Search Results (2,685)

22Cr12NiMoWV martensitic heat-resistant steel serves as a candidate material for underground coiler sector plates, whereas the coupling relationship between partial austenitization, precipitate/carbide evolution, martensitic interfaces and mechanical response under medium-temperature quenching–tempering conditions is still ambiguous. This work systematically explores four key heat treatment variables to clarify the microstructure–property correlation and strengthening rebalance mechanism. In the 790–830 °C partial austenitization interval, the austenite fraction increases from 36.49 wt.% to 72.32 wt.% with a concurrent decline of M₂₃C₆ carbides from 5.34 wt.% to 4.92 wt.%, demonstrating competitive evolution between austenite generation and carbide retention. Specimens quenched at 810 °C for 2 h deliver a yield strength of 1015.4 ± 13.8 MPa and tensile strength of 1192.9 ± 17.8 MPa, 24.9% and 19.9% higher than conventional QT samples, owing to synergistic reinforcement from α′ martensite matrix, orientation interfaces and Cr-Mo-W-V-rich precipitates. After 400 °C × 4 h tempering, the steel still maintains superior strength, and its average misorientation falls from 40.41° to 31.17°. Though its engineering ductility is inferior to the quenched state, the mixed dimple–quasi-cleavage fracture mode suggests a partial recovery of ductile fracture characteristics compared with over-treated samples. The uncovered strengthening mechanism provides microstructural theoretical support for process optimization. Compared with the conventional quenching and tempering process, the optimized medium-temperature process (810 °C × 2 h quenching + 400 °C × 4 h tempering) reduces energy consumption and the production cycle and provides solid theoretical and experimental data for a green and low-cost industrial heat treatment of coil plates. Full article

The development of plant-based functional beverages is often limited by inherent matrix defects, particularly undesirable off-flavors, astringency, and colloidal instability. Water kefir (WK), a highly resilient multispecies symbiotic consortium, offers a robust biorefining platform to address these challenges. This review systematically elucidates the underlying micro-ecological logic and biochemical mechanisms of WK-mediated plant matrix remodeling. We first detail how spatial niche differentiation and cross-feeding networks among lactic acid bacteria, yeasts, and acetic acid bacteria drive ecological homeostasis. Next, we highlight core molecular events that elevate sensory quality: protein unfolding for off-flavor elimination, enzymatic depolymerization of phenolics to mitigate astringency, and exopolysaccharide synthesis for rheological and flavor diffusion control. Finally, to overcome industrial scale-up challenges, we outline a precision fermentation framework, integrating systems multi-omics, real-time biomimetic monitoring, and sensory topological modeling. Ultimately, this synthesis provides theoretical guidance for the reverse flavor engineering and targeted nutritional design of novel plant-based beverages. Full article

Regular exercise mediates inter-tissue communication between skeletal muscle and the immune system through skeletal muscle-derived secretory factors, providing an important molecular basis for the beneficial effects of exercise on chronic inflammation, metabolic dysregulation, and impaired tissue repair. As key effector cells of the innate immune system, macrophages do not simply conform to a dichotomous classification of classically activated M1 macrophages and alternatively activated M2 macrophages; rather, their functional states constitute a dynamic spectrum shaped by exercise load, recovery time window, tissue microenvironment, and disease context. This review focuses on recent advances in exercise-induced skeletal muscle secretory factors involved in macrophage functional remodeling. Representative signals, including interleukin-6 (IL-6), irisin, meteorin-like protein (METRNL), fibroblast growth factor 21 (FGF21), oncostatin M (OSM), decorin, myostatin, chemokines, and extracellular vesicles, are systematically summarized in terms of their exercise responsiveness, evidence for skeletal muscle origin, and evidence supporting macrophage regulation. Based on these dimensions, an evidence-strength grading framework is further proposed. Moreover, this review integrates key signaling axes, including glycoprotein 130 (gp130)/Janus kinase (JAK)/signal transducer and activator of transcription (STAT), signal transducer and activator of transcription 6 (STAT6)/peroxisome proliferator-activated receptor gamma (PPARγ), AMP-activated protein kinase (AMPK)/nuclear factor erythroid 2-related factor 2 (Nrf2)/nuclear factor kappa B (NF-κB), transforming growth factor beta (TGF-β)/Smad, and chemokine receptor pathways, to explain how exercise-induced secretory networks participate in the dynamic regulation of the macrophage functional spectrum through immune cell recruitment, inflammatory clearance, immunometabolic reprogramming, matrix remodeling, and repair-niche formation. Current evidence indicates the translational potential of exercise-induced skeletal muscle secretory factors in skeletal muscle repair, metabolic inflammation, aging-related functional decline, and cancer rehabilitation. However, this field still faces several major challenges, including insufficient tracing of skeletal muscle-derived signals, limited direct causal validation, a lack of human tissue-level evidence, and unclear exercise dose–response relationships. Future studies should combine tissue-specific genetic interventions, receptor blockade, single-cell and spatial omics, metabolic flux analysis, and standardized human exercise interventions to further clarify the mechanistic basis and application boundaries of exercise-induced skeletal muscle–macrophage communication, thereby providing a theoretical foundation for precision exercise prescription and chronic inflammation intervention. Full article

