Search Results (380)

To address the critical challenge of ensuring bottom water-inrush stability during the excavation of ultra-deep foundation pits for riverside suspension-bridge anchorages under complex geological conditions involving high-pressure confined groundwater, we investigate the application of D-RJP high-pressure rotary jet grouting pile technology for ground improvement. Its effectiveness is systematically validated through a case study of the South Anchorage Foundation Pit for the North Channel Bridge of the Zhangjinggao Yangtze River Bridge. The D-RJP method led to the successful construction of a composite foundation within the soft soil that satisfies the permeability coefficient, interface friction coefficient, bearing capacity, and shear strength requirements, significantly improving the geotechnical performance of the anchorage foundation. A series of field experiments were conducted to optimize the critical construction parameters, including the lifting speed, water–cement ratio, and stroke spacing. Core sampling and laboratory testing revealed the grout columns to have good structural integrity. The unconfined compressive strength of the high-pressure jet grout columns reached 5.45 MPa in silty clay layers and 8.21 MPa in silty sand layers. The average permeability coefficient ranged from 1.67 × 10⁻⁷ to 2.52 × 10⁻⁷ cm/s. The average density of the columns was 1.66 g/cm³ in the silty clay layer and 2.08 g/cm³ in the silty sand layer. The cement content in the return slurry varied between 18% and 27%, with no significant soil squeezing effect observed. The foundation interface friction coefficient ranged from 0.44 to 0.52. After excavation, the composite foundation formed by D-RJP columns was subjected to static load and direct shear testing. The results showed a characteristic bearing capacity value of 1200 kPa, the internal friction angle exceeded 24.23°, and the cohesion exceeded 180 kPa. This study successfully verifies the feasibility of applying D-RJP technology to construct high-performance artificial composite foundations in complex strata characterized by deep soft soils and high-pressure confined groundwater, providing valuable technical references and practical insights for similar ultra-deep foundation pit projects involving suspension bridge anchorages. Full article

With the widespread application of unmanned aerial vehicle technology in search and detection, express delivery and other fields, the requirements for unmanned aerial vehicle dynamic area coverage algorithms has become higher. For an unknown dynamic environment, an improved Dual-Attention Mechanism Double Deep Q-network area coverage algorithm is proposed in this paper. Firstly, a dual-channel attention mechanism is designed to deal with flight environment information. It can extract and fuse the features of the local obstacle information and full-area coverage information. Then, based on the traditional Double Deep Q-network algorithm, an adaptive exploration decay strategy and a coverage reward function are designed based on the real-time area coverage rate to meet the requirement of a low repeated coverage rate. The proposed algorithm can avoid dynamic obstacles and achieve global coverage under low repeated coverage rate conditions. Finally, with Python 3.12 and PyTorch 2.2.1 environment as the training platform, the simulation results show that, compared with the Soft Actor–Critic algorithm, the Double Deep Q-network algorithm, and the Attention Mechanism Double Deep Q-network algorithm, the proposed algorithm in this paper can complete the area coverage task in a dynamic and complex environment with a lower repeated coverage rate and higher coverage efficiency. Full article

Partial discharge (PD) is not only a critical indicator but also a major accelerating factor of insulation degradation in power transformers. Accurate identification of PD types is essential for diagnosing insulation defects and locating faults in transformers. Traditional methods based on phase-resolved partial discharge (PRPD) patterns typically rely on expert interpretation and manual feature extraction, which are increasingly being supplanted by Convolutional Neural Networks (CNNs) due to their ability to automatically extract features and deliver high classification accuracy. However, the inherent subtlety and diversity of characteristic differences among PRPD patterns, coupled with substantial noise resulting from complex electromagnetic interference, present significant hurdles to achieving accurate identification. This paper proposes a transformer partial discharge identification method based on Deep Residual Shrinkage Network (DRSN) to address these challenges. The method integrates dual-path feature extraction to capture both local and global features, incorporates a channel-domain adaptive soft-thresholding mechanism to effectively suppress noise interference, and utilizes the Focal Loss function to enhance the model’s attention to hard-to-classify samples. To validate the proposed method, given the scarcity of diverse real-world transformer PD data, an experimental platform was utilized to generate and collect PD data by artificially simulating various discharge defect models, including tip discharge, surface discharge, air-gap discharge and floating discharge. Data diversity was then enhanced through sample augmentation and noise simulation, to minimize the gap between experimental data and real-world on-site data. Experimental results demonstrate that the proposed method achieves superior partial discharge recognition accuracy and strong noise robustness on the experimental dataset. For future work, it is essential to collect more real transformer PD data to further validate and strengthen the model’s generalization capability, thereby ensuring its robust performance and applicability in practical scenarios. Full article

