Search Results (712)

This study systematically examines the low-carbon transition challenges faced by the Yellow River Basin, a core strategic energy base in China with a coal-dominated energy system, under the dual carbon goals. Existing studies based on traditional econometric models or single-province analyses are mostly limited to static analysis, failing to simultaneously capture the nonlinear spatiotemporal evolution, cross-regional spillover effects, and long-term changing trends of carbon emissions in the basin. To fill this gap, this study builds an Energy–Economy–Carbon (EEC) analytical framework, and develops an integrated TFT-ASTGCN deep learning framework. Specifically, we employ the Temporal Fusion Transformer (TFT) for high-precision multivariate time-series simulation and peak forecasting, while the Attention-based Spatial–Temporal Graph Convolutional Network (ASTGCN) is used to identify complex spatial dependencies of inter-provincial emissions. The empirical results confirm that: (1) Basin carbon emissions show significant coal-driven carbon lock-in, with initial decoupling between economic growth and emissions. (2) Most provinces will maintain rising emissions under the current development mode, posing severe challenges to carbon peaking. (3) Asymmetric spatial spillover effects are prominent, underscoring cross-regional collaborative governance as a critical pathway for achieving an early and stable carbon peak in the basin. Full article

In insurance portfolios, classifying customers without a prior history at a given company is particularly challenging due to the absence of historical behavior, extreme class imbalance, heavy-tailed loss distributions, and strict operational constraints. Traditional machine learning approaches, including the baseline methodology proposed in previous studies, typically optimize global predictive accuracy and therefore fail to capture business-critical outcomes, especially the identification of high-risk clients. This study extends the existing approach by evaluating two complementary business-aware classification strategies: (i) a balanced bagging ensemble specifically designed to handle class imbalance and maximize expected profit under explicit customer-omission constraints, and (ii) a lightweight Transformer-based architecture capable of learning richer feature representations. Both approaches incorporate the asymmetric financial cost structure of insurance and operate under operational selection limits. The empirical analysis is conducted on a proprietary large-scale auto insurance dataset comprising 51,618 customers and is complemented by validation on nine synthetic datasets to assess robustness. Model performance is evaluated using statistical tests (ANOVA, Friedman, and pair-wise comparisons) together with business-oriented metrics. The results show that both proposed approaches consistently outperform the baseline methodology (p < 0.001) in terms of profit, with the ensemble offering a better balance of performance and efficiency, while the Transformer shows stronger robustness and generalization under data perturbations. The balanced ensemble provides the most favourable trade-off between predictive performance, robustness, interpretability, and computational efficiency, making it suitable for deployment in regulated insurance environments, while the Transformer achieves competitive results and exhibits stronger generalization under data perturbations. The proposed approach aligns machine learning with actuarial portfolio optimization by explicitly integrating profit-driven objectives and operational constraints, offering two practical and scalable solutions for risk-based decision-making in real-world insurance settings. Full article

Subway-hub blocks are critical areas where the pressures of metropolitan populations and environmental quality are closely interconnected. This study constructs a “pressure–context–carrier–response” (PCRC) framework (F1–F7) to systematically reveal the correlations between built-environment characteristics and environmental performance. The results demonstrate that resource allocation (F7) and comprehensive response (F5) display notable “asymmetric differentiation”. The socio-economic environment (F2, F3) considerably influences the concentration of green-space resource allocations (F7) (p < 0.01), with affluent blocks demonstrating a clear advantage in resource distribution. The thermo-ecological composite response (F5), which includes NDVI and LST, demonstrates “statistical convergence” (p = 0.894) across various block types, indicating that resource inputs cannot be linearly transformed into environmental efficiency. This disconnection is ascribed to two physical limitations: firstly, the stochastic nature of spatial distribution (Global Moran’s I ≈ 0) restricts the scale effects of green spaces; secondly, the nonlinear limitations of the physical medium indicate that under conditions of high pressure load (F1) and elevated spatial capacity (F6), the regulatory effectiveness of greening demonstrates a significant diminishing marginal return effect. Therefore, intervention planning must shift from controlling macro-level indicators to optimising micro-level accuracy to address ecological performance constraints in densely populated metropolitan areas. Full article

