Search Results (174)

Land-use change and its interaction with petrochemical accident risk are critical for sustainable coastal development. This study established a multi-source data-integrated risk assessment framework, employing fuzzy C-means clustering to stratify petrochemical accident risk into six distinct levels. The analysis revealed the relationship between these risk levels and land-use type changes. Furthermore, the Takagi–Sugeno fuzzy dynamic model was applied to evaluate potential risks at representative coastal petrochemical enterprises. The findings were as follows: (1) Risk concentrates in small-to-medium private, newly established firms, primarily as explosion accidents. (2) The highest risk occurs in Bohai Bay, followed by Jiangsu, Zhejiang, and Guangdong; national policies have reduced affected zones from 352.61 km² (2019) to 43.67 km² (2022). (3) The total potential risk zone spans 2986.21 km², with high-risk cores in Hebei, Zhejiang, and Fujian (36.52%) and medium-risk in Shandong Peninsula (32.01%). (4) Risk primarily affects farmland and construction land; urban expansion has increased affected built-up areas from 16.36% (2012) to 47.02% (2022), shifting effects from ecological to combined socio-ecological consequences. These findings provide critical theoretical support and actionable management recommendations for integrating coastal land-use planning, urban expansion control, and coordinated petrochemical risk governance. Full article

The deep-sea convergence zone (CZ) is a critical phenomenon for long-range underwater acoustic propagation. Accurate prediction of its distance, width, and gain is essential for enhancing sonar detection performance. However, conventional ray-tracing models, which assume vertically stratified sound speed profiles (SSPs), fail to account for horizontal sound speed gradients in the mixed layer, leading to significant prediction errors. To address this, we propose a novel ray-tracing model that incorporates horizontally inhomogeneous SSPs in the mixed layer. Our approach combines empirical orthogonal function (EOF) decomposition with the Del Grosso sound speed formula to construct a continuous 3D sound speed field. We further derive a modified ray equation including horizontal gradient terms and solve it using a fourth-order Runge–Kutta method. Simulation and experimental validation in the South China Sea demonstrate that our model reduces the prediction error for the first CZ distance by 2.26%, width by 2.66%, and gain deviation by 5.85% compared to the Bellhop model. These results confirm the effectiveness of our method in improving CZ parameter prediction accuracy. Full article

Mesoscale eddies have a significant influence on primary productivity and upper-ocean variability, particularly in stratified and monsoon-driven basins like the Bay of Bengal (BoB). This study analyzes mesoscale eddies in the BoB from January 2010 to March 2020 using post-processed and gridded daily sea surface height anomaly (SLA) data from the Copernicus Marine Environment Monitoring Service. We used a hybrid detection method combining the Okubo–Weiss parameter and SLA contour analysis to identify 1880 anticyclonic and 1972 cyclonic eddies. Cyclonic eddies were mainly found in the western BoB along the east Indian coast, while anticyclonic eddies were less frequent in this area. Analysis of eddy lifespans revealed that short-lived (1-week) eddies were nearly equally distributed between anticyclonic (48.81%) and cyclonic (51.19%) types. However, for longer-lived eddies, cyclonic eddies became more prevalent, comprising 83.33% of 30-week eddies. A notable, consistent eddy presence was observed east of Sri Lanka, influencing the East India Coastal Current. Most eddies (91%) propagated west/southwestward along the western slope of the Andaman Archipelago, likely influenced by ocean currents and coastal topography, with concentrations in the Andaman Sea and central BoB. These patterns suggest significant interactions between eddies, coastal upwelling zones, and boundary currents, impacting nutrient transport and marine ecosystem productivity. This study contributes valuable insights into the dynamics of ocean circulation and the impacts of eddies, which can inform fisheries management strategies, advance climate resilience measures, expand scientific knowledge, and guide policies related to conservation and sustainable resource utilization. Full article

