Search Results (6,726)

Sustainable port development in coastal regions necessitates robust frameworks for quantifying hydrodynamic risks under climate change. To bridge the gap between generic guidelines and site-specific resilience planning, this study proposes and applies a numerical modeling-based risk assessment framework. Within the context of the Port Master Plan, the framework is applied to the critical case of Takoradi Port in West Africa, employing a two-dimensional hydrodynamic model to simulate current fields under three current regimes, “Normal”, “Stronger”, and “Estimated Extreme” scenarios, for the first time. The model quantifies key hydrologic parameters such as current velocity and direction in critical zones (the approach channel, port basin, and berths), providing actionable data for the Port Master Plan. Key new findings include the following: (1) Northeastward surface currents, driven by the southwest monsoon, dominate the study area; breakwater sheltering creates a prominent circulation zone north of the port entrance. (2) Under extreme conditions, the approach channel exhibits amplified currents (0.3–0.7 m/s), while inner port areas maintain stable conditions (<0.1 m/s). (3) A stark spatial differentiation in designed current velocities for 2–100 years return periods, where the 100-year extreme current velocity in the external approach channel (0.87 m/s at P1) exceeds the range in the internal zones (0.01–0.15 m/s) by approximately 5 to 86 times. The study validates the framework’s utility in assessing hydrodynamic risks. By integrating numerical simulation with risk assessment, this work provides a scalable methodological contribution that can be adapted to other port environments, directly supporting the global pursuit of sustainable and resilient ports. Full article

Carbon sequestration and oxygen release (CSOR) are core regulating functions of terrestrial ecosystems. However, regional assessments often fail to (i) separate scale-driven high supply from per-area efficiency, (ii) detect structural instability and degradation risk from long-term trajectories, and (iii) provide evidence that is comparable across units for management prioritization. Using Minnesota, USA, we integrated satellite-derived net primary productivity (NPP; 1998–2021) with a Quantity–Intensity–Structure (Q–I–S) framework to quantify CSOR, detect trends and change points (Mann–Kendall and Pettitt tests), map spatial clustering and degradation risk (Exploratory Spatial Data Analysis, ESDA), and attribute natural and human drivers (principal component regression and GeoDetector). CSOR increased overall from 1998 to 2021, with a marked shift around 2013 from a slight, variable decline to sustained recovery. Spatially, CSOR showed a persistent north–south gradient, with higher and improving services in northern Minnesota and lower, more degraded services in the south; persistent degradation was concentrated in a central high-risk belt. The Q–I–S framework also revealed inconsistencies between total supply and condition, identifying high-supply yet degrading areas and low-supply areas with recovery potential that are not evident from the totals alone. Climate variables primarily controlled CSOR quantity and structure, whereas human factors more strongly influenced intensity; the interactions of the two further shaped observed patterns. These results provide an interpretable and transferable basis for diagnosing degradation and prioritizing restoration under long-term environmental change. Full article

Non-AIDS-defining cancers (NADCs) have emerged as an increasingly prominent cause of non-AIDS-related morbidity and mortality among people living with HIV (PLWH). However, the scarcity of NADC clinical samples, compounded by privacy and security constraints, continues to present formidable obstacles to advancing pathological and clinical investigations. In this study, we adopted a joint analysis strategy and deeply integrated and analyzed transcriptomic data from 12,486 PLWH and cancer patients to systematically identify potential key regulators for 23 NADCs. This effort culminated in NADCdb—a database specifically engineered for NADC pathological exploration, structured around three mechanistic frameworks rooted in the interplay of immunosuppression, chronic inflammation, carcinogenic viral infections, and HIV-derived oncogenic pathways. The “rNADC” module performed risk assessment by prioritizing genes with aberrant expression trajectories, deploying bidirectional stepwise regression coupled with logistic modeling to stratify the risks for 21 NADCs. The “dNADC” module, synergized patients’ dysregulated genes with their regulatory networks, using Random Forest (RF) and Conditional Inference Trees (CITs) to identify pathogenic drivers of NADCs, with an accuracy exceeding 75% (in the external validation cohort, the prediction accuracy of the HIV-associated clear cell renal cell carcinoma model exceeded 90%). Meanwhile, “iPredict” identified 1905 key immune biomarkers for 16 NADCs based on the distinct immune statuses of patients. Importantly, we conducted multi-dimensional profiling of these key determinants, including in-depth functional annotations, phenotype correlations, protein–protein interaction (PPI) networks, TF-miRNA-target regulatory networks, and drug prediction, to deeply dissect their mechanistic roles in NADC pathogenesis. In summary, NADCdb serves as a novel, centralized resource that integrates data and provides analytical frameworks, offering fresh perspectives and a valuable platform for the scientific exploration of NADCs. Full article

