Search Results (276)

Background/Objectives: Serial passage in embryonated eggs is widely used to attenuate the infectious bronchitis virus (IBV) for vaccine production; however, the evolutionary processes underlying attenuation and immunogenicity remain incompletely understood. Here, we analyzed genome-wide viral evolution during serial passages to investigate how mutations emerge, persist, are lost, or become fixed over time and how these dynamics relate to changes in pathogenicity and immunogenicity. Methods: Deep sequencing was performed on 11 representative serial passages (P2–P79) of the UY/11/CA/18 strain, including two derivative lineages: P7 VIR (virulent) and P53 VAC (attenuated and immunogenic). Results: This study identified an early adaptive phase characterized by a limited set of mutations potentially associated with genome replication, viral RNA processing, and virion assembly, including a key change in non-structural protein 14 and variants in M and 3c (E). This phase was followed by a broader expansion of the variant spectrum across replicase genes and delayed accumulation of Spike protein variants. Most Spike changes emerged during later passages and exhibited transient dynamics, and only a subset reached a high frequency after the establishment of early replicase- and structural-associated changes. Consistent with these dynamics, P7 VIR diverged before the late accumulation of Spike variants and retained a pathogenic phenotype, whereas P53 VAC diverged after the emergence of early high-frequency variants but before the extensive late-stage Spike variation observed in P79, which was associated with reduced immunogenicity. Conclusions: These findings support a multi-step model of IBV attenuation in which progressive filtering of genome-wide variation shapes distinct evolutionary outcomes during serial passages. This evolutionary framework provides insight into the relationship between attenuation and immunogenicity and may help guide the rational design of live attenuated vaccines. Full article

Rural e-commerce is reshaping agricultural markets, yet its environmental consequences remain insufficiently understood. This study examines how the Rural E-commerce Comprehensive Demonstration (RECD) program affects agricultural carbon outcomes in China. Using a balanced panel of 2152 counties from 2010 to 2022, we employ a multi-period difference-in-differences (DID) model to identify the effect of the RECD policy. The results show that the RECD policy significantly increases total agricultural carbon emissions. Evidence for production expansion and production restructuring suggests that improved market access and stronger price incentives encourage output expansion and a shift toward more market-oriented production, thereby raising aggregate emissions. At the same time, the RECD policy significantly reduces the carbon emission intensity and improves the carbon emission efficiency, indicating better carbon performance per unit of agricultural output. Further analysis shows that this dual result reflects the coexistence of efficiency gains and scale expansion, with the scale effect dominating the technical effect at the current stage. The emission-increasing effect is more pronounced in balanced agricultural areas, poverty-designated counties, counties with weaker initial e-commerce foundations, and counties with higher initial emission levels, while stronger environmental regulation and green technological innovation significantly mitigate this effect. In addition, the RECD policy generates spillover effects on neighboring counties within 50 km. These findings provide empirical evidence on the effects of the RECD policy on agricultural carbon emissions and offer policy guidance for integrating rural e-commerce policies with low-carbon agricultural transformation. Full article

Polycythemia vera (PV) is a JAK2V617F-driven myeloproliferative neoplasm characterized by erythroid expansion, increased thrombotic risk, and heterogeneous clinical outcomes. Although prior studies have described key transcriptional abnormalities—including Janus kinase–signal transducer and activator of transcription (JAK–STAT) hyperactivation and chronic myeloinflammation—most have examined single hematopoietic compartments. A multi-compartment approach may reveal conserved and lineage-specific disease-associated transcriptional programs. Here, an integrated, multi-compartment transcriptomic analysis of publicly available microarray datasets was performed, spanning bone marrow (BM) CD34+ progenitors, peripheral blood (PB) CD34+ progenitors, and whole blood from PV patients and healthy controls, with independent validation in neutrophils. Differential gene expression, pathway enrichment, and protein–protein interaction network analyses were used to delineate conserved versus compartment-specific transcriptional programs and to evaluate persistence of progenitor-derived signatures into mature myeloid cells. Across compartments, PV demonstrated consistent enrichment of inflammatory, interferon, and JAK–STAT-associated pathways despite limited overlap at the individual gene level, indicating that core disease processes are maintained through lineage- and differentiation-stage-specific transcriptional reprogramming. Network analysis identified highly connected hub genes, which were used to derive a single-sample gene set enrichment (ssGSEA) signature. This signature showed strong diagnostic performance across cohorts; remained enriched in PV neutrophils; and correlated with platelet count, indolent disease status, and reduced levels in post-splenectomy patients. Together, these findings support a model in which PV is driven by stable, progenitor-derived inflammatory programs that persist across myeloid differentiation while incorporating compartment-specific adaptations, and highlight the value of multi-compartment, network-based approaches for translational biomarker development. Full article

