Search Results (10,843)

The Ca Mau Peninsula (CMP), the southernmost region of the Mekong Delta, is increasingly threatened by groundwater salinization, posing severe risks to both the freshwater supply and land sustainability. This study develops a three-dimensional, density-dependent groundwater flow and salinity transport model to investigate salinization dynamics across the CMP’s complex multi-aquifer system. Unlike previous studies that largely rely on model calibration, this research introduces a novel approach by systematically deriving the spatial distribution of longitudinal dispersivity based on sediment characteristics. Moreover, detailed land use mapping is integrated to assign spatially and temporally variable Total Dissolved Solids (TDS) values to the uppermost layers, thereby enhancing the model realism in areas where monitoring data are limited. The model was utilized not only to simulate the regional salinity evolution, but also to critically evaluate conceptual hypotheses related to the mechanisms driving groundwater salinization. Results reveal a strong influence of seasonal and land use factors on salinity variability in the upper aquifers, while deeper aquifers remain largely stable, affected primarily by paleosalinity and localized pumping. This integrated modeling approach contributes to a better understanding of regional-scale groundwater salinization and highlights both the potential and the limitations of numerical modeling under data-scarce conditions. The findings provide a valuable scientific basis for adaptive water resource management in vulnerable coastal zones. Full article

An absolutely conflicting value for the incorporation of the Lode parameter into a fracture criterion was reported in the literature when predicting the ballistic resistance of metallic plates failing through shear plugging. In this study, a combined experimental–numerical investigation was conducted to understand the dynamic shear fracture behavior under compression–shear stress states. Flat-hat-shaped specimens of 30CrMnSiNi2A high-strength steel were loaded using a Split Hopkinson Pressure Bar apparatus, combining the ultra-high-speed photography technique, digital image correlation method, and microstructure observation. Parallel finite element simulations were performed using both a modified Johnson–Cook (MJC) fracture criterion or an extended Xue–Wierzbicki (EXW) fracture criterion with Lode dependence to reveal the value of the Lode parameter incorporation. It was found that deformed shear bands with a width of approximately 0.14 mm form at a critical impact velocity. The EXW criterion correctly predicts the critical fracture velocity and estimates the fracture initiation instants within an error of 5.3%, whereas the MJC fracture criterion overestimates the velocity by 14.3%. Detailed analysis shows that the EXW criterion predicts a combined failure mechanism involving ductile fracture and material instability, while the MJC fracture criterion attributes the failure exclusively to material instability. The improved accuracy achieved by employing the Lode-dependent EXW fracture criterion may be attributed to the compression–shear stress state and the accurate prediction of the failure mechanism of the dynamic shear fracture. Full article

The sebaceous gland (SG) is an integral part of the pilosebaceous unit and is a very active and dynamic organ that contributes significantly to the maintenance of skin homeostasis. In addition to its primary role in sebum production, the SG is involved in the maintenance of skin barrier function, local endocrine/neuroendocrine function, the innate immune response, and the regulation of skin bacterial colonization. Structural and functional alterations of SGs leading to the dysregulation of sebum production/composition and immune response may contribute to the pathogenesis of inflammatory dermatoses. This review summarises the current knowledge on the contribution of SGs to the pathogenesis of common inflammatory skin diseases. These findings are crucial for the development of more effective therapeutic strategies for the treatment of inflammatory dermatoses. Full article

