Search Results (12,217)

Microplastic pollution in riverine systems poses critical environmental challenges, yet predictive modeling remains constrained by data scarcity and the computational limitations of traditional numerical approaches. This study develops a physics-informed neural network (PINN) framework that integrates advection–diffusion equations and turbulence modeling approaches with deep learning architectures to stimulate three-dimensional microplastic transport dynamics. The methodology embeds governing partial differential equations as soft constraints, enabling predictions under sparse observational conditions (requiring approximately three times fewer observation points than conventional numerical models), while maintaining physical consistency. Applied to a representative 15 km Yangtze River reach with 12 months of monitoring data, the model achieves improved performance with a root mean square error of 0.82 particles/m³ and a Nash–Sutcliffe efficiency exceeding 0.88, representing a 34% accuracy improvement over conventional finite volume methods. The framework successfully captures size-dependent transport behavior, identifies three primary accumulation hotspots exhibiting 3–5 times elevated concentrations, and quantifies nonlinear flux–discharge relationships with 6–8-fold amplification during high-flow events. This physics-constrained approach provides practical findings for pollution management and establishes an adaptable computational framework for environmental transport modeling in data-limited scenarios across diverse riverine systems. Full article

The liquid diffusion coefficient is a critical parameter for studying mass transfer processes, calculating mass transfer rates, and facilitating chemical engineering design and development, with its value strongly influenced by factors such as temperature and concentration. Conventionally, determining the concentration-dependent diffusion coefficient relationship D(C) requires multiple measurements across various concentrations followed by fitting, which is time-consuming and prone to cumulative errors, especially under varying thermal conditions encountered in industrial applications. To address this limitation, this study proposes an optimized finite difference numerical method that enables rapid determination of D(C) using only a single diffusion image, significantly enhancing measurement efficiency. This approach was validated by comparison with the shift of equivalent refractive index slice method and ray-tracing simulations. Diffusion coefficients for β-alanine aqueous solutions at different concentrations were measured over the temperature range of 288.15 K to 318.15 K using both techniques. The results from the two methods showed excellent consistency, with diffusion coefficients well described by the Arrhenius equation across temperatures, allowing for the rapid derivation of activation energies. Numerical simulations based on the derived D(C) relationship yielded images that closely matched experimental observations, confirming the accuracy and reliability of the finite difference method. This innovative technique not only offers a streamlined pathway for characterizing concentration-dependent diffusion in amino acid systems like β-alanine—relevant to pharmaceutical and biochemical processes—but also demonstrates broad applicability for obtaining diffusion coefficients and activation energies with minimal experimental effort. Full article

Grassland productivity faces heightened uncertainty under nonlinear climatic forcing. This study characterizes the spatial heterogeneity of nonlinear variations and nonstationary climate sensitivities across the Eurasian Steppe Region (EASR) to provide a scientific basis for its adaptive management. Using the aboveground net primary productivity (ANPP) and climate datasets (1982–2015), we employed piecewise linear regression, LOWESS, and sliding window partial correlation analysis to identify temporal turning points and dynamic climate–productivity relationships. We identified distinct turning points in 1994 and 2008, revealing a phased “Increasing–Decreasing–Increasing” trajectory. A key novelty is the mapping of eight phased trajectory patterns, illustrating significant spatial heterogeneity in productivity trends. Furthermore, we demonstrate temporally reversed climate sensitivities. Notably, the sensitivity of ANPP to temperature shifted from positive to negative as warming-induced water stress intensified. While precipitation remains the dominant driver (68% of the region), its influence is nonstationary and state-dependent. In the Qinghai–Tibet Plateau, the limiting factor transitioned from thermal to water availability. Overall, productivity in the EASR appears to undergo phased reorganization under shifting climatic baselines. Our findings suggest that future ecosystem models should incorporate time-varying sensitivity parameters to account for nonlinear dynamics and potential trend reversals in grassland ecosystems. Full article

