Search Results (19,379)

The earlier onset of vegetation phenology and longer growing seasons resulting from global warming are widely recognized as beneficial for enhancing the carbon sink function of terrestrial ecosystems. However, significant uncertainty remains regarding whether the increased growth during the early growing season can be sustained and converted into growth benefits during the later season or even throughout the entire year. This study focuses on vegetation in northern China. Based on solar-induced chlorophyll fluorescence (SIF) data from 2001 to 2020, it establishes an analytical framework for assessing the “seasonal stability” of vegetation growth. The framework quantifies the evolutionary characteristics of early growth enhancement signals during the late growing season. Furthermore, structural equation modeling (SEM) is employed to elucidate the underlying climate-driven mechanisms. The results indicate: (1) Vegetation growth season stability in northern China has long been dominated by the Strong stabilizing type (accounting for 87.4%), suggesting that early growth enhancement signals are mostly attenuated or suppressed during seasonal progression rather than continuously amplified. (2) This stable pattern exhibits a distinct spatial structure at the interannual scale. The expansive and Weak stabilizing types undergo event-driven expansions during specific climatic years, with different vegetation functional types adopting differentiated regulatory strategies during this process. Shallow-rooted grasslands demonstrate higher growth elasticity, while forest vegetation exhibits stronger ecological inertia. (3) Mechanistic analysis reveals that in water-limited zones, enhanced early growth accelerates transpiration processes, thereby disrupting seasonal soil moisture continuity and exacerbating water deficits during the late growing season. This inhibits late-season photosynthesis, constituting a core hydrological–physiological regulatory mechanism that maintains the dominance of Strong stabilizing in the region. Conversely, in energy-limited zones, late-season temperature emerges as the dominant factor constraining sustained growth. This study examines the transmission and modulation mechanisms of early growth signals to the later growing season from the perspective of intra-seasonal dynamics, providing a new analytical approach for incorporating interseasonal processes into assessments of vegetation growth and carbon sink stability in northern China. Full article

China’s vast rural landscape exhibits pronounced regional disparities in both foundational resources and development potential. In the context of nationwide rural revitalization efforts, the emergent divergence in village development pathways underscores a pressing need for context-specific, classified interventions. To furnish a scientifically grounded typology of villages and inform differentiated development planning, this investigation focuses on Hubei Province as an illustrative case. Synthesizing survey data from 32,457 villages, we developed a multidimensional evaluation framework encompassing four pivotal domains: economic vitality, social service provision, ecological integrity, and cultural value. Leveraging the Self-Organizing Feature Map (SOFM) neural network—an unsupervised machine learning algorithm—we performed a cluster analysis on multi-source, heterogeneous datasets. This technique enabled the objective delineation of spatial typological patterns among Hubei’s villages, elucidated their underlying classification architecture shaped by multifaceted drivers, and demonstrated the methodological robustness and applicability of this approach for large-scale village categorization. Grounded in the derived typologies and informed by strategic directives from higher-tier planning instruments, we conducted a nuanced examination of the distinctive attributes characterizing each village type. The findings provide scientific evidence and decision-making support for village classification and rural revitalization planning in Hubei Province, with valuable implications for other regions with similar development foundations in China. Full article

Background/Objectives: Virtual histological staining offers a rapid, cost-effective alternative to physical reprocessing but faces challenges related to spatial misalignment and staining heterogeneity between Hematoxylin and Eosin (H&E) and Masson’s Trichrome (MT) domains. This study develops a robust framework for H&E-to-MT virtual staining to enable accurate fibrosis assessment without additional tissue consumption. Methods: We propose a transformer-based generative adversarial network (TbGAN) supported by a multi-stage alignment pipeline (SIFT (scale-invariant feature transform) coarse alignment, ORB/homography patch registration, and B-spline free-form deformation) and a weighted fusion mechanism combining four configuration outputs (O/10/3, O/3/10, R/10/3, and R/3/10). The framework was validated on 27 whole-slide images (>100,000 aligned patches) through 24 independent experiments. Results: The fused approach achieved state-of-the-art performance: MI = 0.9815 ± 0.0934, SSIM = 0.7474 ± 0.0597, NCC = 0.9320 ± 0.0220, and CS = 0.9946 ± 0.0014. Statistical analysis confirmed enhanced stability through narrower interquartile ranges, fewer outliers, and tighter 95% confidence intervals compared to individual configurations. Qualitative assessment demonstrated preserved collagen morphology critical for fibrosis staging. Conclusions: Our framework provides a reliable, IRB-compliant solution for virtual MT staining that maintains high structural fidelity suitable for diagnostic support. It enables resource-efficient fibrosis quantification and supports integration into clinical digital pathology workflows without patient-specific recalibration. Full article

