Search Results (1,661)

Expressway networks represent evolving complex systems whose topological properties significantly impact regional development. This paper presents a decision support framework for addressing the expressway infrastructure sequencing problem using computational intelligence. We develop a novel framework that models expressways as L-space networks and evaluates how construction sequences create path-dependent evolutionary trajectories, introducing network science principles into infrastructure planning decisions. Our decision support framework quantifies project impacts on accessibility, connectivity, and reliability using nine topological metrics and a hybrid weighting mechanism that combines domain expertise with entropy-based uncertainty quantification. The system employs a hybrid TOPSIS algorithm that relies on geometric symmetry to simulate network evolution, capturing emergent properties in which each decision restructures possibilities for subsequent choices—a computational challenge that conventional planning approaches have not addressed. The system was validated with real-world Chongqing expressway planning data, demonstrating its ability to identify sequences that maximize synergistic network effects. Results reveal how topologically equivalent projects produce dramatically different system-wide outcomes depending on implementation order. Analysis shows that network science-informed sequencing substantially enhances system performance by exploiting structural synergies. This research advances decision support frameworks by bridging complex network theory with computational decision-making, creating a novel analytical tool that enables transportation authorities to implement evidence-based infrastructure sequencing strategies beyond the reach of conventional planning methods. Full article

(1) Background: Under the dual pressures of global climate change and rapid urbanization, blue–green infrastructure as a nature-based solution is crucial for enhancing urban sustainability. However, there is still a significant cognitive gap regarding the synergy mechanism between its blue and green components and its nonlinear combined impact on sustainability. (2) Method: To fill this gap, this study takes Zhenjiang, a national sponge pilot city in China, as a case and constructs a comprehensive assessment framework. The framework combines multi-source spatio-temporal big data (remote sensing images, point of interest data, mobile phone signaling data) with spatial analysis techniques (geodetectors, Getis-Ord Gi*) to quantify the synergistic effects of blue–green infrastructure on environmental, economic, and social sustainability. (3) Results: The main findings include the following: (1) urban sustainability presents a spatial differentiation pattern of “high in the center, low in the periphery, and multi-core”, and there is a significant positive spatial correlation with the distribution of blue–green infrastructure. (2) The economic dimension, especially daytime population vitality, contributes the most to overall sustainability. (3) Crucially, the co-configuration of sponge facility density and park facility density was identified as the most influential driving mechanism (q = 0.698). In addition, the interaction between the blue infrastructure and the green sponge facilities showed obvious nonlinear enhancement characteristics. Based on spatial matching analysis, the study area was divided into three priority intervention zones: high, medium, and low. (4) Conclusions: This study confirms that it is crucial to view blue–green infrastructure as an interrelated collaborative system. The findings deepen the theoretical understanding of the synergistic empowerment mechanism of blue–green infrastructure and provide scientifically based and actionable policy support for the precise planning of ecological spaces in high-density urbanized areas. Full article

This study investigates the effect of three-dimensional (3D) bioprinted collagen (Col) scaffolds (2% w/v collagen) loaded with autologous bone marrow stromal cells (BMSCs) and enriched with bone morphogenetic protein-2 (BMP-2) and hydroxyapatite-based particles (HAPPs) on bone regeneration in calvarial defects in rabbits. Three implant formulations, Col-(BMP-2) (at a concentration of 80 ng/mL), Col-HAPP (1% w/v) and a mixture of the two—Col-(BMP-2)-HAPP (40 ng/mL final concentration and 0.5% HAPP), were compared with a control group C-Per containing only periosteum to assess the influence of material structure, biochemical signals and cell component on osteogenesis. Histological analysis and quantitative computed tomography (CT) imaging parameters (HU values and residual defect diameter) showed significant differences between the groups, highlighting the role of combined strategies for optimal bone repair. The control group demonstrated the weakest regeneration, expressed by minimal lamellar bone and the largest residual defect. Col-(BMP-2) stimulated moderate osteoinduction with active osteoblasts but without a fully organised lamellar structure. Col-HAΡΡ provided more advanced regeneration, with histologically observed thick osteoid lamellae, early calcification, and structured lamellar architecture, emphasising the osteoconductive role of HAΡΡs. The strongest regeneration was reported with Col-(BMP-2)-HAΡΡ, where the synergy between BMP-2, HAΡΡs and BMSCs resulted in formed osteons, well-developed cancellous bone and minimal residual defects. The established negative correlation between bone density and residual calvarial defects emphasises the relationship between mineralisation and the degree of defect filling. The new data presented demonstrate that the combination of the abovementioned structural, biochemical and cellular factors in 3D bioprinted scaffolds offers a promising strategy for osteoregeneration of complex bone defects. Full article

