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In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm’s (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.

Methane is one of the most dangerous gases encountered in the mining industry. During mining operations, methane can be broadly classified into three states: mining excavation, stoppage safety check, and abnormal methane concentration, which is usually a precursor to a gas accident, such as a coal and gas outburst. Consequently, it is vital to accurately predict methane concentrations. Herein, we apply three deep learning methods—a recurrent neural network (RNN), long short-term memory (LSTM), and a gated recurrent unit (GRU)—to the problem of methane concentration prediction and evaluate their efficacy. In addition, we propose a novel prediction method that combines classical time series analysis with these deep learning models. The results revealed that GRU has the least root mean square error (RMSE) loss of the three models. The RMSE loss can be further reduced by approximately 35% by using the proposed combined approach, and the models are also less likely to result in overfitting. Therefore, combining deep learning methods with classical time series analysis can provide accurate methane concentration prediction and improve mining safety.

The phecode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) using the electronic health record (EHR). The goal of this paper was to develop and perform an initial evaluation of maps from the International Classification of Diseases, 10th Revision (ICD-10) and the International Classification of Diseases, 10th Revision, Clinical Modification (ICD-10-CM) codes to phecodes. We mapped ICD-10 and ICD-10-CM codes to phecodes using a number of methods and resources, such as concept relationships and explicit mappings from the Centers for Medicare & Medicaid Services, the Unified Medical Language System, Observational Health Data Sciences and Informatics, Systematized Nomenclature of Medicine-Clinical Terms, and the National Library of Medicine. We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM phecode map by investigating phenotype reproducibility and conducting a PheWAS. We mapped >75% of ICD-10 and ICD-10-CM codes to phecodes. Of the unique codes observed in the UKBB (ICD-10) and VUMC (ICD-10-CM) cohorts, >90% were mapped to phecodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease for phenotypes sourced from the ICD-10-CM phecode map. Using the ICD-9-CM and ICD-10-CM maps, we conducted a PheWAS with a Lipoprotein(a) genetic variant, rs10455872, which replicated two known genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P<.001; odds ratio (OR) 1.60 [95% CI 1.43-1.80] vs ICD-10-CM: P<.001; OR 1.60 [95% CI 1.43-1.80]) and chronic ischemic heart disease (ICD-9-CM: P<.001; OR 1.56 [95% CI 1.35-1.79] vs ICD-10-CM: P<.001; OR 1.47 [95% CI 1.22-1.77]). This study introduces the beta versions of ICD-10 and ICD-10-CM to phecode maps that enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for PheWAS in the EHR.

Deep rock masses are typically in complex stress states, and research on the evolution of their strain energy density is of highly important for understanding their failure characteristics. In this work, a true triaxial stress‒balanced unloading test is designed to analyze the u d and u e evolution of sandstone under true triaxial compression conditions. The study results indicate that as σ 1 increases, the elastic strain decreases in the σ 2 and σ 3 directions, whereas the residual strain progressively increases, and the magnitude of decrease in elastic strain exceeds the magnitude of increase in residual strain. Throughout the loading process of σ 1 , u e progressively decreases in the σ 2 and σ 3 directions, whereas u d gradually increases, and the magnitude of decrease in u e surpasses the magnitude of increase in u d . The u d and u e of sandstone under different stress levels were calculated via true triaxial stress‒balanced unloading tests, and the evolution of u d and u e in the three principal stress directions and the overall strain energy density of sandstone followed a linear energy storage law. On the basis of this law and the true triaxial stress‒balanced unloading test, a new method for calculating the true triaxial u d and u e was proposed. A study on the σ 1 unloading stress path revealed that the σ 1 unloading stress path significantly affects the storage and dissipation of the strain energy density in the three principal stress directions of sandstone. On the basis of the research results, the criteria for determining rockbursts were discussed.

Due to the emission of carbon dioxide and other greenhouse gases, the global climate is warming. As the world’s biggest emitter of carbon emissions, China faces a more severe challenge in reducing carbon emissions than developed countries. A reasonable prediction of the carbon peak in China will help the government to formulate effective emission reduction paths. This paper analyzes the changes in carbon emissions in China from 2004 to 2020, uses the STIRPAT model and scenario analysis method to predict carbon emissions from 2021 to 2030, and then calculates the carbon efficiency during carbon peaking to select the most effective carbon peak path for China. The results show that China’s carbon emissions increased year by year from 2004 to 2020. Under the baseline scenario, China is unlikely to reach its carbon peak before 2030. Under the regulatory scenarios, China can reach its carbon peak before 2030. The peak values from high to low are seen with the rapid development-weak carbon control scenario, rapid development-intensified carbon control scenario, slow development-weak carbon control scenario and slow development-intensified carbon control scenario, respectively. Correspondingly, China will peak its carbon emissions in 2029, 2028, 2028 and 2028, respectively, according to these scenarios. The carbon efficiency under the rapid development-weak carbon control scenario is the highest, which means that accelerating the growth rate of population, GDP and urbanization while moderately carrying out the transformation of industrial structure and energy structure is an effective way to achieve the goal of “carbon peak by 2030”.

Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is proposed with variable weight combination method based on residual assignment, which considers not only the time subsequence feature of data, but also the nonlinear characteristics of data. During the data preprocessing, the optimal parameters of gas concentration prediction are determined through the analysis of the Pearson correlation coefficients of different sensor data. The experimental results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are 1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination model is better than that of the two above models, respectively. In this way, this study provides a novel idea and method for gas accident prevention based on gas concentration prediction.

Deep rock are often in a true triaxial stress state. Studying the impacts of varying unloading speeds on their strain energy (SE) density is highly significant for predicting rock stability. Through true triaxial unloading principal stress experiments and true triaxial stress equilibrium unloading experiments on sandstone, this paper proposes a method to compute the SE density in a true triaxial compressive unloading principal stress test. This method aims to analyze the SE variation in rocks under the action of true triaxial unloading principal stresses. Acoustic emission is used to verify the correctness of the SE density calculation method in this paper. This study found that: (1) Unloading in one principal stress direction causes the SE density to rise in the other principal stress directions. This rise in SE, depending on its reversibility, can be categorized into elastic and dissipated SE. (2)When unloading principal stresses, the released elastic SE density in the unloading direction is influence by the stress path and rate. (3) The higher the unloading speed will leads to greater increases in the input SE density, elastic SE density, and dissipative SE density in the other principal stress directions. (4) The dissipated SE generated under true triaxial compression by unloading the principal stress is positively correlated with the damage to the rock; with an increase in unloading rate, there is a corresponding increase in the formation of cracks after unloading. (5) Utilizing the stress balance unloading test, we propose a calculation method for SE density in true triaxial unloading principal stress tests.

This study investigates the influence of geometric parameters of internally threaded tubes on heat transfer and resistance characteristics. Experimental analyses were conducted on pressure drop for 9.52 mm outer diameter tubes with various industry-standard geometric parameter combinations. Using R410A as the working fluid under turbulent flow conditions (Re = 20,000–60,000), experimental parameters included the following: mass velocity 50–600 kg/(m2·s), condensation temperature 45 ± 0.2 °C, and geometric ranges of thread height (e = 0.0001–0.0003 m), helix angle (α = 17–46°), crest angle (β = 16–53°), and number of ribs (Ns = 50–70). Results demonstrate that the newly developed correlation based on Webb and Ravigururajan friction factor models shows improved prediction accuracy for R410A condensation pressure drop in ribbed tubes. Model II achieved a mean absolute percentage error (MAPE) of 7.08%, with maximum and minimum errors of 27.66% and 0.76%, respectively. The standard deviation decreased from 0.0619 (Webb-based Model I) to 0.0362. Integration of SVR machine learning further enhanced tube selection efficiency through optimized correlation predictions.

Background Intracellular DNA-sensing pathway cGAS-STING, inflammasomes and pyroptosis act as critical natural immune signaling axes for microbial infection, chronic inflammation, cancer progression and organ degeneration, but the mechanism and regulation of the crosstalk network remain unclear. Main body of the abstract Cellular stress disrupts mitochondrial homeostasis, facilitates the opening of mitochondrial permeability transition pore and the leakage of mitochondrial DNA to cell membrane, triggers inflammatory responses by activating cGAS-STING signaling, and subsequently induces inflammasomes activation and the onset of pyroptosis. Meanwhile, the inflammasome-associated protein caspase-1, Gasdermin D, the CARD domain of ASC and the potassium channel are involved in regulating cGAS-STING pathway. Importantly, this crosstalk network has a cascade amplification effect that exacerbates the immuno-inflammatory response, worsening the pathological process of inflammatory and autoimmune diseases. Given the importance of this crosstalk network of cGAS-STING, inflammasomes and pyroptosis in the regulation of innate immunity, it is emerging as a new avenue to explore the mechanisms of multiple disease pathogenesis. Therefore, efforts to define strategies to selectively modulate cGAS-STING, inflammasomes and pyroptosis in different disease settings have been or are ongoing. In this review, we will describe how this mechanistic understanding is driving possible therapeutics targeting this crosstalk network, focusing on the interacting or regulatory proteins, pathways, and a regulatory mitochondrial hub between cGAS-STING, inflammasomes, and pyroptosis. Short conclusion This review aims to provide insight into the critical roles and regulatory mechanisms of the crosstalk network of cGAS-STING, inflammasomes and pyroptosis, and to highlight some promising directions for future research and intervention.

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management.