Search Results (8,891)

Value co-creation among diverse actors in digital innovation ecosystems (DIEs) exhibits characteristics of high complexity and dynamic evolution. Grounded in the Quadruple Helix Theory, this study develops a conceptual model that interlinks “supervisory guides, knowledge providers, technology transformers, and user demand parties.” This model is defined by organizational oversight as its nexus, knowledge and technology as its foundation, outcome transformation as its core, and user needs as its orientation. Building upon this conceptual foundation, we establish a four-party evolutionary game model involving “innovation regulators (government), innovation producers (academic/research institutions), innovation decomposers (enterprises), and innovation consumers (users).” This analytical framework is then applied to systematically investigate the dynamic evolutionary mechanisms and collaborative pathways for value co-creation in DIEs. We construct the payoff matrix and replicator dynamics to derive the system’s Evolutionarily Stable Strategies (ESSs). Numerical simulations via MATLAB R2023b identify the stability conditions for each party’s strategic choices and unravel the influence mechanisms of key parameters. The results demonstrate nine distinct ESSs, categorized into three types: low-level stability, regulation-dominated transitional stability, and high-level cooperative stability. While the agents’ initial strategies do not alter the system’s final equilibrium state, they significantly impact the speed of evolutionary convergence. Critical factors—including regulators’ intervention costs, subsidy and penalty mechanisms, producers’ and decomposers’ cooperation and default costs, and consumer feedback behaviors—collectively drive the system toward the ideal (1, 1, 1, 1) equilibrium. Theoretically, this study enriches the perspective on multi-agent collaboration in value co-creation by introducing a dynamic quantitative analytical framework, thereby addressing a notable gap in the literature. Practically, it provides actionable insights for mechanism design and a solid foundation for policy optimization, aiming to foster a synergistic governance system that integrates “regulatory guidance, market incentives, and social feedback.” Full article

Estimating the state of charge (SoC) is a critical task for effective management of electric vehicle batteries. Simple machine learning methods (LR, KNN, etc.) often suffer from limited prediction accuracy, while deep learning approaches (LSTM, CNN, etc.) generally require high computational resources and behave as black-box models with limited explainability. To overcome these limitations, the present work proposes a SoC estimation approach based on the Light Gradient Boosting Machine (LightGBM). The proposed model provides a balanced trade-off between prediction accuracy and computational efficiency. Furthermore, feature engineering is performed to derive additional informative features, improving the model’s ability to learn driving conditions and battery dynamics. In addition, the study incorporates a seasonal analysis by evaluating the model under both summer and winter conditions, allowing the impact of environmental variations on SoC estimation performance to be investigated. Moreover, Explainable Artificial Intelligence (XAI) techniques are employed to interpret the model predictions. Evaluation on real-world on-road data demonstrated that the proposed model achieved substantial improvements in estimation performance compared to recent studies. Full article

Depression is a highly heterogeneous psychiatric disorder with its pathogenesis increasingly linked to dysregulated neuroinflammation. Microglia, as the resident immune cells of the central nervous system (CNS), play a pivotal role in the initiation and progression of the neuroinflammation and the pathophysiology of depression. These cells exhibit a dual role in pro- and anti-inflammatory processes, dynamically regulating immune responses through immunometabolic reprogramming in response to environmental cues. This review elaborates how metabolic remodeling in microglia, particularly within glucose, lipid, and amino acid pathways, drives their polarization toward a pro-inflammatory phenotype. This shift promotes depression pathogenesis via the release of inflammatory factors, disruption of synaptic plasticity, and mediation of neurotoxicity. We further discuss the impact of existing antidepressants on cellular metabolism and highlight the promise and challenges of targeting specific microglial metabolic pathways as a novel therapeutic strategy. This synthesis provides new insights into the immunometabolic mechanisms of depression and outlines directions for developing targeted treatments. Full article

