Search Results (562)

Background: Computed tomography (CT)-derived body composition parameters, including skeletal muscle and fat indices, are prognosticators in oncology. Most studies focus on baseline body-composition parameters; however, changes during treatment may provide better prognostic value. Standardized methods for measuring/reporting these parameters remain limited. Methods: This retrospective study included patients who were treated with immunotherapy for non-small cell lung cancer (NSCLC), renal cell carcinoma (RCC), or melanoma between 2017 and 2024 and had technically adequate baseline and follow-up CT scans. Body composition was analyzed using a novel, fully automated software (CompoCT) for L3 slice selection and segmentation. Body composition indices (e.g., skeletal muscle index [SMI]) were calculated by dividing the cross-sectional area by the patient’s height squared. Results: The cohort included 376 patients (mean [SD] age 66.4 [11.4] years, 67.3% male, 72.6% NSCLC, 14.6% RCC, and 12.8% melanoma). During a median follow-up of 21 months, 220 (58.5%) died. Baseline body composition parameters were not associated with mortality, except for a weak protective effect of higher SMI (HR = 0.98, p = 0.043). In contrast, longitudinal decreases were strongly associated with increased mortality. Relative decreases in SMI (HR, 1.17; 95% CI, 1.07–1.27) or subcutaneous fat index (SFI) (HR, 1.11; 95% CI, 1.07–1.15) significantly increased mortality risk. Multivariate models showed similar concordance (0.65) and identified older age, NSCLC tumor type, and relative decreases in SMI and SFI (per 5% units) as independent predictors of mortality. Conclusions: Longitudinal decreases in skeletal muscle and subcutaneous fat were independent predictors of mortality in immunotherapy-treated patients. Automated CT-based body composition analysis may support treatment decisions during immunotherapy. Full article

Accurate diagnosis of aero-engine faults and precise signal characterization are crucial to ensuring operational reliability and service life prediction. The structural complexity of engines and the variability of operating conditions pose significant challenges for fault diagnosis and identification. Based on an analysis and emphasis on the critical importance of aero-engine fault signal recognition and diagnosis, this paper comprehensively reviews and discusses the classification and evolution of aero-engine fault signal recognition techniques. The review traces this evolution along its developmental trajectory, from classical methods to emerging approaches such as quantum signal processing for weak feature extraction. It also examines characteristics of different types of aviation engine failures and the progression of diagnostic research over time. This review provides multiple tables to compare the applicability, advantages, and limitations of various signal recognition methods and deep learning diagnostic architectures. Detailed discussions synthesize the relative merits of different approaches and their selection trade-offs. Based on this overview, the paper outlines the complexity of real aero-engine faults and key research directions. Building on these developments in fault signal recognition and diagnosis, the paper addresses the complexity and the research areas receiving particular attention within real aero-engine faults. It highlights key research areas, including handling data imbalance, adapting to variable and cross-domain conditions, and advancing diagnostic and data enhancement methods for weak composite faults. Finally, the paper analyzes the multifaceted challenges in the field and identifies future trends in aero-engine fault signal recognition and intelligent diagnosis. Full article

To address the problems of weak geometric features, low signal response amplitude, and insufficient spatial resolvability of near-surface defects in metal substrates, a high-resolution spatiotemporal-coded eddy-current array probe is proposed. The probe adopts an array topology with time-multiplexed excitation and adjacent differential reception, achieving a balance between high common-mode rejection ratio and high-density spatial sampling. First, a theoretical electromagnetic coupling model between the probe and the metal substrate is established, and finite-element simulations are conducted to investigate the evolution of the skin effect, eddy-current density distribution, and differential impedance response over an excitation frequency range of 1–10 MHz. Subsequently, a 64-channel M-DECA probe and an experimental testing platform are developed, and frequency-sweeping experiments are carried out under different excitation conditions. Experimental results indicate that, under a 50 kHz excitation frequency, the array eddy-current response achieves an optimal trade-off between signal amplitude and spatial geometric consistency. Furthermore, based on the pixel-to-physical coordinate mapping relationship, the lateral equivalent diameters of near-surface defects with different characteristic scales are quantitatively characterized, with relative errors of 6.35%, 4.29%, 3.98%, 3.50%, and 5.80%, respectively. Regression-based quantitative analysis reveals a power-law relationship between defect area and the amplitude of the differential eddy-current array response, with a coefficient of determination ${\mathrm{R}}^2=0.9034$ for the bipolar peak-to-peak feature. The proposed M-DECA probe enables high-resolution imaging and quantitative characterization of near-surface defects in metal substrates, providing an effective solution for electromagnetic detection of near-surface, low-contrast defects. Full article