This study aims to reduce the phase transition temperature (PTT) of W-doped vanadium dioxide (VO₂) multilayer thin films. We designed and fabricated two asymmetric multilayer thin film structures; namely, TiO₂/VO₂-5%W/ITO and ITO/VO₂-5%W/TiO₂. The W-doped VO₂-based Trilayer thin films were deposited using an electron beam evaporation combined with the ion-assisted deposition (IAD) technique. An experimental study was conducted on the temperature-dependent residual stress and optical properties of the two asymmetric VO₂-based three-layer structures. The VO₂-based thin films were characterized using UV–Vis–NIR spectrophotometry, Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, and an improved Twyman–Green interferometer combined with fast Fourier transform (FFT) analysis for residual stress measurement. The trilayer structures incorporated a ~60 nm thick W-doped VO₂ middle layer, which plays a critical role in modulating thermochromic behavior and residual stress evolution. The results show that both trilayer thin films demonstrated excellent optical performance in transmission spectra. Raman spectral analysis revealed a blue shift in the characteristic W-doped VO₂ peaks, accompanied by a decrease in peak intensity as the temperature increased. Heating experiments on asymmetric W-doped VO₂ trilayer thin films revealed that the critical transition temperature of the ITO/VO₂-5%W/TiO₂/B270 trilayer film structure was significantly reduced to 45 °C. This demonstrates the effectiveness of our proposed multilayer film design in improving the PTT of W-doped VO₂ thin films. Analysis of the changes in residual stress of the trilayer thin films during heating experiments revealed that the residual stress shifted from compressive to tensile in the temperature range of 40 °C to 50 °C. The thermal expansion coefficient and biaxial modulus of the TiO₂/VO₂-5%W/ITO trilayer film structure were 5.37 × 10⁻⁶ °C⁻¹ and 295.7 GPa, respectively. In addition, the thermal expansion coefficient and biaxial modulus of the ITO/VO₂-5%W/TiO₂ trilayer film structure were 6.65 × 10⁻⁶ °C⁻¹ and 745.0 GPa. Full article

Hydrogen peroxide (H₂O₂) plays a vital role as an eco-friendly oxidizer, extensively used in environmental cleanup, energy transformation, and organic production. Nonetheless, the conventional method of creating anthraquinones is intricate, resulting in significant energy and ecological costs, which calls for the development of more eco-friendly and efficient substitute technologies. The article methodically examines the reaction processes and methods for improving efficiency in photocatalytic H₂O₂ generation in the past few years. This review summarizes the design principles and key structural features of various novel catalytic materials, focusing on light absorption, charge separation and migration, surface redox reactions, and enhanced mass transfer. Approaches such as expanding the range of bandgap absorption, building conjugated structures, and incorporating metal nanoclusters can significantly enhance the efficiency of light absorption. In the charge separation process, constructing built-in electric fields at the interfaces of heterojunctions, homojunctions, and Schottky junctions is crucial for improving reaction efficiency. Additionally, defect engineering may encourage targeted carrier movement and minimize recombination. The review highlights the latest advancements in enhancing selectivity and reducing H₂O₂ breakdown in surface redox reactions, achieved by regulating active sites, introducing new functional groups, and developing dual-channel reaction pathways. Furthermore, constructing three-phase interfaces, regulating asymmetric wettability, and designing cyclic/flow reactors provide innovative engineering solutions to address the challenges of insufficient oxygen supply and large-scale continuous production. Ultimately, the potential for producing H₂O₂ in photocatalytic systems is detailed. Full article

Multi-omnidirectional mobile robot formations offer significant advantages for applications in unstructured environments. However, under constraints such as limited field of view and high operator cognitive load, existing teleoperation frameworks struggle to guarantee formation safety and stability. In this study, a bilateral shared control framework for multi-robot formation that integrates intent perception and vortex-field modulation is proposed. First, an Intent-Mediated Asymmetric Vortex Modulation (IM-AVM) strategy is developed, where the operator’s micro-intentions are mapped to determine the topological orientation of a vortex field. By constructing a dynamic asymmetric modulation matrix, saddle points in the potential field are geometrically eliminated, enabling deadlock-free obstacle avoidance while maintaining a rigid formation. Second, a multi-dimensional perception-based dynamic authority arbitration and topological deadlock escape mechanism is constructed, facilitating a seamless transition from assisted deadlock to autonomous escape. Finally, a formation coordination system based on anisotropic flow field modulation and adaptive sliding mode control is designed. Rigid formation constraints are transformed into a tangential safe flow field, and robust tracking is subsequently achieved through an Adaptive Nonsingular Fast Terminal Sliding Mode Controller (ANFTSMC). Theoretical analysis and experimental results demonstrate that the proposed framework achieves collision-free navigation for the formation in simulated environments. Full article

