Search Results (49,849)

Artificial intelligence (AI) technology has become the core force driving industrial transformation in today’s world. In-depth exploration of the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries is of great significance for optimizing the industrial structure, preventing systemic risks in the industrial system, and achieving high-quality development. Based on the DY and BK spillover index model under the TVP-VAR framework, this paper analyzes the spillover effects between artificial intelligence and new energy, as well as high-carbon-emission industries from a time–frequency perspective, and constructs a spillover network to analyze the risk spillover transmission path. Finally, it explores the optimal investment portfolio weights and investment hedging strategies in the financial market. The results show that there is a significant static spillover effect between artificial intelligence and new energy, as well as high-carbon-emission industries. The intensity of this effect follows the pattern of “short-term > medium-term > long-term”. Moreover, new energy and some high-carbon-emission industries (such as the non-ferrous metals industry, the petrochemical industry, and the chemical industry) are the net spillover sources, while artificial intelligence and some high-carbon-emission industries (such as the power industry, the building materials industry, and the aerospace industry) are the net receiving parties. The dynamic spillover effect exhibits significant time-varying characteristics, being significantly impacted by major events such as environmental protection policies, the COVID-19 pandemic, and technological innovations. The chemical industry is the largest spillover outputter in all frequency domains, while the building materials industry is the largest receiver. From the perspective of the spillover network, the artificial intelligence industry, as a key node of the spillover network, plays a crucial role in the transmission of risk spillover. From the perspective of investment practice, the minimum connectedness portfolio (MCoP) performs well in terms of risk hedging effectiveness and return performance and may be the best choice for investors to balance risk and return. Full article

Few-shot remote sensing scene classification aims to accurately recognize unseen scene categories using only a scarce number of labeled samples, which has emerged as a research hotspot in the field of remote sensing image interpretation. However, remote sensing images intrinsically suffer from large intra-class variations, high inter-class similarities, and complex background interferences. Traditional few-shot learning methods typically perform feature metric learning in Euclidean space, making it difficult to capture the non-Euclidean geometric distribution characteristics of remote sensing features, and they often neglect the spatial structural information embedded in feature maps. To address these issues, this paper proposes a novel few-shot remote sensing scene classification method, termed ZSFS-RGBN, which integrates a Zigzag Scanning Feature Sequence with a Riemannian Geometric Barycenter Network. Specifically, ResNet12 is first employed as the backbone to extract deep convolutional feature maps from both the support and query sets. Second, a Zigzag scanning strategy is introduced to reorganize the two-dimensional feature maps into one-dimensional feature sequences, thereby effectively preserving the spatial locality and structural continuity of the features. Third, an autoregressive moving average (ARMA) model is constructed to characterize the spatial dependencies of the feature sequences, and its state parameters are mapped onto a symmetric positive definite (SPD) matrix manifold, enabling the deep semantic representations of remote sensing scenes in a non-Euclidean geometric space. Finally, a Riemannian geometric barycenter network is designed to learn the Riemannian barycenter of each category on the SPD manifold, where a joint loss function is introduced to simultaneously optimize intra-class compactness and inter-class separability. Comprehensive experiments are conducted on three public remote sensing scene datasets: NWPU-RESISC45, UC Merced Land-Use, and WHU-RS19. Experimental results demonstrate that the proposed method consistently outperforms several representative state-of-the-art approaches under both 5-way 1-shot and 5-way 5-shot settings. Furthermore, ablation studies verify the effectiveness of each component within the proposed framework. Full article

Diesel-powered equipment is widely used in underground coal mines for auxiliary transportation, material handling, and equipment relocation because of its long operating endurance, convenient refueling, and strong adaptability to complex operating conditions. However, carbon monoxide (CO) emissions from such equipment can accumulate locally under restricted ventilation, idling, and frequent start–stop operation, thereby threatening occupational health and mine safety. This review focuses on CO purification technologies for diesel-powered mining equipment. The operating characteristics and influencing factors are analyzed, and different technical routes are compared, including in-cylinder control, wet scrubbing, adsorption, non-thermal plasma (NTP), and catalytic oxidation. Recent advances in noble-metal catalysts, transition-metal and CeO₂-based reducible oxide catalysts, and single-atom catalyst (SAC) design strategies are summarized. Research progress in exhaust aftertreatment systems is also discussed. Overall, CO purification for diesel-powered mining equipment requires coordinated optimization of low-temperature activity, safety-oriented thermal management, flow resistance, and long-term operational stability. Future research should focus on structured catalytic units, durability under coupled exhaust conditions, online monitoring, and field validation to improve the compatibility of CO purification systems with underground mining conditions. Full article