Background: Carotid plaque composition is central to stroke risk, but some aspects of plaque characterization are derived from ex vivo imaging, while clinical decision-making relies on in vivo ultrasound (US) and computed tomography (CT). High correlation of clinical in vivo and ex vivo imaging is necessary when including ex vivo plaque features in artificial intelligence (AI) models, but the extent of this correlation between CT and US remains poorly understood. Methods: Patients undergoing carotid endarterectomy (n = 188) were enrolled. Preoperative carotid US (n = 182) and CT (n = 156) were performed. Plaque specimens from 187 patients were imaged on ex vivo CT and US. Quantitative metrics included plaque volumes, relative calcified/non-calcified volumes, HU and grayscale distributions, Agatston and calcification scores, and heterogeneity indices (coefficient of variation). Qualitative US parameters (echogenicity, juxtaluminal echolucency, discrete white areas) were visually graded. Correlation between in vivo and ex vivo imaging was assessed, and agreement was quantified for parameters with the highest correlation with Bland–Altman analysis. Results: CT of patients and ex vivo CT showed moderate to strong correlation for total, calcified, and non-calcified plaque volumes and whole-plaque mean HU (r = 0.55–0.79; CCC = 0.43–0.74). Agatston and calcification scores correlated strongly (r = 0.78–0.80; CCC = 0.63–0.76). In contrast, most non-calcified and heterogeneity metrics showed negligible-to-weak correlation. Correlations between in vivo and ex vivo US were substantially weaker (maximum correlation: 75th grayscale percentile r = 0.35). In vivo CT overestimated calcified volume (bias: 8.7%) and in vivo US underestimated the 75th grayscale quantile (bias: −25.5 grayscale). Conclusions: Quantitative CT metrics—particularly relative calcified plaque volume and calcium scores—translate reasonably well from ex vivo to in vivo imaging and represent robust candidates for radiomics and AI-based stroke risk models, even ex vivo. Ultrasound parameters show limited translational validity, underscoring the need for volumetric clinical US and discouraging the inclusion of ex vivo ultrasound features for machine learning applications. Full article

Large-Format Additive Manufacturing (LFAM) offers the ability to 3D print composites at multi-meter scale and high throughput by utilizing a screw-based extrusion system that is compatible with pelletized feedstock. As such, LFAM systems like the Big Area Additive Manufacturing (BAAM) system provide a pathway for incorporating AM techniques into industry-scale production. Despite significant growth in LFAM techniques and usage in recent years, typical Multi-Material (MM) techniques induce weak points at discrete material boundaries and encounter a higher frequency of delamination failures. A novel dual-hopper configuration was developed for the BAAM platform to enable in situ switching between material feedstocks that creates a graded transition region in the printed part. This research studied the influence of extrusion screw speed, component design, transition direction, and material viscosity on the transition behavior. Material transitions were monitored using compositional analysis as a function of extruded volume and modeled using a standard Weibull cumulative distribution function (CDF). Screw speed had a negligible influence on transition behavior, but averaging the Weibull CDF parameters of transitions printed using the same configurations demonstrated that designs intended to improve mixing increased the size of the blended material region. Further investigation showed that the relative difference and change in complex viscosity influenced the size of the blended region. These results indicate that tunable properties and material transitions can be achieved through selection and modification of composite feedstocks and their complex viscosities. Full article

