Search Results (2,398)

After the 2011 earthquake, lake water depletion has become a widespread issue in Sikkim, especially in regions classified as high to very high seismic zones, where many lakes have turned into seasonal water bodies. This study investigates Chochen Lake in the Barapathing area of Sikkim’s Pakyong district, which is facing severe water seepage and instability. The problem, intensified by the 2011 seismic event and ongoing local construction, is examined through subsurface fracture mapping using Vertical Electrical Sounding (VES) and profiling techniques. A statistical factor method, applied to interpret VES data, helped identify fracture patterns beneath the lake. Results from two sites (VES-1 and VES-2) reveal significant variations in weathered and semi-weathered soil layers, indicating fractures at depths of 17–50 m (VES-1) and 20–55 m (VES-2). Higher fracture density near VES-1 suggests increased settlement risk and ground displacement compared to VES-2. Contrasting resistivity values emphasize the greater instability in this zone and the need for cautious construction practices. The findings highlight the role of seismic-induced fractures in ongoing water depletion and underscore the importance of continuous dewatering to stabilize the swampy terrain. 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

A square patch metasurface is designed, simulated, fabricated, and experimentally tested to investigate polarization characteristics quantitatively. The metasurface consists of one layer unit cell in the form of a square patch with one via and a lumped resistor, which is used for harvesting RF (radio frequency) energy. FR4 dielectric is used as a substrate supported by a metal ground plane. Polarization-dependent properties with specific surface current patterns and voltage dip are obtained when simulating under normal incidence of a plane wave. This characteristic results from changes in surface current conditions when the polarization angle is varied. A voltage dip appears at a specific polarization angle when the surface current pattern is symmetrical. This condition occurs when the position of the lumped resistor from the center of the patch is perpendicular to the linearly polarized incident electric field. A couple of 10 × 10 arrays with different resistor positions are fabricated and tested. The experimental results are in good agreement with the simulated results. The proposed design demonstrates a symmetric unit cell structure with one via and a resistor that exhibits polarization-dependent behavior for linear polarization. An asymmetric patch design is explored through both simulation and measurement to mitigate polarization dependence by suppressing the dip behavior, albeit at the expense of reduced absorption efficiency. This study provides a complete polarization analysis for both symmetric and asymmetric patch metasurfaces with a single via and a single lumped resistor, and introduces a predictive relation between the position of the resistor relative to the center of the patch and the resulting voltage dip behavior. Full article

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

When emergency incidents, such as bridge damage, abruptly occur on highways and lead to traffic disruptions, the multidimensionality and complexity of driver behaviors present significant challenges to the design of effective emergency response mechanisms. This study introduces a multi-level collaborative emergency mechanism grounded in driver behavior characteristics, aiming to enhance both traffic safety and emergency response efficiency through hierarchical collaboration and dynamic optimization strategies. By capitalizing on human drivers’ perception and decision-making attributes, a driver behavior classification model is developed to quantitatively assess the risk response capabilities of distinct behavioral patterns (conservative, risk-taking, and conformist) under emergency scenarios. A multi-tiered collaborative framework, comprising an early warning layer, a guidance layer, and an interception layer, is devised to implement tailored emergency strategies. Additionally, a rear-end collision risk propagation model is constructed by integrating the risk field model with probabilistic risk assessment, enabling dynamic adjustments to interception range thresholds for precise and real-time emergency management. The efficacy of this mechanism is substantiated through empirical case studies, which underscore its capacity to substantially reduce the occurrence of secondary accidents and furnish scientific evidence and technical underpinnings for emergency management pertaining to highway bridge damage. Full article

