The Weighted Support Vector Machine Based on Hybrid Swarm Intelligence Optimization for Icing Prediction of Transmission Line

Not only can the icing coat on transmission line cause the electrical fault of gap discharge and icing flashover but also it will lead to the mechanical failure of tower, conductor, insulators, and others. It will bring great harm to the people’s daily life and work. Thus, accurate prediction of ice thickness has important significance for power department to control the ice disaster effectively. Based on the analysis of standard support vector machine, this paper presents a weighted support vector machine regression model based on the similarity (WSVR). According to the different importance of samples, this paper introduces the weighted support vector machine and optimizes its parameters by hybrid swarm intelligence optimization algorithm with the particle swarm and ant colony (PSO-ACO), which improves the generalization ability of the model. In the case study, the actual data of ice thickness and climate in a certain area of Hunan province have been used to predict the icing thickness of the area, which verifies the validity and applicability of this proposed method. The predicted results show that the intelligent model proposed in this paper has higher precision and stronger generalization ability

Construction and Application of “Active Prediction-Passive Warning” Joint Impact Ground Pressure Resilience Prevention System: Take the Kuan Gou Coal Mine as an Example

AbstractWith the increasing depth and intensity of coal mining, the impact on ground pressure has become one of the main disasters facing mining, seriously threatening mine safety. Introducing the concept of toughness urban design, building a joint toughness prevention and control system based on active prediction and analysis of the impact pressure risk at the back mining face according to the geological deposit conditions and mining technology conditions and passive warning using monitoring data to explore the impact precursor characteristics is an important basis for impact pressure management and has important engineering significance to ensure the safe back mining. In this paper, firstly, the whole working face is divided into small unit areas, and the BP neural network prediction model is constructed to predict and analyze each small unit separately, and the distribution of impact ground pressure hazard level in different areas of the working face is derived. Next, a FLAC numerical model was established to analyze the stress distribution and migration characteristics at different retrieval distances of the working face and to explore the main distribution areas of impact hazard. Finally, the trend method, critical value method, and dynamic rate of change method were applied to determine the early warning indicators of impact ground pressure in the Kuan Gou coal mine, establish a comprehensive early warning method of impact ground pressure applicable to the Kuan Gou coal mine, and carry out field application with good effect. The findings of this paper have good scientific significance and reference value for promoting impact hazard analysis and early warning in mines with similar geological conditions and mining technology conditions in China

In Ovo Monitoring of Smooth Muscle Fiber Development in the Chick Embryo: Diffusion Tensor Imaging with Histologic Correlation

, and to determine the correlation between histologically-derived muscle fiber fraction, day of incubation and diffusion tensor imaging fractional anisotropy values and length of tracked fibers.From a set of 82 normally developing fertile chicken eggs, 5 eggs were randomly chosen each day from incubation days 5 to 18 and cooled using a dual-cooling technique prior to and during magnetic resonance imaging at 3.0 Tesla. Smooth muscle fibers of the gizzard were tracked using region of interests placed over the gizzard. Following imaging, the egg was cracked and the embryo was fixated and sectioned, and a micrograph most closely corresponding to the acquired magnetic resonance image was made. Smooth muscle fiber fraction was determined using an automated computer algorithm. development of smooth muscle tissue

Construction and application of an intelligent prediction model for the coal pillar width of a fully mechanized caving face based on the fusion of multiple physical parameters

The scientific and reasonable width of coal pillars is of great significance to ensure safe and sustainable mining in the western mining area of China. To achieve a precise analysis of the reasonable width of coal pillars in fully mechanized caving face sections of gently inclined coal seams in western China, this paper analyzes and studies various factors that affect the retention of coal pillars in the section, and calculates the correlation coefficients between these influencing factors. We selected parameters with good universality and established a data set of gently inclined coal seams based on 106 collected engineering cases. We used the LSTM algorithm loaded with a simulated annealing algorithm for training, and constructed a coal pillar width prediction model. Compared with other prediction algorithms such as the original LSTM algorithm, the residual sum of squares and root mean square error were reduced by 27.2% and 24.2%, respectively, and the correlation coefficient was increased by 12.6%. An engineering case analysis was conducted using the W1123 working face of the Kuangou Coal Mine. The engineering verification showed that the SA-CNN-LSTM coal pillar width prediction model established in this paper has good stability and accuracy for multi-parameter nonlinear coupling prediction results. We have established an effective solution for achieving the accurate reservation of coal pillar widths in the fully mechanized caving faces of gently inclined coal seams