The synergistic development of digitalization and green transition has become a key driver for promoting China’s high-quality economic development. To elucidate the impact and mechanism of digital–green policy synergy on urban carbon emissions, this paper utilizes the intersection of the “National Big Data Comprehensive Pilot Zones” (BDPZ) and “Low-Carbon City Pilot” (LCCP) programs as a quasi-natural experiment. Based on panel data from 300 prefecture-level cities in China from 2005 to 2023, a multi-period DID model is constructed for empirical research. The empirical results indicate the following: (1) The synergy between digital and green policies significantly curbs urban carbon emissions, and this conclusion remains robust after parallel trend tests and a series of robustness checks. (2) Compared with single digital or green policies, the digital–green synergy exhibits a significantly superior carbon reduction effect. (3) Mechanism analysis reveals that digital–green synergy promotes low-carbon transition primarily through three pathways: driving green technology innovation, promoting the agglomeration of scientific and technological talent, and optimizing the allocation efficiency of capital factors. (4) Heterogeneity analysis reveals stronger emission reduction effects in non-resource-based, eastern, and developed cities, highlighting how structural rigidities and the digital divide constrain the policy’s effectiveness. We suggest strengthening policy integration and adopting differentiated strategies to break path dependence and achieve “Dual Carbon” goals. Full article

Amid intensifying global competition, key core technology breakthroughs have become central to advancing technological self-reliance and strengthening national productive capacity. Using panel data on Chinese A-share listed firms from 2011 to 2023, we adopt a difference-in-differences framework to identify the effect of digital–intelligence policy synergy on firm-level key core technology breakthroughs. The empirical results show that digital–intelligence policy synergy significantly promotes firms’ key core technology breakthroughs, and this finding remains robust to a battery of robustness checks, including a double machine learning approach. Mechanism analyses indicate that digital–intelligence policy synergy promotes breakthroughs through three channels: deeper technology convergence between the digital economy and the real economy, improved industry–research compatibility, and the accumulation of human capital trained for digital–intelligence. Heterogeneity analyses further suggest that the effect is more pronounced among state-owned enterprises, firms in strategic emerging industries, and firms located in regions with stronger intellectual property protection. Overall, this study offers empirical evidence that orchestrating policy synergies is critical for fostering an innovation ecosystem conducive to technological self-reliance. Full article

As an important industrial organizational form in the era of the digital economy, digital industry agglomeration exerts a profound impact on urban ecological resilience. Using panel data of 281 prefecture-level cities in China from 2011 to 2021, this study measures the level of digital industry agglomeration by means of the location entropy method, and constructs an urban ecological resilience evaluation system based on the “Pressure-State-Response (PSR)” model. It systematically examines the impact effects and action mechanisms of digital industry agglomeration on urban ecological resilience. The results show that: (1) The spatio-temporal evolution of the two presents a gradient pattern of “eastern leadership and central-western catch-up”, and their spatial correlation deepens over time, with the synergy maturity in the eastern region being significantly higher than that in the central and western regions. (2) Digital industry agglomeration significantly promotes the improvement in urban ecological resilience, and this conclusion remains valid after endogeneity treatment and robustness tests. (3) The promotional effect is more prominent in central cities, coastal cities, and key environmental protection cities, whose advantages stem from digital infrastructure and innovation endowments, industrial synergy and an open environment, and the adaptability of green technologies under strict environmental regulations, respectively. (4) Digital industry agglomeration empowers ecological resilience by driving green innovation and improving the efficiency of land resource allocation, while the construction of digital infrastructure plays a positive regulatory role. Full article