Electrocardiogram (ECG) interpretation using deep learning models holds immense potential for improving cardiac diagnosis. However, existing models often suffer from overconfident predictions and lack the capability to directly quantify uncertainty, leading to unreliable clinical guidance. To address this challenge, we propose a model for uncertainty-aware ECG interpretation. The model employs a deep convolutional architecture with max-pooling residual modules to capture both local and global spatiotemporal features from raw ECG signals. The architectural design respects the symmetry inherent in ECG waveforms—such as periodicity and morphological consistency across cardiac cycles—enabling the network to extract clinically relevant features more effectively. Then, unlike conventional models that rely on softmax-based probability outputs, our approach parameterizes class distributions using the Dirichlet distribution, while Subjective Logic translates these parameters into interpretable belief masses and uncertainty scores. We evaluate the model on the PhysioNet Challenge 2017 dataset, our model achieves an accuracy of 86.12%, an F1 score of 83.14%, a Precision-Recall Area Under the Curve (PR-AUC) of 85.25%, and a Receiver Operating Characteristic Area Under the Curve (ROC-AUC) of 92.87%—outperforming baseline models in three out of four metrics. Critically, the model reduces overconfidence to 0.59% (compared to 12–22% in softmax-based baselines), aligning prediction confidence with true accuracy. By progressively increasing the uncertainty threshold u, the model dynamically filters low-confidence predictions, leading to consistently improved performance—reaching up to 93.59% accuracy, 93.22% F1 score, 89.17% PR-AUC, and 95.10% ROC-AUC at u = 0.1. These results validate the model’s capacity for reliable ECG interpretation while leveraging physiological signal symmetry for enhanced feature extraction. Full article

Optimizing airflow organization is essential for ensuring the energy-efficient and secure operation of data centers. To address common airflow distribution issues in air-cooled systems, such as uneven air supply and cooling capacity imbalance, this study investigates a bidirectional airflow data center room located in a hot-summer and warm-winter region. A computational fluid dynamics (CFD) model was developed based on field-measured data to analyze the airflow distribution characteristics and evaluate the existing thermal conditions. Three optimization strategies were systematically examined: (1) Installation of rack blanking panels, (2) cold aisle containment with varying degrees of closure, and (3) combined implementations of these measures. Performance evaluation was conducted using three thermal metrics: the Return Temperature Index (RTI), Supply Heat Index (SHI), and Rack Cooling Index (RCIHI). The results demonstrate that among individual optimization strategies, rack blanking panels achieved the most significant improvement, reducing SHI by 42.61% while effectively eliminating local hotspots. For combined optimization strategies, the rack blanking panels and fully contained cold aisle containment showed optimal performance, improving cooling utilization efficiency by 88.26%. The optimal retrofit solution for this data center is the rack blanking panels with fully contained cold aisle containment. When considering budget constraints, the secondary option would be rack blanking panels with cold aisle top-only containment. These findings provide practical guidance for energy efficiency improvements in similar data center environments. Full article

Forest ecosystems critically regulate land surface temperature (LST) from local to regional scales. Over the last three decades (1986–2016), increasingly frequent and severe disturbances have substantially altered the European forest canopy structure and carbon storage. However, the biophysical interactions between forest disturbance severity (FDS) and LST, particularly their spatiotemporal dynamics, remain insufficiently quantified at regional-to-continental scales. This study integrated multi-source, high-resolution remote sensing data spanning 1986–2016 to systematically investigate European FDS and its biophysical control over LST. We find significant spatiotemporal heterogeneity in FDS, which decreased markedly from 5.92 ± 4.6 in 1986 to 0.35 ± 2.36 in 2016, stabilizing after a sharp decline pre-2000. Concurrently, the mean regional LST exhibited significant warming trends, increasing from −27.04 ± 10.15 K to 16.47 ± 10.67 K, and declining FDS indirectly contributed up to 65% of this temperature rise. Mechanistically, the reduced FDS enhanced the secondary forest leaf area index (LAI), decreasing surface albedo and increasing net radiation absorption, thereby inducing positive radiative feedback that drives surface warming. Our findings demonstrate that the carbon sequestration benefits accrued during forest recovery can be partially offset by associated biophysical warming effects. This evidence is crucial for optimizing European forest management strategies to balance carbon sink enhancement and climate regulation functions. Full article