Natural fractures dominate fracturing effects and well production. Conventional fracturing fails to fully activate multi-scale fractures, and most simulations adopt homogeneous rock assumptions, lacking systematic analysis on fracture propagation and seepage evolution in heterogeneous fractured formations, while the natural fracture activation mechanism of pulsed fracturing remains unclear. This work constructs a pulsed fracturing model for heterogeneous fractured rock to simulate fracture growth and permeability evolution in intact rock and formations with various fracture attitudes, revealing the coupled laws of fracture propagation and seepage change. Results show rock mechanical heterogeneity determines fracture network complexity in intact rock; pulsed loading slows main fracture breakthrough and stimulates microcracks, creating a near-well dense and far-well sparse fracture distribution. Single-orientation fractures drive directional asymmetric fracture extension following near-weak-zone priority, with matrix heterogeneity merely causing local fracture deflection. Multi-orientation fractures display layered activation: low-angle and near-well fractures initiate first, and cross-fracture interactions raise network complexity and coverage. Fracture growth is jointly governed by weak bedding, pulse fatigue damage and matrix properties. Pulsed fracturing achieves remote non-contact activation of natural fractures, with fracture-permeability evolution showing strong spatiotemporal coupling; main fracture breakthrough triggers abrupt permeability growth. Serving as both mechanical weak planes and preferential flow paths, natural fractures build composite seepage systems of main channels and micro flow zones. This study provides theoretical support for parameter optimization and efficient permeability improvement in fractured reservoirs. Full article

The core predicament of energy transition lies not in the availability of clean technologies, but in whether an economy possesses the institutional capacity and social foundation to systematically regulate its carbon-energy metabolic processes. Drawing upon co-evolutionary theory from evolutionary economics, this paper constructs a composite indicator of Low-Carbon Green Energy Eco-Co-evolution (LCEE) encompassing three functional dimensions: efficiency advancement, kinetic energy replacement, and boundary adherence. Concurrently, by integrating innovation ecosystem theory with inclusive development theory, we propose the concept of “Inclusive Potential of Regional Innovation Ecosystems” (IEP), characterizing the systemic potential for transforming innovation outcomes into social welfare across four dimensions: Knowledge Matrix Abundance (KMF), Cultural Capillary Permeation (CCP), Technological Community Succession (TCS), and Social Root Nourishment (SRN). Taking China’s 2016 intellectual property (IP) powerhouse construction pilot as the institutional prototype of Industrial Intellectual Property Upgrading Reform (IPR), we incorporate IPR, IEP, and LCEE into a unified causal analytical framework, proposing a testable transmission logic of ‘institutional supply → ecological development → co-evolutionary synergy. Using panel data from 30 Chinese provincial-level administrative regions over 2010–2022, we employ a Spatial Durbin Difference-in-Differences (SDM-DID) model to identify the direct and spatial spillover effects of IPR on LCEE, and embed a Double Machine Learning (DML) framework to test the mediating mechanism of IEP while controlling for high-dimensional nonlinear interference. The findings reveal that IPR exerts a significant and robust direct promoting effect on LCEE, generating positive spatial spillovers to neighboring regions through the public disclosure of patent information. IEP significantly promotes local LCEE, yet its spatial spillover lacks statistical support due to structural conflicts in inter-dimensional transmission attributes. IEP plays a significant partial mediating role between IPR and LCEE, with the indirect effect accounting for over one-third of the total effect, a finding robust to alternative machine learning algorithms, sample split adjustments, and exclusion of contemporaneous competing policies. Sub-path tests reveal that KMF bears the strongest mediating efficacy, serving as the primary transmission channel, while CCP exhibits full mediation—the institutional effect on LCEE in the cultural dimension depends almost entirely on the mediating transformation through the public cultural service system. Heterogeneity analysis further demonstrates full mediation in the Low-Carbon Green Energy Eco-Kinetic Replacement (KER) dimension, indicating that the institutional catalytic effect on clean energy substitution must be realized through IEP transformation. This paper provides empirical evidence for the proposed causal pathway through which institutional public goods indirectly enhance the synergistic quality of carbon-energy transition via the inclusive potential of innovation ecosystems, providing theoretical foundations and policy implications that, while grounded in China’s institutional context, may offer valuable reference points for emerging market economies facing similar dual pressures of technological constraints and green transition. Full article