With the rapid expansion of the smart clothing market, designers face increasing pressure to balance functional performance, material suitability, environmental impact, and development efficiency. Conventional design workflows and rule-based assistance methods often struggle to provide adaptive and data-driven support for multi-constraint decision-making. To address this issue, this study proposes an AI-driven decision support system for sustainable smart clothing design based on a multi-scale dynamic graph convolutional network (MDGCN). The proposed system integrates material properties, environmental indicators, and user-oriented design requirements into a unified decision-support framework and further enhances feature extraction through an attention mechanism. Two datasets, the Wearable Technology Material Properties Dataset (WTMPD) and the Environmental Impact Assessment Dataset (EIAD), were used to validate the model and system effectiveness. Experimental results showed that the MDGCN-based model achieved accuracies of 0.964 and 0.943, with recalls of 0.923 and 0.920 on the WTMPD and EIAD datasets, respectively. In system-level evaluation, the proposed decision support system reduced design time from 120 h to 60 h, improved material selection accuracy to 90.2%, and achieved superior operational performance in terms of resource utilization (77.45%), energy consumption (115.25 kWh), and response time (1.56 s). These results demonstrate that the proposed framework can effectively support complex design decision-making while improving efficiency, sustainability, and adaptability in smart clothing development. The study provides a practical AI-enabled system innovation approach for sustainable smart clothing design by linking flexible material selection, environmental impact prediction, and designer-oriented decision support. In addition, the prototype deployment demonstrates the feasibility of applying the proposed system as a design-stage wearable AI tool for mediating human, technological, and environmental considerations in smart clothing development. Full article

Land-use planning in ecologically sensitive landscapes requires balancing biodiversity conservation, ecosystem service provision, agricultural production, settlement expansion, and infrastructure demand within a single spatial system. This challenge is particularly significant in Mediterranean environments, where long-term land transformations and increasing development pressures intensify conflicts among competing land-use priorities. Accordingly, the present study develops an integrated spatial zoning and decision-support framework for Osmaniye Province, southern Türkiye. The framework integrates fuzzy multi-criteria evaluation, CatBoost-based machine learning, SHAP-based interpretability, and NSGA-II multi-objective optimization. The workflow followed a sequential decision process in which an expert-derived zoning surface was first established through fuzzy evaluation, reconstructed from continuous spatial predictors using CatBoost, interpreted through SHAP, and refined through NSGA-II under explicit spatial constraints. By using the expert-derived zoning surface as the learning target, the CatBoost stage aimed to evaluate the internal consistency and spatial learnability of the planning logic within a present-day zoning context. The results indicated that the integrated framework distinguished conservation, controlled-use, and development priorities while identifying the key environmental and anthropogenic drivers shaping class-specific zoning outcomes. The final zoning structure allocated 37.9% of the study area to conservation, 43.6% to controlled use, and 18.5% to development. The study shows that by including a transitional zone with varying proportions of conservation, controlled use, and development, a more balanced distribution among the three goals can be achieved compared to a fixed partition into these three zones. The findings further demonstrate that this approach is more effective than current zoning, which does not accommodate such trade-offs. Full article

As a novel urban leisure activity, Citywalk is reshaping the spatial organization of urban tourism resources and spatial experience patterns. This phenomenon provides a crucial entry point for understanding new tourist–destination relationships from the perspective of spatial behavior. This paper takes Harbin, an Internet-Famous City (IFC), as a case study and integrates multi-source data, including pedestrian trajectories, social media texts, and urban infrastructure. A cross-modal analytical framework for Citywalk networks is constructed to examine the structural evolution of Citywalk networks and the relationship between digital-space and physical-space in the context of IFCs. The results indicate that: (1) During its rise as an IFC, Harbin’s Citywalk network transformed from a single-core agglomeration structure to a multi-nodal radial structure, exhibiting a pattern of core reinforcement and outward expansion. (2) Online visibility was associated with the emergence of new nodes and network expansion, but a structural misalignment was observed between digital-space association and physical-space linkage. (3) Emotional differentiation among newly visible nodes further reflected the uneven development of the Citywalk network, while concentrated digital attention was accompanied by persistent structural imbalance. This study highlights the digital–physical misalignment in urban tourism networks, suggests the important role of social media in shaping tourists’ route imagination and emotional evaluation, and provides references for the spatial optimization and sustainable management of urban tourism resources in the new development stage. Full article

