Search Results (11,784)

The growth of rural residents’ disposable income is essential for narrowing the income gap between urban and rural areas and promoting integrated development. This study explores how China’s National Main Functional Zoning Plan influences rural household income through its regulatory impact on construction land expansion. Using data from county−level administrative units across China, the research identified the construction land regulation index as a key mediating variable linking zoning policy to changes in household income. By shifting the analytical perspective from a traditional urban–rural classification to a framework aligned with the National Main Functional Zoning Plan, the study reveals how spatial planning tools, particularly differentiated land quota allocations, influence household income. The empirical results confirm a structured causal chain in which zoning policy affects land development intensity, which in turn drives rural income growth. This relationship varies across different functional zones. In key development zones, strict land control limits income potential by constraining land supply. In main agricultural production zones, moderate regulatory control enhances land use efficiency and contributes to higher income levels. In key ecological function zones, ecological constraints require diverse approaches to value realization. The investigation contributes both theoretical and practical insights by elucidating the microeconomic effects of national spatial planning policies and offering actionable guidance for optimizing land use regulation to support income growth tailored to regional functions. Full article

Existing studies on the abrasive cutting process have primarily focused on the influence of cutting conditions on key parameters such as temperature, cut-off wheel wear, and machined surface quality. However, the choice of working conditions is often made based on the experience of qualified personnel or using data from reference sources. The literature also provides optimal values for the cutting mode elements, but these are only valid for specific methods and cutting conditions. This article proposes a new multi-objective optimization approach for determining the conditions for the implementation of the abrasive cutting process that leads to Pareto-optimal solutions for improving economic efficiency, evaluated by production rate and manufacturing net cost parameters. To demonstrate this approach, the elastic abrasive cutting process of structural steels C45 and 42Cr4 has been selected. Theoretical–experimental models for production rate and manufacturing net cost have been developed, reflecting the complex influence of the conditions of the elastic abrasive cutting process (compression force of the cut-off wheel on the workpiece and rotational frequency of the workpiece). Multi-objective compromise optimization based on a genetic algorithm has been conducted by applying two methods—the determination of a compromise optimal area for the conditions of the elastic abrasive cutting process and the generalized utility function method. Optimal conditions for the implementation of the elastic abrasive cutting process have been determined, ensuring the best combination of high production rate and low manufacturing net cost. Full article

This study aimed to investigate the efficacy of a composite coating composed of 150 mg/L ε-Poly-L-lysine (ε-PL) and 5 g/L chitosan (CTS) in extending the shelf life and maintaining the postharvest quality of fresh Tremella fuciformis. Freshly harvested T. fuciformis were treated by surface spraying, with distilled water serving as the control. The effects of the coating on storage quality, physicochemical properties, reactive oxygen species (ROS) metabolism, and membrane lipid metabolism were evaluated during storage at (25 ± 1) °C. The results showed that the ε-PL/CTS composite coating significantly retarded quality deterioration, as evidenced by reduced weight loss, maintained whiteness and color, and higher retention of soluble sugars, soluble solids, and soluble proteins. The coating also effectively limited water migration and loss. Mechanistically, the coated T. fuciformis exhibited enhanced antioxidant capacity, characterized by increased superoxide anion (O₂⁻) resistance capacity, higher activities of antioxidant enzymes (SOD, CAT, APX), and elevated levels of non-enzymatic antioxidants (AsA, GSH). This led to a significant reduction in malondialdehyde (MDA) accumulation, alongside improved DPPH radical scavenging activity and reducing power. Furthermore, the ε-PL/CTS coating preserved cell membrane integrity by inhibiting the activities of lipid-degrading enzymes (lipase, LOX, PLD), maintaining higher levels of key phospholipids (phosphatidylinositol and phosphatidylcholine), delaying phosphatidic acid accumulation, and consequently reducing cell membrane permeability. In conclusion, the ε-PL/CTS composite coating effectively extends the shelf life and maintains the quality of postharvest T. fuciformis by modulating ROS metabolism and preserving membrane lipid homeostasis. This study provides a theoretical basis and a practical approach for the quality control of fresh T. fuciformis. Full article