(1) Background: The continuous growth of rehabilitation literature makes evidence synthesis increasingly challenging. Lexical network analysis may provide quantitative information on the organization of scientific knowledge. This study aimed to characterize the shoulder rehabilitation literature through an International Classification of Functioning, Disability and Health (ICF)-oriented lexical network approach. (2) Methods: PubMed was searched for papers on shoulder rehabilitation published from 2016 to 2025. ICF-related lexical terms associated with the shoulder were selected from the official ICF framework, and their frequency inside the titles and abstracts of the found papers was calculated. A binary text-term matrix was generated and converted into a bipartite network. Graph-theoretical analysis, including feature reduction, was performed to identify representative topological descriptors and relationships. (3) Results: The analysis identified 6424 paper-word connections. SHOULDER, PAIN, and FUNCTION represented the principal lexical hubs of the network. Feature reduction enabled the characterization of the network by eigencentrality and neighborhood connectivity. Participation- and environment-related terms showed comparatively limited representation. PARTICIPATION showed the highest neighborhood connectivity. (4) Conclusions: The proposed ICF-oriented lexical network approach provides a quantitative framework for exploring the lexical organization of rehabilitation literature, providing complementary information to conventional literature reviews and supporting future evidence synthesis and research planning. Full article

Tensegrity structures, due to their lightweight and self-equilibrating characteristics, have found extensive applications across various engineering fields. The introduction of equal cable length as an additional geometric constraint enables a high degree of geometric symmetry, resulting in uniform internal force distribution and predictable mechanical responses. However, existing stiffness matrix assembly methods predominantly rely on conventional node-element topological connectivity matrices confined to classical one-to-one force-displacement systems, struggling to exploit the geometric regularities inherent in equal-length constraints and highly symmetric configurations. To address this, the paper proposes a stiffness matrix modeling method tailored for equal-cable-length tensegrity structures within the linear stiffness framework. A generalized connectivity matrix is introduced to unify the topological description of struts and cables while integrating displacement compatibility, internal equilibrium, and geometric constraints into a cohesive algebraic system. Leveraging symmetry properties and member categorization by loading type, the method embeds equal-length and symmetry grouping information directly into assembly, significantly reducing independent variables and construction complexity. A finite element model is established for numerical implementation, and experiments on a three-bar tensegrity structure validate the theoretical model, with minor deviations confirming its reliability. Full article

Urban park green spaces are key infrastructure for improving the quality of the living environment, enhancing residents’ well-being, and strengthening ecological resilience. Targeting 1760 county seats in China, this study combines the stock and incremental synergy analysis matrix, exploratory spatial data analysis, and explainable machine learning (EML) methods (SHAP) to systematically reveal the spatiotemporal patterns, spatial association characteristics, and nonlinear paths of influencing factors regarding the spatial configuration of park green spaces in county seats from 2015 to 2024. The low-stock expansion zone is dominant and concentrated in the western region and non-core urban agglomerations. The high-stock expansion zone is of the dominant type, concentrated in the eastern coastal areas and core urban agglomerations. The low-stock contraction zone and high-stock contraction zone are, respectively, marginalized lock-in and degradation risk types, requiring priority intervention. The dynamics of the park green space configuration, including the stock, increment, and their synergy, all exhibit significant positive spatial autocorrelation. The influences of the nine factors exhibit four major characteristics: directional mixing, intensity gradation, path nonlinearity, and regional heterogeneity. The threshold effect is widespread, and the inflection point value varies depending on the factor and region type. This study constructs a nonlinear and interpretable analytical paradigm and, based on empirical results, proposes a new governance framework of “zoning–grading–staging–synergy”. It provides large-sample empirical evidence and theoretical support for the transformation of small-town park green spaces from “sectoral management” to “spatial governance”, offering significant policy value toward achieving the precise distribution and equitable sharing of regional green space resources. Full article