In arid inland watersheds, the compounding impacts of climate change and intensive human activities have severely altered hydrological regimes and accelerated landscape degradation. However, conventional spatial planning often overlooks the critical coupling between subsurface hydrological processes and surface landscape dynamics. Taking the Manas River Watershed in northwestern China as a representative case, this research investigates the multi-scale dynamics of landscape patterns and their underlying spatial determinants. Integrating multi-period land-use data (2000–2020), landscape metrics, and the GeoDetector model, we diverge from conventional uniform buffer approaches by redefining riparian boundaries utilizing four distinct River–Groundwater Transformation (RGT) patterns. This methodological shift reveals critical eco-hydrological heterogeneities previously masked by fixed-width approaches. Our multi-scale analyses demonstrate that watershed-level landscapes exhibited a trajectory of declining diversity, transient recovery, and ultimately, intensified fragmentation, while riparian patches concurrently expanded and became increasingly homogenized. GeoDetector assessments indicate a fundamental shift in driving forces: early-stage variations were constrained by natural factors, whereas post-2010 dynamics became overwhelmingly dominated by socio-economic determinants, particularly agricultural expansion and GDP growth. Crucially, our RGT-coupled spatial analysis reveals a strong spatial association between agricultural sprawl and landscape risk hotspots concentrated within groundwater overflow zones—a pattern consistent with, but not directly demonstrating, disrupted vertical hydrological connectivity. Direct verification of subsurface mechanisms would require continuous piezometric monitoring beyond the scope of this study. Consequently, rather than generic zoning, we propose a multi-scale “hydro-spatial” governance framework featuring targeted interventions. By establishing strict agricultural redlines in vulnerable overflow zones and implementing eco-hydrological restoration tailored to specific RGT regimes, this paradigm delivers robust methodological insights for advancing precision spatial planning in fragile arid ecosystems. Full article

Coastal urbanization is increasingly constrained by legacy land-use patterns and escalating climate risks, yet long-term morphological trajectories remain poorly quantified due to the absence of multispectral data in pre-satellite archives. This study introduces a scalable deep learning pipeline that bridges a century-scale domain gap through an attention-enhanced Pix2Pix colorization stage and a few-shot U-Net++ segmentation stage, enabling automated reconstruction of urban expansion from panchromatic historical aerial imagery (1920–1971) and digital aerial photographs (1997) to contemporary very-high-resolution satellite data (2024) in Les Sables-d’Olonne, France. The novelty of the approach lies in coupling generative colorization with epoch-specific fine-tuning to overcome radiometric and annotation bottlenecks that have historically prevented quantitative urban reconstruction from pre-satellite archives. The colorization stage achieved high spectral fidelity (PSNR 35.21 dB, SSIM 0.9762), and segmentation performed strongly on modern imagery (mIoU 0.9789). While the segmentation model performed strongly on modern imagery, direct transfer to historical data exhibited substantial domain shift due to radiometric discrepancies. Few-shot adaptation on year-specific calibration sets recovered reliable building footprints (mIoU 0.53–0.65) across the full timeline. Multi-scalar analysis of the reconstructed footprints revealed constrained anisotropic expansion: early saturation of the coastal historic core, followed by rapid inland peri-urbanization post-1971 driven by geographic barriers. This spatiotemporal shift has entrenched spatial lock-in, placing recent development in retro-littoral zones that are vulnerable to submersion and characterized by severe vegetation loss. The framework unlocks previously inaccessible historical archives for quantitative urban monitoring, providing critical insights into legacy effects of unconstrained growth and informing resilient coastal planning under climate change. Full article

Urban design that improves microclimate can significantly enhance the ecological livability of human settlements, while the climate-adaptive wisdom of applying local water-net landscapes to modern urban renewal requires further validation. To investigate the optimization mechanism of waterscape on microclimate comfort, this study focuses on the public space of Xinsheng District in the Suzhou water-net region. By integrating continuous incremental multi-scenario form design, computational fluid dynamics (CFD) multi-physics simulation, and climate sensation evaluation, we reproduce the spatial differentiation of microclimate and comfort gradients across multi-hour periods during hot summer daytime within the built-up environment involving waterbodies, vegetation, and buildings. Consequently, an indicator of comfort improvement efficiency (CIE) is proposed to measure the spatial effectiveness of per-unit-area water surface expansion on climate sensation. Results show that when controlling other morphological parameters and designing three incremental waterbody scenarios—no water surface, 50% water, and 100% waterscape—the relative comfort area expanded across all time periods as water increased. This implies that waterscape variations exert a positive effect on microclimate suitability. However, during the expansion of water area at each time, the CIE was higher in the 0–50% initial stage of water surface increase compared to the 50–100% later morphological stage. Therefore, this study reveals the stepwise nonlinear trend by which increased water area in the built-up environment improves the climate suitability of waterfront spaces. Furthermore, under constraints of equivalent area and other geometric forms, a more dispersed and networked waterscape was found to be a superior spatial strategy. This confirms the microclimate wisdom of the water-net landscape in the Jiangnan locality, providing form optimization guidance for ecologically oriented urban renewal design. Full article

