. Full article

Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization problem. Traditional mathematical programming and heuristic approaches often struggle with the “curse of dimensionality” and fail to provide real-time responses under stochastic disturbances. To address these challenges, this paper proposes a novel framework based on Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we model the TTR problem as a decentralized cooperative process and utilize the Multi-Agent Advantage Actor-Critic (MAA2C) algorithm to optimize train schedules dynamically. The proposed framework is implemented within the Flatland simulation environment, which allows for the representation of complex arbitrary topologies. We design a composite reward function that minimizes total delay deviation while maximizing passenger satisfaction, subject to constraints such as headway, operating time, and train capacity. Furthermore, to enhance the robustness of the model against high-dimensional state uncertainties, random disturbances following a negative exponential distribution are introduced during training. Experimental results across various scenarios—ranging from simple dual-track to complex random networks—demonstrate that the MAA2C-based approach significantly outperforms traditional baselines. It not only achieves faster convergence in small-scale scenarios but also demonstrates superior computational efficiency and scalability in large-scale environments, effectively minimizing passenger waiting times. This study validates the potential of MADRL in solving high-dimensional traffic control problems for intelligent transportation systems. Full article

Rapid urbanization has promoted socioeconomic growth but has exacerbated spatial-structure imbalances. This study investigates 41 prefecture-level cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using nighttime light data, we compute the Comprehensive Nighttime Light Index (CNLI) to track urbanization dynamics and delineate built-up areas. Furthermore, we apply random-forest recursive feature elimination with cross-validation (RF-RFECV) and a Shapley additive explanations (SHAP)-based interpretation framework to quantify the spatiotemporal evolution of urbanization drivers. The results indicate that urbanization in the YRD increased steadily overall during the study period. Shanghai maintained its core leadership, Jiangsu and Zhejiang advanced steadily, and Anhui rapidly caught up driven by regional integration policies. Although regional disparities generally converged, persistent absolute gaps in small and medium-sized cities and inland areas remain a prominent challenge to balanced development. Spatially, urbanization exhibits a gradient differentiation of “higher in the east and lower in the west, and higher along rivers and coasts than inland.” The regional spatial structure gradually shifted from an early “pole-core–belt” pattern to a polycentric and networked urban agglomeration system, with metropolitan areas and economic belts serving as important carriers for promoting spatial balance. Furthermore, built-up areas exhibit a trajectory of “core agglomeration, corridor-oriented expansion, and intensive transition.” The shrinking coverage of the standard deviational ellipse and a slowdown in expansion rates suggest a shift from extensive outward sprawl to more concentrated development. Regarding driving mechanisms, YRD urbanization has evolved from early-stage factor-scale expansion to a later-stage efficiency- and innovation-driven trajectory. While population density remained the dominant driver, early-stage reliance on transport infrastructure and fiscal decentralization was largely replaced by the strengthening effects of per capita output and green innovation. Overall, these findings provide empirical evidence for optimizing spatial patterns and designing differentiated policies for high-quality urbanization in the YRD. Full article

Two-dimensional materials have broad application prospects in the field of optoelectronic devices. As a next-generation power electronic device, AlN materials have obvious advantages in power processing, and their monolayer structure has excellent optoelectronic properties, which is of great significance for the study of 2D AlN monolayers. Properties such as electronic and optical properties of metal-adsorbed AlN (M-AlN) systems have been systematically investigated using density functional theory from first principles. The results of the energy bands of the M-AlN system indicate that the adsorption of Al, Li, Ag, Au, Bi, Cr, Mn, Na, Pb, Sn, Ti, and K metals makes the monolayer AlN magnetic, the incorporation of two metals, Al and Li, is the transition of the monolayer AlN from a semiconductor to a semi-metal, and the introduction of K metal makes the monolayer AlN transition from a semiconductor to a metal. The work function of the M-AlN system shows that the introduction of the metal reduces the work function of the monolayer AlN, especially for K-AlN, which is reduced by 56.12% compared to the monolayer AlN. In addition, the results of the optical absorption spectra of the M-AlN system revealed that the introduction of the metals made the monolayer AlN exhibit high absorption peaks in the visible and near-infrared regions; in particular, the intensity of the absorption peaks of the Ti-AlN system at 557.8 nm reached 7.4 × 10⁴ cm⁻¹ and the intensity of the absorption peaks of the K-AlN system at 1109.3 nm reached 1.01 × 10⁵ cm⁻¹. This indicates that the introduction of Ti and K metal atoms enhances the absorption properties of monolayer AlN in the visible and near-infrared regions. Finally, the time-domain finite difference using spherical metal nanoparticles is used to excite the localized surface plasmon resonance, and the results show a small area of strong electric field around the electric field hotspot of Cr and Li particles, and a good concentration of the electric field strength in the x and y directions. In summary, the system of metal atoms adsorbed on AlN will be favorable for the design of spintronics, field-emitting devices and solar photovoltaic devices. Full article