Gully development is a significant geomorphological and environmental process that affects land degradation worldwide, with ephemeral gullies (EGs) and permanent gullies (PGs) being the two most common types. These two gully types are often spatially connected, and with such EG-PG connectivity can accelerate erosion. However, systematic research on this phenomenon remains limited, particularly at the regional scale. This study focuses on the spatial connectivity between EGs and PGs in the Songnen black soil region of northeast China. An unequal probability stratified sampling was used to establish 977 small watershed units, and a database of gullies and their connectivity was constructed based on sub-meter imagery. Among them, 55 representative units were randomly selected within geomorphic zones for field surveys and UAV validation to ensure data accuracy. Spatial patterns of gully connectivity were analyzed, and dominant controlling factors were identified using the Geodetector, which quantifies spatial stratified heterogeneity and evaluates the explanatory power of potential driving factors. The results are as follows: (1) Gully connectivity varies significantly across the region, with hotspot areas where more than 50% of permanent gullies are connected to ephemeral gullies, and cold spot clusters elsewhere. (2) Permanent gullies connected to ephemeral gullies differ significantly from unconnected ones in both length and width, with the former exhibiting a more elongated morphology. (3) Slope length and mean annual precipitation are the primary drivers of gully connectivity, both showing significant positive effects. Moreover, the interaction between mean annual precipitation and slope length shows the strongest explanatory power, indicating that precipitation, in combination with topographic features, plays a dominant role in shaping gully connectivity. By examining the spatial patterns of gully connectivity, this study contributes to a more refined understanding of gully morphological evolution and offers empirical insights for enhancing gully erosion models and optimizing regional soil and water conservation strategies. Full article

Multiple sclerosis (MS), a chronic disease of the central nervous system, is known to cause structural and vascular changes in the retina. Although optical coherence tomography (OCT) and fundus photography can detect retinal thinning and circulatory abnormalities, these findings are not specific to MS. This study explores the potential of Infrared Scanning-Laser-Ophthalmoscopy (IR-SLO) imaging to uncover vascular morphological features that may serve as MS-specific biomarkers. Using an age-matched, subject-wise stratified k-fold cross-validation approach, a deep learning model originally designed for color fundus images was adapted to segment optic disc, optic cup, and retinal vessels in IR-SLO images, achieving Dice coefficients of 91%, 94.5%, and 97%, respectively. This process included tailored pre- and post-processing steps to optimize segmentation accuracy. Subsequently, clinically relevant features were extracted. Statistical analyses followed by SHapley Additive exPlanations (SHAP) identified vessel fractal dimension, vessel density in zones B and C (circular regions extending 0.5–1 and 0.5–2 optic disc diameters from the optic disc margin, respectively), along with vessel intensity and width, as key differentiators between MS patients and healthy controls. These findings suggest that IR-SLO can non-invasively detect retinal vascular biomarkers that may serve as additional or alternative diagnostic markers for MS diagnosis, complementing current invasive procedures. Full article

The spatial variation of forest ecosystem services at regional scales remains poorly understood, and few studies have explicitly analyzed how ecosystem services are distributed across different forest management types. This study assessed the spatial overlap between forest management types and ecosystem service hotspots in the Southeastern United States (SEUS) and the Pacific Northwest (PNW) forests. We used the InVEST suite of tools and GIS to quantify carbon storage and water yield. Carbon storage was estimated, stratified by forest group and age class, and literature-based biomass pool values were applied. Average annual water yield and its temporal changes (2001–2020) were modeled using the annual water yield model, incorporating precipitation, potential evapotranspiration, vegetation type, and soil characteristics. Ecosystem service outputs were classified to identify hotspot zones (top 20%) and to evaluate the synergies and tradeoffs between these services. Hotspots were then overlaid with forest management maps to examine their distribution across management types. We found that only 2% of the SEUS and 11% of the PNW region were simultaneous hotspots for both services. In the SEUS, ecological and preservation forest management types showed higher efficiency in hotspot allocation, while in PNW, production forestry contributed relatively more to hotspot areas. These findings offer valuable insights for decision-makers and forest managers seeking to preserve the multiple benefits that forests provide at regional scales. Full article