An analytical methodology of rock burst with fully mechanized top-coal caving mining in steeply inclined thick coal seam

Rock burst disaster is still one of the most serious dynamic disasters in coal mining, seriously restricting the safety of coal mining. The b value is the main parameter for monitoring rock burst, and by analyzing its changing characteristics, it can effectively predict the dangerous period of rock burst. This article proposes a method based on deep learning that can predict rock burst using data generated from microseismic monitoring in underground mining. The method first calculates the b value from microseismic monitoring data and constructs a time series dataset, and uses the dynamic time warping algorithm (DTW) to reconstruct the established b value time series. A bidirectional short-term and short-term memory network (BiLSTM) loaded with differential evolution algorithm and attention mechanism was used for training, and a prediction model for the dangerous period of rock burst based on differential algorithm optimization was constructed. The study used microseismic monitoring data from the B1+2 fully mechanized mining face and B3+6 working face in the southern mining area of Wudong Coal Mine for engineering case analysis. The commonly used residual sum of squares, mean square error, root mean square error, and correlation coefficient R2 for time series prediction were introduced, which have significant advantages compared to basic LSTM algorithms. This verifies that the prediction method proposed in this article has good prediction results and certain feasibility, and can provide technical support for the prediction and prevention of rock burst in steeply inclined thick coal seams in strong earthquake areas

Precursor Information Recognition of Rockburst in the Coal-Rock Mass of Meizoseismal Area Based on Multiplex Microseismic Information Fusion and Its Application: A Case Study of Wudong Coal Mine

AbstractIn recent years, the rockburst induced by steeply inclined coal seam mining in the Urumqi mining area has become serious. In this paper, the evolution law of multiplex microseismic information before and after the rockburst is obtained through in-depth mining of the field microseismic data. In addition, the evolution characteristics of microseismic activities before and after the rockburst of steeply inclined coal-rock mass in the meizoseismal area are revealed from three important scales: time, space, and strength. The results show the following: (1) The microseismic activity of the Wudong Coal Mine is mainly of stress migration type. The sandwiched rock pillar is the primary inducement of rockburst, and the b value decreases greatly with the mining progresses (by 23.9%). It indicates that the risk of rockburst induced by the local failure of rock mass in this area is increasing. (2) From the time scale and strength index, the precursory indexes of rockburst are put forward, respectively: ① the daily total energy and the frequency of microseisms suddenly rise and fall rapidly at the same time in the shock start-up period (5 days before rockburst), and the daily total energy of microseisms decreases to the abnormal valley value within 30 days. ② The abnormal growth rate of microseismic events exceeded 60% in a certain stage, and “induced shock events” appeared. (3) The shock risk is positively correlated with the decline rate of energy index, the growth rate of cumulative apparent volume, and Schmidt. It is determined that the rockburst will occur within 19 days after entering the shock early warning period. The results of prediction examples show that this method has a good prediction effect on rockburst in strong meizoseismal areas, which can provide a reference for rockburst prevention in the mining process in strong meizoseismal areas

Experimental study on mechanical damage characteristics of water-bearing tar-rich coal under microwave radiation

As a recognized special resource, tar-rich coal can extract the country's scarce oil and gas resources and generate semi-coke that can replace anthracite and coking coal. The tar-rich coal in northern Shaanxi is prominent, but due to the dense structure and high strength of tar-rich coal, it is easy to cause frequent dynamic disasters in coal mining. Therefore, the realization of pressure relief and disaster reduction has become the primary problem in mining tar-rich coal. There are many shortcomings in conventional pressure relief methods, so a new method of microwave-weakening coal is proposed. Through different water saturation treatments of tar-rich coal samples, the longitudinal wave velocity degradation trend and surface crack expansion law of water-bearing coal after microwave irradiation were analyzed, and the strength softening characterization and energy evolution relationship under the combined action of microwave and water were studied. Fractal dimension and its internal correlation based on the equivalent side length-mass of coal sample fragments. The experimental results show that: (1) Under the same microwave radiation condition, with the increase of water saturation, the deterioration trend of physical and mechanical parameters such as longitudinal wave velocity and peak strength is obvious. (2) After microwave radiation, the uniaxial compression results show that the coal sample is damaged by load, there is still a high residual strength, the ratio of elastic energy to dissipation energy decreases, and the possibility of rockburst of the coal sample decreases. The strength softening degree of coal specimen under the degradation of microwave and water is the highest, followed by microwave and water. (3) The fractal dimension is inversely proportional to the moisture content and microwave radiation intensity, and the fractal dimension has a significant positive correlation with the peak intensity and longitudinal wave velocity. The mechanical damage law of water-bearing tar-rich coal under microwave action is revealed, which aims to solve the problem of weakening and reducing the impact of hard coal on-site to a certain extent, ensure the safety of working face, and improve the mining efficiency of tar-rich coal. It provides basic theoretical support for microwave-assisted hydraulic fracturing technology and effective weakening measures for hard roof treatment