The mevalonate (MVA) pathway is a central metabolic route responsible for the biosynthesis of isoprenoids with broad biological and biotechnological relevance. Due to its importance, the MVA pathway has attracted increasing interest in studies of enzymatic regulation, structural biology, metabolic engineering, and synthetic biology, particularly in fungi. This review provides a comprehensive overview of the MVA pathway, addressing its distribution across different domains of life, evolutionary aspects, and metabolic organization, with emphasis in fungi. Special attention is given to the biochemical and structural characterization of MVA-pathway enzymes, including catalytic mechanisms, structural features, and regulatory processes. The methylerythritol phosphate pathway is also presented as an alternative route for isoprenoid precursor biosynthesis and discussed in terms of its taxonomic distribution and metabolic significance. Recent advances in synthetic biology, enzyme regulation, and pathway engineering are highlighted, emphasizing their contributions to metabolic engineering and synthetic biology. Special emphasis is given to fungi, in which the MVA pathway plays a central role in ergosterol biosynthesis, protein prenylation, and secondary metabolite production. Advances in the engineering of fungal cells, including Saccharomyces cerevisiae and other emerging fungal species, are discussed in the context of sustainable isoprenoid production. Finally, strategies for optimizing microbial production are presented, highlighting the importance of fungal synthetic biology in advancing biotechnological applications. Full article

We revise the Soft Landing Problem deriving the Optimal Guidance Policy and the Value Function of the minimum time problem in its Controllable Set. Using this Guidance Policy the lander can be guided not only along the nominal trajectory or in its neighborhood but in all conditions that can have a possible successful landing, thus providing a safer approach to changes in planning, failures or other unknown conditions. In addition to the classic solutions of this problem we have fully described a new class of broken extremals which occur when the lander is falling with an high vertical speed and can be used in case of failures or unplanned conditions. Full article

To explore potential strategies for improving the applicability of cyanidin-3-O-glucoside (C3G) and to avoid the health risks associated with the in vivo accumulation of aluminum by intake of traditional aluminum-based lake pigments, food-grade CaCO₃ was used as a matrix to prepare two types of edible lake pigments, namely C3G-CaCO₃ and MA-CaCO₃, via coprecipitation method using purified cyanidin-3-O-glucoside (C3G) and non-purified mulberry anthocyanins (MA). The effect of pH on adsorption was systematically investigated, and various characterization methods were used to analyze the physicochemical properties and formation mechanism of lake pigments. The results showed that pH 9.5 was the optimal condition for CaCO₃ to adsorb MA. The introduction of C3G altered the particle size, surface charge, and other characteristics of CaCO₃ without changing its calcite crystal form. The adsorption of MA and C3G on the CaCO₃ surface was multilayer physical adsorption, dominated by the Freundlich model. The isothermal adsorption results showed that CaCO₃ exhibited a higher adsorption capacity for C3G than for MA at equivalent equilibrium concentrations, demonstrating C3G’s superior binding affinity. C3G primarily binds to calcium carbonate through surface adsorption, with possible partial diffusion of molecules into the matrix, without forming new chemical bonds, and slightly regulated the thermal stability of CaCO₃. This study successfully constructed a lake pigment system based on CaCO₃, systematically elucidated its adsorption behavior and structural characteristics toward anthocyanins, and provided a material foundation for the further application of this type of carrier in the food sector. Full article