This study developed a copper mineral prospectivity map for the Koldar massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatialgenetic link was identified between faults and hydrothermal mineralization, with faults acting as key conduits for ore-bearing fluids. Lineament analysis and density mapping confirmed the high permeability of the Koldar massif, indicating its structural prospectivity. Hyperspectral and multispectral data (ASTER, PRISMA, WorldView-3) were applied for detailed mapping of hydrothermal alteration (phyllic, propylitic, argillic zones), which are critical for discovering porphyry copper deposits. In particular, WorldView-3 imagery facilitated the identification of new prospective zones. The transformation of magnetic and gravity data successfully delineated geological features and structural boundaries, confirming the fractured nature of the massif, a key structural factor for mineralization. The resulting map of prospective zones, created by normalizing and integrating four evidential layers (lineament density, PRISMA-derived hydrothermal alteration, magnetic, and gravity anomalies), is thoroughly validated, successfully outlining the known Aktogay, Aidarly, and Kyzylkiya deposits. Furthermore, new, previously underestimated prospective areas were identified. This work fills a significant knowledge gap concerning the Koldar massif, which had not been extensively studied using satellite methods previously. The key advantage of this research lies in its comprehensive approach and the successful application of high-quality hyperspectral imagery for mapping new prospective zones, offering a cost-effective and efficient alternative to traditional ground-based investigations. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for integrated sensing and communication (ISAC) due to their mobility and deployment flexibility. By adaptively adjusting their flight trajectories, UAVs can maintain favorable line-of-sight (LoS) communication links and sensing angles, thus enhancing overall system performance in dynamic and complex environments. However, ensuring physical layer security (PLS) in such UAV-assisted ISAC systems remains a significant challenge, particularly in the presence of mobile users and potential eavesdroppers. This manuscript proposes a joint optimization framework that simultaneously designs robust transmit beamforming and UAV trajectories to secure downlink communication for multiple ground users. At each time slot, the UAV predicts user positions and maximizes the secrecy sum-rate, subject to constraints on total transmit power, multi-target sensing quality, and UAV mobility. To tackle this non-convex problem, we develop an efficient optimization algorithm based on successive convex approximation (SCA) and constrained optimization by linear approximations (COBYLA). Numerical simulations validate that the proposed framework effectively enhances the secrecy performance while maintaining high-quality sensing, achieving near-optimal performance under realistic system constraints. Full article

Semantic change detection technology based on remote sensing data holds significant importance for urban and rural planning decisions and the monitoring of ground objects. However, simple convolutional networks are limited by the receptive field, cannot fully capture detailed semantic information, and cannot effectively perceive subtle changes and constrain edge information. Therefore, a dynamic graph reasoning network with layer-by-layer semantic decomposition for semantic change detection in remote sensing data is developed in response to these limitations. This network aims to understand and perceive subtle changes in the semantic content of remote sensing data from the image pixel level. On the one hand, low-level semantic information and cross-scale spatial local feature details are obtained by dividing subspaces and decomposing convolutional layers with significant kernel expansion. Semantic selection aggregation is used to enhance the characterization of global and contextual semantics. Meanwhile, the initial multi-scale local spatial semantics are screened and re-aggregated to improve the characterization of significant features. On the other hand, at the encoding stage, the weight-sharing approach is employed to align the positions of ground objects in the change area and generate more comprehensive encoding information. Meanwhile, the dynamic graph reasoning module is used to decode the encoded semantics layer by layer to investigate the hidden associations between pixels in the neighborhood. In addition, the edge constraint module is used to constrain boundary pixels and reduce semantic ambiguity. The weighted loss function supervises and optimizes each module separately to enable the network to acquire the optimal feature representation. Finally, experimental results on three open-source datasets, such as SECOND, HIUSD, and Landsat-SCD, show that the proposed method achieves good performance, with an SCD score reaching 35.65%, 98.33%, and 67.29%, respectively. Full article

The aim of the article is to analyze the possibilities of using a lightweight dynamic cone probe DCP to determine the quality of compaction of surface layers of embankments (from 0.10 m to approx. 0.80 m below ground level). For this purpose, comparative tests of non-cohesive soil used for the construction of embankments were carried out using the DCP test and direct tests of the degree of compaction I_S in a calibration chamber with the following dimensions: height 1.10 m and diameter 0.75 m. The subsoil was prepared from difficult-to-compact sand (Sa) with a uniformity coefficient of C_U = 3.10 and curvature coefficient of C_C = 0.99. The soil in the laboratory in the calibration chamber was compacted in layers using a vibratory plate compactor. A database for statistical analysis was obtained, n = 68 cases described by seven variables: z, ρ, w, ρ_d, I_S, PI, N₁₀(DCP). It was found that the DCP probe can be used to assess the degree of compaction of embankments made of non-cohesive soil, using the developed relationship I_S = f(z, N₁₀(DCP)). Full article

