Search Results (267)

After the original support system in the auxiliary transportation roadway of the northern wing of the Zhaoxian Mine failed, the extent of damage and deformation varied significantly across different sections of the drift. A single support method could not meet the engineering requirements. Therefore, this paper conducted research on the classification of roadway damage and zoning repair. The overall damage characteristics of the roadway are described by three indicators: roadway deformation, development of rock mass fractures, and water seepage conditions. These are further refined into nine secondary indicators. In summary, a rock mass damage combination weighting evaluation model based on the FAHP–entropy weight TOPSIS method is proposed. According to this model, the degree of damage to the roadway is divided into five grades. After analyzing the damage conditions and support requirements at each grade, corresponding zoning repair plans are formulated by adjusting the parameters of bolts, cables, channel steel beams, and grouting materials. At the same time, the reliability of partition repair is verified using FLAC3D 6.0 numerical simulation software. Field monitoring results demonstrated that this approach not only met the support requirements for the roadway but also improved the utilization rate of support materials. This provides valuable guidance for the design of support systems for roadways with similar heterogeneous damage. Full article

Bud eye identification is a critical step in the intelligent seed cutting process for potatoes. This study focuses on the challenges of low testing accuracy and excessive weighted memory in testing models for potato bud eye detection. It proposes an improved potato bud eye detection method based on YOLOv5s, referred to as the YOLO-SCA model, which synergistically optimizing three main modules. The improved model introduces the ShuffleNetV2 module to reconstruct the backbone network. The channel shuffling mechanism reduces the model’s weighted memory and computational load, while enhancing bud eye features. Additionally, the CBAM attention mechanism is embedded at specific layers, using dual-path feature weighting (channel and spatial) to enhance sensitivity to key bud eye features in complex contexts. Then, the Alpha-IoU function is used to replace the CloU function as the bounding box regression loss function. Its single-parameter control mechanism and adaptive gradient amplification characteristics significantly improve the accuracy of bud eye positioning and strengthen the model’s anti-interference ability. Finally, we conduct pruning based on the channel evaluation after sparse training, accurately removing redundant channels, significantly reducing the amount of computation and weighted memory, and achieving real-time performance of the model. This study aims to address how potato bud eye detection models can achieve high-precision real-time detection under the conditions of limited computational resources and storage space. The improved YOLO-SCA model has a size of 3.6 MB, which is 35.3% of the original model; the number of parameters is 1.7 M, which is 25% of the original model; and the average accuracy rate is 95.3%, which is a 12.5% improvement over the original model. This study provides theoretical support for the development of potato bud eye recognition technology and intelligent cutting equipment. Full article

Synthetic aperture radar (SAR) exhibits all-day and all-weather capabilities, granting it significant application in remote sensing. However, interpreting SAR images requires extensive expertise, making SAR-to-optical remote sensing image translation a crucial research direction. While conditional generative adversarial networks (CGANs) have demonstrated exceptional performance in image translation tasks, their massive number of parameters pose substantial challenges. Therefore, this paper proposes ILF-BDSNet, a compressed network for SAR-to-optical image translation. Specifically, first, standard convolutions in the feature-transformation module of the teacher network are replaced with depthwise separable convolutions to construct the student network, and a dual-resolution collaborative discriminator based on PatchGAN is proposed. Next, knowledge distillation based on intermediate-layer features and channel pruning via weight sharing are designed to train the student network. Then, the bio-inspired dynamic search of channel configuration (BDSCC) algorithm is proposed to efficiently select the optimal subnet. Meanwhile, the pixel-semantic dual-domain alignment loss function is designed. The feature-matching loss within this function establishes an alignment mechanism based on intermediate-layer features from the discriminator. Extensive experiments demonstrate the superiority of ILF-BDSNet, which significantly reduces number of parameters and computational complexity while still generating high-quality optical images, providing an efficient solution for SAR image translation in resource-constrained environments. Full article