Curcumin, the major polyphenolic constituent of Curcuma longa, has been widely investigated as a hepatoprotective adjunct due to its antioxidant and immunomodulatory properties. This review evaluates the relevance of curcumin for the prevention and management of liver dysfunction and hepatitis in pigs by synthesizing available porcine evidence and integrating mechanistic insights from translational liver injury models where pig-specific data remain limited. Across experimental hepatic injury contexts, curcumin administration is most consistently associated with reduced biochemical and structural indicators of hepatocellular damage, including decreased aminotransferase activity, attenuation of lipid peroxidation, and enhancement of endogenous antioxidant defenses. These effects are mechanistically linked to suppression of pro-inflammatory signaling pathways, particularly NF-κB-related transcriptional activity and inflammasome-associated responses, together with reduced expression of key cytokines such as TNF-α, IL-1β, and IL-6. Concurrent activation of Nrf2-centered cytoprotective pathways and induction of phase II antioxidant enzymes (including HO-1, GST, and NQO1) appear to constitute a conserved axis supporting hepatic oxidative stress resilience. In swine-relevant infectious settings, available data further support antiviral activity against selected porcine pathogens, including classical swine fever virus and porcine reproductive and respiratory syndrome virus, potentially mediated through interference with lipid-dependent stages of viral replication and modulation of Kupffer cell activation. Although combination strategies with established hepatoprotective approaches are conceptually attractive, current synergy evidence remains heterogeneous and largely extrapolated. Overall, curcumin represents a plausible adjunct candidate for supporting porcine liver health; however, translation into practice will depend on resolving formulation-dependent bioavailability constraints and strengthening the pig-specific evidence base. Full article

High-precision autonomous localization remains a critical challenge for intelligent mining vehicles in GNSS-denied and unstructured coal mine roadways, where traditional odometry-based methods suffer from severe cumulative drift and perceptual aliasing. Inspired by the synergy between mammalian visual cues and cognitive neural mechanisms, this paper proposes a robust biomimetic localization framework that integrates multi-source perception with a prior cognitive map. The core contributions are three-fold: First, a semantic-enhanced biomimetic localization method is developed, leveraging roadway sign data as absolute spatial anchors to suppress long-distance cumulative errors. Second, an optimized head direction (HD) cell model is formulated by incorporating a speed balance factor, kinematic constraints, and a drift correction influence factor, significantly improving the precision of angular perception. Third, boundary-adaptive and sign-based semantic constraint terms are integrated into a continuous attractor network (CAN)-based path integration model, effectively preventing trajectory deviation into non-navigable regions. Comprehensive evaluations conducted in large-scale underground scenarios demonstrate that the proposed framework consistently outperforms conventional IMU-odometry fusion, representative 3D SLAM solutions, and baseline biomimetic algorithms. By effectively integrating semantic landmarks as spatial anchors, the system exhibits superior resilience against cumulative drift, maintaining high localization precision where standard methods typically diverge. The results confirm that our approach significantly enhances both trajectory consistency and heading stability across extensive distances, validating its robustness and scalability in handling the inherent complexities of unstructured coal mine environments for enhanced intrinsic safety. Full article

Cultural ecosystem services (CESs) are the non-material benefits people derive from ecosystems and are important for human well-being. Most research has focused on individual CES supply–demand relationships, with little systematic study of the overall CES structure, interactions, and mechanisms in metropolitan areas. This study takes the Nanjing Metropolitan Area as a case study, integrating multi-source geospatial data and employing the MaxEnt model, self-organizing maps (SOMs), Spearman correlation analysis, and the Optimal Parameters-based Geographical Detector (OPGD). It analyzes supply–demand matching, trade-offs, synergies, and drivers for four CES categories: aesthetic (AE), recreational entertainment (RE), knowledge education (KE), and cultural diversity (CD). The main findings are as follows: (1) CES supply and demand are spatially zoned: the core area has surplus supply, secondary centers are balanced, and the periphery has both weak supply and demand. (2) Three supply–demand bundles have distinct synergy and trade-off patterns: Bundle 1 primarily exhibits strong synergy between AE and CD; Bundle 2 shows a weak trade-off relationship; and Bundle 3 forms a synergy centered on AE. (3) The explanatory power of driving factors exhibits pronounced spatial heterogeneity: Bundle 1 is dominated by non-quantifiable social factors; Bundle 2 features dual synergistic drivers of population and transportation; and Bundle 3 demonstrates synergistic effects driven by facilities and economic factors. Overall, this study contributes an integrated metropolitan-scale framework that connects CES supply–demand mismatch patterns with bundle typologies, interaction structures, and bundle-specific drivers. The results provide an operational basis for targeted planning and coordinated ecological–cultural governance in the Nanjing Metropolitan Area and offer a transferable reference for other metropolitan regions. Full article