Full-vehicle crash simulations that account for occupant injury are essential for automobile safety assessment; however, they are computationally intensive and time-consuming. In particular, dash panel deformation plays a key role in transmitting impact loads to an occupant’s lower extremities. To address this issue, this study proposes a two-stage machine learning framework for occupant lower-limb injury assessment. In the first stage, the deformation behavior of the dash panel is predicted using a machine learning model, enabling efficient generation of a wide range of deformation patterns. In the second stage, occupant lower-limb injury metrics are evaluated based on the predicted deformation using a sled model. While the ultimate objective is to establish the complete two-stage framework, the present paper is limited to the first stage. It investigates the feasibility of machine learning-based deformation prediction. Deformation distributions of simplified structural components are predicted using an XGBoost-based machine learning model, in which principal component scores derived from geometric and deformation data serve as input features. The objective is to efficiently generate representative deformation modes from limited training data rather than optimizing prediction accuracy for individual deformation responses. Numerical experiments are conducted to investigate the effectiveness of the proposed prediction framework. The results of the proposed approach show good agreement with crash simulations in overall deformation behavior, while local deformation is not reproduced perfectly. These findings demonstrate the feasibility of machine learning-based dash panel deformation prediction as the first step toward the proposed two-stage framework for lower-limb injury assessment. Full article

Orthotropic laminates are highly desirable in composite structures because they eliminate bending–twisting coupling, simplify structural behavior, improve analytical predictability, and facilitate structural design, optimization, and certification. However, achieving fully orthotropic behavior in laminated composites remains challenging because conventional laminate architectures generally retain stiffness couplings arising from anisotropic ply orientations and stacking-sequence effects. This work introduces the Tailored Antisymmetric Composite (TAC) concept, a laminate architecture that provides the closest practical approximation to full orthotropy while preserving broad stiffness-tailoring capability and manufacturability. TAC laminates are constructed from tailored antisymmetric sublaminates that enforce D₁₆ = D₂₆ = 0 while maintaining extremely small extension–bending coupling terms B₁₆ and B₂₆. Representative TAC and symmetric Quad laminates were compared analytically, statistically, and experimentally. Monte Carlo simulations comprising 100,000 realizations with realistic ±0.1° AFP/ATL fiber-orientation deviations showed that the distributions of the extension–bending coupling terms \(B_{16}^*\) and \(B_{26}^*\) remained nearly indistinguishable for both laminate architectures, with probability-density overlap coefficients between 0.87 and 0.98. In contrast, the bending–twisting coupling terms \(D_{16}^*\) and \(D_{26}^*\) were 140–600 times lower in TAC laminates than in the corresponding Quad laminates, and their statistical distributions exhibited complete separation. Experimental measurements of post-cure warpage confirmed that TAC laminates achieved dimensional stability comparable to symmetric Quad laminates while exhibiting lower variability. These results demonstrate that TAC laminates combine exact elimination of bending–twisting coupling with negligible extension–bending coupling, statistical robustness to realistic manufacturing variability, and excellent dimensional stability, establishing TAC as a practical and systematic route toward full orthotropy in laminated composite structures. Full article

This review covers literature published up to June 2026. Detecting various water pollutants quickly and reliably remains a challenge. Boron-doped diamond (BDD) electrodes, particularly when fabricated as nanostructured thin films such as nanocones or nanowalls, offer a wide electrochemical window, low background current, and excellent chemical stability, making them promising tools for electrochemical sensing. However, unmodified BDD electrodes face an inherent trade-off among conductivity, active site density, and interfacial stability, a phenomenon termed herein the “sensitivity-selectivity-stability triangle bottleneck”, which severely limits practical performance. In this review, we demonstrate how multi-scale structural regulation can circumvent this bottleneck. Specifically, a triple strategy comprising boron doping, surface modification, and post-annealing treatment is proposed and evaluated. First, the effect of boron doping level on conductivity and active site density is discussed. Second, two common surface modification approaches are examined: carbon nanomaterials (which increase surface area and form conductive networks) and metal nanoparticles (which enhance catalytic activity and interfacial charge transfer). Third, post-annealing is highlighted as a key synergistic step that locks the modified layer and stabilizes the interface. Together, these three components form an integrated framework. To provide concrete guidance, the performance of each strategy is compared for representative water pollutants, including heavy metal ions, phenolic compounds, and emerging contaminants such as antibiotics and pesticides, with emphasis on sensitivity, selectivity, and stability. Representative detection limits achieved include 0.01 μg/L for Pb²⁺, 5 nM for acetaminophen, and 0.32 fM for PCB-77, demonstrating the effectiveness of the triple structural regulation strategy. Finally, in line with the theme of this Nanomaterials Special Issue on nanostructured thin films, current challenges in structural regulation are summarized, and future directions, including multi-parameter optimization, AI-assisted high-throughput screening, and real-world testing, are outlined. The goal is to offer practical structure-performance guidelines for designing BDD-based electrochemical sensors that are both high-performing and durable. Full article