Urban heat islands (UHIs) have intensified in rapidly urbanizing regions like New York, exacerbating thermal discomfort, public health risks, and energy consumption. While previous research has highlighted various environmental and socioeconomic contributors, most existing studies lack interpretable, fine-scale models capable of quantifying the effects of specific drivers—limiting their utility for targeted planning. To address this challenge, we develop an interpretable machine learning framework using Random Forest and XGBOOST to predict land surface temperature across 1800+ census tracts in the New York metropolitan area, incorporating vegetation indices, water proximity, urban morphology, and socioeconomic factors. Both models performed strongly (mean R² ≈ 0.90), with vegetation coverage and water proximity emerging as the most influential cooling factors, while built form features played supporting roles. Socioeconomic vulnerability indicators showed weak correlations with temperature, suggesting a relatively equitable thermal landscape. Optimization simulations further revealed that increasing vegetation to a threshold level could lower average surface temperatures by up to 6.38 °C, with additional but smaller gains achievable through adjustments to water access and urban form. These findings provide evidence-based guidance for climate-adaptive urban design and green infrastructure planning. More broadly, the study illustrates the potential of explainable machine learning to support data-driven environmental interventions in complex urban systems. Full article

Clarifying the residential space differentiation in urban fringe areas and its influencing factors are crucial for land use planning and sustainable urban development. This study investigates residential space differentiation and its influencing factors in the urban fringe area of Beijing from the perspective of housing rent. Utilizing multi-source data, including housing rent statistics from the China Real Estate Price Platform, remote sensing imagery, and POI big data, we employ the residential dissimilarity index for tenants, geographical detector, and MGWR model to analyze spatial patterns and driving mechanisms. The results show the following: (1) The residential space differentiation in the urban fringe area of Beijing is obvious, showing an “X”-shaped fragmentation pattern, with the northeast and southwest regions forming high differentiation values, while the northwest and southeast regions form low differentiation values. (2) The residential space differentiation in the marginal area shows a strong scale effect, which originates from the historic “collage” development mode of Beijing. (3) The differentiation of residential space in Beijing’s urban fringe area is sensitive to the spatial accessibility of residential areas to other facilities, and is less affected by the spatial proximity, such as the number of facilities. (4) The central potential and traffic potential factors are still the core driving forces shaping the differentiation pattern of residential space in the marginal area; the role of leisure supporting factors has become increasingly prominent, and it has gradually become the key factor strengthening residential space differentiation; and the influence of medical and commercial supporting factors is relatively weak. Full article

Wetlands store large amounts of soil organic carbon stock (SOCS), making them crucial for global climate regulation. However, climate change, poor management, and weak protection policies threaten these stocks. To assess the contribution of different wetland types for national and international climate targets and to monitor the effectiveness of protection measures, additional research is required. Therefore, we assessed SOCS and disturbances from climate change, land use/land cover (LULC), and soil chemical composition in saline and eutrophic Ramsar sites in Serbia. Analyzing a total of 96 samples, we accounted for soil depth, reference soil group (RSG), and habitat/vegetation type. Mean SOCS in the saline site ranged from approximately 36 t·ha⁻¹ at 0–30 cm to 26 t·ha⁻¹ at 30–60 cm, whereas values were much higher for the eutrophic sites, ranging from 81 to 82 t·ha⁻¹ at 0–30 cm and 47–63 t·ha⁻¹ at 30–60 cm. Differences between groups for the whole soil columns (0–60 cm) were significant at the 0.1% level. While SOCS generally decreases with depth, it showed notable local variability, including occasional instances at deeper layers, indicating complex environmental and anthropogenic influences. Spatial mapping of soil chemistry parameters (pH, humus, P₂O₅, and K₂O) along with land use/land cover (LULC) data revealed nutrient dynamics influenced by agricultural activities. An analysis of regional climate data revealed temperature increases relative to the reference period of 1971–2000 by 0.5 °C for the decade 2001–2010 and of 1.5 °C for 2011–2020. Climate projections under the RCP4.5 and 8.5 scenarios predict further warming trends, as well as increased rainfall variability and drought risks. The results of our study contribute to quantifying the important, though variable, contribution of wetland sites to global climate regulation and show the influence of geogenic, pedogenic, and anthropogenic factors on SOCS. National policies should be adapted to safeguard these stocks and to limit negative effects from surrounding agricultural areas, as well as to develop strategies to cope with expected regional climate change effects. Full article