This study aims to investigate the processes associated with mass movements and their relationship with the behavior of the Amazon River delta-estuary (ADE) wetlands. The methodological approach involves using water spectral indices and ground-penetrating radar (GPR) to diagnose areas of soil water saturation and characterize regions affected by mass movements in Amazonian cities. It also involves identifying areas of critical saturation content and consequent mass movements. Analysis of risk and land use data revealed that the affected areas coincide with zones of high susceptibility to mass movements induced by water. The results showed the following: the accumulated annual precipitation ranged from 70.07 ± 55.35 mm·month⁻¹ to 413.34 ± 127.51 mm·month⁻¹; the response similarity across different sensors obtained an accuracy greater than 90% for NDWI, MNDWI, and AWEI for the same targets; and a landfill layer with a thickness variation between 1 and 2 m defined the mass movement concentration in Abaetetuba city. The interaction between infiltration, water saturation, and human-induced land alteration suggests that these areas act as wetlands with unstable dynamics. The analysis methodology developed for this study aimed to address this scenario by systematically mapping areas with mass movement potential and high-water saturation. Due to the absence of geological and geotechnical data, remote sensing was employed as an alternative, and in situ ground-penetrating radar (GPR) evaluation was suggested as a means of investigating the causes of a previously observed movement. Full article

In retail logistics, one typical task is to arrange a delivery schedule that guides the intake of inventory from the distribution center to stores. It is essential to accurately predict the out-of-stock (OOS) outcome for various delivery schedules to identify the optimal patterns for minimizing the OOS ratio. This paper investigates the feasibility of utilizing a neural network to accurately predict the out-of-stock (OOS) risk under each delivery pattern. Due to the zero-inflated distribution of the target values, it is necessary to evaluate two prediction accuracies simultaneously: the accuracy on data with a positive ground truth OOS rate and the accuracy on data with a zero ground truth OOS rate. In this paper, I examine how a selection of features associated with delivery schedules and the choice of activation function at the output layer, would impact the accuracy of the model. Full article

Leaf loss caused by pest infestations poses a serious threat to forest health. The leaf loss rate (LLR) refers to the percentage of the overall tree-crown leaf loss per unit area and is an important indicator for evaluating forest health. Therefore, rapid and accurate acquisition of the LLR via remote sensing monitoring is crucial. This study is based on drone hyperspectral and LiDAR data as well as ground survey data, calculating hyperspectral indices (HSI), multispectral indices (MSI), and LiDAR indices (LI). It employs Savitzky–Golay (S–G) smoothing with different window sizes (W) and polynomial orders (P) combined with recursive feature elimination (RFE) to select sensitive features. Using Random Forest Regression (RFR) and Convolutional Neural Network Regression (CNNR) to construct a multidimensional (horizontal and vertical) estimation model for LLR, combined with LiDAR point cloud data, achieved a three-dimensional visualization of the leaf loss rate of trees. The results of the study showed: (1) The optimal combination of HSI and MSI was determined to be W11P3, and the LI was W5P2. (2) The optimal combination of the number of sensitive features extracted by the RFE algorithm was 13 HSI, 16 MSI, and hierarchical LI (2 in layer I, 9 in layer II, and 11 in layer III). (3) In terms of the horizontal estimation of the defoliation rate, the model performance index of the CNNR_HSI model (MPI = 0.9383) was significantly better than that of RFR_MSI (MPI = 0.8817), indicating that the continuous bands of hyperspectral could better monitor the subtle changes of LLR. (4) The I-CNNR_HSI+LI, II-CNNR_HSI+LI, and III-CNNR_HSI+LI vertical estimation models were constructed by combining the CNNR_HSI model with the best accuracy and the LI sensitive to different vertical levels, respectively, and their MPIs reached more than 0.8, indicating that the LLR estimation of different vertical levels had high accuracy. According to the model, the pixel-level LLR of the sample tree was estimated, and the three-dimensional display of the LLR for forest trees under the pest stress of larch caterpillars was generated, providing a high-precision research scheme for LLR estimation under pest stress. Full article

