Search Results (78,173)

The external relations of small towns are an important dimension in the regional urban system. However, the “metropolitan bias” in existing studies results in a lack of empirical verification of their characteristics, hindering effective regional policymaking. Applying Central Flow Theory (CFT), mobile phone data, and a multiscale geographically weighted regression (MGWR) model, this study investigates the spatially non-stationary associations between built environment factors and the “city-ness” and “town-ness” of small towns in the Yangtze River Delta. The results show: (1) Enterprise density in metropolitan shadow areas is positively associated with cross-city jobs–housing separation; in peripheral areas, both enterprise density and housing prices exhibit a strong correlation with intra-municipal jobs–housing separation. (2) Middle schools consistently correlate with localized intra-municipal flows, suggesting a plausible spatial anchoring role; around metropolises, medical and commercial facilities link to recreational flows and commuting town-ness, while in distal small towns, medical facilities coincide with intratown jobs–housing balance, and commercial facilities correlate with localized consumption and cross-town employment mobility. (3) Higher road network density corresponds to a shrinking commuting radius near metropolises and intra-municipal intertown interconnection in distal towns, rather than mere external relation channels. This study empirically supports CFT at the small-town scale, explores plausible mechanisms, and informs differentiated planning strategies. Full article

Type 2 endoleaks (EL2s) are potentially life-threatening complications, defined as persistent arterial perfusion of the excluded aneurysmal sac after endovascular aneurysm repair (EVAR). Most EL2s are managed endovascularly, through embolization of the aneurysmal sac and its arterial feeders. During embolization, attention should be given to anatomical variants such as “corona mortis”, an arterial anastomosis connecting external iliac (via inferior epigastric) and internal iliac (via obturator) arteries. We present the case of an 88-year-old male previously treated with EVAR for a left common iliac artery aneurysm (CIAA), complicated by EL2 originating from the ipsilateral ilio-lumbar branch of the internal iliac artery. Successful embolization of the endoleak was achieved through catheterization of the inferior epigastric artery, taking advantage of the “corona mortis” variant. This route allowed access to the sac and embolization with ethylene-vinyl-alcohol-copolymer. This approach represents a safe alternative to direct sac puncture or superior gluteal artery access in patients exhibiting this anatomical variant. Full article

Rising consumer demand for functional beverages has accelerated the development of health-promoting, fruit-based ready-to-drink (RTD) products. This study investigated the effects of incubation temperature (50–80 °C) and time (60–240 min) on pectinase-assisted extraction (0.1% v/v) of date palm (Phoenix dactylifera L., Bahi variety) juice and subsequently formulated antioxidant-rich RTD beverages by blending the optimized extract with quince and jujube juices. The optimal extraction condition (50 °C, 60 min) was selected based on maximizing bioactive compound recovery rather than yield, achieving total phenolic content of 326.33 mg GAE/100 mL, total carotenoid content of 1.08 mg β-carotene equivalents/100 mL, and strong antioxidant activity (DPPH: 514.06; FRAP: 595.38 µmol TE/100 mL). Although maximum yield (81.25%) was obtained at 60 °C for 240 min, functional quality was prioritized. Six RTD formulations were developed using a constrained simplex-lattice mixture design. All blends exhibited significantly enhanced phenolic content, carotenoids, and antioxidant capacity compared to the control, while pH and acidity remained stable (p > 0.05). Sensory evaluation indicated that the formulation containing 70% date palm, 15% quince, and 15% jujube achieved the highest acceptability (6.50). These findings highlight the potential of this tri-fruit blend as a functional RTD beverage, warranting further studies on shelf-life stability. Full article

The increasing deployment of autonomous vehicles in port logistics requires safety assessment methods that remain valid in mixed traffic environments. This study evaluates the safety of mixed automated guided vehicle (AGV) and human-driven vehicle (HDV) traffic in a seaport terminal connected to an external urban road network. A microscopic traffic model was developed in AIMSUN Next to represent gate areas, internal roads, storage-yard access, berth interfaces, and external container-truck traffic. HDVs were modeled using a Gipps-based car-following model, whereas AGVs were represented through an Adaptive Cruise Control framework. Vehicle trajectories were exported to the Surrogate Safety Assessment Model (SSAM), where Time-to-Collision (TTC) and Post-Encroachment Time (PET) were used to detect and classify conflicts. Six staged fleet-composition scenarios were evaluated in 36 simulation runs, ranging from fully human-driven operation to full automation. Total conflicts decreased from 89 in the fully human-driven scenario to 43 in the fully automated scenario (−51.7%), while rear-end conflicts decreased from 70 to 30 (−57.1%). Crossing conflicts remained relatively stable across scenarios. At the same time, mean TTC decreased from 0.80 to 0.24 s and mean PET from 1.57 to 0.38 s, indicating tighter but more coordinated interactions under automated control. These results show that automation improves longitudinal safety performance in port traffic, but also that conventional TTC and PET thresholds calibrated for human-driven traffic may not be directly applicable to automated port operations. Automation-sensitive surrogate safety criteria are therefore needed for seaport mixed-traffic evaluation. Full article