Location-based services generate a large amount of location and trajectory data, which contain rich spatiotemporal and semantic information. Publishing these data without proper protection can seriously threaten users’ trajectory privacy. Existing trajectory privacy protection schemes generally fail to consider the dependency between a stay point and its preceding location and also overlook the relationship between the semantic information of location and privacy. Moreover, they often suffer from issues such as over-protection. Therefore, this paper proposes a trajectory privacy protection scheme based on the replacement of stay points. First, a stay point extraction algorithm is proposed, which extracts users’ stay points by setting distance and time thresholds based on the principle of the sliding window. Then, this paper proposes a location perturbation algorithm based on the vector indistinguishability mechanism and introduces different protection strategies for ordinary stay points and long-duration stay points, respectively. Finally, the perturbed trajectory is adjusted by generating a certain number of location points near the replacement points to maintain the temporal continuity and integrity of the trajectory. The experimental results indicate that it is necessary to provide more meticulous protection for long-duration stay points. Compared with similar schemes, the proposed scheme in this paper achieves higher data utility while ensuring privacy. Full article

This study systematically compared spray drying (SD) and freeze drying (FD) for microencapsulating Idesia polycarpa oil using a soy protein isolate/maltodextrin (SPI/MD) wall system. SD produced predominantly spherical and compact microcapsules with higher solubility (51.33%), encapsulation efficiency (81.9%), and superior oxidative stability (oxidation induction period, 6.05 h), together with improved thermal resistance, indicating its suitability for applications requiring enhanced stability and aroma retention. In contrast, FD yielded irregular and porous microcapsules with significantly higher emulsifying activity (29.12 m² g⁻¹, p < 0.05) but lower solubility and encapsulation efficiency. Integrated physicochemical characterization-including scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), particle size and polydispersity index (PDI), ζ-potential, differential scanning calorimetry (DSC), oxidative stability index (OSI) measurements, and volatile profiling via odor activity value (OAV) analysis—revealed clear process-dependent structure–function relationships. The denser SPI/MD matrix formed during SD restricted lipid molecular mobility and oxygen diffusion, thereby suppressing lipid oxidation and promoting the retention of key lipid-derived odorants. Conversely, the porous structure generated by FD facilitated interfacial functionality but increased molecular diffusion pathways. Overall, this work demonstrates that SPI/MD-based microencapsulation functions as a molecular stabilization platform for highly unsaturated plant oils and provides mechanistic guidance for selecting drying strategies to tailor Idesia polycarpa oil microcapsules for specific food applications. Full article

Urban parks play an increasingly important role in supporting social interaction, ecological services, and everyday well-being in rapidly urbanizing cities, yet prevailing planning practices still rely on equal-provision logics and linear modeling frameworks, implicitly assuming that park vitality increases proportionally with facilities and surrounding services. Such assumptions overlook the possibility that park vitality responds to built-environment factors in nonlinear, threshold-based, and configuration-dependent ways. This study develops an interpretable machine learning approach to identify the nonlinear effects and structural configurations that drive urban park vitality in Changsha, China. We integrate Baidu Huiyan population heat data with AOI-defined park boundaries and multi-source POI indicators to characterize internal facilities and surrounding built-environments for 147 parks in the city’s main urban area. An XGBoost model is trained to predict park vitality, and SHAP values, partial dependence analysis, and bivariate interaction plots are employed to examine variable importance, threshold behaviors, and synergistic or substitutive relationships among key factors. The results show that sports and leisure facilities are the most influential driver of vitality, followed by shopping services and government service facilities. Their impacts are strongly nonlinear: sports and leisure facilities and public amenities display clear saturation thresholds, while high-density shopping services generate substantial gains in vitality only beyond specific concentration levels. Interaction effects further indicate that park vitality emerges from particular configurations of internal facilities and surrounding residential and service environments, rather than from the additive accumulation of isolated factors. These findings demonstrate the value of interpretable machine learning for shifting urban park planning from equal-provision paradigms toward structurally informed configuration strategies and more efficient public space governance. Full article