CO₂ foam fracturing has emerged as a promising stimulation technology for enhancing permeability and improving production performance in deep coalbed methane (CBM) reservoirs while providing additional potential for carbon utilization. However, the multiscale relationship between local mechanical degradation and macroscopic mechanical deterioration and fracture instability induced by CO₂ foam treatment remains insufficiently understood. In this study, four candidate coal samples originating from the Carboniferous–Permian No. 8+9 coal seam system were first comparatively characterized. Based on petrographic characteristics, mineralogical composition, and specimen integrity, representative bright coal and semi-dull coal samples from the Lüliang mining area were selected for subsequent multiscale mechanical investigations. Based on petrographic characteristics, mineralogical composition, and specimen integrity, representative bright coal and semi-dull coal samples from the Lüliang mining area were selected for petrographic analysis, X-ray diffraction (XRD), nanoindentation, conventional triaxial compression, and cracked chevron-notched Brazilian disc (CCNBD) fracture toughness tests. Coal specimens were immersed in CO₂ foam under reservoir-relevant conditions (50 °C, 20 MPa, foam quality of 65%) for different durations (0–6 days), and the coupled evolution of micromechanical properties, macroscopic mechanical behavior, and fracture resistance was evaluated. The results indicate that both coal types exhibit pronounced heterogeneity in maceral composition and mineral distribution. Bright coal is characterized by high vitrinite content and low mineral abundance, whereas semi-dull coal contains higher proportions of inertinite and minerals. Nanoindentation results reveal that mineral-rich regions possess significantly higher Young’s modulus and hardness than organic-matter-rich regions, highlighting pronounced micromechanical heterogeneity within the coal matrix. With increasing immersion time, the micromechanical properties of both coals exhibit a two-stage evolution characterized by rapid initial deterioration followed by a gradual stabilization trend. After 6 days of immersion, the average Young’s modulus and hardness of bright coal decreased by 40% and 30%, respectively, whereas those of semi-dull coal decreased by 30% and 17%. Simultaneously, macroscopic mechanical properties and fracture resistance continuously declined, with fracture toughness reductions of 74% and 55% for bright coal and semi-dull coal, respectively. Compared with semi-dull coal, bright coal exhibited higher damage sensitivity, evolving from dominant single-fracture failure to granular fragmentation, whereas semi-dull coal maintained a multi-crack composite shear failure mode. Combined micromechanical and macroscopic observations suggest that the observed mechanical deterioration may be associated with coupled effects of fluid–coal interaction, matrix softening, and progressive damage evolution. Although pore and crack evolution were not directly observed, the results suggest that coal structure plays an important role in governing damage transfer across scales and thereby influences fracture behavior and mechanical weakening. These findings provide insight into the multiscale mechanical response of coal under CO₂ foam treatment and may support the optimization of stimulation strategies for deep CBM reservoirs. Full article