Art therapy serves as a crucial intervention modality for children with autism spectrum disorder (ASD), demonstrating unique value in emotional expression, sensory integration, and social communication. However, current practice presents critical challenges, including the disconnect between design expertise and clinical needs, unclear mechanisms of environmental factors’ impact on therapeutic outcomes, and insufficient evidence-based support for technology integration. Purpose: This study aimed to construct an evidence-based theoretical framework for art therapy environment design for children with autism, clarifying the relationship between environmental design elements and therapeutic effectiveness. Methodology: Based on the Web of Science database, this study employed a dual-track approach comprising bibliometric analysis and micro-qualitative content analysis to systematically examine the knowledge structure and developmental trends. Research hotspots were identified through keyword co-occurrence network analysis using CiteSpace, while 24 representative design cases were analyzed to gain insights into design concepts, emerging technologies, and implementation principles. Key Findings: Through keyword network visualization analysis, this study identified ten primary research clusters that were systematically categorized into four core design elements: sensory feedback design, behavioral guidance design, emotional resonance design, and therapeutic support design. A responsive therapeutic environment conceptual framework was proposed, encompassing four interconnected components based on the ABC model from positive psychology: emotional, sensory, environmental, and behavioral dimensions. Evidence-based design principles were established emphasizing child-centeredness, the promotion of multisensory expression, the achievement of dynamic feedback, and appropriate technology integration. Research Contribution: This research establishes theoretical connections between environmental design elements and art therapy effectiveness, providing a systematic design guidance framework for interdisciplinary teams, including environmental designers, clinical practitioners, technology developers, and healthcare administrators. The framework positions technology as a therapeutic mediator rather than a driver, ensuring technological integration supports rather than interferes with children’s natural creative impulses. This contributes to creating more effective environmental spaces for art therapy activities for children with autism while aligning with SDG3 goals for promoting mental health and reducing inequalities in therapeutic access. Full article

Introduction/Objective: Post-bariatric surgery patients require long-term, coordinated care to address complex nutritional, physiological, and behavioral challenges. Personalized smart nutrition, combining individualized dietary strategies with targeted monitoring, has emerged as a valuable direction for optimizing recovery and long-term outcomes. This article examines how traditional medical protocols can be enhanced by digital solutions in a multidisciplinary framework. Methods: The study analyzes current clinical practices, including personalized meal planning, physical rehabilitation, biochemical marker monitoring, and psychological counseling, as applied in post-bariatric care. These established approaches are then analyzed in relation to the NutriMonitCare system, a digital health system developed and tested in a laboratory environment. Used here as an illustrative example, the NutriMonitCare system demonstrates the potential of digital tools to support clinicians through real-time monitoring of dietary intake, activity levels, and physiological parameters. Results: Findings emphasize that medical protocols remain the cornerstone of post-surgical management, while digital tools may provide added value by enhancing data availability, supporting individualized decision making, and reinforcing patient adherence. Systems like the NutriMonitCare system could be integrated into interdisciplinary care models to refine nutrition-focused interventions and improve communication across care teams. However, their clinical utility remains theoretical at this stage and requires further validation. Conclusions: In conclusion, the integration of digital health tools with conventional post-operative care has the potential to advance personalized smart nutrition. Future research should focus on clinical evaluation, real-world testing, and ethical implementation of such technologies into established medical workflows to ensure both efficacy and patient safety. Full article

