Numerical Simulation of Fluid-Solid Coupling Heat Transfer in Excavating Roadway

Heat damage is an urgent problem to be solved in deep mining. The relevant factors in the excavation roadway significantly affect the cooling effect in the roadway. In this paper, the fluid-solid coupling heat transfer model of excavating roadway is established using numerical simulation, and the influence of inlet air temperature, wind speed, original rock temperature and surrounding rock thermal conductivity on roadway temperature is studied. The results show that the larger the initial rock temperature, the higher the airflow temperature in the roadway, and the greater the decrease of the airflow temperature after ventilation. The lower the inlet air temperature, the more significant the decrease in the airflow temperature in the roadway after ventilation, and the airflow temperature in the roadway gradually stabilizes with the extension of the ventilation time. The greater the wind speed, the lower the airflow temperature in the roadway, and the more significant the decrease of the airflow temperature in the roadway after ventilation. When the wind speed reaches a specific value, the cooling effect of ventilation is minimal. The higher the thermal conductivity of the surrounding rock, the higher the outlet temperature simultaneously

The APC Algorithm of Solving Large-Scale Linear Systems: A Generalized Analysis

A new algorithm called accelerated projection-based consensus (APC) has recently emerged as a promising approach to solve large-scale systems of linear equations in a distributed fashion. The algorithm adopts the federated architecture, and attracts increasing research interest; however, it's performance analysis is still incomplete, e.g., the error performance under noisy condition has not yet been investigated. In this paper, we focus on providing a generalized analysis by the use of the linear system theory, such that the error performance of the APC algorithm for solving linear systems in presence of additive noise can be clarified. We specifically provide a closed-form expression of the error of solution attained by the APC algorithm. Numerical results demonstrate the error performance of the APC algorithm, validating the presented analysis.Comment: 6 pages, 3 figure

A consensus linkage map of the grass carp (Ctenopharyngodon idella) based on microsatellites and SNPs

<p>Abstract</p> <p>Background</p> <p>Grass carp (<it>Ctenopharyngodon idella</it>) belongs to the family Cyprinidae which includes more than 2000 fish species. It is one of the most important freshwater food fish species in world aquaculture. A linkage map is an essential framework for mapping traits of interest and is often the first step towards understanding genome evolution. The aim of this study is to construct a first generation genetic map of grass carp using microsatellites and SNPs to generate a new resource for mapping QTL for economically important traits and to conduct a comparative mapping analysis to shed new insights into the evolution of fish genomes.</p> <p>Results</p> <p>We constructed a first generation linkage map of grass carp with a mapping panel containing two F1 families including 192 progenies. Sixteen SNPs in genes and 263 microsatellite markers were mapped to twenty-four linkage groups (LGs). The number of LGs was corresponding to the haploid chromosome number of grass carp. The sex-specific map was 1149.4 and 888.8 cM long in females and males respectively whereas the sex-averaged map spanned 1176.1 cM. The average resolution of the map was 4.2 cM/locus. BLAST searches of sequences of mapped markers of grass carp against the whole genome sequence of zebrafish revealed substantial macrosynteny relationship and extensive colinearity of markers between grass carp and zebrafish.</p> <p>Conclusions</p> <p>The linkage map of grass carp presented here is the first linkage map of a food fish species based on co-dominant markers in the family Cyprinidae. This map provides a valuable resource for mapping phenotypic variations and serves as a reference to approach comparative genomics and understand the evolution of fish genomes and could be complementary to grass carp genome sequencing project.</p

DSCA-PSPNet: Dynamic spatial-channel attention pyramid scene parsing network for sugarcane field segmentation in satellite imagery