This study presents the scientific and methodological foundation of Spain’s first national framework for the ethical management of community cat populations: the Action Plan for the Management of Community Cat Colonies (PACF), launched in 2025 under the mandate of Law 7/2023. This pioneering legislation introduces a standardized, nationwide obligation for trap–neuter–return (TNR)-based management of free-roaming cats, defined as animals living freely, territorially attached, and with limited socialization toward humans. The PACF aims to support municipalities in implementing this mandate through evidence-based strategies that integrate animal welfare, biodiversity protection, and public health objectives. Using standardized data submitted by 1128 municipalities (13.9% of Spain’s total), we estimated a baseline population of 1.81 million community cats distributed across 125,000 colonies. These data were stratified by municipal population size and applied to national census figures to generate a model-ready demographic structure. We then implemented a stochastic simulation using Vortex software to project long-term population dynamics over a 25-year horizon. The model integrated eight demographic–environmental scenarios defined by a combination of urban–rural classification and ecological reproductive potential based on photoperiod and winter temperature. Parameters included reproductive output, mortality, sterilization coverage, abandonment and adoption rates, stochastic catastrophic events, and territorial carrying capacity. Under current sterilization rates (~20%), our projections indicate that Spain’s community cat population could surpass 5 million individuals by 2050, saturating ecological and social thresholds within a decade. In contrast, a differentiated sterilization strategy aligned with territorial reproductive intensity (50% in most areas, 60–70% in high-pressure zones) achieves population stabilization by 2030 at approximately 1.5 million cats, followed by a gradual long-term decline. This scenario prioritizes feasibility while substantially reducing reproductive output, particularly in rural and high-intensity contexts. The PACF combines stratified demographic modeling with spatial sensitivity, offering a flexible framework adaptable to local conditions. It incorporates One Health principles and introduces tools for adaptive management, including digital monitoring platforms and standardized welfare protocols. While ecological impacts were not directly assessed, the proposed demographic stabilization is designed to mitigate population-driven risks to biodiversity and public health without relying on lethal control. By integrating legal mandates, stratified modeling, and realistic intervention goals, this study outlines a replicable and scalable framework for coordinated action across administrative levels. It exemplifies how national policy can be operationalized through data-driven, territorially sensitive planning tools. The findings support the strategic deployment of TNR-based programs across diverse municipal contexts, providing a model for other countries seeking to align animal welfare policy with ecological planning under a multi-level governance perspective. Full article

This study presents a numerical and experimental evaluation of axial load transfer mechanisms in deep foundations constructed in stratified cohesive soils in İzmir, Türkiye. A full-scale bi-directional static load test equipped with strain gauges was conducted on a barrette pile to investigate depth-dependent mobilization of shaft resistance. A finite element model was developed and calibrated using field-observed load–settlement and strain data to replicate the pile–soil interaction and deformation behavior. The analysis revealed a shaft-dominated load transfer behavior, with progressive mobilization concentrated in intermediate-depth cohesive layers. Sensitivity analysis identified the undrained stiffness (E_u) as the most influential parameter governing pile settlement. A strong polynomial correlation was established between calibrated E_u values and SPT N₆₀, offering a practical tool for preliminary design. Additionally, strain energy distribution was evaluated as a supplementary metric, enhancing the interpretation of mobilization zones beyond conventional stress-based methods. The integrated approach provides valuable insights for performance-based foundation design in layered cohesive ground, supporting the development of site-calibrated numerical models informed by full-scale testing data. Full article

Precision neurosurgery is rapidly evolving as a medical specialty by merging genomic medicine, multi-omics technologies, and artificial intelligence (AI) technology, while at the same time, society is shifting away from the traditional, anatomic model of care to consider a more precise, molecular model of care. The general purpose of this review is to contemporaneously reflect on how these advances will impact neurosurgical care by providing us with more precise diagnostic and treatment pathways. We hope to provide a relevant review of the recent advances in genomics and multi-omics in the context of clinical practice and highlight their transformational opportunities in the existing models of care, where improved molecular insights can support improvements in clinical care. More specifically, we will highlight how genomic profiling, CRISPR-Cas9, and multi-omics platforms (genomics, transcriptomics, proteomics, and metabolomics) are increasing our understanding of central nervous system (CNS) disorders. Achievements obtained with transformational technologies such as single-cell RNA sequencing and intraoperative mass spectrometry are exemplary of the molecular diagnostic possibilities in real-time molecular diagnostics to enable a more directed approach in surgical options. We will also explore how identifying specific biomarkers (e.g., IDH mutations and MGMT promoter methylation) became a tipping point in the care of glioblastoma and allowed for the establishment of a new taxonomy of tumors that became applicable for surgeons, where a change in practice enjoined a different surgical resection approach and subsequently stratified the adjuvant therapies undertaken after surgery. Furthermore, we reflect on how the novel genomic characterization of mutations like DEPDC5 and SCN1A transformed the pre-surgery selection of surgical candidates for refractory epilepsy when conventional imaging did not define an epileptogenic zone, thus reducing resective surgery occurring in clinical practice. While we are atop the crest of an exciting wave of advances, we recognize that we also must be diligent about the challenges we must navigate to implement genomic medicine in neurosurgery—including ethical and technical challenges that could arise when genomic mutation-based therapies require the concurrent application of multi-omics data collection to be realized in practice for the benefit of patients, as well as the constraints from the blood–brain barrier. The primary challenges also relate to the possible gene privacy implications around genomic medicine and equitable access to technology-based alternative practice disrupting interventions. We hope the contribution from this review will not just be situational consolidation and integration of knowledge but also a stimulus for new lines of research and clinical practice. We also hope to stimulate mindful discussions about future possibilities for conscientious and sustainable progress in our evolution toward a genomic model of precision neurosurgery. In the spirit of providing a critical perspective, we hope that we are also adding to the larger opportunity to embed molecular precision into neuroscience care, striving to promote better practice and better outcomes for patients in a global sense. Full article