Over the past decade, the field of object detection has advanced remarkably, especially in the accurate recognition of medium- and large-sized objects. Nevertheless, detecting small objects is still difficult because their low-resolution appearance provides insufficient discriminative features, and they often suffer severe occlusions, particularly in the safety-critical context of autonomous driving. Conventional detectors often fail to extract sufficient information from shallow feature maps, which limits their ability to detect small objects with high precision. To address this issue, we propose the ECAN-Detector, an efficient context-aggregation method designed to enrich the feature representation of shallow layers, which are particularly beneficial for small-object detection. The model first employs an additional shallow detection layer to extract high-resolution features that provide more detailed information for subsequent stages of the network, and then incorporates a dynamic scaled transformer (DST) that enriches spatial perception by adaptively fusing global semantics and local context. Concurrently, a context-augmentation module (CAM) embedded in the shallow layer complements both global and local features relevant to small objects. To further boost the average precision of small-object detection, we implement a faster method utilizing two reparametrized convolutions in the detection head. Finally, extensive experiments conducted on the VisDrone2012-DET and VisDrone2021-DET datasets verified that our proposed method surpasses the baseline model, and achieved a significant improvement of 3.1% in AP and 3.5% in $A{P}_s$. Compared with recent state-of-the-art (SOTA) detectors, ECAN Detector delivers comparable accuracy yet preserves real-time throughput, reaching 54.3 FPS. Full article

Although some progress has been made in deep learning-based remote sensing image change detection, the complexity of scenes and the diversity of changes in remote sensing images lead to challenges related to background interference. For instance, remote sensing images typically contain numerous background regions, while the actual change regions constitute only a small proportion of the overall image. To address these challenges in remote sensing image change detection, this paper proposes a Dynamic Adaptive Context Attention Network (DACA-Net) based on an exchanging dual encoder–decoder (EDED) architecture. The core innovation of DACA-Net is the development of a novel Dynamic Adaptive Context Attention Module (DACAM), which learns attention weights and automatically adjusts the appropriate scale according to the features present in remote sensing images. By fusing multi-scale contextual features, DACAM effectively captures information regarding changes within these images. In addition, DACA-Net adopts an EDED architectural design, where the conventional convolutional modules in the EDED framework are replaced by DACAM modules. Unlike the original EDED architecture, DACAM modules are embedded after each encoder unit, enabling dynamic recalibration of T1/T2 features and cross-temporal information interaction. This design facilitates the capture of fine-grained change features at multiple scales. This architecture not only facilitates the extraction of discriminative features but also promotes a form of structural symmetry in the processing pipeline, contributing to more balanced and consistent feature representations. To validate the applicability of our proposed method in real-world scenarios, we constructed an Unmanned Aerial Vehicle (UAV) remote sensing dataset named the Guangxi Beihai Coast Nature Reserves (GBCNR). Extensive experiments conducted on three public datasets and our GBCNR dataset demonstrate that the proposed DACA-Net achieves strong performance across various evaluation metrics. For example, it attains an F1 score (F1) of 72.04% and a precision(P) of 66.59% on the GBCNR dataset, representing improvements of 3.94% and 4.72% over state-of-the-art methods such as semantic guidance and spatial localization network (SGSLN) and bi-temporal image Transformer (BIT), respectively. These results verify that the proposed network significantly enhances the ability to detect critical change regions and improves generalization performance. Full article

Large-scale windstorms represent an important atmospheric hazard in the Northeastern US (NE) and are associated with substantial socioeconomic losses. Regional simulations performed with the Weather Research and Forecasting (WRF) model using lateral boundary conditions from three Earth System Models (ESMs: Geophysical Fluid Dynamics Laboratory (GFDL), Hadley Centre Global Environment Model (HadGEM) and Max Planck Institute (MPI)) are used to quantify possible future changes in windstorm characteristics and/or changes in the parent cyclone types responsible for windstorms. WRF nested within MPI ESM best represents important aspects of historical windstorms and the cyclone types responsible for generating windstorms compared with a reference simulation performed with the ERA-Interim reanalysis for the historical climate. The spatial scale and frequency of the largest windstorms in each simulation defined using the greatest extent of exceedance of local 99.9th percentile wind speeds (U > U₉₉₉) plus 50-year return period wind speeds (U_50,RP) do not exhibit secular trends. Projections of extreme wind speeds and windstorm intensity/frequency/geolocation and dominant parent cyclone type associated with windstorms vary markedly across the simulations. Only the MPI nested simulations indicate statistically significant differences in windstorm spatial scale, frequency and intensity over the NE in the future and historical periods. This model chain, which also exhibits the highest fidelity in the historical climate, yields evidence of future increases in 99.9th percentile 10 m height wind speeds, the frequency of simultaneous U > U₉₉₉ over a substantial fraction (5–25%) of the NE and the frequency of maximum wind speeds above 22.5 ms⁻¹. These geophysical changes, coupled with a projected doubling of population, leads to a projected tripling of a socioeconomic loss index, and hence risk to human systems, from future windstorms. Full article