Residents’ health is the foundation of social civilization and progress, an important symbol of national prosperity and strength, and a common pursuit of the general public. Ecological environment quality, as a key link connecting sustainable development and residents’ health, its governance effect is directly related to the achievement of Sustainable Development Goals. Based on the data of 31 provinces in China from 2010 to 2022, this paper empirically tests the impact of the ecological and environmental protection supervision policy (EEPS) on residents’ health by adopting the double machine learning method. The research results show that (1) the ecological and environmental protection supervision policy can significantly improve residents’ health level, laying a solid human capital foundation for sustainable development. (2) In contrast, the policy has a more prominent effect in areas with low population density, regions where government attention is below the median, and areas with relatively weak economic development. (3) The policy can enhance residents’ health through the synergistic effect of government environmental investment and public environmental participation. This study strengthens the research on how environmental policies promote residents’ health and provides valuable references for advancing sustainable development. Full article

Enhancing herders’ livelihoods is essential in balancing human–land interactions and promoting inclusive, sustainable development within protected area management. Using a household survey (N = 3539; March–June 2025) and a mixed-methods quantitative approach (weighted TOPSIS, obstacle degree, Spatial Durbin Model, and hierarchical regression), we assessed household livelihood resilience in the Lancang River source area of Sanjiangyuan National Park. Key findings included the following. Overall livelihood resilience was moderate, with a mean score of 0.411. This was characterized by a marked weakness in learning capacity (0.358) and relative strength in self-organization (0.431). Major barriers to resilience included cooperative participation (obstacle degree: 8.14%), education levels (7.58%), skills training (7.18%), household savings (6.40%), and information acquisition abilities (5.97%). The spatial analysis revealed a core-periphery pattern of resilience, evidenced by significant negative spatial autocorrelation (W×HLR coefficient = −0.787, p = 0.001), suggesting competitive interactions among villages. Within this pattern, cooperative participation induced significant positive spatial spillovers (W×X₈ coefficient = 0.147, p < 0.001), while benefits derived from information acquisition abilities remained localized (Direct Effect = 0.061, p < 0.001). The pathways to resilience were associated with household heterogeneity. Associations between key factors and resilience varied across demographic groups, with women and youth benefiting more from skills training and education. Livelihood strategies were linked to information utilization, with cordyceps-dependent households exhibiting greater sensitivity to information acquisition abilities (interaction coefficient = 0.009, p = 0.009). The institutional environment shaped organizational benefits; the positive association with cooperative participation diminished in the core protected zone (interaction coefficient = −0.011, p = 0.036). These findings highlight household heterogeneity as a key factor influencing diverse resilience pathways. They also emphasize the need for targeted, spatially specific, and group-oriented governance strategies. Full article