Single-walled carbon nanotube (SWCNT) films are promising materials for thermoelectric power generation; however, the dependence of their transport properties on their thickness remains insufficiently understood. This study examined the relationship between the transport properties and the internal structure of SWCNT films with thicknesses ranging from 28 to 193 µm. The structural, mechanical, thermoelectric, and phonon transport properties exhibited a discontinuous dependence on the film thickness. Films up to 72 µm in thickness formed a uniform, dense network that maximized electrical conductivity, whereas films exceeding 97 µm exhibited a coarse and densely layered morphology. This coarse-dense structure increased the contact resistance between SWCNT bundle layers, leading to a reduction in electrical conductivity. Additionally, the increased number of layered interfaces increased phonon scattering, which decreased thermal conductivity and phonon mean free path. These findings provide insights into phonon transport in SWCNT films and have implications for SWCNT-based thermoelectric generator design and optimization. Full article

Exploring the coupling coordination relationship and underlying mechanisms within Production–Living–Ecological Space (PLES) and revealing its spatial interactions are of great significance for promoting the sustainable development of national territorial space. Taking the Beipanjiang River Basin as a case study, this research employs the PLES classification system, the Coupling Coordination Degree (CCD) model, Exploratory Spatiotemporal Data Analysis (ESTDA), and a geodetector to examine the spatiotemporal differentiation patterns and impact mechanisms of PLES coupling coordination from 2010 to 2023 in the karst plateau basin. The results indicate that (1) from 2010 to 2023, the spatial functions of PLES showed significant heterogeneity. Both production and living functions increased gradually, while the ecological function declined slightly. (2) From 2010 to 2020, the coupling degree and coordination level of PLES improved steadily but remained at a low coupling and imbalanced stage overall. From 2020 to 2023, marked growth occurred, though regional disparities persisted. (3) From 2010 to 2023, the overall spatial structure of the CCD of the PLES remained relatively stable, demonstrating significant spatial integration. Regional development exhibited clear path dependence, and the positive spatial correlation effect continued to strengthen. (4) Natural, social, and economic factors jointly influenced the PLES coordination level. Among them, nighttime light intensity, population density, and annual average Normalized Difference Vegetation Index (NDVI) were key drivers of its spatiotemporal differentiation. Factor interactions showed significant two-factor enhancement and nonlinear enhancement effects. These findings provide a scientific basis for optimizing the PLES in the Beipanjiang River Basin and offer a practical reference for spatial governance and planning in ecologically fragile karst plateau river basins. Full article

Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation. Full article

Dissolved oxygen (DO) is a cornerstone of aquatic ecosystem vitality, yet conventional in situ monitoring methods, reliant on field probes, buoys, and lab analyses, struggle to capture the spatiotemporal variability of DO at regional or global scales. Satellite remote sensing has revolutionized water quality assessment by enabling systematic, high-frequency, and spatially continuous monitoring of surface waters, transcending the logistical and financial constraints of traditional approaches. This systematic review critically evaluates satellite-based methodologies for estimating DO concentrations, emphasizing their capacity to address global environmental challenges such as eutrophication, hypoxia, and climate-driven deoxygenation. Following the PRISMA 2020 guidelines, large bibliographic databases (Scopus, Web of Science, and Google Scholar) identified that studies on satellite-derived DO concentrations are focused on both spectral and thermal foundations of DO retrieval, including empirical relationships with proxy variables (e.g., Chlorophyll-a, sea surface temperature, and turbidity) as well as direct optical signatures linked to oxygen absorption in the red and near-infrared spectra. The 77 results included in this review (accessed on 27 November 2025) indicate that the reported advances in sensor technologies (e.g., Sentinel-2,3’s OLCI and MODIS) have greatly expanded the ability to monitor DO levels across different types of water bodies, and that there has been a significant paradigm shift towards more complex and sophisticated machine learning and deep learning architectures. Recent work demonstrates that advanced machine learning and deep learning models can effectively estimate DO from remote sensing proxies, achieving high predictive performance when validated against in situ observations. Overall, this review indicates that their effectiveness depends heavily on high-quality training data, rigorous validation, and careful recalibration. Global case studies illustrate applications showcasing the scalability of remote sensing solutions. An OSF project was created to enhance transparency, while the review protocol was not prospectively registered, which is consistent with the PRISMA 2020 guidelines for non-registered reviews. Full article