Introduction: Accurate knowledge of the external carotid artery (ECA) anatomy is essential for head and neck surgery, interventional procedures, and imaging interpretation. Although its branching pattern is classically described as relatively constant, clinically relevant anatomical variations are frequently encountered. Cadaveric dissection remains fundamental for identifying rare vascular configurations. Materials and Methods: During an anatomical teaching dissection of a 72-year-old male cadaver, a right-sided lateral cervicotomy was performed to expose the carotid sheath. After mobilisation of the sternocleidomastoid muscle, the ECA and its proximal branches were skeletonised, allowing detailed three-dimensional assessment of their origin, calibre, and neurovascular relationships. Results: The superior thyroid artery originated from the proximal segment of the external carotid artery, in close proximity to the carotid bifurcation. The main anatomical finding was a lingual artery of relatively small initial calibre exhibiting an atypical superior trajectory: after its origin, it crossed superior to the hypoglossal nerve before continuing toward the tongue. This configuration differs from classical descriptions and modified the anatomical arrangement of Beclard’s and Pirogoff’s triangles, creating a potential site of close neurovascular contact. Conclusions: This cadaveric study describes a rare trajectory-based variant of the external carotid artery characterised by a lingual artery crossing superior to the hypoglossal nerve. Awareness of such rare patterns is essential for improving anatomical interpretation and enhancing surgical safety in the head and neck region. Full article

Standardized driving cycles often inadequately represent the driving patterns specific to a particular city, and variations in vehicle types within the same city further contribute to discrepancies in driving cycles. This study seeks to characterize the driving patterns of a specific vehicle model within a designated city and to provide robust data support for precise predictions of energy consumption and driving range. To achieve this objective, a driving cycle was developed and analyzed using real-world operational data collected from a battery electric vehicle (BEV) in Qingdao, China. The driving cycle was constructed through a process involving data preprocessing, dimensionality reduction via principal component analysis (PCA), and Improved Grey Wolf Optimizer K-means (IGWO-K-means). The analysis of energy consumption per 100 km is concluded by the study. Validation of the constructed driving cycle against the preprocessed data yielded an average relative error of 2.31%, providing a reference for the real-world driving cycle of BEVs in Qingdao, China. Furthermore, a comparative analysis of the driving cycles for BEVs in Qingdao, China, and internal combustion engine vehicles (ICEVs) in Fuzhou and Nanjing, China, revealed notable differences. This underscores the critical need for developing driving cycles that are specifically tailored to distinct cities and vehicle models. The examination of energy consumption per 100 km further corroborated the representativeness of the constructed driving cycle. Furthermore, a comparative assessment of energy consumption across varying ambient temperature ranges demonstrated that it increases as temperatures decrease. Full article

Background/Objectives: This study evaluated the clinical image quality of three-dimensional synthetic MRI (3D SI) compared with conventional MRI (cMRI), focusing on tissue contrast, anatomical detail, and motion sensitivity. Methods: Patients with nonspecific neurological symptoms were included. Both cMRI and 3D SI were acquired on single-vendor 1.5 T and 3 T scanners with slice thicknesses of 1.0–1.7 mm. Two experienced neuroradiologists and one fellow independently evaluated matched scans using a 0–100 scale. Assessed parameters included signal-to-noise ratio (SNR), gray–white matter contrast, artifacts, motion robustness, and confidence in detecting perivascular spaces, white matter lesions, and subtle pathology. Interrater agreement was measured using Krippendorff’s alpha and ICC2. Multiple linear regression analyzed associations between image quality ratings and imaging method. Results: Images of 31 patients were analyzed. Three-dimensional SI demonstrated sufficient-to-good overall image quality and high robustness to motion. Cortical-surface-to-cerebrospinal-fluid contrast on FLAIR was rated lower for 3D SI than for cMRI. False-positive lesion detection occurred more frequently on 3D SI FLAIR, particularly among experienced readers. cMRI achieved significantly higher T1-weighted SNR than 3D SI (8.76 points, p < 0.001). Experienced readers consistently rated SNR and tissue contrast higher than the fellow. Vascular signal range was broader on 3D SI, reducing sensitivity to vascular abnormalities. Conclusions: Three-dimensional synthetic MRI provides clinically usable image quality and fulfills its primary diagnostic purpose, offering advantages in acquisition efficiency and robustness to motion. Nevertheless, limitations in cortical contrast, vascular signal characterization, and reader-dependent interpretive variability constrain its reliability for subtle or detail-critical findings. Full article