Tea garden irrigation suffers from time delays, nonlinear interference, and phenological biomass fluctuations caused by plucking, leading to the failure of traditional Proportional–Integral–Derivative (PID) and fixed-threshold models in precise water supply. This study proposes a precision irrigation system for smart tea gardens integrating Phenology-Aware Collaborative Decision-Making and an Adaptive Gain Predictive Super-Twisting Sliding Mode Control (AG-PSTC) algorithm. A “temperature–time–water” phenological reference model was constructed, and Crop Water Stress Index (CWSI) was introduced to decouple shoot density changes into phenology-driven and water stress components, realizing dynamic target soil moisture (W_target) setting. The AG-PSTC algorithm combined an improved Smith predictor for phase compensation and a barrier function-based adaptive super-twisting term for chattering elimination and finite-time convergence. Simulations showed AG-PSTC reduced rise time by 78% and steady-state error by four orders of magnitude compared with PID, with robust performance under ±40% time-delay perturbation. Field tests confirmed the system suppressed false irrigation during plucking, with soil moisture standard deviation within 1.51%. This study provides a vertical integration framework from crop physiological models to precision control, promoting the transition of tea garden irrigation from experience-based to demand-based. Full article

Developing sensitive and reversible CO sensors requires precise control of material–analyte interactions. Using DFT, we investigate CO sensing on bimetallic (Fe, Pt) anchored on N-doped graphene (TM₂–N₄–C), focusing on active-site density effects. Three densities are considered: low (12.7 Å), medium (8.5 Å), and high (4.2 Å). FePt–N₄–C band gaps exhibit non-monotonic tuning, approaching metallicity at high density. CO chemisorbs on Fe sites, but physisorbs on Pt sites. FePt exhibits stronger synergistic adsorption than homonuclear counterparts. While adsorption generally strengthens with density, spin-polarized calculations qualitatively reorder this trend via spin delocalization. High temperatures drastically improve recovery; low-density FePt–N₄–C reaches 65 s at 498 K. Three design principles emerge: low-density heteronuclear systems for reversible sensing, medium-density high-spin states for ultra-sensitive capture, and high-density configurations for work function sensors. This work establishes active site density as a key electronic and kinetic knob for graphene-based CO sensors. Full article

Rapid urbanization-induced extreme rainstorms severely disrupt social functions. Previous research often focused on “de-densification” strategies, which are difficult to adapt to high-density Sponge City Stormwater Management Units (SMUs) that carry core development functions. This study uses Shenzhen as a case study, utilizing Keep movement big data as a “social sensor” for system function perception and introducing the Socio-Ecological-Technological Systems (SETS) theory to construct a “recovery (RCN)–resistance (MI)” binary assessment framework. Through systematic clustering and hierarchical regression models, the driving mechanisms of blue landscape patterns, topography, road networks, and the built environment on social behavioral resilience are systematically parsed. The results show: (1) Road network morphology dominates resistance, while multi-dimensional elements collaborate for recovery. Resistance (MI) is primarily dominated by macro road network detour resistance (TPD2000, β = 0.956), while recovery depends on the synergistic support of blue space interspersion (Blue_IJI), topography, and micro-circulation road networks. (2) Green infrastructure fails in the model due to efficiency bottlenecks, empirical evidence of weakened regulation caused by green space fragmentation in ultra-high-density environments. (3) Low-density, eco-centric built environments provide dual synergistic gains for resilience. Based on this, a “Bidirectional Socio-Ecological Resilience Needs Pyramid” model is constructed, identifying four governance types such as the “Synergistic Balanced Type”. This study provides a quantitative basis for the transition from administrative control to precise morphological governance in high-density cities. Full article

