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The longwall top-coal caving technology is an effective method for extracting thick and ultra-thick coal seams, and it has become a hallmark technology in China's coal mining industry. This paper reviews the research progress on the "Four elements" coal caving theory, the relationship between the top coal recovery rate and the rock mixed ratio, a recovery rate prediction model based on block distribution, and the relationship between instantaneous rock mixed ratio and cumulative rock mixed ratio. The challenges of intelligent coal caving technology are analyzed, emphasizing that the rock mixed ratio is a key factor affecting the top coal recovery rate and coal quality. Rapid and accurate calculation of the rock mixed ratio during the coal caving process is crucial for breakthroughs in intelligent coal caving technology. This technology is categorized into two types: non-image recognition and image recognition. The research progress, advantages, disadvantages, and usage conditions of different technologies are discussed in detail. Non-image recognition intelligent coal caving technology includes memory coal caving technology, sound and vibration signal detection technology, γ-ray detection technology, ground penetrating radar technology, microwave irradiation combined with infrared detection technology, and laser scanning coal caving monitoring technology. Image-based intelligent coal caving technology encompasses precise control of underground illumination environment, dust removal algorithms for coal caving images, accuracy assurance strategies for rock mixed ratio calculations, and infrared image recognition of coal and rock.

Tumor extracellular matrix (ECM) is the center component of tumor microenvironment (TME), ECM diversity constitutes the inherent heterogeneity of TME that contributes to tumor growth, dormancy, drug resistance, and metastasis. Discoidin domain receptor 1 is one of the ECM receptors that interact with multiple ECM ligands. It also regulates the occurrence and development of tumors. Accordingly, DDR1 plays an increasingly important role in the prevention, diagnosis, and treatment of cancer. In this review, we primarily summarize the research of ECM and its receptors with components, regulation, cell receptors, and signaling pathways in tumor progression.

PurposeTumor genomic features have been of particular interest because of their potential impact on the tumor immune microenvironment and response to immunotherapy. Due to the substantial heterogeneity, an integrative approach incorporating diverse molecular features is needed to characterize immunologic features underlying primary resistance to immunotherapy and for the establishment of novel predictive biomarkers.Experimental designWe developed a pan-cancer deep machine learning model integrating tumor mutation burden, microsatellite instability, and somatic copy-number alterations to classify tumors of different types into different genomic clusters, and assessed the immune microenvironment in each genomic cluster and the association of each genomic cluster with response to immunotherapy.ResultsOur model grouped 8,646 tumors of 29 cancer types from The Cancer Genome Atlas into four genomic clusters. Analysis of RNA-sequencing data revealed distinct immune microenvironment in tumors of each genomic class. Furthermore, applying this model to tumors from two melanoma immunotherapy clinical cohorts demonstrated that patients with melanoma of different genomic classes achieved different benefit from immunotherapy. Interestingly, tumors in cluster 4 demonstrated a cold immune microenvironment and lack of benefit from immunotherapy despite high microsatellite instability burden.ConclusionsOur study provides a proof for principle that deep learning modeling may have the potential to discover intrinsic statistical cross-modality correlations of multifactorial input data to dissect the molecular mechanisms underlying primary resistance to immunotherapy, which likely involves multiple factors from both the tumor and host at different molecular levels.

Since widely accepted by international scholars, Voussoir Beam Theory proposed by Academician Minggao Qian helps the research on underground mining pressure realize leapfrog development from the stage of qualitative hypothesis to quantitative analysis. In the past forty years, an integral system has been formed for strata movement theory and strata control technique, contributing to safe, efficient and green mining of underground coal seams. By underlining the voussoir beam theory, critical developments on strata movement and its control have been reviewed. Important topics are proposed for future research. In the voussoir beam theory, rupture characteristics of main roof are studied with beam and thin plate models. It is revealed fracturing line of main roof locates ahead of the longwall face. Due to supporting effects provided by coal wall and caving materials, the rotation of broken blocks of main roof results in the occurrence of compressive force, forming the voussoir beam structure. Both slipping and rotating instability modes of the structure are identified. The S-R instability conditions are determined. Based on such understanding, determination method for roof load is proposed through keeping the balance of voussoir beam structure, realizing quantitative analysis on the relation between hydraulic support and surrounding rock. Until now, the voussoir beam structure stands as the most impeccable model for explaining underground mining pressure. It is a major breakthrough for conventional qualitative hypothesis, which makes historic contributions to living safety and working efficiency of the miners. The Voussoir Beam Theory further brings about many achievements on strata control, including three-coupling relation between hydraulic support and surrounding rock, dynamic load model for roof control, medium plate model, far-near field model in longwall face with large space etc. Based on the voussoir beam theory, the key stratum theory is moreover proposed by Minggao Qian in the middle of the 1990s. Mechanical relation between different overburden strata is established during movement process. Coordinative analysis on mining pressure, strata movement and surface subsidence is realized. The Voussoir Beam Theory and key stratum theory provide significant guidance on the research of coal mining pressure and strata movement, contributing to a series of theoretical developments, such as mining pressure control at the face area, mining induced fracture distribution in strata, mining induced stress distribution, strata movement and surface subsidence. Such achievements serve as the foundation of green mining and scientific mining. Further development of the theory should pay attention to unified field theory for strata movement, strata movement based accident and damage prevention technique, strata movement under complex mining conditions, visualization of strata movement and intelligent strata control.