This study systematically verified the applicability and accuracy of the Probability Density Evolution Method (PDEM) in the probabilistic modeling of the dynamic response of anchored rock slopes under random seismic action through large-scale shaking table model tests. Across 144 sets of non-stationary random ground motions and 7 sets of white noise excitations, key response data such as acceleration, displacement, and changes in anchor axial force were collected. The PDEM was used to model the instantaneous probability density function (PDF) and cumulative distribution function (CDF), which were then compared with the results of normal distribution, Gumbel distribution, and direct sample statistics from multiple dimensions. The results show that the PDEM does not require a preset distribution form and can accurately reproduce the non-Gaussian, multi-modal, and time evolution characteristics of the response; in the reliability assessment of peak responses, its prediction deviation is much smaller than that of traditional parametric models; the three-dimensional probability density evolution cloud map further reveals the law governing the entire process of the response PDF from “narrow and high” in the early stage of the earthquake, “wide and flat” in the main shock stage, to “re-convergence” after the earthquake. The study confirms that the PDEM has significant advantages and engineering application value in the analysis of random seismic responses and the dynamic reliability assessment of anchored slopes. Full article

With the widespread adoption of renewable energy, distribution grids face increasing challenges in efficiency, safety, and economic performance due to stochastic generation and fluctuating load demand. Traditional operational models often exhibit limited adaptability, weak coordination, and insufficient holistic optimization, particularly in early-/mid-stage distribution planning where feeder-level network information may be incomplete. Accordingly, this study adopts a planning-oriented formulation and proposes a distributed energy storage system (DESS) planning strategy to enhance distribution network resilience under high uncertainty. First, representative wind and photovoltaic (PV) scenarios are generated using an improved Gaussian Mixture Model (GMM) to characterize source-side uncertainty. Based on a grid-based network partition, a priority index model is developed to quantify regional storage demand using quality- and efficiency-oriented indicators, enabling the screening and ranking of candidate DESS locations. A mixed-integer linear multi-objective optimization model is then formulated to coordinate lifecycle economics, operational benefits, and technical constraints, and a sequential connection strategy is employed to align storage deployment with load-balancing requirements. Furthermore, a node–block–grid multi-dimensional evaluation framework is introduced to assess resilience enhancement from node-, block-, and grid-level perspectives. A case study on a Zhejiang Province distribution grid—selected for its diversified load characteristics and the availability of detailed historical wind/PV and load-category data—validates the proposed method. The planning and optimization process is implemented in Python and solved using the Gurobi optimizer. Results demonstrate that, with only a 4% increase in investment cost, the proposed strategy improves critical-node stability by 27%, enhances block-level matching by 88%, increases quality-demand satisfaction by 68%, and improves grid-wide coordination uniformity by 324%. The proposed framework provides a practical and systematic approach to strengthening resilient operation in distribution networks. Full article

Accurate interpretation of missense variants in cancer-associated genes remains a critical challenge in precision oncology, as most sequence-based predictors lack mechanistic explanations. Receptor tyrosine kinases like FGFR2 exemplify this problem: their function depends on conformational dynamics, yet most variants remain classified as variants of uncertain significance (VUS). In this paper we present DyVarMap, an interpretable structural-learning framework that integrates AlphaFold2-based ensemble generation with physics-driven refinement, manifold learning, and supervised classification using five biophysically motivated geometric features. Applied to FGFR2, the framework generates diverse conformational ensembles, identifies metastable states through nonlinear dimensionality reduction, and classifies pathogenicity while providing mechanistic attributions via SHAP analysis. External validation on ten kinase-domain variants yields an AUROC of 0.77 with superior calibration (Brier score = 0.108) compared to PolyPhen-2 (0.125) and AlphaMissense (0.132). Feature importance analysis consistently identifies K659–E565 salt-bridge distance and DFG motif dihedral angles as top predictors, directly linking predictions to known activation mechanisms. Case studies of borderline variants (A628T, E608K, L618F) demonstrate the framework’s ability to provide structurally coherent mechanistic explanations. DyVarMap bridges the gap between static structure prediction and dynamics-aware functional assessment, generating testable hypotheses for experimental validation and demonstrating the value of incorporating conformational dynamics into variant effect prediction for precision oncology. Full article