Thermal management is a key factor for the efficiency, performance, and reliability of battery electric vehicles (BEVs), particularly in systems with strongly coupled components and heterogeneous thermal dynamics. This study proposes a centralized nonlinear model predictive control (NMPC) strategy for component cooling in BEVs, designed to maintain temperatures within optimal ranges while minimizing energy consumption and respecting actuator constraints. A reduced-order physics-based model is developed in MATLAB/Simulink R2024b, and the NMPC is implemented using CasADi, incorporating coolant temperatures as stabilizing states and a systematic parametrization of sampling time, prediction horizon, and weighting factors. The considered thermal management system consists of hydraulically coupled subsystems with different overall time constants, for which a single-horizon NMPC formulation is applied. Simulation results show that the proposed controller accurately tracks thermal dynamics across components with varying inertia and effectively captures cross-coupling effects. Sensitivity analyses indicate that variations in sampling time and prediction horizon have a limited impact on temperature trajectories and energy consumption, demonstrating robustness and real-time applicability. Compared to a rule-based controller, the NMPC achieves up to 30% reduction in energy consumption depending on ambient conditions and driving cycles, while improving temperature regulation, particularly for the high-voltage battery, with up to 2 K lower peak temperatures and a more balanced temperature distribution. These findings demonstrate that centralized NMPC is a suitable and efficient approach for thermal management in directly coupled BEV subsystems with heterogeneous dynamics. Full article

The digital economy empowers the development efficiency of the accessible industry, which is crucial for its sustainable development. Previous studies have focused on a single part of the accessible industry, lacking an overall grasp of the industry. Furthermore, they have not yet elaborated on the driving role of the digital economy in the accessible industry. This paper constructs an index system for evaluating the development efficiency of the accessible industry empowered by the digital economy, and uses sample data from 31 provinces (cities) in China. By comprehensively employing the three-stage DEA model method, this paper explores the reasons for the differences in development efficiency among accessible industries, empirically analyzes their influencing factors and the mechanism of efficiency improvement, and fills the gap in research on the digital economy’s impact on the accessible industry. The purpose is to deeply understand the development model of the accessible industry empowered by the digital economy through systematic evaluation and analysis, to accurately identify efficiency bottlenecks and clarify paths for improvement. Full article

As an important state parameter in battery management systems, accurate state of charge (SOC) estimation is of great significance for the safe and reliable use of batteries. In this paper, a Temporal Convolutional Network–Transformer (TCN–Transformer) model is proposed for achieving accurate estimation of SOC. First, the TCN is integrated in series with the Transformer model. This integration not only extracts the local characteristics of time-series data but also captures broader spatiotemporal correlations, thereby enhancing the feature representation and achieving highly accurate estimation. However, since the hyperparameter settings of neural networks have a significant impact on model performance, this study employs the advanced hippo optimization (HO) algorithm to determine the optimal values for the number of filters, filter size, number of residual blocks, and number of encoder layers, ultimately improving the model’s stability and efficiency. Finally, the proposed model was tested under various dynamic driving conditions at different temperatures. Experimental validation on the CALCE dataset demonstrates that the proposed HO–TCN–Transformer achieves RMSE and MAE both under 0.7%, representing an approximately 50% overall error reduction compared to the standalone TCN. Cross-validation across five folds confirms robust performance with <7% standard deviation. Full article

The construction industry, a global economic pillar and carbon emission giant, faces a critical gap between digital transformation and risk management, which ultimately undermines the sector’s capacity for risk management. This study combines social technical systems theory with the technology–organization–environment framework, using panel data from Chinese listed construction firms to explore how digital transformation affects project risk management. Key findings reveal that digital transformation significantly boosts risk management through two distinct pathways. While environmental governance capacity and green innovation efficiency both serve as significant mediators, the study identifies a notable disparity in the driving forces: digital transformation exerts a stronger impact on green innovation efficiency (17.8%) compared to environmental governance (4.4%). However, the resulting mediating effects of these two paths are found to be remarkably similar (0.0060 vs. 0.0068). Furthermore, labor investment efficiency is identified as a critical boundary condition, with a threshold effect (−0.385) below which the benefits of digital transformation weaken. These findings provide empirical evidence from Chinese context regarding the “technology-institution” co-evolution mechanism in construction. While centered on China, the study offers valuable insights for global stakeholders on how to harness digitalization to mitigate project risks and enhance sustainability. Full article