Deep learning (DL) models have demonstrated strong performance in dermatological applications, particularly when trained on dermoscopic images. In contrast, tabular clinical data—such as patient metadata and lesion-level descriptors—are difficult to integrate into DL-based pipelines due to their heterogeneous, non-spatial, and often low-dimensional nature. As a result, these data are commonly handled using separate classical machine learning (ML) models. In this work, we present a proof-of-concept study that investigates whether dermatological tabular data can be transformed into two-dimensional image representations to enable convolutional neural network (CNN)-based learning. To this end, we employ the Low Mixed-Image Generator for Tabular Data (LM-IGTD), a framework designed to transform low-dimensional and heterogeneous tabular data into two-dimensional image representations, through type-aware encoding and controlled feature augmentation. Using this approach, we encode low-dimensional clinical metadata, high-dimensional lesion-level statistical features extracted from dermoscopic images, as well as their feature-level fusion, into grayscale image representations. The resulting image representations serve as input to CNNs, and the performance is compared with ML models trained on tabular data. Experiments conducted on the Derm7pt and PH2 datasets show that traditional ML models generally achieve the highest Area Under the Curve values, while LM-IGTD-based representations provide comparable performance and enable the use of CNNs on tabular clinical data used in dermatology. Full article

As a result of China’s rapid urbanization, new urban districts are characterized by a superblock development paradigm that contrasts sharply with core urban areas, where service facilities remain largely congruent with the population distribution. This planning approach has resulted in a pronounced spatial mismatch, with an intensive concentration of public service facilities within commercial cores and a critical lack of facilities proximate to high-density residential clusters. Within the framework of the 15 min community life circle policy, evaluating and optimizing these configurations is imperative for mitigating such structural imbalances. Using Xi’an’s Qujiang New District as a representative empirical case, this study integrates Point of Interest (POI) geospatial data with 330 resident behavioral questionnaires to assess facility distribution and utilization patterns. The findings reveal a distinct spatial pattern of core–periphery polarization, which is significantly influenced by cultural landscapes and commercial land values. Furthermore, the utilization patterns differ markedly across age groups. The reliance of young and middle-aged groups on digital life circles should be viewed not only as a lifestyle preference but also as an adaptation to mitigate physical facility deficits. While digital services compensate for physical facility shortages, they mask the actual lack of community spaces. This further disadvantages older adults, who still rely heavily on walking to access daily services. Addressing the unique characteristics of new urban districts, this study proposes a synergistic physical–digital dual-tier system in which physical infrastructure safeguards the equity baseline, while digital platforms enhance operational efficiency, providing a scientific basis for constructing age-friendly communities. Full article

Rapid urbanization has significantly exacerbated the Urban Heat Island (UHI) effect in high-density megacities, driven by the intensifying competition between built-up morphology and natural cooling infrastructure. Current research, however, often fails to accurately predict land surface temperatures (LST) because traditional models frequently overlook the complex spatial dependencies and neighborhood spillover effects inherent in urban environments. Existing studies often ignore the spatial dependence of heat transfer. This study proposes an explainable machine learning framework incorporating spatial lag variables to capture the thermal spillover from adjacent neighborhood context—such as green space cooling diffusion or built-up heat accumulation—which is frequently treated as noise in traditional models. Taking Shenzhen as a case study, we integrated multi-source data (Landsat 8, building vectors, DEM) and developed an XGBoost regression model (R² = 0.806) augmented with SHAP (Shapley Additive exPlanations) to quantify the contributions of local and contextual features. The results revealed that: (1) Non-linear Thresholds: Vegetation cooling exhibits a saturation effect, with the highest marginal benefit observed in the NDVI range of 0.2–0.4, while building warming effects converge at extremely high densities due to mutual shading; (2) Neighborhood Spillovers: Spatial interaction analysis confirms significant cool island synergy (where clustered green spaces provide amplified cooling) and heat island agglomeration effects—e.g., green spaces surrounded by high ecological backgrounds provide amplified cooling benefits; (3) Spatial Antagonism: A novel Interaction Balance Index (IBI) based on game-theoretic SHAP contributions was constructed to map the source-sink competition patterns, identifying distinct heat-dominated (West) and cool-dominated (East) zones. Unlike traditional area-weighted source-sink landscape metrics, IBI enables a pixel-level additive decomposition of warming and cooling factors, quantifying the net thermal outcome of local morphology and neighborhood spillover. By explicitly encoding spatial context into non-linear modeling, this study provides a more mechanistically robust understanding of urban thermal environments. The identified thresholds and dominant driver maps offer precise, spatially differentiated guidance for urban climate-adaptive planning and ecological restoration. Full article