With the continuous expansion of high-density urban forms, residents’ opportunities for daily contact with natural environments have been increasingly reduced, making the equity of urban green space allocation a critical challenge for sustainable urban development. Existing studies have largely focused on green space quantity or accessibility at single time points, lacking systematic investigations into the spatiotemporal evolution of green space exposure (GSE) and its equity from the perspective of residents’ actual environmental experiences. GSE refers to the integrated level of residents’ contact with urban green spaces during daily activities across multiple dimensions, including visual exposure, physical accessibility, and spatial distribution, emphasizing the relationship between green space provision and lived environmental experience. Based on this framework, this study takes the central urban area of Hangzhou as the study area and integrates multi-temporal remote sensing imagery with large-scale street view data. A deep learning–based approach is developed to identify green space exposure, combined with spatial statistical methods and equity measurement models to systematically analyze the spatiotemporal patterns and evolution of GSE and its equity from 2013 to 2023. The results show that (1) GSE in Hangzhou increased significantly over the study period, with accessibility exhibiting the most pronounced improvement. However, these improvements were mainly concentrated in peripheral areas, while changes in the urban core remained relatively limited, revealing clear spatial heterogeneity. (2) Although overall GSE equity showed a gradual improvement, pronounced mismatches between low exposure and high demand persisted in densely populated areas, particularly in older urban districts and parts of newly developed residential areas. (3) The spatial patterns and evolutionary trajectories of equity varied significantly across different GSE dimensions. Composite inequity characterized by “low visibility–low accessibility” formed stable clusters within the urban core. This study further explores the mechanisms underlying green space exposure inequity from the perspectives of urban renewal patterns, land-use intensity, and population concentration. By constructing a multi-dimensional and temporally explicit analytical framework for assessing GSE equity, this research provides empirical evidence and decision-making references for refined green space management and inclusive, sustainable urban planning in high-density cities. Full article

The digitalization of machining processes is increasingly recognized as essential for achieving higher productivity, reliability, and traceability. However, access to reliable in-process sensor data remains limited, particularly in multi-axis CNC machining, where dimensional accuracy and surface integrity strongly depend on stable and optimized process conditions. This study investigates sensor-based monitoring as a practical approach for evaluating process performance in five-axis CNC milling. Electric current and vibration signals were acquired during three machining operations, under distinct cutting parameters, using current clamps and a plug-and-play MEMS accelerometer. The signals were processed using the root mean square method to assess the correlation between sensor data and machining conditions. Dimensional inspection of each workpiece was carried out to verify geometric conformity. The results show that spindle current measurements exhibit a strong linear correlation with material removal rate and cutting power, supporting their use as indicators of cutting forces and energy consumption. Vibration signals revealed pronounced dynamic behaviour for specific tool orientations, particularly in transverse to tool axis direction. The proposed methodology provides a simple and low-cost framework for integrating sensor-based monitoring into five-axis CNC milling, particularly relevant for semi-roughing operations, and offers a basis for future studies on process optimization and real-time condition monitoring. Full article

This work investigates a data-driven approach for detecting structural damage in the wing of a Cessna 172 aircraft using reduced-order finite element (FE) models. This study focuses on the ability of machine learning methods to generalize across different structural conditions, aiming to support reliable Structural Health Monitoring (SHM) in aeronautical applications. The wing was first modeled in detail using the FiniteElement Method, followed by the development of a simplified FE model to reduce computational cost while maintaining accuracy. The similarity between the two models was evaluated through modal analysis and the Modal Assurance Criterion (MAC). Dynamic excitation representing turbulence effects was applied to simulate healthy and damaged conditions, producing acceleration data used to train one-dimensional and two-dimensional neural network classifiers. The 1D models processed raw vibration signals, while the 2D models used image representations of the same data. Both architectures were tested against results from the detailed FE model to assess their generalization capability. The 2D networks achieved higher classification accuracy, demonstrating improved robustness in identifying both minor and severe damage. The findings highlight the potential of combining reduced FE models with data-driven methods for efficient and accurate aircraft wing damage detection. Full article