Lepidolite deposits are rare-metal deposits in which lepidolite is the principal industrial mineral. Owing to thin overburden and widespread open-pit mining, their exploitation supports raw material supply for the new energy industry but also continuously disturbs mining ecosystems, thereby threatening regional ecological security. Under the combined effects of fragile natural conditions and human-induced mining disturbance, traditional fixed-weight evaluation methods have difficulty identifying stage-wise changes and localized high-risk characteristics of ecological security in lithium mining areas. Taking the lithium mining area of Huaqiao Township, Yichun, as a case study, this study constructed an ecological-security evaluation system based on the Driver–Pressure–State–Impact–Response–Management (DPSIRM) framework and introduced variable weight (VW) theory to develop a penalty-dominated state variable weight model. This model enabled the dynamic adjustment of indicator weights across years and evaluation units, while the geographic detector was used to identify the main driving factors. Results showed that (1) from 2010 to 2024, ecological security exhibited a stage-wise pattern of initial improvement followed by degradation, and low-security areas first contracted and then expanded outward; (2) vegetation coverage was a key driving factor, while interactions between any two factors were stronger than the effect of a single factor, indicating that cumulative multi-stressor effects strongly shaped spatial differentiation; and (3) compared with the constant weight (CW) method, the VW method produced finer stratification within the severely degraded tail at the Shixiawo mining site across the four assessment years, demonstrating applicability at a representative mining site in this Huaqiao case study. These findings provide a scientific basis for ecological assessment, restoration, and coordinated resource management in lithium mining areas. Full article

Continuous urban expansion and the resulting land use and land cover (LULC) changes significantly exacerbate the urban heat island effect and intensify heatwaves. While the cooling effects of blue–green spaces are widely documented, most studies focus on single landscape types or specific time frames. Few investigations systematically explore the comprehensive thermal regulation mechanisms of gray–green spaces, or their nonlinear driving factors and interactive effects across coupled seasonal and diurnal scales. To address these gaps, this study focuses on Chengdu, a typical expanding city in China, to establish a comprehensive indicator system for urban gray–green spaces. This system encompasses four key dimensions: coverage, fragmentation, aggregation, and morphological spatial pattern. After evaluating 12 machine learning models, the optimal model was selected for further analysis using SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP). This research investigates the nonlinear thresholds and interactive effects of composite gray–green space indicators on land surface temperature (LST) across varying seasonal and diurnal cycles. The results indicate that: (1) The impact of gray–green spaces on LST varies significantly across seasonal and diurnal contexts. Green spaces primarily exert a cooling effect during spring, summer, and autumn, whereas gray spaces dominate heat retention during winter and across all nocturnal periods. (2) The morphological spatial pattern of green spaces (GMSPA) outperforms traditional coverage indicators (G1) in providing cooling benefits across multiple scenarios. (3) The cooling efficiency of GMSPA peaks between −0.8 and −0.5, reaching saturation at 0.53. Conversely, LST exhibits a sharp, step-like increase when gray space aggregation (B3) exceeds −0.58. (4) Optimizing areas with high GMSPA can significantly mitigate heat exposure risks in expanding cities. These findings offer robust theoretical insights and actionable guidelines for spatial planning aimed at thermal resilience, urban thermal environment management, and building energy conservation in rapidly growing urban areas. Full article

In autonomous driving and intelligent transportation systems, the degradation of image quality under low-light conditions severely impacts the reliability of subsequent object detection. Existing methods predominantly employ data-driven deep learning models for image enhancement, often lacking physical interpretability and struggling to maintain robustness in complex lighting-varying traffic scenarios. To address this, this paper proposes a Physically Guided Transformer–CNN Hybrid Network (Physically Guided Transformer–CNN Hybrid Network, PGT-Net) for end-to-end joint optimization of low-light enhancement and object detection. PGT-Net innovatively integrates the atmospheric scattering physical model with deep learning architecture: first, a learnable physical guidance branch estimates the scene’s atmospheric illumination map and transmittance map, providing explicit physical priors for the network; second, a dual-branch enhancement backbone is designed, where the local CNN branch (based on an improved UNet) restores fine textures, while the Global Transformer Branch (based on Swin Transformer) models long-range dependencies to correct global uneven illumination, with features adaptively combined via a Physical Fusion Module to ensure enhancement results align with physical laws while retaining rich visual features; finally, the enhanced images are directly fed into a lightweight detection head (e.g., YOLOv7) for joint training and optimization. Comprehensive experiments on public datasets (ExDark, BDD100K-night, etc.) demonstrate that PGT-Net significantly outperforms mainstream methods (e.g., RetinexNet, KinD, Zero-DCE) in both low-light image enhancement quality (PSNR/SSIM) and object detection accuracy (mAP), while maintaining high inference efficiency. This research offers an interpretable, high-performance solution for visual perception tasks under adverse lighting conditions, holding strong theoretical significance and practical value. Full article