In security applications, visible-light pedestrian detectors are highly sensitive to changes in illumination and fail under low-light or nighttime conditions, while infrared sensors, though resilient to lighting, often produce blurred object boundaries that hinder precise localization. To address these complementary limitations, we propose a practical multimodal pipeline—Adaptive Energy–Gradient–Contrast (EGC) Fusion with AIFI-YOLOv12—that first fuses infrared and low-light visible images using per-pixel weights derived from local energy, gradient magnitude and contrast measures, then detects pedestrians with an improved YOLOv12 backbone. The detector integrates an AIFI attention module at high semantic levels, replaces selected modules with A2C2f blocks to enhance cross-channel feature aggregation, and preserves P3–P5 outputs to improve small-object localization. We evaluate the complete pipeline on the LLVIP dataset and report Precision, Recall, mAP@50, mAP@50–95, GFLOPs, FPS and detection time, comparing against YOLOv8, YOLOv10–YOLOv12 baselines (n and s scales). Quantitative and qualitative results show that the proposed fusion restores complementary thermal and visible details and that the AIFI-enhanced detector yields more robust nighttime pedestrian detection while maintaining a competitive computational profile suitable for real-world security deployments. Full article

Navigating ships through one-way channels introduces significant uncertainties due to their unique navigational constraints, yet a comprehensive and tailored risk evaluation system for such channels remains notably underdeveloped. Recognizing its critical role as a global maritime artery, this study selects the Suez Canal as the case study to address this gap. The study begins by analyzing the navigational characteristics of one-way channels, systematically identifying key risk factors such as channel width, traffic density, and environmental conditions. Building on this, a novel risk evaluation model is developed, integrating the entropy weight method to assign objective weights, fuzzy logic to handle uncertainty, and Evidential Reasoning (ER) to aggregate multi-criteria assessments. The Suez Canal is then utilized as a case study to demonstrate the model’s effectiveness and practical applicability. The results reveal that Channel C exhibits the highest risk utility value, consistent with its history of the most grounding incidents, including the notable “Ever Given” event during 2021–2023. These findings not only provide valuable insights for enhancing Suez Canal management strategies but also contribute to filling the existing void in risk evaluation frameworks for one-way channels, paving the way for future research into dynamic risk assessment methodologies. Full article

Failures of rotating machinery, such as bearings and gears, are a critical concern in industrial systems, leading to significant operational downtime and economic losses. A primary research challenge is achieving accurate fault diagnosis under complex industrial noise, where weak fault signatures are often masked by interference signals. This problem is particularly acute in demanding applications like offshore wind turbines, where harsh operating conditions and high maintenance costs necessitate highly robust and reliable diagnostic methods. To address this challenge, this paper proposes a novel Multi-Scale Domain Convolutional Attention Network (MSDCAN). The method integrates enhanced adaptive multi-domain feature extraction with a hybrid attention mechanism, combining information from the time, frequency, wavelet, and cyclic spectral domains with domain-specific attention weighting. A core innovation is the hybrid attention fusion mechanism, which enables cross-modal interaction between deep convolutional features and domain-specific features, enhanced by channel attention modules. The model’s effectiveness is validated on two public benchmark datasets for key rotating components. On the Case Western Reserve University (CWRU) bearing dataset, the MSDCAN achieves accuracies of 97.3% under clean conditions, 96.6% at 15 dB signal-to-noise ratio (SNR), 94.4% at 10 dB SNR, and a robust 85.5% under severe 5 dB SNR. To further validate its generalization, on the Xi’an Jiaotong University (XJTU) gear dataset, the model attains accuracies of 94.8% under clean conditions, 95.0% at 15 dB SNR, 83.6% at 10 dB SNR, and 63.8% at 5 dB SNR. These comprehensive results quantitatively validate the model’s superior diagnostic accuracy and exceptional noise robustness for rotating machinery, establishing a strong foundation for its application in reliable condition monitoring for complex systems, including wind turbines. Full article