This paper introduces the Copula Asymmetry Index (CAI), a rolling, rank-based measure of asymmetric tail dependence between equity returns and implied-volatility proxies. CAI is defined as the difference between the empirical frequency of joint “equity-down & volatility-up” tail events and that of the mirror state (“equity-up & volatility-down”) within a rolling window. Building on this core asymmetry measure, we develop CAI++, an implementation framework that transforms CAI into an operational defensive allocation signal through smoothing, standardization, delayed execution, hysteresis, and cost-aware portfolio mapping. Using daily data from 2000 onward across a broad cross-section of 50 equity-volatility pairs, we evaluate the CAI++ strategy against buy-and-hold equity, a 60/40 benchmark, an inverse-volatility risk-parity portfolio, and a moving-average timing rule. Cross-sectional results indicate that CAI improves terminal outcomes relative to equity-only exposure for most pairs and shows particularly strong performance versus 60/40 in both final wealth and Sharpe. However, CAI does not dominate structurally diversified low-volatility allocations: risk parity retains a pronounced advantage in downside risk and risk-adjusted metrics. Overall, the findings support CAI as a tail-aware overlay for equity-centric and balanced portfolios rather than a substitute for institutional low-volatility baselines. Full article

Dynamic economic growth and climate change increase pressure on water resources, posing a challenge to achieving sustainable development goals, especially in regions with diverse hydrological conditions and development trajectories. This study aims to quantitatively assess the dynamic asymmetry between water management efficiency and the level of sustainable development in the European Union and the Three Seas Initiative (3SI) countries, with particular emphasis on cumulative mechanisms, regional divergence, and the potential low equilibrium trap. The values of the analysed indicators were calculated for 2015, 2021, and 2022, and subsequently their changes were determined for 2021/2015 and 2022/2021. This study was conducted using Eurostat data, applying the CRITIC method for objective weight determination, the TOPSIS technique for constructing synthetic measures, the Kruskal–Wallis and Mann–Whitney tests to assess inter-group differences, and linear regression to identify dependencies. Countries were grouped according to the dynamics of changes in the synthetic water management index. The results indicate a clear asymmetry: the water sector is characterised by a cumulative mechanism and strong divergence (particularly evident in the short period), whereas sustainable development remains significantly more stable, homogeneous, and weakly linearly correlated with previous water achievements. In 3SI countries, a higher rate of improvement in water indicators was observed compared to the rest of the EU; however, no significant synergy with progress in sustainable development was found. The negative impact of the Water Exploitation Index on sustainable development is statistically noticeable but does not confirm the existence of a clear “low equilibrium trap” across the entire 3SI region. This study highlights the need for regionally differentiated, asymmetrical water policies and the integration of water management with broader ecological transformation strategies. Full article

In side-channel analysis, simple power analysis (SPA) is a widely used technique for recovering secret information by exploiting differences between operations in traces. However, in realistic measurement environments, SPA is often hindered by noise, temporal misalignment, and weak or transient leakage, which obscure secret-dependent features in single or very few power traces. In this paper, we provide a systematic analysis of moving-skewness-based trace preprocessing for enhancing asymmetric leakage characteristics relevant to SPA. The method computes local skewness within a moving window along the trace, transforming the original signal into a skewness trace that emphasizes distributional asymmetry while suppressing noise. Unlike conventional smoothing-based preprocessing techniques, the proposed approach preserves and can even amplify subtle leakage patterns and spike-like transient events that are often attenuated by low-pass filtering or moving-average methods. To further improve applicability under different leakage conditions, we introduce feature-driven window-selection strategies that align preprocessing parameters with various leakage characteristics. Both simulated datasets and real measurement traces collected from multiple cryptographic platforms are used to evaluate the effectiveness of the approach. The experimental results indicate that moving-skewness preprocessing improves leakage visibility and achieves higher SPA success rates compared to commonly used preprocessing methods. Full article