Recently, multi-view learning techniques based on non-negative matrix factorization (NMF) have demonstrated remarkable efficacy in clustering multi-source data. However, existing methods often overlook the intrinsic similarity between views, and conventional NMF struggles to consistently yield sufficiently sparse solutions. To address these limitations, this paper proposes a novel NMF-based multi-view clustering approach. First, a pairwise co-regularization mechanism is introduced to capture cross-view structural correlations by measuring the similarity between view-specific coefficient matrices. Second, by imposing logarithmic norm sparsity constraints on the coefficient matrix, a pairwise co-regularized NMF model with sparsity constraints is constructed. An efficient iterative update algorithm is derived for the proposed model, and its convergence is rigorously guaranteed in theory. Extensive experiments on eight benchmark datasets validate the effectiveness of the proposed algorithm and its superior sparse representation capability. Furthermore, as a practical engineering optimization, a stochastic acceleration strategy is incorporated to expedite convergence. Empirical results show that this strategy reduces the runtime by approximately 48% on average while preserving clustering performance. Future work will focus on the theoretical underpinnings of this stochastic strategy and its applicability to broader scenarios. Full article

DNA hydrogels are an emerging class of biomaterials with programmability, biodegradability, biocompatibility, and dynamic responsiveness, enabling precise regulation of osteoblast and mesenchymal stem cell (MSC) proliferation and differentiation, activation of key signaling pathways, and promotion of angiogenesis and bone matrix mineralization. In contrast, conventional bone repair materials exhibit limitations including poor mechanical strength, uncontrollable degradation, and inadequate matching with native bone properties, restricting their application in osteoporotic defect repair. Current osteoporotic defect therapies, mainly anti-resorptive and anabolic agents, remain insufficient for many patients. Here, we propose pure and hybrid DNA hydrogels as novel therapeutic platforms to restore the dynamic balance between bone resorption and formation, thereby enhancing osteogenesis and facilitating bone regeneration and remodeling under osteoporotic conditions. Although challenges such as high production cost and long-term safety persist, integration with advanced technologies (e.g., 3D printing and gene editing) may provide theoretical support for further investigation of personalized and intelligent therapeutic strategies at the pre-clinical research stage, offering new insights into osteoporotic bone defects and bone tissue regeneration. Full article

Using bismuth oxide (Bi₂O₃) as the matrix and employing a doping modification strategy to introduce the rare-earth element La, this study prepared La/Bi₂O₃ visible-light-responsive photocatalysts with different doping ratios. The research systematically investigated the regulation mechanisms of La doping on the material’s phase structure, microstructure, band structure characteristics, and visible-light photocatalytic performance. The results indicate that an appropriate amount of La³⁺ equivalently substitutes Bi³⁺ in the lattice, inducing the complete transformation of pure α-Bi₂O₃ into the tetragonal β-Bi₂O₃ phase while maintaining the integrity of the crystal framework. Meanwhile, the modulation of the local electronic structure caused by La³⁺ substitution effectively narrows the bandgap width and broadens the visible-light response range; it also acts as an electron trap to significantly suppress the recombination of photo-generated electron–hole pairs, thereby enhancing charge transport efficiency. Visible-light catalytic degradation experiments confirmed that 4% La/Bi₂O₃ exhibits the optimal degradation kinetics for RhB, achieving a 72.88% degradation rate of Rhodamine B within 60 min of visible-light irradiation. The first-order reaction rate constant was 23 times that of pure Bi₂O₃, and the material demonstrated good stability under repeated cycles. Radical trapping experiments indicated that the order of contribution of active species was ·O₂⁻ > h⁺ > ·OH, with the superoxide radical (·O₂⁻) being the dominant active species. This study confirms that appropriate lattice doping with La can synergistically optimize the structure and optoelectronic properties of Bi₂O₃, providing experimental evidence and theoretical references for the rational design of highly efficient and stable visible-light-responsive Bi₂O₃-based photocatalytic materials. Full article

In this work, we theoretically investigate the valley-dependent electron transport properties of graphene modulated by two strained regions and a single ferromagnetic stripe based on the Dirac equation and the transfer-matrix method. By constructing a multi-region model, the conductances of the K and K’ valleys as well as the corresponding valley polarization are systematically calculated. The effects of the magnetic vector-potential, the strain-induced gauge potentials, the widths of the strained regions, and the competition parameter $\lambda$ on the valley-resolved transport behavior are analyzed in detail. The results show that both the conductance and the valley polarization are highly sensitive to the external modulation parameters and the geometric structural parameters. By properly tuning the relative strength and spatial distribution of the magnetic vector-potential and strain, an effective control of valley polarization can be achieved. This work deepens the understanding of valley-dependent transport mechanisms in graphene and provides theoretical guidance for tunable valley filtering in graphene-based systems. Full article