Humanoid robots have attracted considerable attention for their anthropomorphic structure, extended workspace, and versatile capabilities. This paper presents a novel humanoid upper-body robotic system comprising a pair of 8-degree-of-freedom (DOF) arms, a 3-DOF head, and a 3-DOF torso—yielding a 22-DOF architecture inspired by human biomechanics and implemented via standardized hollow joint modules. To overcome the critical reliance of zeroing neural network (ZNN)-based trajectory tracking on the Jacobian matrix derivative, we propose an integration-enhanced matrix derivative observer (IEMDO) that incorporates nonlinear feedback and integral correction. The observer is theoretically proven to ensure asymptotic convergence and enables accurate, real-time estimation of matrix derivatives, addressing a fundamental limitation in conventional ZNN solvers. Workspace analysis reveals that the proposed design achieves an 87.7% larger total workspace and a remarkable 3.683-fold expansion in common workspace compared to conventional dual-arm baselines. Furthermore, the observer demonstrates high estimation accuracy for high-dimensional matrices and strong robustness to noise. When integrated into the ZNN controller, the IEMDO achieves high-precision trajectory tracking in both simulation and real-world experiments. The proposed framework provides a practical and theoretically grounded approach for redundant humanoid arm control. Full article

The incorporation of waste polyethylene terephthalate (PET) as a modifier for asphalt presents a promising approach to addressing the environmental pollution associated with waste plastics while simultaneously extending the service life of road surfaces. This study investigates the fundamental physical properties and rheological properties of asphalt modified with waste PET at both high and low temperatures. Utilizing the theory of fractional derivatives, performance evaluation indicators, such as the deformation factor and viscoelasticity factor, have been developed for the assessment of waste PET-modified asphalt. The underlying mechanism of this modification was examined through scanning electron microscopy and Fourier transform infrared spectroscopy. The results indicate that the addition of waste PET enhances the high-temperature stability of the base asphalt but reduces its resistance to cracking at low temperatures. The fractional derivative model effectively describes the dynamic shear rheological properties of waste PET-modified asphalt, achieving a maximum correlation coefficient of 0.99991. Considering the performance of modified asphalt at both high and low temperatures, the optimal concentration of waste PET was determined to be 6%. At this concentration, the minimum creep stiffness of the PET-modified asphalt was approximately 155 MPa at −6 °C. Additionally, the rutting factor of the waste PET-modified asphalt achieved a maximum value of 527.12 KPa at 52 °C. The interaction between waste PET and base asphalt was primarily physical, with mutual adsorption leading to the formation of a spatial network structure that enhanced the deformation resistance of the asphalt. This study provides a theoretical foundation and technical support for the engineering application of waste PET as a modifier in asphalt. Full article

The parameter identification of Autonomous Underwater Vehicles (AUVs) serves as a fundamental basis for achieving high-precision motion control, state monitoring, and system development. Currently, AUV parameter identification typically relies on the complete motion information obtained from onboard sensors. However, in practical applications, it is often challenging to accurately measure key state variables such as velocity and angular velocity, resulting in incomplete measurement data that compromises identification accuracy and model reliability. This issue is particularly pronounced in vertical motion tasks involving low-speed, large pitch angles, and highly maneuverable conditions, where the strong coupling and nonlinear characteristics of underwater vehicles become more significant. Traditional hydrodynamic models based on full-state measurements often suffer from limited descriptive capability and difficulties in parameter estimation under such conditions. To address these challenges, this study investigates a parameter identification method for AUVs operating under vertical, large-amplitude maneuvers with constrained measurement information. A control autoregressive (CAR) model-based identification approach is derived, which requires only pitch angle, vertical velocity, and vertical position data, thereby reducing the dependence on complete state observations. To overcome the limitations of the conventional Recursive Least Squares (RLS) algorithm—namely, its slow convergence and low accuracy under rapidly changing conditions—a Multi-Innovation Least Squares (MILS) algorithm is proposed to enable the efficient estimation of nonlinear hydrodynamic characteristics in complex dynamic environments. The simulation and experimental results validate the effectiveness of the proposed method, demonstrating high identification accuracy and robustness in scenarios involving large pitch angles and rapid maneuvering. The results confirm that the combined use of the CAR model and MILS algorithm significantly enhances model adaptability and accuracy, providing a solid data foundation and theoretical support for the design of AUV control systems in complex operational environments. Full article