In this paper, we propose a quantitative evaluation method for navigation safety in coastal waters based on unstructured grids. Initially, a comprehensive analysis was conducted on various factors affecting navigation safety, including natural conditions, traffic conditions, and marine hydro-meteorological conditions, to construct a multi-source fused spatiotemporal dataset. Subsequently, channel boundary extraction was performed using Constrained Delaunay Triangle–Alpha-Shapes, and the precise extraction of ship navigation areas was performed based on Constrained Delaunay Triangle–Voronoi diagrams. Additionally, temporal feature grids were constructed based on the spatiotemporal characteristics of marine hydro-meteorological data. Finally, unstructured grids for evaluating navigation safety were established through spatial overlay analysis. Based on this foundation, a quantitative analysis and evaluation model for comprehensive navigation safety assessment was developed using the fuzzy evaluation method. By calculating the fuzzy relation matrix and weight vectors, quantitative assessments were conducted for each grid cell, yielding safety risk levels from both spatial and temporal dimensions. An analysis was performed using maritime data within the geographic boundaries of lon.119.17–120.41° E and lat.34.40–35.47° N. The results indicated that the unstructured grid division and channel boundary extraction in the demonstrated sea area were closely related to parameters such as the ship traffic flow patterns and the spatiotemporal characteristics of the marine environmental factors. A quantitative evaluation and analysis of the 186 unstructured grid cells revealed that the high risk levels primarily corresponded to restricted navigation areas, the higher-risk grid cells were mainly anchorages, and the low to lower risk levels were primarily associated with channels and navigable areas for ships. Full article

The uplink of 5G networks allows selecting the transmit waveform between cyclic prefix orthogonal frequency division multiplexing (CP-OFDM) and discrete Fourier transform spread OFDM (DFT-S-OFDM) to cope with the diverse operational conditions of the power amplifiers (PAs) in different user equipment (UEs). CP-OFDM leads to higher throughput when the PAs are operating in their linear region, which is mostly the case for cell-interior users, whereas DFT-S-OFDM is more appealing when PAs are exhibiting non-linear behavior, which is associated with cell-edge users. Therefore, existing waveform selection solutions rely on predefined signal-to-noise ratio (SNR) thresholds that are computed offline. However, the varying user and channel dynamics, as well as their interactions with power control, require an adaptable threshold selection mechanism. In this paper, we propose an intelligent waveform-switching mechanism based on deep reinforcement learning (DRL) that learns optimal switching thresholds for the current operational conditions. In this proposal, a learning agent aims at maximizing a function built using available throughput percentiles in real networks. Said percentiles are weighted so as to improve the cell-edge users’ service without dramatically reducing the cell average. Aggregated measurements of signal-to-noise ratio (SNR) and timing advance (TA), available in real networks, are used in the procedure. In addition, the solution accounts for the switching cost, which is related to the interruption of the communication after every switch due to implementation issues, which has not been considered in existing solutions. Results show that our proposed scheme achieves remarkable gains in terms of throughput for cell-edge users without degrading the average throughput. Full article

Lidar serves as a vital active remote sensing instrument for exploring the atmosphere. However, the detection range of lidar is significantly constrained by the dynamic range of photo-detectors. To mitigate this limitation, atmospheric lidars are commonly equipped with multiple channels to capture signals from different altitude ranges, making the high-quality gluing of multi-channel echo signals crucial for accurate data retrieval. In this paper, an efficient dual-channel signal gluing method based on the improved whale optimization algorithm (IMWOA) and the entropy weight method (EWM), named IMWOA-EWM, was proposed. Here, the IMWOA method was used to optimize the fitness function, achieving higher computational efficiency. The weights of the correlation coefficient R, regression stability coefficient S and mean fit deviation D were determined using EWM, which together constitute the fitness function. Through signal gluing experiments conducted with ground-based aerosol lidar data, IMWOA-EWM can accurately identify the optimal gluing region, due to IMWOA’s excellent global search capability and the higher weight assigned to the objective function S by EWM. Meanwhile, regarding computational efficiency, its runtime is only half that of IGWO-RSD. Additionally, the applicable conditions of the weights in IMWOA-EWM were explored, which indicate that IMWOA-EWM has good robustness for atmospheric lidar signal gluing. Full article