With the accelerated launch of spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) satellites, GNSS-R has gradually emerged as an important technique for remote sensing. However, due to its pseudo-random observation mode, the use of a single system makes it difficult to provide continuous spatiotemporal coverage over a specific area within the short term. Although interpolation methods can partially alleviate the coverage gaps, their application is limited by accuracy and reliability constraints, which still restrict the practical use of GNSS-R in terrestrial surface monitoring. To address this issue, conducting joint analyses and data fusion of multi-satellite GNSS-R observations has become an important approach to improving the continuity and accuracy of surface monitoring. However, systematic studies on the integration of multi-satellite GNSS-R data remain relatively limited. Moreover, differences in orbital inclination, antenna design, and signal bandwidth among various spaceborne GNSS-R systems lead to discrepancies in their land observations. Therefore, this study systematically analyzes the reflectivity differences among multiple GNSS-R satellites (e.g., the Cyclone Global Navigation Satellite System (CYGNSS), Fengyun-3 (FY-3), and Tianmu-1 (TM-1)) under consistent surface roughness and land cover conditions, with the aim of providing a theoretical and methodological foundation for the fusion and integrated application of multi-satellite GNSS-R data. The results show that, except for desert regions, the spatial distribution of the correlation coefficients from the least squares fitting of reflectivity between different spaceborne GNSS-R satellites exhibits a pattern similar to that of an established variable, i.e., the vegetation–roughness composite variable (VR), with higher inter-system correlations occurring in areas characterized by lower VR values. Significant reflectivity deviations were observed near water bodies and river networks, such as the Amazon, Paraná, Congo, Niger, Nile, Ganges, Mekong, and Yangtze, where both the fitting intercepts and biases are relatively large. In addition, the reflectivity correlations between CYGNSS–TM-1 and CYGNSS–FY-3 are both strongly influenced by surface vegetation cover type. As the correlation increases, the proportion of non-vegetated and forested areas decreases, while that of grasslands, shrublands, and cropland/vegetation mosaics increases. Analysis of inter-system reflectivity correlations across different land cover types indicates that forested areas exhibit low-to-moderate correlations but maintain stable structural characteristics, whereas wooded areas show moderate correlations slightly lower than those of forests. Grasslands, shrublands, and croplands are mainly distributed within regions of moderate surface roughness and correlation, among which croplands have the highest proportion of highly correlated grids, demonstrating the greatest potential for multi-source data fusion. Wetlands display high roughness and low correlation, largely influenced by dynamic water variations, while bare soils show low roughness (0.2–0.4) but still weak correlations. Full article

Targeting the purification of high-salinity mine water in ecologically vulnerable mining areas in Western China, this study conducted batch adsorption experiments using coal gangue from goaf areas to investigate the effects of initial Ca²⁺ concentration, treatment time, pH, temperature, and particle size on Ca²⁺ removal. The adsorption process was further elucidated through isotherm, kinetic, and thermodynamic modeling. The results demonstrate that the unique slit-shaped/plate-like mesoporous structure of coal gangue provides a favorable physical foundation for adsorption. Batch experiments identified optimal conditions at pH = 8 and 40 °C, achieving a maximum adsorption capacity of 12.4 mg/g. The process followed the Langmuir isotherm model (R² = 0.994, χ² = 0.122) and the pseudo-second-order kinetic model (R² = 0.952, χ² = 0.057), reaching equilibrium within 120 min. Thermodynamic analysis confirmed a spontaneous, endothermic, and entropy-driven process, with a relatively low heat of adsorption (ΔH < 20 kJ/mol) indicating physical adsorption as the dominant mechanism. Collectively, the adsorption system is characterized as a complex process governed by physical adsorption accompanied by weak chemical interactions and modulated by multiple environmental factors. Three mechanisms (electrostatic interaction, ion exchange, and surface complexation) jointly contribute to Ca²⁺ adsorption on coal gangue. This study enhances the understanding of the water purification mechanism by coal gangue, provides a theoretical basis for the application of underground coal mine reservoirs, and proposes a novel technical approach to mitigate membrane scaling caused by Ca²⁺ during mine water treatment. Full article

Federated Learning (FL) is revolutionizing Machine Learning (ML) by enabling devices in different locations to collaborate and learn from user-generated data without centralizing it. In dynamic and non-stationary environments like Internet of Things (IoT), Concept Drift (CD) is the phenomenon of data changing/evolving over time. Traditional FL frameworks struggle to maintain performance when local data distributions evolve, as they lack mechanisms for detecting and adapting to concept drift. However, the use of FL in such environments, where data changing/evolving continuously and Continual Learning (CL) is required to adapt to concept drift, remains a relatively unexplored area. This study specifically addresses this gap by examining strategies for continuous adaptation within federated systems when faced with non-stationary data. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study systematically reviews existing literature on FL adaptation to concept drift. To the best of our knowledge, this is the first systematic review that consolidates and reinterprets existing studies under the emerging framework of Federated Drift-Aware Learning (FDAL), bridging Federated and Continual Learning research toward adaptive and drift-resilient federated systems. We conducted an extensive systematic survey, including an analysis of state-of-the-art methods and the latest developments in this field. Our study highlights their strengths, weaknesses, and datasets used, identifies key challenges faced by FL systems in these scenarios, and explores potential future directions. Additionally, we categorize the limitations and future directions into major thematic areas that highlight core gaps and research opportunities. The results of this study will support researchers in overcoming the adaptation challenges that FL systems face when dealing with changing environments due to concept drift and serve as a critical resource for advancing adaptive federated intelligence. Full article