In the Chan Buddhism koan (gong’an 公案) tradition, the act of “slaying the snake” functions as a signature gesture imbued with complex, historically layered cultural meanings. Rather than merely examining its motivations, this paper emphasizes tracing the semantic transformations that this motif has undergone across different historical contexts. It argues that “snake-slaying” operated variously as an imperial narrative strategy reinforcing ruling class ideology; as a form of popular resistance by commoners against flood-related disasters; as a dietary practice among aristocrats and literati seeking danyao (elixirs) 丹藥 for reclusion and transcendence; and ultimately, within the Chan tradition, as a method of spiritual cultivation whereby masters sever desires rooted in attachment to both selfhood and the Dharma. More specifically, first, as an imperial narrative logic, snake-slaying embodied exemplary power: both Liu Bang 劉邦 and Guizong 歸宗 enacted this discursive strategy, with Guizong’s legitimacy in slaying the snake deriving from the precedent set by Liu Bang. Second, as a folk strategy of demystification, snake-slaying acquired a moral aura—since the snake was perceived as malevolent force, their slaying appeared righteous and heroic. Finally, as a mode of self-cultivation among the aristocracy, snake-slaying laid the groundwork for its later internalization. In Daoism, slaying the snake was a means of cultivating the body; in Chan Buddhism, the act is elevated to a higher plane—becoming a way of cultivating the mind. This transformation unfolded naturally, as if predestined. In all cases, the internalization of the snake-slaying motif was not an overnight development: the cultural genes that preceded its appearance in the Chan tradition provided the fertile ground for its karmic maturation and discursive proliferation. Full article

This study developed a full solid waste-based cementitious material (ISWs-CM) using steel slag (SS), ground granulated blast furnace slag (GGBFS), phosphorus slag (PS), carbide slag (CS), and desulfurized gypsum (DG) to completely replace cement. A two-layer optimization strategy, combining three chemical moduli and simplex lattice experiments, was employed to determine the proportion and to investigate the impact of proportions on the uniaxial compressive strength of mortar. As an application case, the ISWs-CM with the optimal proportion was employed to stabilize aeolian sand, and its effectiveness as a cement substitute and the underlying mechanisms were investigated. The results indicated that the ISW proportion that maximized the strength of the mortar was SS:GGBFS:PS:CS = 5:20:20:40. The strength of the mortar was enhanced when the proportion of GGBFS exhibiting the highest reactivity was increased and also increased initially and then decreased with an increase in CS when the dosage of GGBFS was fixed. The aeolian sand stabilized by ISW-CM exhibited higher strength than that stabilized with cement. The greater number and variety of hydration products resulted in denser connections and encapsulation of sand particles, which highlights the synergistic effect of ISWs and the potential of ISW-CM as a cement replacement across diverse applications including aeolian sand stabilization. Full article

In this paper, we presents a case study involving the implementation experience and a methodological framework through a comprehensive comparative analysis of the YOLOX and YOLOv12 object detection models for agricultural automation systems deployed in the Jetson AGX Orin edge computing platform. We examined the architectural differences between the models and their impact on detection capabilities in data-imbalanced potato-harvesting environments. Both models were trained on identical datasets with images capturing potatoes, soil clods, and stones, and their performances were evaluated through 30 independent trials under controlled conditions. Statistical analysis confirmed that YOLOX achieved a significantly higher throughput (107 vs. 45 FPS, p < 0.01) and superior energy efficiency (0.58 vs. 0.75 J/frame) than YOLOv12, meeting real-time processing requirements for agricultural automation. Although both models achieved an equivalent overall detection accuracy (F1-score, 0.97), YOLOv12 demonstrated specialized capabilities for challenging classes, achieving 42% higher recall for underrepresented soil clod objects (0.725 vs. 0.512, p < 0.01) and superior precision for small objects (0–3000 pixels). Architectural analysis identified a YOLOv12 residual efficient layer aggregation network backbone and area attention mechanism as key enablers of balanced precision–recall characteristics, which were particularly valuable for addressing agricultural data imbalance. However, NVIDIA Nsight profiling revealed implementation inefficiencies in the YOLOv12 multiprocess architecture, which prevented the theoretical advantages from being fully realized in edge computing environments. These findings provide empirically grounded guidelines for model selection in agricultural automation systems, highlighting the critical interplay between architectural design, implementation efficiency, and application-specific requirements. Full article