As an efficient clean energy technology, water electrolysis for hydrogen production has its efficiency limited by the sluggish oxygen evolution reaction (OER) kinetics, which drives the demand for the development of high-performance anode OER catalysts. This work constructs bimetallic (Al, Mn) co-doped nanoporous spinel CoFe₂O₄ (np-CFO) with a tunable structure and composition as an OER catalyst through a simple two-step dealloying strategy. The as-formed np-CFO (Al and Mn) features a hierarchical flaky configuration; that is, there are a large number of fine nanosheets attached to the surface of a regular micron-sized flake, which not only increases the number of active sites but also enhances mass transport efficiency. Consequently, the optimized catalyst exhibits a low OER overpotential of only 320 mV at a current density of 10 mA cm⁻², a minimal Tafel slope of 45.09 mV dec⁻¹, and exceptional durability. Even under industrial conditions (6 M KOH, 60 °C), it only needs 1.83 V to achieve a current density of 500 mA cm⁻² and can maintain good stability for approximately 100 h at this high current density. Theoretical simulations indicate that Al and Mn co-doping could indeed optimize the electronic structure of CFO and thus decrease the energy barrier of OER to 1.35 eV. This work offers a practical approach towards synthesizing efficient and stable OER catalysts. Full article

The accelerating integration of electric vehicles (EVs) into contemporary transportation infrastructure has underscored significant limitations in traditional charging paradigms, particularly in accommodating heterogeneous user requirements within dynamic operational environments. This study presents a differentiated optimization framework for EV charging strategies through the systematic classification of user types. A multidimensional decision-making environment is established for three representative user categories—residential, commercial, and industrial—by synthesizing time-variant electricity pricing models with dynamic carbon emission pricing mechanisms. A bi-level optimization architecture is subsequently formulated, leveraging deep reinforcement learning (DRL) to capture user-specific demand characteristics through customized reward functions and adaptive constraint structures. Validation is conducted within a high-fidelity simulation environment featuring 90 autonomous EV charging agents operating in a metropolitan parking facility. Empirical results indicate that the proposed typology-driven approach yields a 32.6% average cost reduction across user groups relative to baseline charging protocols, with statistically significant improvements in expenditure optimization (p < 0.01). Further interpretability analysis employing gradient-weighted class activation mapping (Grad-CAM) demonstrates that the model’s attention mechanisms are well aligned with theoretically anticipated demand prioritization patterns across the distinct user types, thereby confirming the decision-theoretic soundness of the framework. Full article

Neural networks (NNs) achieve breakthroughs in computer vision and natural language processing,yet their “black box” nature persists. Traditional methods prioritise parameter optimisation and loss design, overlooking NNs’ fundamental structure as topologically organised nonlinear computational systems. This work proposes a complex network theory framework decoding structure–function coupling by mapping convolutional layers, fully connected layers, and Dropout modules into graph representations. To overcome limitations of heuristic compression techniques, we develop a topology-sensitive adaptive pruning algorithm that evaluates critical paths via node strength centrality, preserving structural–functional integrity. On CIFAR-10, our method achieves 55.5% parameter reduction with only 7.8% accuracy degradation—significantly outperforming traditional approaches. Crucially, retrained pruned networks exceed original model accuracy by up to 2.63%, demonstrating that topology optimisation unlocks latent model potential. This research establishes a paradigm shift from empirical to topologically rationalised neural architecture design, providing theoretical foundations for deep learning optimisation dynamics. Full article