Animal venoms represent an extraordinary, yet largely untapped, biochemical reservoir for oncological drug discovery. This review provides a comprehensive analysis of venom-derived enzyme inhibitors as emerging anticancer agents, emphasizing their chemical diversity, structure–activity relationships (SAR), molecular targets, and mechanistic pathways. Venom-derived peptides and proteins exhibit exceptional binding affinity and structural rigidity, characteristics frequently enforced by conserved disulfide networks. This specific architecture allows them to selectively modulate critical cancer-associated enzymes, including matrix metalloproteinases, phospholipases A₂, serine proteases, and kinases. Inhibiting these highly specific targets successfully disrupts tumour angiogenesis, extracellular matrix remodelling, and metastatic dissemination, while simultaneously inducing apoptosis through unique pathways such as reactive oxygen species generation. Modern computational approaches, encompassing deep learning algorithms, molecular docking, and molecular dynamics simulations, are substantially accelerating and transforming the discovery pipeline by rapidly mapping intricate peptide–receptor interactions and guiding rational drug design. Translating these potent molecules into clinical therapeutics remains heavily challenged by pharmacokinetic instability, rapid proteolytic degradation, and systemic toxicity. The integration of computationally optimized scaffolds with advanced targeted delivery platforms, such as nanocarriers and liposomal encapsulation, offers a highly viable strategy to overcome these barriers, ultimately paving the way for next-generation, venom-inspired cancer therapies. Full article

To clarify the transformation patterns and driving mechanisms of the explicit and implicit morphology of rural settlements in peri-urban areas of large mountainous cities in Southwest China, this study examines the central urban area of Chongqing. Using land-use, point-of-interest (POI), socio-economic and digital elevation model (DEM) data from 2008 to 2024, we constructed an evaluation system for explicit and implicit rural settlement morphology. Kernel density estimation, the Mann–Kendall test and the moving t-test were used to identify morphological evolution, while the coupling coordination degree model and structural equation modeling (SEM) were applied to examine coordination relationships and driving mechanisms. The results show that: (1) during the study period, explicit morphology showed continuous contraction, whereas implicit morphology exhibited fluctuating improvement and polarized differentiation, indicating an overall gradual transformation; (2) no statistically significant abrupt changes were detected in either morphology, while temporal changes in coupling coordination divided the process into three stages—stable coordination, intensified imbalance and weak recovery—reflecting structural adjustment; (3) the coupling coordination degree declined overall, shifting from primary coordination towards near imbalance and indicating an uncoordinated transformation characterized by advanced contraction of explicit morphology and lagged improvement of implicit morphology; and (4) SEM results indicate that transportation infrastructure is the core driver of morphological transformation, with a significant positive effect on explicit morphology and a significant negative effect on implicit morphology. Natural factors positively support both morphologies, socio-economic factors exert negative or weak effects, and public services and real estate negatively affect explicit morphology but significantly promote implicit morphology. These findings provide a scientific basis for optimizing the layout and improving the functions of rural settlements in mountainous cities. Full article

To address the problems in high-speed railway communication, such as large signal penetration loss through carriages, difficulty in long-distance strip coverage, and limited coverage range of traditional base station antennas, this paper designs a cylindrical Luneburg lens antenna operating at the 1800/FA frequency bands. A dual-polarized feed antenna with a dipole structure is designed, loaded with X-shaped metal strips for out-of-band suppression, and integrated with a four-layer dielectric stratified cylindrical Luneburg lens, which uses its graded permittivity distribution to achieve beam focusing, enhance gain, narrow the horizontal beamwidth, and maintain a wide vertical beamwidth. Simulation results show that the lens can stably improve the gain by about 5 dBi; measured results indicate that the antenna has port isolation higher than 35 dB, good impedance matching, and measured gain of 12.4–13.3 dBi within the 1.7–2.1 GHz band, which is highly consistent with the simulation. This antenna can effectively adapt to the long-distance strip coverage scenario along high-speed railways, reduce the base station deployment density, and provide an engineering solution for the optimization of high-speed railway communication coverage. Full article