We propose a hierarchical federated learning (HFL) framework for predictive pollutant analysis in advanced green analytical chemistry (AGAC), addressing the limitations of centralized approaches in scalability and data privacy. The system integrates localized sub-models with hybrid neural architectures, combining LSTM and attention mechanisms to capture temporal dependencies and feature importance in distributed analytical data, while raw measurements remain decentralized. A global aggregator dynamically adjusts model weights based on validation performance and data heterogeneity, ensuring robust adaptation to diverse environmental conditions. The framework interfaces seamlessly with AGAC infrastructure, processing inputs from analytical instruments into standardized sequences and mapping predictions back to pollutant concentrations through calibration curves. Implemented with PyTorch Federated and edge-cloud deployment, the system employs homomorphic encryption for secure data transmission, prioritizing spectral features critical for organic pollutant detection. Our approach achieves superior accuracy and privacy preservation compared to traditional centralized methods, offering a transformative solution for scalable environmental monitoring. The proposed method demonstrates significant potential for real-world applications, particularly in scenarios requiring distributed data collaboration without compromising analytical integrity. Full article

In response to the need for cable outer sound source localization, this paper proposes a collaborative localization method based on an improved generalized cross-correlation phase transform (GCC-PHAT) and multi-sensor fusion. By constructing a secondary cross-shaped sensor array model, employing a phase transform weighting function to suppress environmental noise, and incorporating an adaptive environmental compensation algorithm to eliminate multipath effects, a set of spatial localization equations is established. Innovatively, a dynamic weighting factor linked to the startup threshold is introduced; the Levenberg–Marquardt optimization algorithm is then used to iteratively solve the nonlinear equations to achieve preliminary localization in a single-pile coordinate system. Finally, a dynamic weighted fusion model is constructed through DBSCAN spatial clustering to determine the final sound source position. Experimental results demonstrate that this method reduces the mean square error of time delay estimation by 94.7% in a 90 dB industrial noise environment, decreases the localization error by 65.4% in multi-obstacle scenarios, and ultimately maintains localization accuracy within 3 m over a range of 100 m. This performance is significantly superior to that of traditional TDOA and SRP-PHAT methods, offering a high-precision localization solution for underground cable protection. Full article

In this article, we propose a nonlinear mathematical model that incorporates a discrete time delay. The model is used to analyze the dynamics of a socioeconomic system that includes economic growth, corruption, and unemployment. We introduce the time delay in the logistic economic growth term due to the effect of the previous state of the economic growth on its current state. A local stability analysis is performed to investigate the dynamics of the socioeconomic system. We established conditions for the existence of Hopf bifurcations and the appearance of economic limit cycles. We found threshold values for the discrete-time delay in which these Hopf bifurcations occur. We corroborate the theoretical findings by performing numerical simulations for a variety of scenarios. We find various interesting socioeconomic situations where different socioeconomic limit cycles occur. Finally, we present a discussion and future directions of research. Full article