To simulate the erosion damage behavior of thermal barrier coatings (TBCs) under actual service conditions in an aircraft engine environment, this study developed a multi-factor coupled test setup capable of simulating combined loading under high-temperature (1150 °C), high-speed (0.4 Mach), and solid-particle erosion conditions. Yttria-stabilized zirconia (YSZ) TBCs were prepared using electron beam physical vapor deposition (EB-PVD). For different erosion durations (2 h, 5 h, 8 h, 12 h), the evolution of macroscopic and microscopic morphologies as well as the development of residual stresses in the thermally grown oxide (TGO) layer were systematically investigated. The results indicate that the erosion process of the YSZ coating can be divided into three stages. During the initial high-erosion-rate stage (8.17 g/kg), erosion damage was confined to the grain tips of the columnar crystals, primarily caused by brittle fracture at the grain tips, and the TGO stress was relatively low (−0.6 GPa). During the intermediate stage, the erosion rate was lower (2.74 g/kg). Impact stresses induced microcracks within the columnar grains, which gradually connected to form intergranular fractures. This led to the expansion of localized spalling pits. The interface began to wrinkle, and the stress rose to −2.2 GPa. In the final accelerated failure stage (5.88 g/kg), horizontal cracks fully propagated, leading to large-scale peeling of the coating. The stress was released to −0.9 GPa. The coating failure mechanism evolves from surface damage to interfacial peeling, which is closely related to the coating structure, stress evolution, and interfacial state. Full article

Accurate extrinsic calibration of multi-camera systems is a central problem in three-dimensional computer vision, as errors in the relative positioning of sensors directly propagate into geometric distortions that critically degrade the quality of downstream applications. This paper proposes an incremental extrinsic camera parameter initialization method that improves upon the baseline iterative registration algorithm based on the Perspective-n-Point (PnP) problem. Unlike board-based calibration frameworks, the proposed approach operates on individually placed markers with no prior knowledge of their mutual positions, enabling recalibration without dedicated calibration sessions. The accuracy improvement is achieved through the introduction of heuristic weighting of fiducial marker detections using AprilTags, as well as the application of a multi-view triangulation algorithm for dynamic refinement of marker spatial coordinates at each stage of scene expansion. Theoretical analysis demonstrates that the incorporation of these mechanisms does not increase the overall asymptotic computational complexity of the complete calibration cycle (including the global optimization stage), despite the higher computational cost of the initialization stage itself. Empirical validation of the method is performed on both synthetic datasets with known ground-truth camera parameters and real-world capture data through the evaluation of geometric errors and their comparison with the baseline method. Experimental results, supplemented by an ablation study, indicate that the proposed algorithm achieves statistically significant improvements on synthetic data in more than 80% of cases, while on real data it is on average 85% more accurate in terms of reprojection error. Full article

With the advancement of new-type power systems and smart grids, the structure of power protection and control systems has become increasingly complex, and their reliability exhibits dynamic evolution, multi-factor coupling, and full life cycle characteristics. Against this background, this paper presents a review of the evolution of reliability analysis methods for power protection and control systems. Early research has focused on parametric modeling based on statistical data and structural logic combination analysis, establishing a static reliability analysis framework grounded in the relationship between component failure probability and system structure. Subsequently, to characterize temporal process features such as state transitions, fault dependencies, and maintenance recovery, dynamic modeling methods such as state-space models and dynamic fault trees were developed and applied. In recent years, with the continuous accumulation of full life cycle operational data, multi-source information fusion and data-driven technologies have gradually been introduced into reliability research, promoting the expansion of the analysis framework from stage-based evaluation to full-process evolutionary modeling. On this basis, the modeling concepts, applicable scenarios, and inherent limitations of different methods are summarized and compared. Furthermore, the development trend of an integrated reliability analysis system that deeply combines mechanism models with data-driven methods is discussed, aiming to provide a theoretical foundation for the improvement of reliability analysis systems. Full article