The present work analyzes the combined viscoelectric and steric effects on electroosmotic flow in a soft channel with polyelectrolyte coating. The structured channel surface, which controls the electric potential, creates two different flow regions: the electrolyte flow within the permeable polyelectrolyte layer (PEL) and the bulk electrolyte. Thus, this study discusses the interaction of various electrostatic effects to predict the electroosmotic flow field. The nonlinear governing equations describing the fluid flow are the modified Poisson–Boltzmann equation for the electric potential distribution, the mass conservation equation, and the modified Navier–Stokes equations for the flow field, which are solved numerically using a one-dimensional (1D) scheme. The results indicate that the flow enhances when increasing the electric potential magnitude across the channel cross-section via the rise in different dimensionless parameters, such as the PEL thickness, the steric factor, and the ratio of the electrokinetic parameter of the PEL to that of the electrolyte layer. This research demonstrates that the PEL significantly enhances control over electroosmotic flow. However, it is crucial to consider that viscoelectric effects at high electric fields and the friction generated by the grafted polymer brushes of the PEL can reduce these benefits. Full article

Despite advances in Convolutional Neural Networks (CNNs) for intelligent fault diagnosis in CNC machine tools, bearing fault diagnosis in CNC feed systems remains challenging, particularly in multi-scale feature extraction and generalization across operating conditions. This study introduces an enhanced multi-scale feature network (MSFN) that addresses these limitations through three integrated modules designed to extract critical fault features from vibration signals. First, a Soft-Scale Denoising (S2D) module forms the backbone of the MSFN, capturing multi-scale fault features from input signals. Second, a Multi-Scale Adaptive Feature Enhancement (MS-AFE) module based on long-range weighting mechanisms is developed to enhance the extraction of periodic fault features. Third, a Dynamic Sequence–Channel Attention (DSCA) module is incorporated to improve feature representation across channel and sequence dimensions. Experimental results on two datasets demonstrate that the proposed MSFN achieves high diagnostic accuracy and exhibits robust generalization across diverse operating conditions. Moreover, ablation studies validate the effectiveness and contributions of each module. Full article

Soft manipulators have garnered significant research attention in recent years due to their flexibility and adaptability. However, the inherent flexibility of these manipulators imposes limitations on their load-bearing capacity and stability. To address this, this study compares various variable stiffness technologies and proposes a novel design concept: leveraging the phase-change characteristics of low-melting-point alloys (LMPAs) with distinct melting points to fulfill the variable stiffness requirements of soft manipulators. The pneumatic structure of the manipulator is fabricated via 3D-printed molds and silicone casting. The manipulator integrates a pneumatic working chamber, variable stiffness chambers, heating devices, sensors, and a central channel, achieving multi-stage variable stiffness through controlled heating of the LMPAs. A steady-state temperature field distribution model is established based on the integral form of Fourier’s law, complemented by finite element analysis (FEA). Subsequently, the operational temperatures at which the variable stiffness mechanism activates, and the bending performance are experimentally validated. Finally, stiffness characterization and kinematic performance experiments are conducted to evaluate the manipulator’s variable stiffness capabilities and flexibility. This design enables the manipulator to switch among low, medium, and high stiffness levels, balancing flexibility and stability, and provides a new paradigm for the design of soft manipulators. Full article

In the earthquakes that occurred in the Pazarcık (Mw = 7.7) and Elbistan (Mw = 7.6) districts of Kahramanmaraş Province on 6 February 2023, many buildings collapsed in the Hayrullah neighborhood of the Onikişubat district. In this study, we investigated whether there was a soil amplification effect on the damage occurring in the Hayrullah neighborhood of the Onikişubat district of Kahramanmaraş Province. Firstly, borehole, SPT, MASW (multi-channel surface wave analysis), microtremor, electrical resistivity tomography (ERT), and vertical electrical sounding (VES) tests were carried out in the field to determine the engineering properties and behavior of soil. Laboratory tests were also conducted using samples obtained from bore holes and field tests. Then, an idealized soil profile was created using the laboratory and field test results, and site dynamic soil behavior analyses were performed on the extracted profile. According to The Turkish Building Code (TBC 2018), the earthquake level DD-2 design spectra of the project site were determined and the average design spectrum was created. Considering the seismicity of the project site and TBC (2018) criteria (according to site-specific faulting, distance, and average shear wave velocity), 11 earthquake ground motion sets were selected and harmonized with DD-2 spectra in short, medium, and long periods. Using scaled motions, the soil profile was excited with 22 different earthquake scenarios and the results were obtained for the equivalent and non-linear models. The analysis showed that the soft soil conditions in the area amplified ground shaking by up to 2.8 times, especially for longer periods (1.0–2.5 s). This level of amplification was consistent with the damage observed in mid- to high-rise buildings, highlighting the important role of local site effects in the structural losses seen during the Kahramanmaraş earthquakes. Full article