With the shortage of natural aggregates and the massive accumulation of iron tailings (ITs) solid waste restricting the sustainable development of asphalt pavement engineering, replacing natural aggregates with ITs has become a promising low-carbon solution with prominent economic and social benefits. However, the poor interfacial adhesion between ITs and asphalt severely restricts the engineering application of tailings, and the micro-interaction mechanism at their interface still lacks systematic clarification, which is the key research gap addressed in this work. Different from conventional macro road performance tests, this study innovatively combined molecular dynamics (MD) simulation with microscopic characterization, including Fourier transform infrared spectroscopy (FT-IR) and atomic force microscopy (AFM), to comprehensively reveal the interfacial interaction mechanism between ITs and asphalt at the molecular and microscales. The results indicate that asphalt molecules exhibit higher aggregation concentration and diffusivity on Al₂O₃ and Fe₂O₃ surfaces than on SiO₂ surfaces, proving stronger interfacial interaction between asphalt and iron-rich oxide minerals. Moderate temperature optimizes the adhesion performance of asphalt with Al₂O₃ and Fe₂O₃, while the interfacial bonding of asphalt on CaCO₃ and SiO₂ weakens as temperature rises. The silane coupling agent KH-550 can effectively react with acidic minerals, SiO₂ minerals in ITs, which significantly increases the concentration, diffusion coefficient, and distribution uniformity of asphalt molecules at the interface. FT-IR results verify that the combination of ITs and asphalt mainly relies on physical adsorption without generating new chemical bonds. AFM tests further confirm that alkaline minerals improve the surface roughness of asphalt mastic, and KH-550 greatly enhances the micro-adhesion force of the interface. The novelty of this work lies in clarifying the mechanism of typical mineral components in ITs and revealing the modification enhancement law of silane coupling agent and alkali minerals at the micro level. This study provides a scientific theoretical support for the high-value engineering utilization of ITs in asphalt pavement, and offers a reference for optimizing the interfacial modification design of solid waste aggregate. Full article

Digital economy and agricultural technological innovation are key drivers of agricultural economic resilience and sustainable development. However, existing research has yet to clarify how they jointly affect agricultural economic resilience, particularly through potential nonlinear patterns and spatial spillover effects. Using panel data from 30 Chinese provinces, this study measures digital economy development and agricultural economic resilience via the entropy weight method. It systematically examines the direct impact, transmission mechanisms, threshold effects, and spatial spillover effects using two-way fixed effects, mediation, threshold regression, and spatial Durbin models. The findings are as follows. First, the digital economy significantly improves agricultural economic resilience, a result robust to various tests and endogeneity treatments. Second, agricultural technological innovation plays a partial mediating role, accounting for 19.37% of the total effect. Third, the resilience-enhancing effect of agricultural technological innovation exhibits a double-threshold pattern: its positive impact gradually strengthens as the digital economy develops to a higher level. Fourth, the digital economy generates a positive spatial spillover effect on agricultural economic resilience. Fifth, although the digital economy and agricultural technological innovation show synergistic development, their coupling coordination degree remains relatively low, indicating substantial untapped potential for synergy. From a sustainable development perspective, this study reveals the mechanisms through which the digital economy and agricultural technological innovation enhance agricultural economic resilience, providing empirical evidence and policy insights for strengthening agricultural risk resistance and achieving agricultural sustainability via digital transformation and technological progress. Full article

The growing global population and increasing pressure on conventional food systems have intensified the search for sustainable and nutrient-rich protein sources. Blue foods derived from marine and freshwater organisms offer significant nutritional advantages and lower environmental footprints compared with many terrestrial animal proteins. However, challenges related to resource sustainability, processing, preservation, and product traceability limit their full potential. This review provides a broad overview of emerging technologies shaping the future of blue food systems, covering innovative production strategies, advanced processing techniques, and omics-based analytical approaches. Key developments in cellular aquaculture and cellular mariculture are discussed as promising alternatives to traditional fisheries and aquaculture, enabling the production of blue food through controlled cell cultivation. Additionally, alternative protein platforms including plant-based, fermentation-derived, and cultivated blue food analogues are assessed for their potential to enhance sustainability and diversify aquatic protein sources. Advanced structuring technologies such as extrusion, electrospinning, wet spinning, and 3D printing are highlighted for their roles in developing blue food analogues with improved texture and sensory attributes. Furthermore, non-thermal preservation techniques, including cold plasma (CP), high-pressure processing (HPP), pulsed electric fields (PEFs), and ultraviolet-based treatments, are reviewed for their effectiveness in improving microbial safety and extending shelf life while maintaining nutritional quality. The integration of omics technologies (proteomics, metabolomics, and lipidomics) provides deeper molecular insights into product quality, authenticity, and traceability within blue food supply chains. Collectively, these interdisciplinary advancements demonstrate strong potential to transform blue food production into a more resilient, sustainable, and technology-driven sector. Future progress will depend on overcoming challenges related to scalability, regulatory frameworks, and consumer acceptance to enable the successful commercialization of next-generation blue food products. Full article