Background/Objectives: This study investigates the utility of multiparametric PET/MRI in delineating changes in physiologically distinct intratumoral habitats during trastuzumab-induced alterations in a preclinical HER2+ breast cancer model. Methods: By integrating diffusion-weighted MRI, dynamic contrast-enhanced MRI, [¹⁸F]Fluorodeoxyglucose- and [¹⁸F]Fluorothymidine-PET, voxel-wise parametric maps were generated capturing cellular density, vascularity, metabolism, and proliferation. BT-474 tumor-bearing mice have high expression of HER2 and, in response to trastuzumab, an anti-HER2 antibody, effectively show changes in proliferation and tumor microenvironment alterations that result in decreases in tumor volume through time. Results: Single imaging metrics and changes in metrics were incapable of identifying treatment-induced alterations early in the course of therapy (day 4) prior to changes in tumor volume. Hierarchical clustering identified five distinct tumor habitats, which enabled longitudinal assessment of early treatment response. Tumor habitats were defined based on imaging metrics related to biology and categorized as highly vascular (HV), hypoxic responding (HRSP), transitional zone (TZ), active tumor (ATMR) and responding (RSP). The HRSP cluster volume significantly decreased in trastuzumab-treated tumors compared to controls by day 4 (p = 0.015). The volume of ATMR cluster was significantly different at baseline between cohorts (p = 0.03). The TZ cluster, indicative of regions transitioning more to necrosis, significantly decreased in treated tumors (p = 0.031), suggesting regions had already transitioned. Multiparametric image clustering showed a significant positive linear correlation with histological multiparametric mapping, with R² values of 0.56 (HRSP, p = 0.013, 0.64 (ATMR, p = 0.0055), and 0.49 (responding cluster, p = 0.024), confirming the biological relevance of imaging-derived clusters. Conclusions: These findings highlight the potential utility of multiparametric PET/MRI to capture biological alterations prior to any single imaging metric which has potential for better understanding longitudinal changes in biology, stratifying tumors based on those changes, optimizing therapeutic monitoring and advancing precision oncology. Full article

As a critical link connecting groundwater and vegetation, the vadose zone’s lithological structural heterogeneity directly influences soil water distribution and vegetation growth. A comprehensive understanding of the ecological effects of the vadose zone can provide scientific evidence for groundwater ecological protection and natural vegetation conservation in arid regions. This study, taking the Minqin Basin in the lower reaches of China’s Shiyang River as a case, reveals the constraining effects of vadose zone lithological structures on vegetation water supply, root development, and water use strategies through integrated analysis, field investigations, and numerical simulations. The findings highlight the critical ecological role of the vadose zone. This role primarily manifests through two mechanisms: regulating capillary water rise and controlling water-holding capacity. They directly impact soil water supply efficiency, alter the spatiotemporal distribution of water deficit in the root zone, and drive vegetation to develop adaptive root growth patterns and stratified water use strategies, ultimately leading to different growth statuses of natural vegetation. During groundwater level fluctuations, fine-grained lithologies in the vadose zone exhibit stronger capillary water response rates, while multi-layered lithological structures (e.g., “fine-over-coarse” configurations) demonstrate pronounced delayed water release effects. Their effective water-holding capacities continue to exert ecological effects, significantly enhancing vegetation drought resilience. Full article

Satellite-derived bathymetry (SDB) enables the efficient mapping of shallow waters such as coastal zones but typically requires extensive local ground truth data to achieve high accuracy. This study evaluates the effectiveness of transfer learning in reducing this requirement while keeping estimation accuracy at acceptable levels by adapting a deep learning model pretrained on data from Puck Lagoon (Poland) to a new coastal site in Agia Napa (Cyprus). Leveraging the open MagicBathyNet benchmark dataset and a lightweight U-Net architecture, three scenarios were studied and compared: direct inference to Cyprus, site-specific training in Cyprus, and fine-tuning from Poland to Cyprus with incrementally larger subsets of training data. Results demonstrate that fine-tuning with 15 samples reduces RMSE by over 50% relative to the direct inference baseline. In addition, the domain adaptation approach using 15 samples shows comparable performance to the site-specific model trained on all available data in Cyprus. Depth-stratified error analysis and paired statistical tests confirm that around 15 samples represent a practical lower bound for stable SDB, according to the MagicBathyNet benchmark. The findings of this work provide quantitative evidence on the effectiveness of deploying data-efficient SDB pipelines in settings of limited in situ surveys, as well as a practical lower bound for clear and shallow coastal waters. Full article