The intelligent surrounding rock control at longwall working face is the necessary condition to realize the intelligent mining of working face. This paper redefined the connotation of the surrounding rock system at longwall working face and clarified the scope of surrounding rock control and clarified the scope of surrounding rock control, which includes small structure of support surrounding rock within its basic topic range in the vertical direction, and the far field rock layer that will have an effect on surrounding rock control at longwall working face. The factors of surrounding rock system at longwall working face are classified, there are "controllable factors" and "uncontrollable factors" according to the mode of action. The points and basic principles of intelligent surrounding rock control are determined. Multi-parameter intelligent detection, precise analysis of pattern discriminations, self-determination, quick implementation and dynamic evaluation of control effects which is technological framework for intelligent control is established. Furthermore, scientific issues and critical technical problems of intelligent surrounding rock control at longwall working face are elaborated. The four key technology assumptions based on this are "intelligent mining system at working face", "adaptive intelligent control technology of equipment-surrounding rock", "intelligent control of complex conditions surrounding rock", and "stability analysis of surrounding rock system at longwall working face under unified coordinate system". The extractability of the information on the working conditions of support is analyzed with the actual case, and the intelligent prediction of mine pressure at longwall working face is realized by the stacked LSTM algorithm.

A novel block–particle discrete-element simulation method that matches the double medium of overlying rock (OLR) and loose layer (LSL) in coal mining is developed in this study. This method achieves the collaborative failure characteristics of mining damage under the conduction of double media between the OLR and LSL by combining the self-weight stress loading of the LSL and the breakage morphology of the bedrock top. Based on this, the conduction law of high-strength mining damage in the double medium in a western mining area is simulated and analyzed. The combining effect of the OLR breakage morphology and LSL characteristics on the surface-subsidence characteristics is analyzed and verified based on on-site measurements. The results indicate that the OLR is guided by the "double-control layer and thick-soft rock buffer layer" and shows "grouping subsidence", whereas the surface forms collaborative subsidence with the thick-soft rock buffer layer. In the ultra-full mining stage, the surface presents an "asymmetric inverted trapezoidal" subsidence trough shape. The simulation results agree well the on-site measurements in terms of the surface-subsidence and bedrock-subsidence coefficients. The proposed simulation method provides a scientific approach for investigating the micro-conduction mechanism of mining damage under the effect of high-strength mining in western mining areas. It will benefit future investigations pertaining to the characteristics of OLR breakage and surface subsidence under conditions such as LSL thickness and proportion. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) integrated with computer vision technology have emerged as an effective method for information acquisition in various applications. However, due to the small proportion of target pixels and susceptibility to background interference in multi-angle UAV imaging, missed detections and false results frequently occur. To address this issue, a small target detection algorithm, EDANet, is proposed based on YOLOv8. First, the backbone network is replaced by EfficientNet, which can dynamically explore the network size and the image resolution using a scaling factor. Second, the EC2f feature extraction module is designed to achieve unique coding in different directions through parallel branches. The position information is effectively embedded in the channel attention to enhance the spatial representation ability of features. To mitigate the low utilization of small target pixels, we introduce the DTADH detection module, which facilitates feature fusion via a feature-sharing interactive network. Simultaneously, a task alignment predictor assigns classification and localization tasks. In this way, not only is feature utilization optimized, but also the number of parameters is reduced. Finally, leveraging logic and feature knowledge distillation, we employ binary probability mapping of soft labels and a soft label weighting strategy to enhance the algorithm's learning capabilities in target classification and localization. Experimental validation on the UAV aerial dataset VisDrone2019 demonstrates that EDANet outperforms existing methods, reducing GFLOPs by 39.3% and improving Map by 4.6%. [ABSTRACT FROM AUTHOR]

Identifying tumor-relevant T cell subsets in the peripheral blood (PB) has become a potential strategy for cancer treatment. However, the subset of PB that could be used to treat cancer remains poorly defined. Here, we found that the CX3CR1+ T cell subset in the blood of patients with lung cancer exhibited effector properties and had a higher TCR matching ratio with tumor-infiltrating lymphocytes (TILs) compared to CX3CR1− T cells, as determined by paired single-cell RNA and TCR sequencing. Meanwhile, the anti-tumor activities, effector cytokine production, and mitochondrial function were enhanced in CX3CR1+ T cells both in vitro and in vivo. However, in the co-culture system of H322 cells with T cells, the percentages of apoptotic cells and Fas were substantially higher in CX3CR1+ T cells than those in CX3CR1− T cells. Fas-mediated apoptosis was rescued by treatment with an anti-PD-1 antibody. Accordingly, the combination of adoptive transfer of CX3CR1+ T cells and anti-PD-1 treatment considerably decreased Fas expression and improved the survival of lung xenograft mice. Moreover, an increased frequency of CX3CR1+ T cells in the PB correlated with a better response and prolonged survival of patients with lung cancer who received anti-PD-1 therapy. These findings indicate the promising potential of adoptive transfer of peripheral CX3CR1+ T cells as an individual cancer immunotherapy. [ABSTRACT FROM AUTHOR]