The study presents an engineering solution for maintaining traffic flow during road and utility operations, such as trench excavation. The analysis of existing organizational and technical approaches, along with global experience in temporary bridge use, showed that most foreign analogs were developed for military purposes and are not fully suitable for urban conditions in Kazakhstan and CIS countries. As an alternative solution, the development of a mobile overpass adapted for operation in dense urban environments is proposed. The present study continues earlier research focused on optimizing the placement of mobile overpass supports while accounting for the nonlinear deformation behavior of the soil foundation. At the previous stage, a rational distance between the supports and the trench edge was substantiated, and horizontal soil deformations were reduced. In the current study, the primary focus is on the design of the undercarriage, which determines the mobility, stability, and operational feasibility of the structure. A morphological analysis and synthesis method is applied to select a rational configuration of the undercarriage. A 3D model and a 1:4 scale test bench were developed, followed by load tests of 50–200 kg. The maximum deflection of −1.19 mm at 200 kg demonstrated an almost linear deformation pattern. The constructed regression model ($R^2=0.97$) confirmed the accuracy and reliability of the design. The developed mobile overpass is versatile, cost-effective, and practical, improving the resilience of urban transport infrastructure, reducing traffic congestion during roadworks, and creating a foundation for serial production in Kazakhstan and CIS countries. Full article

Personalized federated learning (PFL) faces significant challenges in resource-constrained edge environments, where strict communication budgets and severe system heterogeneity must be jointly addressed. Although one-shot federated learning reduces communication overhead, existing methods typically impose unified model architectures or rely on coarse manual selection strategies, limiting their adaptability to highly heterogeneous data distributions and restricting personalized representation capability. To overcome these limitations, we propose Personalized Federated Zero-shot Knowledge Distillation (pFedZKD), a data-free one-shot federated learning framework designed for structurally heterogeneous scenarios. The framework follows a decouple-and-reconstruct collaborative paradigm. On the client side (decoupling stage), we introduce Particle Swarm Optimization-based Federated Neural Architecture Search (PSO-FedNAS), a gradient-free neural architecture search method that enables each client to autonomously discover a customized convolutional architecture aligned with its local data distribution, eliminating the need for architectural consistency across clients. On the server side (reconstruction stage), to address parameter-space incompatibility caused by structural heterogeneity, we develop an architecture-agnostic multi-teacher zero-shot knowledge distillation mechanism (Multi-ZSKD). This method synthesizes pseudo-samples in latent space to extract semantic consensus from heterogeneous client models and transfers the aggregated knowledge to a unified global student model without accessing real data. The entire collaborative process is completed within a single communication round, substantially reducing communication cost while enhancing privacy preservation. Extensive experiments on MNIST, FashionMNIST, SVHN, and CIFAR-10 under heterogeneous data settings demonstrate that pFedZKD consistently achieves superior personalization accuracy, global generalization performance, and communication efficiency compared with state-of-the-art PFL methods. Full article

Temporal lobe epilepsy (TLE) is the most common focal epilepsy in adults, but cell-type-specific molecular alterations in the epileptic cortex remain incompletely characterized. We performed single-nucleus RNA sequencing on temporal cortex from three patients with drug-resistant TLE and two non-epileptic controls, retaining 66,932 nuclei. Seven major cell types were annotated. Neuropeptide Y (NPY) was significantly upregulated in microglia and oligodendrocytes under stringent criteria (|log₂FC| > 1, adjusted p < 0.01), whereas changes in other cell types did not meet this threshold. Microglia showed enrichment of neuropeptide- and inflammatory-related pathways, together with reduced oxidative phosphorylation signatures. Oligodendrocytes showed altered lipid metabolism, together with reduced mitochondrial energy-related signatures. Inferred intercellular communication was globally reduced in the TLE samples. qPCR in an independent small set showed an upward trend of NPY expression, though not statistically significant. Given the limited cohort size, these results should be interpreted as exploratory. They provide a cell-type-resolved candidate framework for future mechanistic studies of glial-associated responses in human epilepsy. Full article