This review examines how distinct gut microbial community configurations—characterized by differential enrichment of Bacteroides, Prevotella, Ruminococcus, Bifidobacterium, and Lachnospira—may be associated with variations in host redox homeostasis through microbiota-derived metabolites, including short-chain fatty acids, secondary bile acids, and tryptophan derivatives. These compositional patterns represent reproducible features across populations and correlate with differential disease susceptibility in metabolic disorders. While microbial communities exist along compositional continua rather than discrete clusters, stratification based on dominant patterns offers a pragmatic framework for interpreting large-scale microbiome datasets and guiding precision nutrition interventions. Observational evidence suggests Bacteroides-enriched communities may associate with pro-inflammatory signatures, whereas Prevotella- Ruminococcus, Proteobacteria, Bifidobacterium, and Lachnospira-enriched configurations may exhibit anti-inflammatory or antioxidant characteristics in certain populations. However, inter-population variability and species- and strain-level heterogeneity limit generalization. Condition-dependent effects are exemplified by Prevotella copri, which demonstrates pro-inflammatory responses in specific settings despite beneficial profiles in others. When dysbiosis compromises intestinal barrier integrity, microbial translocation may amplify chronic oxidative stress and immune activation. We evaluate therapeutic potential of beneficial genera including Lactobacillus and Bifidobacterium while examining the dose-dependent, context-specific, and sometimes paradoxical effects of key metabolites. Microbiota-stratified therapeutic strategies—personalizing dietary, probiotic, or prebiotic interventions to baseline community composition—show promise but remain at proof-of-concept stage. Current evidence derives predominantly from cross-sectional and preclinical studies; prospective interventional trials linking community stratification with oxidative stress biomarkers remain scarce. The community–redox relationships presented constitute a hypothesis-generating framework supported by mechanistic plausibility and observational associations, rather than established causal pathways. Future research should prioritize intervention studies assessing whether aligning therapeutic approaches with baseline microbial configurations improves outcomes in oxidative stress-related metabolic disorders. Full article

In recent years, numerous studies have been carried out on the landscape of the 5th-15th centuries in Catalonia. When studying settlement, we will assess research on the morphogenesis of villages and highlight differences across regions. We will also see the characteristics of the hamlets of the Early Middle Ages and those of the Pyrenean lands. Farmsteads, which were made up of a house and some land that depended on it, were a fundamental element of the landscape of many regions of Catalonia. To understand the characteristics of the agricultural areas, we will be interested in the concentric shapes and coaxial strips. Furthermore, to understand the landscape of the regions of Lleida and Tortosa, we must understand the transformations that occurred in the Islamic era and the diffusion of ditches and irrigated spaces. Likewise, we will examine the relationship we discover between the coombs and the first medieval settlements and necropolises. It is also important to determine when and why the terraces were built. This study will address the evolution of the landscape throughout Catalonia, with special emphasis on the most recent contributions relating to the regions of Barcelona and Lleida. This research has been based primarily on the study of written documents and the analysis of what is preserved on the ground, which we can learn about above all through aerial photographs. Full article