Soil heavy metal contamination in mining areas poses a serious environmental challenge, requiring monitoring approaches with both wide coverage and high accuracy. Hyperspectral remote sensing provides an effective solution, yet its performance in complex mining environments is often limited by mixed-pixel effects and nonlinear spectral responses. To address these issues, this study proposes a Physically-Constrained Collaborative Endmember Extraction (PCCEE) framework that integrates spectral unmixing with machine learning for multi-element inversion. Using Gaofen-5 hyperspectral imagery, a collaborative workflow combining Pixel Purity Index (PPI), Vertex Component Analysis (VCA), and prior-spectral-constrained Spectral Angle Mapper (SAM) was developed to improve endmember purity and physical interpretability. Among three unmixing models (LMM, NMF, and SVR), the Linear Mixing Model achieved the best balance between accuracy and efficiency. Random Forest regression using retrieved abundances enabled high-accuracy inversion of eight heavy metals (mean R² = 0.85). Spatial analysis revealed significant co-enrichment of Pb, Cd, and Zn related to sulfide weathering, while PCA distinguished compound and independent pollution sources. The proposed PCCEE framework effectively mitigates mixed-pixel interference and provides a transferable approach for heavy metal monitoring and risk assessment in complex mining environments. Full article

Under the intensifying impacts of climate change, tightening agricultural resource constraints, and escalating food security pressures, the development of high-accuracy and interpretable crop yield estimation methods has become a critical technical issue in sustainable agricultural engineering. In this study, multi-temporal and multi-spectral remote sensing imagery are utilized as the core input. A multi-scale visual feature extraction module is designed to characterize canopy texture, field structure, and regional heterogeneity, while a temporal growth modeling module captures the dynamic evolution of crops from emergence to maturity. Yield regression is further integrated with economic mapping and explainability mechanisms, thereby forming an end-to-end prediction framework. Experimental results across multiple regions and years demonstrate that the proposed method outperforms various representative models. In the primary regression experiment, the framework achieves approximately $R^2=0.76$, with MAE reduced to $0.60$ and MSE to $0.62$, representing an error reduction of over $25\%$ compared with traditional regression approaches and classical machine learning models. In classification experiments for yield-grade evaluation, the model attains an accuracy of approximately $0.85$, with both precision and recall exceeding $0.82$, demonstrating its effectiveness in both continuous yield prediction and stable yield-level region identification. Cross-region and cross-year validation further indicate strong generalization capability, with $R^2$ remaining above $0.65$ in unseen regions and around $0.67$ under cross-year prediction settings. Ablation studies confirm the synergistic contributions of multi-scale spatial modeling, temporal growth modeling, and explainability constraints, as performance consistently declines when any individual module is removed. Overall, the results highlight that the proposed framework provides reliable data support for precision agricultural management, resource optimization, and agricultural engineering decision-making, while also offering a scalable and reproducible pathway for sustainable agricultural engineering development. Full article

New-type urbanization (NTU) is a key driver of high-quality development and progress toward the Sustainable Development Goals (SDGs) in China. While existing studies acknowledge the multidimensional nature of this process, they often measure it as a single composite aggregate. This approach masks the system’s local sensitivity to internal structural changes and obscures the spatially stratified heterogeneity of dominant drivers. To address this gap, this study constructs construct a comprehensive evaluation index system using panel data for 280 prefecture-level and above cities in China from 2001 to 2023. This study integrates the entropy-weighted TOPSIS method, a modified coupling coordination degree model (MCCD), geographically and temporally weighted regression (GTWR), and the optimal parameters geographical detector (OPGD). Using this framework, this study investigates the spatio-temporal characteristics of the coordinated high-quality development (CHQD) in NTU, systematically dissecting the spatial heterogeneity of local sensitivities and dominant drivers. The results indicate that the following: (1) CHQD exhibits a continuous upward trajectory characterized by significant regional convergence, with the center of gravity gradually shifting southwest. Structurally, green and social dimensions demonstrate the most rapid growth, progressively superseding spatial expansion as primary growth poles. (2) The structural decomposition reveals clear spatially stratified heterogeneity in local sensitivity. The coastal East faces “diminishing marginal utility” of traditional factor inputs, whereas the Central and Western regions continue to reap “structural dividends” from factor accumulation. (3) The dominant drivers shaping spatial heterogeneity have undergone a sequential evolution from an early “resource-space orientation” to a later “innovation-service orientation.” For instance, in the eastern region, the proportion of construction land (L2) had a single-factor explanatory power (q-statistic) of 0.791. However, its interactions with science and technology expenditure (E3) and other factors yielded q-statistics exceeding 0.820, indicating a marked synergistic effect. These findings support region-specific policy recommendations to promote CHQD and inform sustainable urbanization pathways in China. Full article