Global urbanization has long been hampered by the “metrocentric priority” paradigm, with small towns—core hubs for urban–rural integration—severely undervalued in practical value. Amid China’s transition to high-quality urban–rural integration, unbalanced small town development has become a critical bottleneck for county-level factor flows, demanding systematic research to unlock their strategic value and resolve urban–rural dual predicaments. Existing studies suffer from scientific gaps including unidirectional linear cognition, insufficient complex system thinking, and weak interpretation of regional heterogeneity, remaining at the stage of static correlation description and failing to reveal the two-way reciprocal feedback logic between small towns and urban–rural integration. Meanwhile, the application of complex system theory in urban–rural research is still confined to theoretical narratives, which hinders the advancement of research from descriptive analysis to mechanism interpretation. Taking Henan Province (a typical agricultural and populous province reflecting China’s urban–rural development) as a case, this study builds a “local emergence–global synergy” framework based on complex system theory, establishes a dual indicator system for small towns’ multidimensional performance and county-level urban–rural integration, and integrates spatial statistical analysis, bidirectional regression and coupling coordination models to explore their cross-scale spatiotemporal evolution and reciprocal feedback during 2019–2023. Findings show the following: (1) The multidimensional performance of small towns presents a pattern characterized by polarized expansion of high-value regions and overall improvement of low-value regions, while county-level urban–rural integration evolves into a polycentric structure featured by central dominance and southern growth. (2) There is a significant two-way asymmetric relationship between small towns’ multidimensional performance and county-level urban–rural integration: the positive effect is significantly stronger than the reverse effect, and both direct impacts are significantly weakened after introducing economic variables, indicating that economic development serves as a key transmission channel. (3) The coupling mechanism presents three evolutionary paths with pronounced core–periphery spatial heterogeneity. Grounded in complex system theory, this study constructs a systemic analytical framework of “local emergence of small-town subsystems and global synergy of county-level systems”, verifies the core proposition of two-way interactions between subsystems and the overall system in the urban–rural complex giant system, and enriches the localized application of complex system theory and the urban–rural continuum theory in traditional agricultural regions of China. This study provides a foundational empirical paradigm for the in-depth exploration of nonlinear characteristics and threshold effects in future research. It offers theoretical support for policy formulation of county-level urban–rural integration in traditional agricultural regions of China, and it provides Chinese experiences for the Global South with similar contexts to explore inclusive urbanization pathways, promoting cross-cultural dialogue and practical transformation of urban–rural integration theory. Full article

This study investigates how digital transformation influences corporate breakthrough innovation through supply chain spillovers. Using data from Chinese listed companies between 2006 and 2023, we find that upstream digital transformation significantly promotes downstream breakthrough innovation via three mechanisms: knowledge spillover, digital peer effects, and information synergy, the latter helping to mitigate the bullwhip effect. Robustness checks confirm the reliability of these results. Heterogeneity analyses reveal that the effect is stronger for firms with high absorptive capacity, operating in highly competitive industries, or with concentrated supplier bases. In contrast, downstream digital transformation also affects upstream firms, but the spillover is weaker, asymmetric, and operates only through peer effects. These findings enrich the literature on supply chain dynamics and innovation, offering practical insights for firms to harness digital synergy to expand their innovative capabilities. Full article

Imine reductases (IREDs) have emerged as valuable biocatalysts for the asymmetric synthesis of chiral amines, key intermediates in numerous active pharmaceutical ingredients. Their ability to operate under mild reaction conditions with high chemo- and stereoselectivity provides an attractive alternative to conventional metal-catalyzed or chemical reduction processes. However, the broader industrial application of wild-type IREDs is often constrained by their limited substrate scope and moderate catalytic efficiency. Recent advances in biocatalysis have demonstrated that engineered IREDs can catalyze the reduction of a wide range of natural and non-natural imines, significantly expanding their applicability in pharmaceutical and fine chemical synthesis. In parallel, enzyme immobilization strategies have proven highly effective for improving operational stability, facilitating enzyme reuse, and enabling continuous flow biocatalytic processes. Efficient cofactor regeneration systems have further enhanced the practical implementation of IRED-based transformations. Advances in protein engineering, including structure-guided design, semi-rational mutagenesis, and directed evolution, have generated enzyme variants with improved catalytic activity, stereoselectivity, and substrate tolerance. The integration of high-throughput screening technologies and machine-learning-assisted enzyme design has further accelerated the discovery and optimization of efficient IRED biocatalysts. This review summarizes recent progress in the protein engineering and immobilization of IREDs and discusses future perspectives for their industrial application. 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

N-Heterocyclic carbene (NHC) catalysis has emerged as a powerful and versatile strategy for constructing silicon derivatives, offering a metal-free alternative to traditional transition-metal methods. This review comprehensively summarizes recent advances in the NHC-catalyzed synthesis of organosilicon derivatives. Key transformations discussed include both asymmetric and non-asymmetric silylation reactions, as well as the construction of silicon-stereogenic centers. The content is systematically organized according to the types of silicon products and their underlying catalytic mechanisms. Our own perspectives on future development within this rapidly evolving field are also outlined. Full article

Historically, the sustainable development of education bears the mission of advancing the sustainable development of human society, and the transformation of universities is a crucial link in the sustainable development of higher education. This paper addresses the top-down, government-led transformation of local undergraduate universities, a process currently hampered by ambiguous objectives, insufficient internal motivation, and a mismatch in supporting systems, resources, and institutional culture. To analyze and optimize this process, we establish an asymmetric evolutionary game model involving the local government, local universities, and teachers. By integrating optimization theory, this study determines the optimal equilibrium conditions for the game system. We then use numerical simulations to depict the system’s evolutionary paths under various transformation scenarios. Furthermore, we have analyzed the key influencing factors for promoting university transformation and development, which form the basis for proposing targeted policy recommendations. Full article