This paper studies deep reinforcement learning (DRL)-based joint resource allocation and three-dimensional (3D) trajectory optimization for unmanned aerial vehicle (UAV)–ground access point (GAP) cooperative non-orthogonal multiple access (NOMA) communication in Industrial Internet of Things (IIoT) systems. Cooperative and non-cooperative users adopt different signal transmission strategies to meet diverse, task-oriented, quality-of-service requirements. Specifically, the DRL framework based on the Soft Actor–Critic algorithm is proposed to jointly optimize user scheduling, power allocation, and UAV trajectory in continuous action spaces. Closed-form power allocation and maximum weight bipartite matching are integrated to enable efficient user pairing and resource management. Simulation results show that the proposed scheme significantly enhances system performance in terms of throughput, spectral efficiency, and interference management, while enabling robustness against channel uncertainties in dynamic IIoT environments. The findings indicate that combining model-free reinforcement learning with conventional optimization provides a viable solution for adaptive resource management in dynamic UAV-GAP cooperative communication scenarios. Full article

Due to diverse lighting conditions and photo environments, shadows are almost ubiquitous in images, especially document images captured with mobile devices. Shadows not only seriously affect the visual quality and readability of a document but also significantly hinder image processing. Although shadow removal research has achieved good results in natural scenes, specific studies on document images are lacking. To effectively remove shadows in document images, the dark illumination correction network is proposed, which mainly consists of two modules: shadow detection and illumination correction. First, a simplified shadow-corrected attention block is designed to combine spatial and channel attention, which is used to extract the features, detect the shadow mask, and correct the illumination. Then, the shadow detection block detects shadow intensity and outputs a soft shadow mask to determine the probability of each pixel belonging to shadow. Lastly, the illumination correction block corrects dark illumination with a soft shadow mask and outputs a shadow-free document image. Our experiments on five datasets show that the proposed method achieved state-of-the-art results, proving the effectiveness of illumination correction. Full article

Multi-channel LED drivers are crucial for high-power lighting applications. Maintaining a constant average forward current is essential for stable LED luminous intensity, necessitating drivers capable of consistent current delivery across wide operating ranges. Meanwhile, achieving precise current sharing among channels without incurring high costs and system complexity is a significant challenge. Leveraging the constant-current characteristics of the LCL-T network, this paper presents a multi-channel DC/DC LED driver comprising a full-bridge inverter, a transformer, and a passive resonant rectifier. The driver generates a high-frequency AC bus with series-connected diode rectifiers, a structure that guarantees excellent current sharing among all output channels using only a single control loop. Fully considering the impact of higher harmonics, this paper derives an exact solution for the output current. A step-by-step parameter design methodology ensures soft switching and enhanced switch utilization. Finally, experimental verification was conducted using a prototype with five channels and 200 W, confirming the correctness and accuracy of the theoretical analysis. The experimental results showed that within a wide input voltage range of 380 V to 420 V, the driver was able to provide a stable current of 700 mA to each channel, and the system could achieve a peak efficiency of up to 94.4%. Full article

Collar monopile foundation is a new type of offshore wind power foundation. This paper explores the horizontal bearing performance of collar monopile foundation in saturated cohesive soil through a combination of physical experiments and numerical simulations. After analyzing the deformation characteristics of the pile–soil system under horizontal load through static load tests, horizontal cyclic loading tests were conducted at different cycles to study the cumulative deformation law of the collar monopile. Based on a stiffness degradation model for soft clay, a USDFLD subroutine was developed in Fortran and embedded in ABAQUS. Coupled with the Mohr–Coulomb criterion, it was used to simulate the deformation behavior of the collar monopile under horizontal cyclic loading. The numerical model employed the same geometric dimensions and boundary conditions as the physical test, and the simulated cumulative pile–head displacement under 4000 load cycles showed good agreement with the experimental results, thereby verifying the rationality and reliability of the proposed simulation method. Through numerical simulation, the distribution characteristics of bending moment and the shear force of collar monopile foundation were studied, and the influence of pile shaft and collar on the horizontal bearing capacity of collar monopile foundation at different loading stages was analyzed. The results show that as the horizontal load increases, cracks gradually appear at the bottom of the collar and in the surrounding soil. The soil disturbance caused by the sliding and rotation of the collar will gradually increase, leading to plastic failure of the surrounding soil and reducing the bearing capacity. The excess pore water pressure in shallow soil increases rapidly in the early cycle and then gradually decreases with the formation of drainage channels. Deep soil may experience negative pore pressure, indicating the presence of a suction effect. This paper can provide theoretical support for the design optimization and performance evaluation of collar monopile foundations in offshore wind power engineering applications. Full article