The intensifying global warming and the increasing frequency of extreme weather events have created an urgent need for targeted resilience building in mountainous villages. This study focuses on three typical villages in the Hengduan Mountains region. From the perspective of individual villagers, a disaster resilience evaluation index system was constructed, encompassing four dimensions: disaster prevention capacity, disaster resistance capacity, disaster relief capacity, and recovery capacity. Using the entropy method and a village disaster resilience assessment model, the disaster resilience levels of each village were quantitatively evaluated. The results indicate the following: (1) Disaster resistance capacity is the key factor constraining the disaster resilience level of mountain villages. (2) The overall disaster resilience of mountain villages is at a medium level, with minor differences among villages. (3) Significant disparities exist in capacity dimensions across villages: Qina Village demonstrates the strongest disaster resistance capacity, while Xiamachang Village excels in disaster prevention capacity but shows relative weakness in recovery capacity. (4) Household material endowment has a significant positive impact on disaster prevention, resistance, relief, and recovery capacities, while individual self-rescue capability and individual–government collaboration capacity also significantly enhance disaster prevention, resistance, and relief capacities. We propose the following: Leveraging the rural revitalization strategy as a pivotal point, this approach promotes the diversified development of the village economy. It facilitates the increase in villagers’ income through the implementation of employment skill training programs, thereby strengthening household material foundations to enhance individual disaster resilience. By relying on the mass monitoring and mass prevention mechanism and a disaster information sharing platform, real-time exchange of disaster situation information is achieved, which enhances communication and collaboration between villagers and the government, consequently improving the synergistic efficiency between individuals and governmental bodies. Simultaneously, a villager-centered disaster prevention system is constructed. Through measures such as disaster prevention publicity and practical disaster response drills, villagers’ awareness of disasters and their capabilities for self and mutual rescue are elevated, ultimately strengthening the overall disaster resilience of rural areas in the Hengduan Mountains region. Full article

Rainfall stations in small and medium-sized river basins in China are sparsely distributed and unevenly spaced, resulting in insufficient spatial representativeness of precipitation data and posing challenges to the accuracy of flood forecasting. Spatial interpolation methods for rainfall data are a key tool for bridging the gap between discrete rainfall station data and continuous surface rainfall data; however, their applicability in flood forecasting for small and medium-sized river basins with sparse rainfall stations requires further investigation. Taking the Hezikou basin as the study area and focusing on the Liuxihe model, this study analyzes the distribution characteristics of the seven rainfall stations in the basin and the interpolation effectiveness of the original Thiessen Polygon Interpolation (THI) method in the model. It compares and discusses the applicability of the THI, the Inverse Distance Weighting (IDW) method, and the Trend Surface Interpolation (TSI) method in flood forecasting for this basin. Different rainfall station distribution scenarios (full coverage, upstream only, downstream only, single rainfall station) were set up to study the performance differences in each method under extremely sparse conditions. The results indicate that, under the sparse condition of only 0.0068 rainfall stations per square kilometer in the Hezikou basin, IDW interpolation yields the best flood forecasting results, with model Nash–Sutcliffe Efficiency (NSE) values all above 0.85, Kling–Gupta Efficiency (KGE) values exceeded 0.78, and the Peak Relative Error (PRE) was controlled within 0.09, significantly outperforming THI and TSI. Additionally, as rainfall station sparsity increased, IDW exhibited the smallest decline in performance, showing a weak negative correlation (p ≤ 0.05) between prediction performance and rainfall station sparsity, demonstrating stronger adaptability to sparse scenarios. When station information is extremely limited, IDW performs more stably than THI and TSI in terms of certainty coefficients (NSE, KGE) and flood peak error control. The Inverse Distance Weighting method (IDW) can provide reliable rainfall spatial interpolation results for flood forecasting in small and medium-sized basins with sparse rainfall stations. Full article