Study on the law of stress distribution in the presence of remaining coal pillar in a close-distance coal seam and the reasonable location of the roadway.

In order to reveal the impact of remaining coal pillar on roadway layout of the lower seam after the upper part of the close coal seam is reversely mined, theoretical analysis and numerical simulation are conducted in this paper. With the roadway to be excavated in a mine located in Shanxi as the research object, theoretical analysis and numerical simulation are performed to explore the evolution of stress field in the lower coal seam under the influence of the leftover coal pillar. Also, an analysis is conducted on the mechanism of deformation and destruction occurring to the roadway in the lower coal seam, so as to determine the rational layout of roadway in the lower coal seam and the optimal parameters related to roadway support.

Diffusion-CAD: Controllable Diffusion Model for Generating Computer-Aided Design Models.

Generative methods for creating computer-aided design (CAD) models have gained significant attention over the past two years. However, existing methods lack fine-grained control over the generated CAD models, making it difficult to manage details such as model dimensions and the relative structure of components. To address these limitations, this study introduces Diffusion-CAD, a diffusion-based generative approach that outputs CAD construction sequences.

Intelligent Identification and Prediction of Roof Deterioration Areas Based on Measurements While Drilling.

During roadway excavation, the presence of roof deterioration zones, such as layered spaces and weak interlayers, significantly affects the stability of the surrounding rock. To achieve timely and effective support for roadways, it is essential to utilize drilling measurement signals obtained during the construction of anchorage holes for the identification and prediction of these deterioration zones. This study systematically investigates the response characteristics of thrust, torque, and Y-direction vibration signals to different combinations of rock layers through theoretical analysis, laboratory experiments, ABAQUS dynamic numerical simulations, and field measurements.

Characteristics of deformation and damage of surrounding rock along the top roadway in the working face of an isolated island and its evolution law.

This study investigates the deformation and damage characteristics of the surrounding rock along the top return mining roadway of an isolated island working face at different stages and reveals its damage mechanism and evolution law. Utilizing a mine in Yangquan City, Shanxi Province, China, as the engineering background, this research employs FLAC 3D numerical simulation and on-site measurements. The findings suggest that the evolution of the plastic zone along the top roadway of the 15,106 island face is largely similar during both the excavation and mining periods.

Edge Devices Clustering for Federated Visual Classification: A Feature Norm Based Framework.

Federated learning is a privacy-preserving distributed learning paradigm where multiple devices collaboratively train a model, which is applicable to edge computing environments. However, the non-IID data distributed in multiple devices degrades the performance of the federated model due to severe weight divergence. This paper presents a clustered federated learning framework named cFedFN for visual classification tasks in order to reduce the degradation.

Balanced Federated Semisupervised Learning With Fairness-Aware Pseudo-Labeling.

Federated semisupervised learning (FSSL) aims to train models with both labeled and unlabeled data in the federated settings, enabling performance improvement and easier deployment in realistic scenarios. However, the nonindependently identical distributed data in clients leads to imbalanced model training due to the unfair learning effects on different classes. As a result, the federated model exhibits inconsistent performance on not only different classes, but also different clients.

Self-powered droplet manipulation system for microfluidics based on triboelectric nanogenerator harvesting rotary energy.

Microfluidic technology, as a method for manipulating tiny fluids, has the advantages of low sample consumption, fast reaction, and no cross-contamination. In a microfluidic system, accurate manipulation of droplets is a crucial technology that has been widely investigated. In this work, a self-powered droplet manipulation system (SDMS) is proposed to realize various droplet operations, including moving, splitting, merging, mixing, transporting chemicals and reacting.

High-quality blind defocus deblurring of multispectral images with optics and gradient prior.

This paper presents a blind defocus deblurring method that produces high-quality deblurred multispectral images. The high quality is achieved by two means: i) more accurate kernel estimation based on the optics prior by simulating the simple lens imaging, and ii) the gradient-based inter-channel correlation with the reference image generated by the content-adaptive combination of adjacent channels for restoring the latent sharp image. As a result, our method gains the prominence on both effectiveness and efficiency in deblurring defocus multispectral images with very good restoration on the obscure details.

A Direction Self-Tuning Two-Dimensional Piezoelectric Vibration Energy Harvester.

Most work from the last decade on the piezoelectric vibration energy harvester (PVEHs) focuses on how to increase its frequency bandwidth but ignores the effect of vibration direction on the output performance of the harvester. However, both the frequency and the direction of the vibration in a real environment are time-variant. Therefore, improving the capability of PVEH to harvest multi-directional vibration energy is also important.