Expansive clays damage the foundations, slabs, and utilities of low- and mid-rise buildings, threatening daily operations and incurring billions of dollars in costs globally. This study pioneers a domain-informed machine learning framework, coupled with a collinearity-aware feature selection strategy, to predict soil swell potential solely from routine index properties. Following hard-limit filtering and Unified Soil Classification System (USCS) screening, 291 valid samples were extracted from a public dataset of 395 cases. A random forest benchmark model was developed using five correlated features, and a multicollinearity analysis, as indicated by the variance inflation factor, revealed exact linear dependence among the Atterberg limits. A parsimonious two-variable model, based solely on plasticity index (PI) and clay fraction (C), was retained. On an 80:20 stratified hold-out set, this simplified model reduced root mean square error (RMSE) from 9.0% to 6.8% and maximum residuals from 42% to 16%. Bootstrap analysis confirmed a median RMSE of 7.5% with stable 95% prediction intervals. Shapley Additive Explanations (SHAP) analysis revealed that PI accounted for approximately 75% of the model’s influence, highlighting the critical swell surge beyond PI ≈ 55%. This work introduces a rule-based cleaning pipeline and collinearity-aware feature selection to derive a robust, two-variable model balancing accuracy and interpretability, a lightweight, interpretable tool for foundation design, GIS zoning, and BIM workflows. Full article

Although wind turbine installation vessels (WTIVs) are increasingly operating in deepwater complex geological areas with larger scales, systematic research on and experimental validation of platform jack-up stability remain insufficient. This study aimed to establish a comprehensive evaluation framework encompassing penetration depth, anti-overturning/sliding stability, and punch-through risk, thereby filling the gap in holistic platform stability analysis. An entire four-legged centrifuge test at 150× g was integrated with coupled Eulerian–Lagrangian (CEL) numerical simulations and theoretical methods to systematically investigate spudcan penetration mechanisms and global sliding/overturning evolution in clay/sand. The key findings reveal that soil properties critically influence penetration resistance and platform stability: Sand exhibited a six-times-higher ultimate bearing capacity than clay, yet its failure zone was 42% smaller. The sliding resistance in sand was 2–5 times greater than in clay, while the overturning behavior diverged significantly. Although the horizontal loads in clay were only 50% of those in sand, the tilt angles at equivalent sliding distances reached 8–10 times higher. Field validation at Guangdong Lemen Wind Farm confirmed the method’s reliability: penetration prediction errors of <5% and soil backflow/plugging effects were identified as critical control factors for punch-through risk assessment. Notably, the overturning safety factors for crane operation at 90° outreach and storm survival were equivalent, indicating operational load combinations dominate overturning risks. These results provide a theoretical and decision-making basis for the safe operation of large WTIVs, particularly applicable to engineering practices in complex stratified seabed areas. Full article

Infectious diseases can spread through virus-laden respiratory droplets exhaled into the air. Ventilation systems are crucial in indoor settings as they can dilute or eliminate these droplets, underscoring the importance of understanding their efficacy in the management of indoor infections. Within the field of fluid dynamics methods, the dispersed droplets may be approached through either a Lagrangian framework or an Eulerian framework. In this study, various Eulerian methodologies are systematically compared against the Eulerian–Lagrangian (E-L) approach across three different scenarios: the pseudo-single-phase model (PSPM) for assessing the transport of gaseous pollutants in an office with displacement ventilation (DV), stratum ventilation (SV), and mixing ventilation (MV); the two-fluid model (TFM) for evaluating the transport of non-evaporating particles within an office with DV and MV; and the two-fluid model-population balance equation (TFM-PBE) approach for analyzing the transport of evaporating droplets in a ward with MV. The Eulerian and Lagrangian approaches present similar agreement with the experimental data, indicating that the two approaches are comparable in accuracy. The computational cost of the E-L approach is closely related to the number of tracked droplets; therefore, the Eulerian approach is recommended when the number of droplets required by the simulation is large. Finally, the performances of DV, SV, and MV are presented and discussed. DV creates a stratified environment due to buoyant flows, which transport respiratory droplets upward. MV provides a well-mixed environment, resulting in a uniform dispersion of droplets. SV supplies fresh air directly to the breathing zone, thereby effectively reducing infection risk. Consequently, DV and SV are preferred to reduce indoor infection. Full article