Background/Objectives: Histone deacetylase (HDAC) inhibitors have been shown to prime the response to immunotherapy (IMT) treatment by inducing immune activation and infiltration to target tumor cells. Many studies primarily focus on adaptive immune cells and their expression of pro-inflammatory markers, like somatostatin receptor 2 (SSTR2); however, macrophages are known to help mediate key tumor microenvironment changes. The goal of this study is to evaluate the effects of HDAC inhibitors and IMT on macrophages, their expression of SSTR2, and their impact on the treatment response in triple-negative breast cancer (TNBC). Methods: Cytotoxic effects of HDAC inhibitors on 4T1 mouse mammary carcinoma cells, including suberoylanilide hydroxamic acid (SAHA), were evaluated using flow cytometry. Bone marrow-derived macrophages (BMDMs) were stimulated to M1-like and M2-like phenotypes and treated with SAHA to explore the effects on SSTR2 expression in different macrophage phenotypes. 4T1-tumor-bearing BALB/c mice were used to evaluate the therapy response to four treatments: saline control, SAHA, anti-PD-1 + anti-CTLA-4 checkpoint blockade IMT, or a combination of SAHA + IMT. Additional cohorts of 4T1-tumor-bearing BALB/c mice and NOD SCID mice, which lack adaptive immune cells, were euthanized for early evaluation of tumor-associated macrophage (TAM) populations via flow cytometry and cytokine analysis. One-way independent ANOVAs and log-rank tests were used to compare group differences. Results: SAHA promotes SSTR2 expression on M1-like BMDMs in vitro. SAHA promotes M2-like TAMs in vivo and stimulates pro-inflammatory, anti-tumor cytokine production in combination with IMT. Conclusions: SAHA drives SSTR2 expression and anti-tumor innate immune responses with additive effects in combination with immunotherapy in preclinical TNBC. Full article

We investigate the impact of ultrastrong magnetic fields on the structure of neutron stars within a density-dependent relativistic mean-field framework (DDME2). In the first case, we incorporate a magnetic field framework through Landau quantization of charged particles, yielding anisotropic pressure contributions and showing that field-induced stiffening increases stellar radii, maximum masses, and tidal deformabilities. To capture anisotropic stresses and geometric distortions, we employ axisymmetric equilibrium configurations computed with the XNS 4.0 code under the extended conformally flat condition. For magnetic field strengths up to $4.5\times {10}^{17}$ G, we analyze purely poloidal and toroidal geometries across a representative mass range (1.2–2.0 $M_{\odot }$). Axisymmetric models reveal that purely toroidal fields induce prolate deformations reaching $|\overline{e}| \approx 0.67$ for a 1.2 $M_{\odot }$ star, while purely poloidal fields drive oblate deformations with $\overline{e}\approx 0.24$, both diminishing with increasing stellar mass as greater gravitational binding resists magnetic reshaping. These macroscopic effects, combined with microphysical stiffening, have direct implications for gravitational-wave emission and systematic biases in radius measurements. Our study provides a systematic mapping between magnetic field strength, topology, and dense-matter stiffness, offering constraints relevant to multimessenger observations of magnetized neutron stars. Full article