Elevated limb-bud and heart development (LBH) expression indicates poor prognosis and promotes gastric cancer cell proliferation and invasion via upregulating Integrin/FAK/Akt pathway

The limb-bud and heart development (LBH) gene is a highly conserved, tissue-specific transcription cofactor in vertebrates that regulates multiple key genes in embryonic development. The role of LBH in various cancer types is still controversial, and its specific role and molecular mechanism in the oncogenesis of gastric cancer (GC) remains largely unexplored. In the present study, the prognostic significance and clinicopathological characteristics of LBH in GC was determined. The LBH mRNA expression was first investigated in four independent public datasets (TCGA-STAD, GSE15459, GSE29272, and GSE62254) and then validated with our samples at the protein level. LBH was overexpressed at both the mRNA and protein levels in cancer compared with normal tissues. High LBH expression was correlated with advanced T, N, and M stages. Kaplan–Meier analysis and log-rank test indicated that higher LBH expression was statistically correlated with shorter overall survival (OS) in the public datasets and our study samples. Univariate and multivariate Cox regression analysis showed that LBH was an independent prognostic biomarker for survival in TCGA-STAD, GSE15459, GSE62254 cohorts, and our GC patients. In vitro experiments showed that knockdown of LBH can significantly inhibit the proliferation and invasion of HGC-27 cells, while overexpression of LBH can significantly enhance the proliferation and invasion of BGC-823 cells. Gene Set Enrichment Analysis (GSEA), Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG) indicated that high LBH expression is associated with the PI3K-Akt pathway, focal adhesion, and extracellular matrix (ECM)-receptor interaction. Western blot analysis showed that knockdown of LBH significantly inhibited the expression of integrin α5, integrin β1, p-FAK, and p-Akt. Therefore, results from the present study indicate that LBH is a potential independent prognostic biomarker and promotes proliferation and invasion of GC cells by activating the integrin/FAK/Akt pathway

Polyploidy underlies co-option and diversification of biosynthetic triterpene pathways in the apple tribe

Whole-genome duplication (WGD) plays important roles in plant evolution and function, yet little is known about how WGD underlies metabolic diversification of natural products that bear significant medicinal properties, especially in nonmodel trees. Here, we reveal how WGD laid the foundation for co-option and differentiation of medicinally important ursane triterpene pathway duplicates, generating distinct chemotypes between species and between developmental stages in the apple tribe. After generating chromosome-level assemblies of a widely cultivated loquat variety and Gillenia trifoliata, we define differentially evolved, duplicated gene pathways and date the WGD in the apple tribe at 13.5 to 27.1 Mya, much more recent than previously thought. We then functionally characterize contrasting metabolic pathways responsible for major triterpene biosynthesis in G. trifoliata and loquat, which pre- and postdate the Maleae WGD, respectively. Our work mechanistically details the metabolic diversity that arose post-WGD and provides insights into the genomic basis of medicinal properties of loquat, which has been used in both traditional and modern medicines

A compendium of genetic regulatory effects across pig tissues

The Farm Animal Genotype-Tissue Expression (FarmGTEx) project has been established to develop a public resource of genetic regulatory variants in livestock, which is essential for linking genetic polymorphisms to variation in phenotypes, helping fundamental biological discovery and exploitation in animal breeding and human biomedicine. Here we show results from the pilot phase of PigGTEx by processing 5,457 RNA-sequencing and 1,602 whole-genome sequencing samples passing quality control from pigs. We build a pig genotype imputation panel and associate millions of genetic variants with five types of transcriptomic phenotypes in 34 tissues. We evaluate tissue specificity of regulatory effects and elucidate molecular mechanisms of their action using multi-omics data. Leveraging this resource, we decipher regulatory mechanisms underlying 207 pig complex phenotypes and demonstrate the similarity of pigs to humans in gene expression and the genetic regulation behind complex phenotypes, supporting the importance of pigs as a human biomedical model.</p