Background/Objectives: This study aimed to improve the biological profile of oleanolic acid (OA) through structural modification at the C-17 carboxyl group and the C-3 hydroxyl group, with a focus on the design of short-chain alkyl esters and 3-O-furoyl hybrids. Methods: Two series of OA derivatives were synthesized and characterized using spectroscopic methods, including ¹H NMR, ¹³C NMR and MS. In silico structure–activity relationship (SAR) analysis, ADMETox profiling, and molecular docking to the epidermal growth factor receptor (EGFR) tyrosine kinase domain were performed as predictive and hypothesis-generating tools. Anticancer activity was evaluated in vitro using the MTT assay against human cancer cell lines, including HeLa, MCF-7, A-549, SKBR-3, PC-3 and SKOV-3, as well as non-malignant human dermal fibroblasts (HDFs). Antioxidant properties were assessed using cell-free CUPRAC and DPPH assays. Results: The C-17 esterification markedly enhanced cytotoxic potency compared to the parent OA, while the introduction of the 3-O-furoyl moiety further improved antiproliferative activity in several derivatives. Selected compounds showed low-micromolar IC₅₀ values and moderate selectivity toward cancer cells. Molecular docking suggested favorable accommodation of selected derivatives within the EGFR ATP-binding pocket, mainly through hydrophobic and π-related interactions; however, these results do not confirm direct EGFR binding and require experimental validation. The CUPRAC and DPPH assays provided preliminary insight into chemical redox behavior but should not be directly extrapolated to intracellular antioxidant or pro-oxidant activity. Predicted ADMETox profiles indicated moderate permeability and relatively low predicted risk for selected toxicity endpoints, while also highlighting high lipophilicity, poor aqueous solubility and potential metabolic liabilities. Conclusions: Overall, the results identify several OA derivatives as promising anticancer lead compounds for further optimization and mechanistic investigation. Full article

The middle reaches of the West Liaohe River Basin, a typical semi-arid to semi-humid transition and agro-pastoral ecotone in northern China, exhibit high ecological sensitivity, low resilience, and pronounced fragility. Despite growing concerns, existing studies in this region lack a comprehensive assessment paradigm that effectively couples inherent ecological attributes with nonlinear predictive modeling. To fill this gap, we developed an integrative framework that innovatively combined the SRP conceptual model with a stacking ensemble learning technique. This coupling is methodologically novel because it moves beyond linear assumptions, enables the detection of complex nonlinear response surfaces, and establishes a seamless analytical chain from historical evaluation to future projection. By selecting 13 indicators, including topography, climate, soil, vegetation, and socio-economic factors, the weight was determined by the comprehensive application of the analytic hierarchy process and entropy weight method, and the ecological fragility of the middle reaches of the West Liaohe River Basin from 2000 to 2020 was evaluated at multiple scales. The spatial differentiation driving factors were analyzed using a geographic detector. Therefore, an Ensemble Learning Regression model was used to simulate and predict the ecological fragility pattern in 2030. The results show that from 2000 to 2020, the ecological fragility of the study area showed a decreasing trend overall, with the Ecological Vulnerability Synthetical Index (EVSI) decreasing from 3.48 to 2.68, and the spatial pattern gradually shifting from “high in the northwest, low in the southeast” to “overall stability, local optimization.” The spatial agglomeration of ecological fragility gradually weakened, indicating that high-fragility areas tend to disperse and low-fragility areas expand in contiguous areas, and the ecosystem structure tends to develop towards equilibrium. The driving mechanism shows an evolution characteristic from “soil erosion dominated” to “biological abundance dominated,” with the impact of climate factors first increasing and then stabilizing, and the direct pressure from human activities continuously weakening. Under the assumption that historical trends continue, the ensemble learning model projects that by 2030, the ecological vulnerability pattern will be dominated by Mild and Moderate levels, with the area of extremely vulnerable regions significantly reduced to 0.36%. This study verified the applicability of the SRP model in transitional river basins, and the constructed “evaluation-driving mechanism-prediction” framework can provide a scientific basis for the ecological protection and adaptive management of the West Liaohe River Basin and provide a methodological reference for ecological fragility research in similar areas. However, limitations persist: the indicator system and weight assignment are subject to inherent subjectivity, and the 2030 scenario projection based on the Stacking ensemble learning model relies on the BAU (Business-As-Usual) assumption, which fails to account for abrupt climate extremes or major policy shifts. Future studies should incorporate multi-scenario constraints to reduce predictive uncertainty. Full article