The rapid integration of Large Language Models into high-stakes domains has elevated inference-time attacks into a primary security concern for production deployments. These attacks are adversarial techniques that exploit models exclusively through their input–output interface. The existing survey literature lacks a dedicated and structured treatment that jointly maps the attack surface and systematically evaluates the mitigation strategies developed against it. This paper addresses this gap through two original taxonomic contributions. First, LLM vulnerabilities are organized into a three-layer attack surface taxonomy stratified by lifecycle stage, establishing the theoretical primacy of the inference time category. Second, to directly address how these attacks can be mitigated, a defense taxonomy spanning three axes, namely prompt-level, inference-time, and training-time interventions, is proposed, within which 30 mitigation mechanisms published from 2024 onwards are systematically analyzed. Building on this taxonomy, an intersectional comparative analysis is conducted across three dimensions: defense-attack coverage, security-utility-latency tradeoffs, and white-box versus black-box applicability, in order to evaluate how effectively current mitigation strategies neutralize each attack category. These dimensions are further synthesized into a practitioner decision framework that maps deployment constraints to concrete defense configurations and identifies two structural coverage gaps that persist regardless of access level or latency budget. The resulting Defense-Attack Coverage Matrix demonstrates that no single defense mechanism provides comprehensive protection, and that robust deployment mandates layered, complementary strategies. The analysis further reveals that the fundamental unresolved tension limiting effective mitigation is the trade-off between adversarial robustness and model utility, with over-refusal and capability degradation constituting the primary practical barriers to deploying these defenses. Finally, open challenges related to multimodal attack surfaces, agentic LLM security, and the absence of standardized evaluation frameworks are identified, together with concrete future research directions. The taxonomies and analyses presented are intended to serve as an actionable reference for both researchers and practitioners tasked with mitigating inference-time attacks in secure LLM deployments. Full article

This article proposes the Collective Delivery Demand Matrix (CDD Matrix), which repositions the New Agile as a correspondence property between collective delivery demands and teams’ operating regimes, aiming to form teams that are simultaneously agile, high-performing, and endowed with dynamic capabilities. It addresses a gap in the agility literature: the Agile Manifesto principles have been extended to many contexts, yet no theory explains when they should be maintained, adapted, or replaced. Developed as a theoretical-conceptual essay, the work synthesizes studies on naturalistic decision-making, team learning, neurostrategy, sociotechnical systems, and decision support. It proposes a taxonomy of seven demand typologies along five classificatory axes, and a correspondence matrix that articulates demand types, operating regimes, shared heuristics, sociotechnical configurations, and managerial errors. The matrix clarifies that agility generates value when it first favors effectiveness, delivering the right objective in the right regime at the appropriate risk level, and only then efficiency. The New Manifesto reformulates the Agile Manifesto as conditional correspondence rules sensitive to demand typology, and six testable propositions guide future empirical research. The main finding is that a team becomes simultaneously agile, high-performing, and dynamically capable only when its operating regime corresponds to the demand typology it faces, matching effectiveness before efficiency, calibrating risk, and building a renewable repertoire of shared heuristics. For managers, teams, and organizations, this implies diagnosing the delivery demand before selecting a method, composing and leading teams according to the demand’s dominant axes, and protecting the psychological safety that turns reversible error into collective learning. The contribution integrates the Agile Manifesto, decision theory, the sociotechnical tradition, and neurostrategy into a single theory of Agile, High-Performance, and Dynamic Teams (EAD²), oriented toward verifiable objectives, goals, and results. Full article

Ni60/WC composite coatings reinforced with various WC contents were fabricated on 45 steels via high-speed laser cladding. First-principles calculations were adopted to investigate the interfacial characteristics between Ni and WC. The microstructures of the coating were analyzed by a scanning electron microscope (SEM). The microhardness and wear resistance of the coatings were evaluated by a Vickers hardness tester and a friction and wear tester. Theoretical calculations indicate that the C-terminated WC (001) crystal plane achieves the most stable bonding with the Ni (111) surface through the hcp site, with an interface energy of 9.57 J·m⁻². The interface is mainly provided by Ni-W metal bonds and Ni-C covalent bonds. The microstructure results show that the WC particles have good metallurgical bonding with the Ni matrix. Thus, the good interface ensures efficient load transfer to hard WC particles. The microhardness rose markedly with the increase in WC content, reaching 760 HV_0.2 for the 40 wt.% WC, which is 1.8 times that of Ni60 coatings. Tribological experiments showed that appropriate WC content could significantly improve the wear resistance of the coating. Full article