By addressing the challenges of management difficulties, insufficient integration of driver analysis, and single-dimensional analysis in the governance of illegal land use and illegal construction (collectively referred to as the “Two Illegalities”) under rapid urbanization, this study designs and implements a GIS-based governance system using Xiamen City as the study area. First, we propose a standardized data-processing workflow and construct a comprehensive management platform integrating multi-source data fusion, spatiotemporal visualization, intelligent analysis, and customized report generation, effectively lowering the barrier for non-professional users. Second, utilizing methods integrated into the platform, such as Moran’s I and centroid trajectory analysis, we deeply analyze the spatiotemporal evolution and driving mechanisms of “Two Illegalities” activities in Xiamen from 2018 to 2023. The results indicate that the distribution of “Two Illegalities” exhibits significant spatial clustering, with hotspots concentrated in urban–rural transition zones. The spatial morphology evolved from multi-core diffusion to the contraction of agglomeration belts. This evolution is essentially the result of the dynamic adaptation between regional economic development gradients, urbanization processes, and policy-enforcement synergy mechanisms. Through a modular, open technical architecture and a “Data-Technology-Enforcement” collaborative mechanism, the system significantly improves information management efficiency and the scientific basis of decision-making. It provides a replicable and scalable technical framework and practical paradigm for similar cities to transform “Two Illegalities” governance from passive disposal to active prevention and control. Full article

To clarify the spatial–temporal evolution patterns and climate-driven mechanisms of carbon sinks of forest ecosystems under climate change, we calculated the net ecosystem productivity (NEP) of forests in the Guangxi region using remote sensing and meteorological data from 2000 to 2023. By employing trend analysis, spatial clustering, the Hurst index, and climate contribution evaluation, we analyzed the spatial and temporal changes, sustainability, and the relative contribution of climate impacts on forest carbon sinks. The results are as follows: The carbon sink capacity of forests in Guangxi increased continuously from 2000 to 2023, at a rate of 3.57 g C·m⁻²·a⁻¹, reaching 39.19% higher in 2023 than in 2000. The carbon sink capacity was higher in the southwest and lower in the northeast, with hotspots mainly located in evergreen/deciduous broad-leaved forest areas. The Hurst index indicates that 84.44% of regions are likely to maintain this increasing trend, suggesting stability in forest carbon sink function. The climate contribution rate to forest carbon sinks was moderate, with significant temporal fluctuations. Temperature governed annual variation in forest carbon sinks, influencing up to 36.37% of the area. The annual average contribution rate of climate change to forest carbon sinks was 30.28%, but there were temporal fluctuations and spatial heterogeneity. Over time, climate contributions had a positive driving impact; however, extreme climate events tended to produce a negative effect. The pattern of forest carbon sinks in Guangxi showed a “heat sink-coupling” phenomenon, with 16.23% of the hotspots of forest carbon sinks coinciding with temperature control zones, highlighting the enhancing effect of temperature rise on carbon sinks against a background of water and heat synergy. This study provides a scientific basis for the assessment of forest carbon sink potential and climate suitability management in Guangxi. Full article

Sepsis is a life-threatening syndrome characterized by marked clinical heterogeneity and complex host–pathogen interactions. Although traditional mechanistic studies have identified key molecular pathways, they remain insufficient to capture the highly dynamic, multifactorial, and systems-level nature of this condition. The advent of high-throughput omics technologies—particularly integrative multi-omics approaches encompassing genomics, transcriptomics, proteomics, and metabolomics—has profoundly reshaped sepsis research by enabling comprehensive profiling of molecular perturbations across biological layers. However, the unprecedented scale, dimensionality, and heterogeneity of multi-omics datasets exceed the analytical capacity of conventional statistical methods, necessitating more advanced computational strategies to derive biologically meaningful and clinically actionable insights. In this context, artificial intelligence (AI) has emerged as a powerful paradigm for decoding the complexity of sepsis. By leveraging machine learning and deep learning algorithms, AI can efficiently process ultra-high-dimensional and heterogeneous multi-omics data, uncover latent molecular patterns, and integrate multilayered biological information into unified predictive frameworks. These capabilities have driven substantial advances in early sepsis detection, molecular subtyping, prognosis prediction, and therapeutic target identification, thereby narrowing the gap between molecular mechanisms and clinical application. As a result, the convergence of AI and multi-omics is redefining sepsis research, shifting the field from descriptive analyses toward predictive, mechanistic, and precision-oriented medicine. Despite these advances, the clinical translation of AI-driven multi-omics approaches in sepsis remains constrained by several challenges, including limited data availability, cohort heterogeneity, restricted interpretability and causal inference, high computational demands, difficulties in integrating static molecular profiles with dynamic clinical data, ethical and governance concerns, and limited generalizability across populations and platforms. Addressing these barriers will require the establishment of standardized, multicenter datasets, the development of explainable and robust AI frameworks, and sustained interdisciplinary collaboration between computational scientists and clinicians. Through these efforts, AI-enabled multi-omics research may progress toward reproducible, interpretable, and equitable clinical implementation. Ultimately, the synergy between artificial intelligence and multi-omics heralds a new era of intelligent discovery and precision medicine in sepsis, with the potential to transform both research paradigms and bedside practice. Full article