Motion perception is a fundamental function of biological visual systems, enabling organisms to navigate dynamic environments, detect threats, and track moving objects. Inspired by the mechanisms of biological motion processing, we propose an Unsupervised Artificial Visual System for motion direction detection. Unlike traditional supervised learning approaches, our model employs unsupervised learning to classify local motion direction detection neurons and group those with similar directional preferences to form macroscopic motion direction detection neurons. The activation of these neurons is proportional to the received input, and the neuron with the highest activation determines the macroscopic motion direction of the object. The proposed system consists of two layers: a local motion direction detection layer and an unsupervised global motion direction detection layer. For local motion detection, we adopt the Local Motion Detection Neuron (LMDN) model proposed in our previous work, which detects motion in eight different directions. The outputs of these neurons serve as inputs to the global motion direction detection layer, which employs a Gaussian Mixture Model (GMM) for unsupervised clustering. GMM, a probabilistic clustering method, effectively classifies local motion detection neurons according to their preferred directions, aligning with biological principles of sensory adaptation and probabilistic neural processing. Through repeated exposure to motion stimuli, our model self-organizes to detect macroscopic motion direction without the need for labeled data. Experimental results demonstrate that the GMM-based global motion detection layer successfully classifies motion direction signals, forming structured motion representations akin to biological visual systems. Furthermore, the system achieves motion direction detection accuracy comparable to previous supervised models while offering a more biologically plausible mechanism. This work highlights the potential of unsupervised learning in artificial vision and contributes to the development of adaptive motion perception models inspired by neural computation. Full article

The dissemination of rumors can lead to significant economic damage and pose a grave threat to social harmony and the stability of people’s livelihoods. Consequently, curbing the dissemination of rumors is of paramount importance. The model in the text assumes that the population is homogeneous in terms of transmission behavior. This homogeneity is essentially a manifestation of translational symmetry. This paper undertakes a thorough examination of the impact of time delay on the dissemination of rumors within social networking services. We have developed a model for rumor dissemination, establishing the positivity and boundedness of its solutions, and identified the existence of an equilibrium point. The study further involved determining the critical threshold of the proposed model, accompanied by a comprehensive examination of its Hopf bifurcation characteristics. In the expression of the threshold $R_0$, the parameters appear in a symmetric form, reflecting the balance between dissemination and suppression mechanisms. Furthermore, detailed investigations were carried out to assess both the localized and global stability properties of the system’s equilibrium states. In stability analysis, the symmetry in the distribution of characteristic equation roots determines the system’s dynamic behavior. Through numerical simulations, we analyzed the potential impacts and theoretically examined the factors influencing rumor dissemination, thereby validating our theoretical analysis. An optimal control strategy was formulated, and three control variables were incorporated to describe the strategy. The optimization framework incorporates a specifically designed cost function that simultaneously accounts for infection reduction and resource allocation efficiency in control strategy implementation. The optimal control strategy proposed in the study involves a comparison between symmetric and asymmetric interventions. Symmetric control measures may prove inefficient, whereas asymmetric control demonstrates higher efficacy—highlighting a trade-off in symmetry considerations for optimization problems. Full article

The increasing complexity of data management in the Architecture, Engineering, and Construction (AEC) industry, driven by the adoption of distributed digital twins (dDTws) and cloud-based solutions, presents challenges in interoperability, data sovereignty, and scalability. Existing Building Information Modeling (BIM) and Common Data Environment (CDE) frameworks often fall short in addressing these issues due to their reliance on centralized and proprietary systems. This paper introduces a novel framework that transforms the Information Container for Linked Document Delivery (ICDD) into a dynamic, graph-based architecture. Unlike conventional file-based ICDD implementations, this approach enables fine-grained, semantically rich linking and querying across distributed models while maintaining data sovereignty and version control. The framework is designed to enhance real-time collaboration, ensure secure and sovereign data management, and improve interoperability across diverse project stakeholders. The framework leverages graph databases, semantic web technologies, and ISO standards such as ISO 21597 to facilitate seamless data exchange, automated linking, and advanced version control. Key functionalities include federated data storage, compliance with local and international regulations, and support for multidisciplinary workflows in large-scale AEC projects. To demonstrate the feasibility of the proposed framework, a simplified use case scenario is implemented and analyzed. By addressing critical challenges and enabling seamless integration of emerging technologies such as digital twins, this study advances the state of the art in data management for the AEC industry, providing a robust foundation for future innovations. Full article