Exploring the evolutionary dynamics of urban, agricultural, and ecological spaces is critical for regional sustainable development and spatial governance. However, traditional spatial simulation methods based on Cellular Automata often struggle to accommodate top-down spatial regulation, non-linear development patterns, and coordinated regional growth. The objective of this scientific research is to address these limitations by proposing a deep learning-based framework for simulating the future distribution of these three spaces. Utilizing the Unet++ model and integrating empirical data sources including multi-period remote sensing land-use mapping and prefecture-level socioeconomic statistical data, the framework predicts regional spatial patterns for the year 2030. Empirical results from the Yangtze River Economic Belt demonstrate that the model achieves high precision in large-scale spatial forecasting (with an average test accuracy of 99.32%) and effectively captures non-linear evolutionary characteristics. Predictions across various growth scenarios reveal that a moderate socioeconomic growth rate facilitates ecological preservation; controlling the expansion of urban space to approximately 20% by 2030 can prevent excessive resource depletion and regional imbalances. Consequently, it is recommended to implement the construction land increment targets outlined in current spatial planning to achieve a balance between economic growth and ecological protection. Full article

Geological hazards are characterized by their sudden occurrence, high destructiveness, and wide spatial impact. In particular, landslides and debris flows triggered by earthquakes and intense rainfall often lead to severe casualties and substantial property losses. Therefore, the rapid delineation of affected areas is crucial for disaster assessment and post-disaster reconstruction. To this end, several geohazard datasets have been developed from remote sensing imagery, focusing on specific regions, disaster types, and data sources, providing valuable support for geohazard detection and risk assessment. Our study addresses the diversity of real-world geological disasters in terms of their types, causes, and spatial distribution and constructs the Composite Geological Hazards Dataset (CGHD), a dual-temporal geohazard dataset that enhances generalisation and practical applicability. CGHD incorporates pre- and post-disaster remote sensing images of 14 landslide and debris flow events that occurred worldwide between 2017 and 2024, collected using four remote sensing platforms and encompassing multiple spatial scales and land-cover categories. The affected areas varied significantly in size and shape, with land-cover types including roads, buildings, vegetation, farmland, and water bodies. This resulted in 3963 pairs of pre- and post-disaster images, each with a size of 1024 × 1024 pixels. We validated the reliability of the CGHD through experiments with nine change-detection models and further evaluated its generalisation capability using an unseen dataset. The experimental results demonstrate that CGHD achieves high recognition accuracy and strong generalisation across diverse geographic environments, providing comprehensive data support for intelligent geohazard recognition and disaster assessment. Full article

A keloid is a benign fibroproliferative cutaneous disorder characterized by excessive extracellular matrix deposition, which is driven by persistent fibroblast proliferation and aberrant wound healing. Its complex pathogenesis involves genetic susceptibility, chronic inflammation, mechanical tension and dysregulated cellular signaling, resulting in poor clinical efficacy and high recurrence rates. Cellular senescence has recently become a central focus in exploring keloid pathophysiology, offering a novel perspective for elucidating its initiation, progression and recurrence. This review systematically summarizes the biological roles of cellular senescence in keloid pathology: it elaborates on the basic concepts and core molecular features of cellular senescence, details the spatial heterogeneity of senescent cell accumulation, the activation and pathological effects of senescence-associated secretory phenotype (SASP), and clarifies the molecular link between senescence-resumed proliferation (SRP) and keloid recurrence and treatment resistance. It also summarizes advances in senescence-related markers, the regulatory roles of the p53/p21 and Wnt/β-catenin pathways, and potential senescence-targeted therapies (senolytic, senomorphic, signaling intervention, cell reprogramming). Finally, we discuss the challenges and future perspectives for translating senescence research into clinical keloid treatments, aiming to provide a novel theoretical framework and therapeutic targets for keloid management. Full article

Due to their characteristics of a high power-to-weight ratio, stringent lightweight requirements, and harsh working environments, straight face gears are prone to issues such as tooth fracture and inadequate fatigue strength. Meanwhile, because of the lack of fatigue information and weak fatigue life prediction method, the fatigue life of face gears cannot be effectively evaluated. In this study, the key technologies involved in the hot rolling forming process, fatigue experiments, and numerical modeling of straight face gears are studied. A technical foundation for straight face gears formed by hot rolling processing is established, and a fatigue experiment of the hot rolling forming of straight face gears is carried out. Due to the lack of information on fatigue experiments, a numerical prediction model is constructed. Sample expansion is carried out using a BP neural network–Bootstrap model to calculate the reliable lifespan of hot-rolled straight face gears, and fatigue life prediction for hot-rolled straight face gears is completed via the improved GM(1,1,λ) model based on the artificial bee colony algorithm, and thus the accurate evaluation of the fatigue life of rolling forming face gears is realized. The feasibility and superiority of the improved fatigue life prediction model are demonstrated by comparing it with the traditional prediction model and experimental results. The theoretical basis and technical support for the research of the fatigue resistance and installation application of face gears are provided. Full article