Accurate anatomical landmark localization is essential to automate chest X-ray analysis and improve diagnostic reliability. While global context recognition is essential in medical imaging, the inherently high-resolution nature of these images has long made this task particularly difficult. While the U-Net-based heatmap regression methods show strong performance, they still lack explicit modeling of the global spatial relationships among landmarks. To address this limitation, we propose an integrated structural learning framework that captures anatomical correlations across landmarks. The model generates probabilistic heatmaps with U-Net and derives continuous coordinates via soft-argmax. Subsequently, these coordinates, along with their corresponding local feature vectors, are fed into a Graph Neural Network (GNN) to refine the final positions by learning inter-landmark dependencies. Anatomical priors, such as bilateral symmetry and vertical hierarchy, are incorporated into the loss function to enhance spatial consistency. The experimental results show that our method consistently outperforms state-of-the-art models across all metrics, achieving significant improvements in MRE and SDR at 3, 6, and 9 pxl thresholds. This high precision demonstrates the framework’s strong potential to enhance the accuracy and robustness of clinical diagnostic systems. Full article

Objective: To clarify the pathophysiology of myopic optic neuropathy (MON) and its relationship to glaucomatous optic neuropathy (GON). Background: MON is presumed to be associated with posterior pole ectasia and deformation of the lamina cribrosa (LC) and parapapillary region. Its dependance on intraocular pressure is expected to be weaker than that of GON; however, the characteristics and clinical behavior of MON remain incompletely understood. Methods: A PubMed search using the keywords myopia, glaucoma, retinal nerve fiber, optic disc, and axonal transport identified 234 relevant publications, which were analyzed in this narrative review. Results: In myopic eyes, a large optic disc, thin or defective LC, and parapapillary microvasculature dropout (pMvD) are considered signs of increased vulnerability to glaucomatous injury. Despite these structural risk factors, visual field (VF) progression in myopic patients with glaucoma is often slow. The involvement of MON, which likely develops in young adulthood and stabilizes with aging, may explain this discrepancy. MON may substantially contribute to the development of central VF defects in myopic glaucoma, which are associated with elongation of papillomacular bundle, pMvD, and normal tension glaucoma. Experimental studies demonstrating impaired axonal transport at the optic disc margin provide important insights into the pathogenesis of MON. Additionally, optic disc deformations in myopia including disc tilting, rotation, and focal thinning or defects of the LC may contribute to atypical VF defects and altered susceptibility to glaucomatous damage. Conclusions: Interaction between MON and GON may explain atypical VF defects and the relatively slow VF progression observed in myopic patients with glaucoma-like VF defects. Full article

Increasingly frequent extreme droughts threaten forest vegetation and highlight the need to identify drought-tolerant germplasm. To support conservation and cultivation of Illicium lanceolatum, we investigated ecotypic differences in photosynthetic responses to short-term drought and rewatering under varying light intensity. One-year-old seedlings from four I. lanceolatum ecotypes originating from the Zhejiang (Lin’an, LA; Kaihua, KH), Jiangxi (Wu’ning, WN), and Fujian (Nan’ping, NP) provinces in China were subjected to drought stress by withholding irrigation and subsequent rewatering. Photosynthesis–light response curves were measured before drought; 2, 4, and 7 days after the last watering; and following rewatering. Short-term drought significantly affected photosynthetic traits in an ecotype-dependent manner. Maximum net photosynthetic rate, light saturation point, light compensation point, and apparent quantum yield increased during drought, indicating enhanced utilization of both high and low light. After rewatering, stomatal conductance increased significantly in the WN and KH ecotypes but declined in the NP ecotype when compared with those under the initial water supply. Instantaneous water use efficiency (A/E) recovered rapidly in all ecotypes and exceeded pre-drought levels. Under light intensity above 1500 µmol·m⁻²·s⁻¹, stomatal conductance exhibited a significant nonlinear relationship with water use efficiency. Overall, these physiological responses indicate that I. lanceolatum is moderately drought-tolerant and exhibits mild sensitivity to soil water variation. The WN and KH ecotypes showed superior improvement in water use efficiency under drought and high light, suggesting their potential for breeding drought-resistant cultivars and for afforestation in drought-prone environments. Full article