Flooding is a recurrent problem in many Brazilian cities, resulting in significant losses that affect health, assets, finance, and the environment. The uncertainty regarding extreme rainfall events due to climate change makes this challenge even more severe, compounded by inadequate urban planning and the occupation of risk areas, particularly for the municipality of Catu, in the state of Bahia, which also suffers from recurrent floods. Critical hotspots include the Santa Rita neighborhood and its surroundings, the main supply center, and the city center—the municipality’s commercial hub. The focus of this research is the unprecedented quantification of the socioeconomic impact of these floods on the low-income population and the region’s informal sector (street vendors). This research focused on analyzing and modeling the destructive potential of intense rainfall in the Santa Rita region (Supply Center) of Catu, Bahia, and its effects on the local economy across different recurrence intervals. A hydrological simulation software suite based on computational and geoprocessing technologies—specifically HEC-RAS 6.4, HEC-HMS 4.11, and QGIS— 3.16 was utilized. Two-dimensional (2D) modeling was applied to assess the flood-prone areas. For the socioeconomic impact assessment, a loss procedure based on linear regression was developed, which correlated the different return periods of extreme events with the potential losses. This methodology, which utilizes validated, indirect data, establishes a replicable framework adaptable to other regions facing similar socioeconomic and drainage challenges. The results revealed that the area becomes impassable during flood events, preventing commercial activities and causing significant economic losses, particularly for local market vendors. The total financial damage for the 100-year extreme event is approximately US $30,000, with the loss model achieving an R² of 0.98. The research concludes that urgent measures are necessary to mitigate flood impacts, particularly as climate change reduces the return period of extreme events. The implementation of adequate infrastructure, informed by the presented risk modeling, and public awareness are essential for reducing vulnerability. Full article

Directional swelling pressure is a critical yet often overlooked factor governing the instability of expansive soil slopes. Most existing studies simplify swelling behavior as a uniform or purely vertical stress, thereby underestimating the distinct contribution of horizontal swelling pressure. In this study, an integrated framework combining the Fuzzy Analytic Hierarchy Process (FAHP), multivariate regression analysis based on 35 expansive soil samples, and three-dimensional strength-reduction numerical modeling was developed to systematically evaluate the mechanistic roles of vertical and horizontal swelling pressures in slope deformation. The FAHP and regression analyses indicate that water content is the dominant factor controlling both the free swell ratio and swelling pressure, leading to predictive relationships that link swelling behavior to fundamental physical indices. These empirical correlations were subsequently incorporated into a three-dimensional numerical model of a representative Neogene expansive soil slope. The simulation results demonstrate that neglecting swelling pressure results in substantial discrepancies between predicted and observed displacements. Vertical swelling pressure induces moderate surface uplift but exerts a limited influence on overall failure patterns. In contrast, horizontal swelling pressure markedly amplifies downslope displacement—by more than four times under saturated conditions—reduces the factor of safety by 24.7%, and promotes the progressive development of a continuous slip surface. These findings clearly demonstrate that horizontal swelling pressure is the dominant driver of progressive failure in expansive soil slopes. This study provides new mechanistic insights into swelling-induced deformation and offers a quantitative framework for incorporating directional swelling stresses into slope stability assessment, design optimization, and mitigation strategies for geotechnical structures in expansive soil regions. Full article

Accurate quantification of forest aboveground biomass (AGB) is essential for monitoring terrestrial carbon stocks. While total AGB estimation is widely practiced, resolving component biomass such as canopy, branches, leaves, and trunks enhances the precision of carbon sink assessments and provides critical structural parameters for ecosystem modeling. Most studies rely on a single SAR sensor or a limited range of SAR features, which restricts their ability to represent vegetation structural complexity and reduces biomass estimation accuracy. Here, we propose a phased fusion strategy that integrates backscatter intensity, interferometric coherence, texture measures, and polarimetric decomposition parameters derived from dual-frequency ALOS-2, GF-3, and Sentinel-1A SAR data. These complementary multi-dimensional SAR features are incorporated into a Random Forest model optimized using an Adaptive Genetic Algorithm (RF-AGA) to estimate forest total and component estimation. The results show that the progressive incorporation of coherence and texture features markedly improved model performance, increasing the accuracy of total AGB to R² = 0.88 and canopy biomass to R² = 0.78 under leave-one-out cross-validation. Feature contribution analysis indicates strong complementarity among SAR parameters. Polarimetric decomposition yielded the largest overall contribution, while L-band volume scattering was the primary driver of trunk and canopy estimation. Coherence-enhanced trunk prediction increased R² by 13 percent, and texture improved canopy representation by capturing structural heterogeneity and reducing saturation effects. This study confirms that integrating coherence and texture information within the RF-AGA framework enhances AGB estimation, and that the differential contributions of multi-dimensional SAR parameters across total and component biomass estimation originate from their distinct structural characteristics. The proposed framework provides a robust foundation for regional carbon monitoring and highlights the value of integrating complementary SAR features with ensemble learning to achieve high-precision forest carbon assessment. Full article