Accurate land parcel segmentation in remote sensing imagery is critical for applications such as land use analysis, agricultural monitoring, and urban planning. However, existing methods often underperform in complex scenes due to small-object segmentation challenges, blurred boundaries, and background interference, often influenced by sensor resolution and atmospheric variation. To address these limitations, we propose a dual-stage framework that combines an enhanced YOLOv5s detector with the Segment Anything Model (SAM) to improve segmentation accuracy and robustness. The improved YOLOv5s module integrates Efficient Channel Attention (ECA) and BiFPN to boost feature extraction and small-object recognition, while Soft-NMS is used to reduce missed detections. The SAM module receives bounding-box prompts from YOLOv5s and incorporates morphological refinement and mask stability scoring for improved boundary continuity and mask quality. A composite Focal-Dice loss is applied to mitigate class imbalance. In addition to the publicly available CCF BDCI dataset, we constructed a new WuJiang dataset to evaluate cross-domain performance. Experimental results demonstrate that our method achieves an IoU of 89.8% and a precision of 90.2%, outperforming baseline models and showing strong generalizability across diverse remote sensing conditions. Full article

Soft-growing robots based on the eversion principle are renowned for their ability to rapidly extend along their longitudinal axis, allowing them to access remote, confined, or otherwise inaccessible spaces. Their inherently compliant structure enables safe interaction with delicate environments, while their simple actuation mechanisms support lightweight and low-cost designs. Despite these benefits, implementing effective navigation mechanisms remains a significant challenge. Previous research has explored the use of pneumatic artificial muscles mounted externally on the robot’s body, which, when contracting, induce directional bending. However, this method only offers limited bending performance. To enhance maneuverability, pneumatic artificial muscles embedded in between the walls of double-walled eversion robots have also been considered and shown to offer superior bending performance and force output as compared to externally attached muscle. However, their adoption has been hindered by the complexity of the current manufacturing techniques, which require individually sealing the artificial muscles. To overcome this multi-stage fabrication approach in which muscles are embedded one by one, we propose a novel single-step method. The key to our approach is the use of non-heat-sealable inserts to form air channels during the sealing process. This significantly simplifies the process, reducing production time and effort and improving scalability for manufacturing, potentially enabling mass production. We evaluate the fabrication speed and bending performance of robots produced in this manner and benchmark them against those described in the literature. The results demonstrate that our technique offers high bending performance and significantly improves the manufacturing efficiency. Full article

Despite the economic importance of Protected Designation of Origin (PDO) cheeses in Italy, little research has examined how label attributes affect price premiums. For Italian cheese producers, especially those investing in PDO certification, understanding which attributes generate premiums is crucial for sustainable business strategies. This study examined attributes displayed on 420 validated cheese labels collected across Italy in 2022, focusing on hard cheese, fresh soft cheese, and string cheese. A content analysis was conducted to identify and categorize the attributes displayed on cheese labels. Following this, the hedonic pricing method, supported by multiple linear regression analysis, was used to assess the impact of these attributes—along with brand and distribution channel—on product pricing. Our findings reveal that sustainability attributes show particularly strong effects on price premiums. PDO certification generates significant premiums prominently for hard and fresh soft cheeses, cow breed information for string cheese, while specialized retail channels create higher prices for fresh soft and string cheeses. While brand–price relationships are heterogeneous, the study provides evidence of their impact. These insights enable cheese producers, marketers, and retailers to strategically prioritize product attributes, optimize distribution channels, and make informed decisions about brand positioning to maximize value in competitive cheese markets. Full article

Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. To address the high computational complexity and inadequate feature representation in traditional convolutional networks, this study proposes MA-YOLO, an agricultural pest detection model based on multi-scale fusion and attention mechanisms. The SDConv module reduces computational costs through depthwise separable convolution and dynamic group convolution while enhancing local feature extraction. The LDSPF module captures multi-scale information via parallel dilated convolutions with spatial attention mechanisms and dual residual connections. The ASCC module improves feature discriminability by establishing an adaptive triple-weight system for global, channel, and spatial semantic responses. The MDF module balances efficiency and multi-scale feature extraction using multi-branch depthwise separable convolution and soft attention-based dynamic weighting. Experimental results demonstrate detection accuracies of 65.4% and 73.9% on the IP102 and Pest24 datasets, respectively, representing improvements of 2% and 1.8% over the original YOLOv11s network. These results establish MA-YOLO as an effective solution for automated agricultural pest monitoring with applications in precision agriculture and crop protection systems. Full article