In fish gut microbiome studies, there are no standardized protocols regarding sampling region or post-feeding time, nor clear consensus on whether analyses should target resident (autochthonous) or transient (allochthonous) bacteria. This study examined the dynamics and interactions of both microbial communities in the anterior and posterior intestine of farmed gilthead sea bream and evaluated the resident microbiome at 24 and 48 h post-feeding. Microbial DNA was sequenced using the Oxford Nanopore Technology platform. Data were analyzed through statistical and discriminant approaches, as well as a Bayesian network framework to assess bacterial interactions. Transient communities showed higher richness and diversity, regardless of intestinal section, suggesting a more specialized and stable microbial environment in the mucus layer. The two communities differed markedly in structure and composition. Variations associated with intestinal region were less pronounced, particularly for autochthonous bacteria, and post-feeding fluctuations in the resident microbiome were minimal. Functionally, results indicated relevant synergies between communities. Protein metabolism pathways were enriched in autochthonous bacteria, whereas allochthonous microorganisms contributed mainly to bile acid and carbohydrate metabolism. Overall, resident and transient bacteria constitute distinct communities in the gut of gilthead sea bream, with numerous genera present in both but most being differentially represented and interconnected. Full article

The combined implementation of land consolidation and smart agriculture is crucial for food security and agricultural carbon neutrality. Using 2010–2024 panel data from 279 Chinese prefecture-level cities, this study constructs an integrated assessment system and examines impact mechanisms and spatial effects using dual machine learning, mediation analysis, and dynamic spatial models. Results show that the interaction between land consolidation and smart agriculture significantly enhances food security at the 10% significance level and promotes agricultural carbon neutrality. Mechanism analysis indicates that agricultural industrial agglomeration positively contributes to both outcomes, while technological innovation significantly promotes carbon neutrality but temporarily suppresses food security. Spatial analysis reveals limited direct effects on local food security but positive indirect and total effects on neighboring regions, with carbon neutrality showing positive direct, indirect, and total effects. After controlling for city fixed effects and quadratic terms, the synergy remains significant, indicating robustness. The study suggests strengthening coordinated governance and innovation-driven regional development to jointly advance food security and agricultural carbon neutrality. Full article

This study evaluates the effectiveness of integrating a Residual Shifting (ResShift)-based deep learning super-resolution (SR) technique with the Robust Dense Feature Matching (RoMa) algorithm for high-precision inter-satellite image registration. The key findings of this research are as follows: (1) Enhancement of Structural Details: Quadrupling image resolution via the ResShift SR model significantly improved the distinctness of edges and corners, leading to superior feature matching performance compared to original resolution data. (2) Superiority of Dense Matching: The RoMa model consistently delivered overwhelming results, maintaining a minimum of 2300 correct matches (NCM) across all datasets, which substantially outperformed existing sparse matching models such as SuperPoint + LightGlue (SPLG) (minimum 177 NCM) and SuperPoint + SuperGlue (SPSG). (3) Seasonal Robustness: The proposed framework demonstrated exceptional stability, maintaining registration errors below 0.5 pixels even in challenging summer–winter image pairs affected by cloud cover and spectral variations. (4) Geospatial Reliability: Integration of SR-derived homography with RoMa achieved a significant reduction in geographic distance errors, confirming the robustness of the dense matching paradigm for multi-sensor and multi-temporal satellite data fusion. These findings validate that the synergy between diffusion-based SR and dense feature matching provides a robust technological foundation for autonomous, high-precision satellite image registration. Full article