In the 21st century, rapid urban development during global urbanization has led to high-density environments. These settings have become a significant cause of stress-related health problems for residents. Healing street design plays an important role in helping address mental health challenges caused by this process. Current research often focuses on healing elements and methods from only a single field. As a result, it lacks the integration of multidisciplinary and multi-stakeholder perspectives. To address this gap, this paper formed a Delphi expert panel with multidisciplinary scholars, urban managers, and practicing designers. The panel developed a quantitative evaluation model. This model covers four core dimensions: Safety (0.3210), Attractiveness (0.1080), Friendliness (0.2155), and Comfort (0.3553). It also includes eleven healing elements, such as Pedestrian Right-of-Way (0.4131), Night Lighting (0.3209), Visual Landscape (0.759), Street Furniture (0.4000), and Street Scale (0.3274). Using this model, the healing potential of Jingliu Road in Zhengzhou was assessed. The analysis identified the overall healing potential, core healing dimensions, and shortcomings of the street. This finding provides a clear direction for future healing-oriented street design. This paper builds a healing system for pedestrian spaces in high-density urban streets in China. It thus offers an evidence-based scientific tool for environmental design. Healing environments have expanded from less accessible spaces, such as squares and parks, to interactive and accessible streets. This transition enhances urban spaces’ capacity to address residents’ mental health concerns and promotes public health. Additionally, this paper offers specific recommendations for planners and policymakers to prioritize healing elements in urban renewal projects. 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

This study develops a unified multiscale–machine learning framework to interpret and predict thermo-mechanical wear regime transitions in MWCNT- and nanoclay-reinforced bio-based epoxy composites. A physics-informed master wear formulation integrating real contact mechanics, geometry-dependent shear transfer, interfacial adhesion energetics, and fracture-controlled matrix detachment was combined with interpretable machine learning analytics on a unified tribological dataset. In the CNT system, increasing loading from 0.1 to 0.4 wt.% enhanced interfacial adhesion energy density from 0.00813 to 0.01906 J/m², resulting in a monotonic reduction in the wear rate from 0.00918 to 0.00613 mm³/N·m (~33% reduction). In contrast, nanoclay exhibited an optimum behavior, with a minimum wear at 0.25 wt.% (0.000093 mm³/N·m; 7.9% reduction vs. neat clay baseline), followed by deterioration at a higher loading due to dispersion loss. The unified probabilistic regime classification of low-wear conditions (k < 0.007 mm³/N·m) achieved an ROC − AUC = 0.9256 and balanced accuracy = 94.3%, with thermo-mechanical severity identified as the dominant regime-switching driver. Reinforcement identity significantly modulated regime stability, confirming distinct shear transfer (Carbon Nano Tubes(CNT)) and confinement/tribofilm (clay) mechanisms within a common mathematical framework. By enabling the durability-oriented design of bio-based tribological systems and extending component service life through predictive stability mapping, this work contributes to resource-efficient materials engineering and reduced lifecycle waste, supporting Sustainable Development Goals SDG 9 (Industry, Innovation and Infrastructure), SDG 12 (Responsible Consumption and Production), and SDG 13 (Climate Action). Full article

We propose a fully nonparametric empirical autoregressive copula framework for univariate time series, designed to capture nonlinear and asymmetric serial dependence while exactly preserving the empirical marginal distribution. The method decouples marginal behavior from temporal dependence by (i) constructing a shape-preserving empirical marginal via monotone interpolation and mapping observations to the unit interval, and (ii) estimating the lag–lead dependence through a nonparametric conditional AR(1) copula density on ${(0,1)}^2$. To ensure stable estimation near the boundaries, we employ reflection-based kernel methods that mitigate edge effects and yield well-behaved conditional densities on the unit support. Forecasts are obtained from the implied conditional predictive density: we compute point forecasts either as conditional modes (maximum a posteriori) on the copula scale or as conditional means, and then back-transform exactly using the empirical quantile function, guaranteeing marginal fidelity and support-respecting predictions. Empirically, we evaluate the approach on three CBOE volatility indices (VIX, VXD, and RVX) and benchmark it against linear ARMA models, copula-based parametric competitors, and state-space/heteroskedasticity baselines (Local level, TVP–AR, and ARMA–GARCH). The results highlight that modeling the full conditional transition density nonparametrically can deliver competitive—often best or near-best—forecast accuracy across horizons, particularly in the presence of pronounced volatility regimes and asymmetric adjustments. Full article

Urban green spaces (UGSs) provide critical ecosystem services (ESs) in rapidly urbanising cities but are increasingly threatened by land-use change, population growth, and socio-economic pressures. This study assessed spatial and temporal changes in UGS in Zomba City, Malawi, from 1998 to 2021 using geospatial and remote sensing methods. Landsat imagery from 1998, 2007, 2013, and 2021 was analysed through post-classification change detection to map land-use/land-cover (LULC) transitions, while the relationship between ward-level population density and vegetation condition was evaluated using the Normalised Difference Vegetation Index (NDVI). Results show a decline in total UGS cover from 60% in 1998 to 51% in 2021, primarily due to the expansion of built-up areas. Tree cover increased from 11% to 18%, with NDVI values rising from 0.700 to 0.947; these changes may reflect both natural vegetation growth and targeted restoration, indicating localised improvements in vegetation condition. An inverse relationship was observed between population density and NDVI, though some high-density wards exhibited NDVI gains associated with restoration initiatives. These findings underscore the role of both institutional and community efforts in sustaining urban vegetation and highlight the potential of ecological restoration to mitigate UGS loss and support ESs. Policymakers and planners should prioritise the protection, restoration, and equitable distribution of UGS, particularly in dense and underserved areas, as strategic urban greening enhances city resilience and human well-being. Full article