Under large-scale farming conditions, automated sow estrus detection is crucial for improving reproductive efficiency, optimizing breeding management, and reducing labor costs. Conventional estrus detection relies heavily on human expertise, a practice that introduces subjective variability and consequently diminishes both accuracy and efficiency. Failure to identify estrus promptly and pair animals effectively lowers breeding success rates and drives up overall husbandry costs. In response to the need for the automated detection of sows’ estrus states in large-scale pig farms, this study proposes a method for detecting sows’ vulvar status and estrus based on multi-dimensional feature crossing. The method adopts a dual optimization strategy: First, the Bi-directional Feature Pyramid Network—Selective Decoding Integration (BiFPN-SDI) module performs the bidirectional, weighted fusion of the backbone’s low-level texture and high-level semantic, retaining the multi-dimensional cues most relevant to vulvar morphology and producing a scale-aligned, minimally redundant feature map. Second, by embedding a Spatially Enhanced Attention Module head (SEAM-Head) channel attention mechanism into the detection head, the model further amplifies key hyperemia-related signals, while suppressing background noise, thereby enabling cooperative and more precise bounding box localization. To adapt the model for edge computing environments, Masked Generative Distillation (MGD) knowledge distillation is introduced to compress the model while maintaining the detection speed and accuracy. Based on the bounding box of the vulvar region, the aspect ratio of the target area and the red saturation features derived from a dual-threshold method in the HSV color space are used to construct a lightweight Multilayer Perceptron (MLP) classification model for estrus state determination. The network was trained on 1400 annotated samples, which were divided into training, testing, and validation sets in an 8:1:1 ratio. On-farm evaluations in commercial pig facilities show that the proposed system attains an 85% estrus detection success rate. Following lightweight optimization, inference latency fell from 24.29 ms to 18.87 ms, and the model footprint was compressed from 32.38 MB to 3.96 MB in the same machine, while maintaining a mean Average Precision (mAP) of 0.941; the accuracy penalty from model compression was kept below 1%. Moreover, the model demonstrates robust performance under complex lighting and occlusion conditions, enabling real-time processing from vulvar localization to estrus detection, and providing an efficient and reliable technical solution for automated estrus monitoring in large-scale pig farms. Full article

With the continuous expansion of urban rail transit networks, construction safety of connecting passages—as critical weak links in underground structural systems—has become pivotal for project success. Although artificial ground freezing technology effectively addresses adverse geological conditions (e.g., high permeability and weak self-stability), it is influenced by multi-field coupling effects (temperature, stress, and seepage fields), which may trigger chain risks such as freezing pipe fractures and frozen curtain leakage during construction. This study deconstructed the freezing method workflow (‘drilling pipe-laying → active freezing → channel excavation → structural support’) and established a hierarchical evaluation index system incorporating geological characteristics, technological parameters, and environmental impacts by considering sandy soil phase-change features and hydro-thermal coupling effects. For weight calculation, the Analytic Hierarchy Process (AHP) was innovatively applied to balance subjective-objective assignment deviations, revealing that the excavation support stage (weight: 52.94%) and thawing-grouting stage (31.48%) most significantly influenced overall risk. Subsequently, a Bayesian network-based risk assessment model was constructed, with prior probabilities updated in real-time using construction monitoring data. Results indicated an overall construction risk probability of 46.3%, with the excavation stage exhibiting the highest sensitivity index (3.97%), identifying it as the core risk control link. These findings provide a quantitative basis for dynamically identifying construction risks and optimizing mitigation measures, offering substantial practical value for enhancing safety in subway connecting passage construction within water-rich sandy strata. Full article

Potassium (K) is a critical macronutrient for sugarcane (Saccharum spp.), playing a vital role in metabolic processes, sucrose accumulation, and yield formation. Herein, this study systematically evaluated the effects of potassium oxide (K₂O) application on sugarcane (cultivar YZ1696) growth at the seedling and tillering stages. Hydroponic experiments demonstrated that 6 mmol/L K₂O optimally promoted seedling growth, whereas field trials revealed that 150 kg/ha K₂O maximized growth rate, yield, and sucrose content. Sugarcane growth exhibited a biphasic response—stimulation followed by inhibition—with increasing K₂O dosage at both developmental stages. Transcriptomic profiling of sugarcane roots under low-potassium (K-deficient), optimal potassium, and high-potassium conditions identified 10,266 differentially expressed genes (DEGs), with the most pronounced transcriptional shifts occurring under K deficiency. Functional enrichment analysis identified DEGs associated with potassium transport, calcium signaling, and carbohydrate metabolism. Notably, potassium uptake was mediated by distinct mechanisms: Shaker family channels (AKT1, AKT2, SPIKE) and the TPK family member KCO1 were induced under optimal K supply, whereas HAK/KUP/KT transporters (HAK1/5/10/21/25) exhibited broad activation across K concentrations, underscoring their key role in K homeostasis. Furthermore, calcium signaling genes (e.g., CIPK23) displayed K-dependent expression patterns. Weighted gene co-expression network analysis identified key gene modules that correlated strongly with agronomic traits, including plant height, yield, and sucrose content. Optimal K conditions favored the expression of yield- and sucrose-associated genes, suggesting a molecular basis for K-mediated productivity enhancement. Our findings revealed the genetic and physiological mechanisms underlying K-dependent sugarcane improvement, providing actionable insights for precise potassium fertilization to maximize the yield and sugar content. Full article