Sugarcane plays a vital role in many global economies, and its efficient cultivation is critical for sustainable development. A central challenge in sugarcane yield prediction and cultivation management is the precise segmentation of sugarcane fields from satellite imagery. This task is complicated by numerous factors, including varying environmental conditions, scale variability, and spectral similarities between crops and non-crop elements. To address these segmentation challenges, we introduce DSCA-PSPNet, a novel deep learning model with a unique architecture that combines a modified ResNet34 backbone, the Pyramid Scene Parsing Network (PSPNet), and newly proposed Dynamic Squeeze-and-Excitation Context (D-scSE) blocks. Our model effectively adapts to discern the importance of both spatial and channel-wise information, providing superior feature representation for sugarcane fields. We have also created a comprehensive high-resolution satellite imagery dataset from Guangxi’s Fusui County, captured on December 17, 2017, which encompasses a broad spectrum of sugarcane field characteristics and environmental conditions. In comparative studies, DSCA-PSPNet outperforms other state-of-the-art models, achieving an Intersection over Union (IoU) of 87.58%, an accuracy of 92.34%, a precision of 93.80%, a recall of 93.21%, and an F1-Score of 92.38%. Application tests on an RTX 3090 GPU, with input image resolutions of 512 × 512, yielded a prediction time of 4.57ms, a parameter size of 22.57MB, GFLOPs of 11.41, and a memory size of 84.47MB. An ablation study emphasized the vital role of the D-scSE module in enhancing DSCA-PSPNet’s performance. Our contributions in dataset generation and model development open new avenues for tackling the complexities of sugarcane field segmentation, thus contributing to advances in precision agriculture. The source code and dataset will be available on the GitHub repository https://github.com/JulioYuan/DSCA-PSPNet/tree/main

Diffusion-Dominated Pinch-Off of Ultralow Surface Tension Fluids

We study the breakup of a liquid thread inside another liquid at different surface tensions. In general, the pinch-off of a liquid thread is governed by the dynamics of fluid flow. However, when the interfacial tension is ultralow (2 to 3 orders lower than normal liquids), we find that the pinch-off dynamics can be governed by bulk diffusion. By studying the velocity and the profile of the pinch-off, we explain why the diffusion-dominated pinch-off takes over the conventional breakup at ultralow surface tensions.Comment: 7 pages, 5 figures. Published versio

Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environments

In the NIPS 2017 Learning to Run challenge, participants were tasked with building a controller for a musculoskeletal model to make it run as fast as possible through an obstacle course. Top participants were invited to describe their algorithms. In this work, we present eight solutions that used deep reinforcement learning approaches, based on algorithms such as Deep Deterministic Policy Gradient, Proximal Policy Optimization, and Trust Region Policy Optimization. Many solutions use similar relaxations and heuristics, such as reward shaping, frame skipping, discretization of the action space, symmetry, and policy blending. However, each of the eight teams implemented different modifications of the known algorithms.Comment: 27 pages, 17 figure

Clinical application of Kirschner wires combined with 5-Ethibond fixation for patella fractures

BackgroundPatella fractures that require surgery are conventionally treated using Kirschner wires (K-wires) and stainless steel wires. In recent years, the nonabsorbable polyester has been reported to have excellent outcomes clinically. Therefore, the goal of our study was to evaluate the effects of Kirschner wires combined with 5-Ethibond on treating patellar fractures.MethodsFrom July 2018 to January 2022, 22 patella fracture patients were treated with Kirschner wires combined with 5-Ethibond. Radiographs of the knees were used to evaluate fracture healing and hardware complications. The clinical results were evaluated through the functional score, knee joint range of motion (ROM), and Bostman patella fracture functional score.ResultsThe average age of patients was 57.4 ± 11.9 (range 33–74) years. The mean follow-up time was 15.2 ± 7.6 (range 4–36) months. The mean operation time was 56.8 ± 8.7 (range 45–80) min. The entire patients had bony union at an average of 10.5 ± 1.9 (range 8–14) weeks. At the final follow-up, the mean range of postoperative ROM was 123.4° ± 14.6° (range 95°–140°), and the functional score was 28.7 ± 1.2 (range 26–30) points. No patient exhibited internal fixation failure, and no symptomatic implants or skin complications were recorded.ConclusionsThe fixation approach using K-wires combined with 5-Ethibond has a lower complication rate and delivers superior clinical results. This research reveals that such technology is a safe and prospective substitute for conventional metal fixation approaches