The artificial lateral line sensing principle provides a promising approach for underwater target perception and the navigation of underwater vehicles in complex flow environments. However, the highly nonlinear hydrodynamic mechanisms in complex flow fields make it difficult to establish accurate analytical models, which limits the development of high-precision perception and localization methods for underwater moving targets. In this study, a high-fidelity simulation model is established to characterize the pressure field variations induced by a moving source on an artificial lateral line pressure array. The influences of source velocity and sensing distance on the sensitivity and discretization characteristics of the pressure array are systematically investigated. Simulation results indicate that the sensor density of the pressure array is strongly correlated with the spatial resolution of the acquired pressure data, and a resolution of 50 sensors per meter is selected as the best-performing configuration by balancing sensing accuracy and sensor quantity. Under this configuration, the pressure distribution induced by the moving source exhibits clear and distinguishable spatiotemporal features, making it suitable for deep learning-based modeling. Furthermore, a large-scale temporal pressure dataset is constructed based on high-fidelity simulations under multiple motion directions and velocity conditions, and a spatiotemporal neural network is employed to predict the position of the underwater moving source. Experimental results demonstrate that, for straight-line underwater motion scenarios, the average localization error is within 7 cm, and a classification accuracy of 71% is achieved in practical engineering experiments. These results indicate that the proposed artificial lateral line pressure array design and deep learning-based prediction framework provide a feasible and effective solution for underwater target perception and localization in complex flow environments. Full article

Transect surveys and distance sampling are widely used to estimate wildlife population densities, but these methods can be biased when animals aggregate near features such as waterholes or other resources that occur along survey routes. Using empirical data from the NamibRand Nature Reserve in Namibia, we developed spatial simulations to examine how clumping of oryx (Oryx gazella) near water sources affects density and population estimates. We simulated surveys along a 50 km transect and varied the proportion of the population concentrated at waterholes (5–20%). Our analyses from the simulated surveys show that such aggregation can cause substantial positive bias, as population estimates were inflated by 67% to 967% relative to the known population size. We evaluated two correction approaches: censoring observations and transect segments near waterholes and redistributing animals from waterholes across the landscape. Both methods reduced bias when applied to our simulated survey data, but censoring was simpler and consistently produced more accurate estimates. These findings demonstrate that nonrandom animal distributions near linear survey features can severely compromise distance sampling assumptions. Accounting for such biases is essential for producing reliable population estimates, particularly in arid and semi-arid systems where wildlife strongly congregates around limited water sources. Full article

Visual pollution in urban environments has a significant impact on aesthetic quality, level of environmental complexity, coherence, and emotional well-being. Due to that, it needs to be analysed considering not only physical environment features and indicators but also aspects of environmental psychology and human emotional needs towards the urban environment. Taking into account this approach, in this research, it is studied applying a genotype-based framework using space syntax analysis and emotional mapping. Spatial analysis tools, such as space syntax and visibility graph analysis (VGA) provide reliable tools for statistically analysing this phenomenon. This method evaluates visual exposure and connectedness to polluting components across the map, resulting in locations with the most obvious pollution (The research examines spatial metrics such as integration, connectivity, and visibility, as well as emotional responses, to reveal significant links between urban spatial configurations and the visual pollution index (VPI). Zones with great accessibility and reachable by people, such as parks and public spaces, have positive emotional responses and low VPI scores, suggesting accessibility and visual harmony. On the contrary, low-integrated and fragmented areas have high VPI ratings, suggesting visual clutter, poor maintenance, and user dissatisfaction. Visual pollution affects the quality of urban surroundings by filling the visual space with contrasting and varied elements, resulting in visual dissonance. Common sources of visual pollution include architectural forms, billboards, advertising boards, signage, and poorly maintained building façades, particularly in modernist neighbourhoods. The Dainava neighbourhood in Kaunas city is used as a case study to apply this integrated methodology, revealing spatial and emotional aspects of the neighbourhood relevant to the VPI assessment. The findings highlight the relevance of a complex methodological approach that integrates spatial and emotional qualities of the environment and the importance of targeted actions, such as improving visibility, creating visual relations, and reducing visual clutter, in establishing inclusive, legible, and visually harmonious urban spaces. This methodological framework provides urban planners with a practical tool for the evaluation of visual pollution that integrates egzogenous (physical) and endogenous (emotional) factors and has predictive capacities to indicate the environment that is the most sensitive to visual pollution. Full article