China’s major function-oriented zoning (MFOZ) serves as a crucial policy instrument for functional regulation of land use, playing a significant role in the latest territorial spatial planning. Studies on the implementation of MFOZ have been conducted since its release in 2012, but there is a lack of comprehensive methods to assess the effectiveness of its implementation. In China, the newly initiated City Examination provides novel technical support for verifying MFOZ planning, addressing the gap in comprehensive evaluation methodologies and channels. This study proposes a comprehensive urban assessment framework and a major function classification approach based on City Examination data, enabling the identification of implementation deviations in MFOZ planning based on the current urban conditions reflected by City Examination. The methodology incorporates dimensionality reduction, multi-indicator clustering, entropy-weighted overlays, and natural break classification techniques and examines the degree of strategic deviation in China’s MFOZ through a comprehensive and systematic assessment. Due to the timeliness and long-term nature City Examination data, the method allows for the long-time dynamic tracking and evaluation of the real-time progress in MFOZ. Empirical analysis of Hubei Province revealed that 77.9% of its urban development aligns with the 2011 MFOZ scheme while demonstrating discernible deviation types and hierarchical discrepancies, with geographically clustered patterns observed among cities exhibiting such deviations. Full article

Rotating machinery is essential to modern industrial systems, where rolling bearings play a critical role in ensuring mechanical stability and operational efficiency. Failures in bearings can result in serious safety risks and significant financial losses, which highlights the need for accurate and robust methods for diagnosing bearing faults. Traditional diagnostic methods relying on single-source data often fail to fully leverage the rich information provided by multiple sensors and are more prone to performance degradation under noisy conditions. Therefore, this paper proposes a novel bearing fault diagnosis method based on a multi-level fusion framework. First, the Symmetrized Dot Pattern (SDP) method is applied to fuse multi-source signals into unified SDP images, enabling effective fusion at the data level. Then, a combination of RepLKNet and Bidirectional Gated Recurrent Unit (BiGRU) networks extracts multi-modal features, which are then fused through a cross-attention mechanism to enhance feature representation. Finally, information entropy is utilized to assess the reliability of each feature channel, enabling dynamic weighting to further strengthen model robustness. The experiments conducted on public datasets and noise-augmented datasets demonstrate that the proposed method significantly surpasses other single-source and multi-source data fusion models in terms of diagnostic accuracy and robustness to noise. Full article

Impervious surfaces reduce natural infiltration, leading to increased runoff, erosion, and pollutant transport. The Alabama Department of Transportation (ALDOT) relies on implementing infiltration swales, a linear bioretention-based practice, along roadside drainage channels to reduce surface runoff. This study developed and constructed modified permeameters and infiltrometers to evaluate and optimize media used to construct infiltration swales. The average measured falling head infiltration rate of sandy topsoil used in the media matrix was 0.63 ft/day (0.19 m/day). A series of amended topsoil mixtures were tested to improve the infiltration rate of the media. In particular, the mixture of 80% topsoil and 20% pine bark fines (by weight) significantly improved the infiltration rates of the swale media. Through iterative testing, the F3 design with 6 in. (15.2 cm) mixture and 10 in. (25.4 cm) sand achieved up to 13.73 ft/day (4.18 m/day) of infiltration rate under constant head, far surpassing the infiltration rate of the current ALDOT design. SWMM bioretention cell models were developed to understand the swale infiltration process and revealed that the infiltration rates obtained from column tests were the saturated hydraulic conductivities of the soil layer when there was no other restriction on vertical flow. The simulated swale hydrological performance depends not only on variations in soil conductivity but also on other swale characteristics under field conditions. Findings from this research can be used to enhance the performance of infiltration-based stormwater practices. Full article