Rising urban temperatures have become a critical constraint to urban ecosystem resilience and livability due to rapid urbanization. This study proposes a novel intra-city zoning scheme, named component morphological blocks (CMBs), which classifies built-up areas into six types characterized by multidimensional urban canopy morphologies. The XGBoost-SHAP model, optimized via Bayesian tuning, was employed to examine the relative contributions of 16 potential driving variables to block-scale land surface temperature (LST). The results show that: (1) LST gradually increases with increasing building density in the warm seasons. The average building height (BH) exhibits a positive correlation with shaded area, thereby reducing LST on the block scale; (2) hotspots are mainly concentrated in function-oriented blocks with hotspot distribution indices of 1.85, 1.96, 1.24, and 1.14, respectively. Coldspots are largely observed in blue–green space in the warm seasons; (3) BH dominates the LST across seasons, while the building-related factors make a prominent impact on LST in warm seasons. The contribution of vegetation canopy density is followed by BH during autumn and winter (12.2%, 10.9%); (4) a distinct transition occurs between summer normalized difference built-up index (NDBI) and fractional vegetation cover around an NDBI of 0.1. In winter, the interaction between 2D and 3D vegetation factors indicates a shift in their relative contributions from negative to positive as they increase. This study demonstrates that CMBs serve as an effective choice for characterizing LST patterns at the block scale, providing insights for sustainable urban development aimed at mitigating the urban heat island effect. Full article

To develop high-performance iridium phosphorescent complexes, we designed and synthesized a series of iridium phosphorescent complexes (G-1, G-2, B-1, B-2, R-1, R-2) using 3-hydroxy-2-methyl-4-pyrone (maltol, short for mal) and 3-hydroxy-2-ethyl-4-pyrone (ethyl maltol, short for emal) as auxiliary ligands, in combination with 2-phenylpyridine (ppy), 2-(2,4-difluorophenyl)pyridine (dfppy), and 1-phenylisoquinoline (piq) as cyclometalating ligands. We systematically investigated their crystal structures, photophysical behavior, electrochemical properties, and electroluminescent performance. The results revealed that the combination of a pyranone auxiliary ligand with the highly conjugated piq ligand leads to the formation of R-1 and R-2, which possess high molecular symmetry and display favorable photophysical performance. These complexes exhibit solution-phase phosphorescence quantum yields of 64% and 55%, and electroluminescent devices incorporating them reach a maximum external quantum efficiency of 13.4%, with brightness exceeding 13,000 cd/m² and minimal efficiency roll-off. In contrast, complexes incorporating pyridine-based cyclometalating ligands (ppy, dfppy)—G-1, G-2, B-1, and B-2—display weak emission in solution but show enhanced solid-state emission through π–π stacking, with a maximum quantum yield of 25.8%. Density functional theory calculations and electrochemical analysis indicate that the presence of both the pyranone auxiliary ligand and the piq ligand results in optimized frontier orbital energy alignment, enhanced metal-to-ligand charge transfer, and reduced non-radiative transitions, thereby improving emission efficiency. This study provides a theoretical framework and molecular design strategy for the application of pyranone auxiliary ligands in high-performance iridium phosphorescent materials. Full article

Aqueous supercapacitors are a class of crucial high-power, long-life, safe and reliable energy storage devices, with their performance fundamentally dependent on electrode materials. Two-dimensional (2D) vanadium-based MXenes, possessing rich multivalent redox activity and tunable layered structures, have emerged as one of highly promising electrode candidates, exhibiting significantly superior specific capacitance and pseudocapacitive properties compared to conventional Ti₃C₂T_z. To overcome inherent limitations in conductivity and structural stability, this review summarizes strategies for regulating composition and microstructure through transition metal solid solution and medium-/high-entropy design. These approaches synergistically optimize electron conduction, expand ion migration pathways, and suppress electrode degradation, thereby comprehensively enhancing rate performance, cycle life, and energy density. This review systematically reveals the composition–structure–performance relationships, providing critical design insights and theoretical foundations for developing next-generation high-performance, long-life aqueous MXene-based supercapacitors. Full article