The complex and heterogeneous nature of cancer necessitates advanced modeling techniques to better understand tumor dynamics and inform treatment strategies. This paper explores the application of stochastic modeling in cancer research, focusing on five key areas: tumor growth kinetics, evolutionary dynamics of cancer, treatment response and resistance, spatial modeling of tumor progression, and clinical applications of stochastic models. We first examine how stochastic models capture the randomness in tumor growth and proliferation, providing insights into cellular behaviors that deterministic models may overlook. Next, we investigate the evolutionary dynamics that govern tumor heterogeneity and the emergence of resistance, highlighting the role of genetic mutations and environmental pressures. The paper also discusses how stochastic modeling can improve predictions of treatment responses, elucidating mechanisms behind therapy resistance in various tumor subpopulations. Furthermore, we address the significance of spatial modeling in understanding tumor interactions within their microenvironment, shedding light on processes such as metastasis. Finally, we emphasize the translational potential of these mathematical frameworks, demonstrating how they can enhance personalized medicine approaches in oncology. By integrating stochastic modeling into cancer research, this work contributes to a deeper understanding of cancer biology and paves the way for improved patient outcomes. Full article

Background and Aims: Neurovascular abnormalities, such as aberrant nerve migration in Hirschsprung’s disease and reduced vascular density in necrotizing enterocolitis, are frequently observed in intestinal diseases. Traditional 2-dimensional (2D) staining methods are complicated, time-consuming and fail to comprehensively visualize the intricate neurovascular structures and morphology of the intestine. This study focuses on evaluating a novel 3D staining technique that promises simpler, faster, and more effective visualization of intact neurovascular structures in the colon. Additionally, it aims to compare the strengths and limitations of this 3D method against traditional 2D techniques for analyzing neuronal and vascular changes in two prevalent pathological conditions. Methods: A novel tissue-clearing approach was used to render mouse and patient distal colon tissues transparent. Neural structures and blood vessels were stained. 2D and 3D imaging were performed with laser confocal or tiling light sheet microscopy. Parameters include total imaging time, imaging range, image quality, operational complexity, and post-processing were compared between 2D and 3D methods. Results: Compared to 2D imaging, 3D imaging reveals the complete morphology and trajectory of neurovascular structures. Confocal 3D imaging offers superior clarity, higher transparency, and faster workflow efficiency, whereas light-sheet microscopy provides broader coverage at the expense of lower image quality. Post-processing facilitated spatial modeling and quantitative analyses. Applications included Hirschsprung’s disease, where 3D imaging revealed abnormal nerve distribution, and congenital heart disease, where hypoperfusion impacted vascular development in the colon. Conclusions: Confocal 3D staining and imaging offered a more streamlined workflow and enabled comprehensive visualization of neurovascular architecture, supporting efficient assessment of intestinal neurovascular phenotypic features. Full article

Background/Objectives: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for improving patient outcomes. Although chest computed tomography (CT) enables detailed assessment of lung abnormalities, manual interpretation is time-consuming, requires expert expertise, and is prone to diagnostic variability. To address these challenges, this study proposes DE-SAMNet, a hybrid deep learning framework for automated multi-class LC classification from CT scans. Methods: The model integrates two pre-trained convolutional neural networks—DenseNet121 and EfficientNetB0—operating in parallel to extract complementary multi-scale features. A Spatial Attention Module (SAM) is applied to each feature stream to emphasize clinically important regions. Final classification is performed through a compact fusion mechanism involving global average pooling, batch normalization, and a fully connected layer. DE-SAMNet was evaluated on two datasets: a public dataset (IQ-OTH/NCCD) with benign, malignant, and normal cases, and a private clinical dataset including benign, malignant, cystic, and healthy cases. Results: On the public dataset, the model achieved a 99.00% F1-score, 98.41% recall, 99.64% precision, and 99.54% accuracy. On the private dataset, it obtained 95.96% accuracy, 95.99% precision, 96.04% F1-score, and 96.21% recall, outperforming existing approaches. To enhance reliability, explainable AI (XAI) techniques such as Grad-CAM were used to visualize the model’s decision rationale. The resulting heatmaps effectively highlight lesion-specific regions, offering transparency and supporting clinical interpretability. Conclusions: This explainability strengthens trust in automated predictions and demonstrates the clinical potential of the proposed system. Overall, DE-SAMNet delivers a highly accurate and interpretable solution for early LC detection. Full article