The Qinghai-Tibetan Plateau is one of the world’s most prominent biodiversity hotspots. Understanding the spatial patterns of ecological uniqueness in its plant communities is essential for uncovering the mechanisms of community assembly and informing effective conservation strategies. In this study, we analyzed data from 758 plots across 338 sites on the Qinghai-Tibetan Plateau. For each plot, the vegetation type was classified, and all plant species present, along with their respective abundance or coverage, were recorded in the database. To assess overall compositional variation, community β-diversity was quantified, while a plot-level approach was applied to determine the influence of local environmental conditions and community characteristics on ecological uniqueness. We used stepwise multiple regressions, variation partitioning, and structural equation modeling to identify the key drivers of spatial variation in ecological uniqueness. Our results show that (1) local contributions to β-diversity (LCBD) exhibit significant geographic variation—increasing with longitude, decreasing with latitude, and showing a unimodal trend along the elevational gradient; (2) shrubs and trees contribute more to β-diversity than herbaceous species, and LCBD is strongly linked to the proportion of rare species; and (3) community characteristics, including species richness and vegetation coverage, are the main direct drivers of ecological uniqueness, explaining 36.9% of the variance, whereas climate and soil properties exert indirect effects through their interactions. Structural equation modeling further reveals a coordinated influence of soil, climate, and community attributes on LCBD, primarily mediated through soil nutrient availability. These findings provide a theoretical basis for adaptive biodiversity management on the Qinghai-Tibetan Plateau and underscore the conservation value of regions with high ecological uniqueness. Full article

Background: Ulcerative colitis (UC), characterized by chronic intestinal inflammation, epithelial barrier dysfunction, and immune imbalance demands novel ameliorative strategies beyond conventional approaches. Methods: In this study, the probiotic properties of Lactobacillus paracasei L21 (L. paracasei L21) and its ability to ameliorate colitis were evaluated using an in vitro lipopolysaccharide (LPS)-induced intestinal crypt epithelial cell (IEC-6) model and an in vivo dextran sulfate sodium (DSS)-induced UC mouse model. Results: In vitro, L. paracasei L21 decreased levels of pro-inflammatory cytokines (TNF-α, IL-1β, IL-8) while increasing anti-inflammatory IL-10 levels (p < 0.05) in LPS-induced IEC-6 cells, significantly enhancing the expression of tight junction proteins (ZO-1, occludin, claudin-1), thereby restoring the intestinal barrier. In vivo, both viable L. paracasei L21 and its heat-inactivated postbiotic (H-L21) mitigated weight loss, colon shortening, and disease activity indices, concurrently reducing serum LPS and proinflammatory mediators. Interventions inhibited NF-κB signaling while activating HIF1α/AhR pathways, increasing IL-22 and mucin MUC2 to restore goblet cell populations. Gut microbiota analysis showed that both interventions increased the abundance of beneficial gut bacteria (Lactobacillus, Dubococcus, and Akkermansia) and improved faecal propanoic acid and butyric acid levels. H-L21 uniquely exerted an anti-inflammatory effect, marked by the regulation of Dubosiella, while L. paracasei L21 marked by the Akkermansia. Conclusions: These results highlight the potential of L. paracasei L21 as a candidate for the development of both probiotic and postbiotic formulations. It is expected to provide a theoretical basis for the management of UC and to drive the development of the next generation of UC therapies. Full article

The abandoned goaf resulting from coal resource integration in China poses a significant threat to coal mine safety. The transient electromagnetic method (TEM) has emerged as a crucial technology for detecting goafs in coal mines due to its adaptable equipment and efficient implementation. In recent years, small-loop TEM has demonstrated high resolution and adaptability in challenging terrains with vegetation, such as coal mine ponding areas, karst regions, and reservoir seepage scenarios. By considering the sedimentary characteristics of coal seams and addressing the resistivity changes encountered in single-point inversion, a joint optimization inversion process incorporating lateral weighting factors and vertical roughness constraints has been developed to enhance the connectivity between adjacent survey points and improve the continuity of inversion outcomes. Through an OCCAM inversion approach, the regularization factor is dynamically determined by evaluating the norms of the data objective function and model objective function in each iteration, thereby reducing the reliance of inversion results on the initial model. Using the Xiaolong Coal Mine as a geological context, the impact of lateral and vertical weighting factors on the inversion outcomes of high- and low-resistivity structural models is examined through a control variable method. The analysis reveals that optimal inversion results are achieved with a combination of a lateral weighting factor of 0.5 and a vertical weighting factor of 0.1, ensuring both result continuity and accurate depiction of vertical and lateral electrical interfaces. The practical application of this approach validates its effectiveness, offering theoretical support and technical assurance for old goaf detection in coal mines, thereby holding significant engineering value. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article