The dynamic behavior within the melt pool governs the final quality of components fabricated by laser powder bed fusion (LPBF). To address key technical challenges—rapid keyhole evolution, low absorption contrast from metal vapor, and difficulties in quantifying internal flow fields—this study introduces move contrast X-ray imaging (MCXI), a technique leveraging time-series frequency characteristics. Combined with a multi-scale Horn–Schunck global optical flow method, MCXI enables full-field quantitative extraction of the melt-pool velocity field. Experimental validation across feature points shows a relative deviation of less than 2% compared to independent manual feature-point tracking, confirming consistency with the best available experimental ground truth. Analysis reveals the keyhole tail evolution cycle comprises three distinct dynamic stages: expansion, stratification, and contraction, with its area increasing from 1329 μm² to 6508 μm² before stabilizing. For the first time, pore pinch-off events were quantitatively measured, revealing front and rear wall collision velocities of 7.98 m/s and 8.04 m/s, respectively, consistent with available high-fidelity simulations. Furthermore, analysis of the overall melt-pool momentum field demonstrates a near-equal distribution of positive and negative momentum, providing an internal self-consistency check confirming the absence of systematic directional bias in the extracted velocity field. This study enables quantitative analysis of LPBF melt-pool dynamics, providing a novel tool for process optimization and defect control. Full article

Rural settlements are significant carriers of rural production, living, and land use activities and are also significant subjects for researching regional socio-economic development and spatial structural changes. With regard to the unique topographical environment and transportation situation in the Qiongbei volcanic lava area, a settlement form with prominent topographical constraints and transportation orientation is created. This paper utilizes land use/land cover data from different periods, along with rural settlement expansion patch data, to quantitatively analyze the spatial patterns and expansion characteristics of rural settlements, as well as their influencing factors, from 1990 to 2025 using GIS spatial analysis, buffer gradient analysis (BGA), and multi-order adjacency index (MAI). The research results indicate the following: (1) The spatial pattern of rural settlement distribution in the study area is “peripheral agglomeration and core sparsity,” and the general expansion trend is “rapid in the early period and stable in the late period.” The settlement area expands from 37.21 km² in 1990 to 80.87 km² in 2025. (2) The evolutionary pattern of rural settlements in the study area changes from “core–peripheral extension” in the early period to a mixed “core stabilization and peripheral leapfrogging development” model in the later period. The new patches formed in the peripheral areas have obvious discrete features, such as varying land use patterns and differing population densities compared to the core areas. (3) The spatial correlation factors for rural settlement expansion in the study area exhibit stage differences and distinct spatial non-stationary characteristics. During the early period (1990–2008), with strict limitations imposed by the natural material environment, sunlight (interpretability of 0.367) and water systems (0.286) show significant spatial coherence, indicating the great adaptability of rural settlements to the material conditions of the landforms; during the later period (2008–2025), after the implementation of the rural revitalization strategy, the population density (0.135) and transport-related factors become the main spatial correlation factors. The GWR model also shows the percentage of positive and negative influences by influencing factors at each stage and their significant differences in space, proving that human activities break through in the limitations of natural topology in a discontinuous way. According to this research, “inefficient land use” should be understood in a dialectical manner in volcanic geomorphological areas, and spatial optimization should be achieved on the premise of respecting the physicality of volcanic landscapes and rural identity. The research conclusions have important guiding significance for the spatial resilience planning in tropical volcanic areas and traditional settlement culture preservation. Full article

During natural gas production and transportation, multi-stage pressure regulation is often required to meet downstream pressure demands, resulting in substantial waste of residual pressure energy at high-pressure wellheads. This study focuses on high-pressure natural gas at the wellhead of the XC gas well in western Sichuan. Based on thermodynamic and exergy analysis, Aspen HYSYS was employed to simulate residual pressure power generation processes, and a systematic comparison was conducted between single-stage and multi-stage expansion schemes. Under operating conditions of an inlet pressure of 20 MPa, an inlet temperature of 70 °C, and a flow rate of 50 × 10⁴ m³/d, the influence of operating parameters on power generation performance was analyzed. The results indicate that power output increases with increasing natural gas flow rate and inlet temperature but decreases with increasing outlet pressure. Under large pressure differential conditions, single-stage expansion is unable to meet the requirements of high-pressure wellhead residual pressure power generation due to excessive temperature drop and limitations in existing expander performance. On this basis, two-stage, three-stage, and four-stage expansion power generation processes were further developed, and the effects of intermediate pressure selection on power output, heating demand, and pressure energy recovery efficiency were systematically examined. The results show that operating under equal expansion ratio conditions enhances pressure energy utilization. By comprehensively comparing power generation performance, heating power requirements, and economic feasibility, the two-stage expansion scheme was identified as the most favorable option under the investigated operating conditions, providing a practical reference for process design and engineering applications of high-pressure natural gas wellhead residual pressure power generation. Full article