Cardiovascular diseases remain the leading cause of mortality worldwide, and one of the key mechanisms driving the development of heart failure is pathological remodeling of the myocardium. This process involves complex structural, cellular, and metabolic alterations in which the immune system and its interactions with cardiomyocytes and fibroblasts play a central role. The aim of this work was to present the current state of knowledge on immunometabolism in cardiac remodeling and to discuss its pathophysiological relevance and therapeutic potential. This review focuses on the metabolism of cardiac macrophages, highlighting the differences between the pro-inflammatory (M1) and reparative (M2) phenotypes and their impact on inflammation, fibrosis, and myocardial regeneration. The roles of major metabolic pathways, including glycolysis, oxidative phosphorylation, fatty acid oxidation, and glutaminolysis, are discussed, as well as the importance of the NLRP3 inflammasome and efferocytosis in regulating the inflammatory response. Furthermore, the review briefly incorporates recent insights into neutrophil, T cell, and regulatory T cell (Treg) metabolism and their contributions to inflammation, repair, and fibrotic remodeling. Particular attention is also given to cardiac fibroblasts and their metabolic reprogramming during fibrosis, with emphasis on the pivotal role of transforming growth factor-β (TGF-β) signaling. The review further discusses the role of microRNAs as mediators of intercellular communication integrating immunological and metabolic signals. The work is complemented by a discussion of therapeutic perspectives, including modulation of macrophage metabolism, fibrogenic signaling pathways, mitochondrial function, and miRNA-based therapies. Immunometabolism emerges as a promising research field whose further exploration may contribute to the development of novel, more precise strategies for the treatment of cardiovascular diseases. Full article

This study evaluated the effects of sodium monensin or a blend containing condensed tannins and yeast products on intake, digestibility, performance, and methane emissions in lactating Holstein cows. Nine cows (three rumen-fistulated and six non-fistulated) were assigned to three 3 × 3 Latin squares. The treatments were: a control (CON), sodium monensin (MON; 12 mg/kg of dry matter [DM]), condensed Acacia tannins and Saccharomyces cerevisiae yeast blend (SUP; 2 g/kg of DM). The trial lasted 84 days, with three 28-day periods. Neutral detergent fiber (NDF) intake was higher in CON and SUP (p = 0.029). Milk yield, energy-corrected milk, and milk composition did not differ (p > 0.05). The total methane emissions were not affected by treatments (p > 0.05). Methane yield/Kg of DM intake (DMI), organic matter intake (OMI), and digestible OM tended to be lower in SUP (p = 0.091, p = 0.093, p = 0.086). SUP increased the DM, crude protein (CP), and NDF ingestion rates (p = 0.049, p = 0.028, p = 0.013) and decreased the CP rumen pool (p = 0.014). Rumen pH tended to be higher in SUP (p = 0.067). The potentially digestible NDF digestion rate decreased in MON (p = 0.007). Finally, SUP-treated animals showed a tendency to reduce their methane yield relative to DMI, OMI, and digestible OM. Further studies should investigate the long-term impacts of supplementation, rumen microbiome changes, and underlying mechanisms driving methane mitigation. Full article

Fungal endophytes are symbiotic microorganisms that establish strong relationships inside plant tissues, providing potential advantages, especially in grasses, by enhancing tolerance to both abiotic and biotic stresses. This review investigates the molecular mechanisms through which fungal endophytes mediate stress tolerance, targeting host–pathogen interactions. By modulating pathogen-associated molecular patterns (PAMPs), damage-associated molecular patterns (DAMPs), and effector proteins, fungal endophytes may contribute to priming the plant’s immune system, enhancing its resistance to pathogen invasion. Moreover, endophyte colonization regulates core processes such as osmotic regulation, reactive oxygen species (ROS) detoxification, and secondary metabolite biosynthesis that enable plants to tolerate environmental stresses like drought, heat, and salinity. The review highlights the impact of endophytes on immune priming, systemic acquired resistance (SAR), and the regulation of non-coding RNAs that regulate host gene networks associated with stress tolerance. Furthermore, the integration of advanced multi-omics techniques genomics, transcriptomics, proteomics, metabolomics, and fluxomics has revealed emerging insights into the genetic and metabolic pathways driving these symbiotic associations. However, grass-specific molecular datasets remain limited, and the consistency of endophyte-mediated tolerance across host species and environmental conditions is not yet fully resolved. Fungal endophytes increase grass stress resilience through coordinated pathogen recognition, RNA regulation, and metabolic reprogramming while AI-assisted multi-omics approaches are emerging as tools for identifying candidate regulatory networks, although empirical validation in grass–endophyte systems remains limited. Together, these advances highlight the potential for climate-smart and sustainable crop improvement. Future research integrating functional genomics, field validation, and biosafety assessment will be essential for translating endophyte-based strategies into reliable agricultural applications. Full article