A Coal Mine Tunnel Deformation Detection Method Using Point Cloud Data.

In recent years, the deformation detection technology for underground tunnels has played a crucial role in coal mine safety management. Currently, traditional methods such as the cross method and those employing the roof abscission layer monitoring instrument are primarily used for tunnel deformation detection in coal mines. With the advancement of photogrammetric methods, three-dimensional laser scanners have gradually become the primary method for deformation detection of coal mine tunnels. However, due to the high-risk confined spaces and distant distribution of coal mine tunnels, stationary three-dimensional laser scanning technology requires a significant amount of labor and time, posing certain operational risks. Currently, mobile laser scanning has become a popular method for coal mine tunnel deformation detection. This paper proposes a method for detecting point cloud deformation of underground coal mine tunnels based on a handheld three-dimensional laser scanner. This method utilizes SLAM laser radar to obtain complete point cloud information of the entire tunnel, while projecting the three-dimensional point cloud onto different planes to obtain the coordinates of the tunnel centerline. By using the calculated tunnel centerline, the three-dimensional point cloud data collected at different times are matched to the same coordinate system, and then the tunnel deformation parameters are analyzed separately from the global and cross-sectional perspectives. Through on-site collection of tunnel data, this paper verifies the feasibility of the algorithm and compares it with other centerline fitting and point cloud registration algorithms, demonstrating higher accuracy and meeting practical needs.

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face.

With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model's computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model's computational requirements and volume while maintaining high accuracy.

STW-MD: a novel spatio-temporal weighting and multi-step decision tree method for considering spatial heterogeneity in brain gene expression data.

Gene expression during brain development or abnormal development is a biological process that is highly dynamic in spatio and temporal. Previous studies have mainly focused on individual brain regions or a certain developmental stage. Our motivation is to address this gap by incorporating spatio-temporal information to gain a more complete understanding of brain development or abnormal brain development, such as Alzheimer's disease (AD), and to identify potential determinants of response. In this study, we propose a novel two-step framework based on spatial-temporal information weighting and multi-step decision trees. This framework can effectively exploit the spatial similarity and temporal dependence between different stages and different brain regions, and facilitate differential gene analysis in brain regions with high heterogeneity. We focus on two datasets: the AD dataset, which includes gene expression data from early, middle and late stages, and the brain development dataset, spanning fetal development to adulthood. Our findings highlight the advantages of the proposed framework in discovering gene classes and elucidating their impact on brain development and AD progression across diverse brain regions and stages. These findings align with existing studies and provide insights into the processes of normal and abnormal brain development.

Mediating trade-off between activity and selectivity in alkynes semi-hydrogenation via a hydrophilic polar layer.

As a crucial industrial process for the production of bulk and fine chemicals, semi-hydrogenation of alkynes faces the trade-off between activity and selectivity due to undesirable over-hydrogenation. By breaking the energy linear scaling relationships, we report an efficient additive-free WO3-based single-atom Pd catalytic system with a vertical size effect of hydrogen spillover. Hydrogen spillover induced hydrophilic polar layer (HPL) with limited thickness on WO3-based support exhibits unconventional size effect to Pd site, in which over-hydrogenation is greatly suppressed on Pd1 site due to the polar repulsive interaction between HPL and nonpolar C=C bonds, whereas this is invalid for Pd nanoparticles with higher altitudes. By further enhancing the HPL through Mo doping, activated Pd1/MoWO3 achieves recorded performance of 98.4% selectivity and 10200 h-1 activity for semi-hydrogenation of 2-methyl-3-butyn-2-ol, 26-fold increase in activity of Lindlar catalyst. This observed vertical size effect of hydrogen spillover offers broad potential in catalytic performance regulation.

A Segmented Cross-Correlation Algorithm for Dynamic North Finding Using Fiber Optic Gyroscopes.

Fiber optic gyroscope (FOG)-based north finding is extensively applied in navigation, positioning, and various fields. In dynamic north finding, an accelerated turntable speed shortens the time required for north finding, resulting in a rapid north-finding response. However, with an increase in turntable speed, the turntable's jitter contributes to signal contamination in the FOG, leading to a deterioration in north-finding accuracy. This paper introduces a divide-and-conquer algorithm, the segmented cross-correlation algorithm, designed to mitigate the impact of turntable speed jitter. A model for north-finding error is established and analyzed, incorporating FOG's self-noise and the turntable's speed jitter. To validate the feasibility of our method, we implemented the algorithm on a FOG. The simulation and experimental results exhibited a strong concordance, affirming the validity of our proposed north-finding error model. The experimental findings indicate that, at a turntable speed of 180°/s, the north-finding bias error within a 360 s duration is 0.052°, representing a 64% improvement over the traditional algorithm. These results indicate the effectiveness of the proposed algorithm in mitigating the impact of unstable turntable speeds, offering a solution for north finding with both prompt response and enhanced accuracy.

LMFD: lightweight multi-feature descriptors for image stitching.

Image stitching is a fundamental pillar of computer vision, and its effectiveness hinges significantly on the quality of the feature descriptors. However, the existing feature descriptors face several challenges, including inadequate robustness to noise or rotational transformations and limited adaptability during hardware deployment. To address these limitations, this paper proposes a set of feature descriptors for image stitching named Lightweight Multi-Feature Descriptors (LMFD). Based on the extensive extraction of gradients, means, and global information surrounding the feature points, feature descriptors are generated through various combinations to enhance the image stitching process. This endows the algorithm with formidable rotational invariance and noise resistance, thereby improving its accuracy and reliability. Furthermore, the feature descriptors take the form of binary matrices consisting of 0s and 1s, not only facilitating more efficient hardware deployment but also enhancing computational efficiency. The utilization of binary matrices significantly reduces the computational complexity of the algorithm while preserving its efficacy. To validate the effectiveness of LMFD, rigorous experimentation was conducted on the Hpatches and 2D-HeLa datasets. The results demonstrate that LMFD outperforms state-of-the-art image matching algorithms in terms of accuracy. This empirical evidence solidifies the superiority of LMFD and substantiates its potential for practical applications in various domains.

Tailoring a local acid-like microenvironment for efficient neutral hydrogen evolution.

Electrochemical hydrogen evolution reaction in neutral media is listed as the most difficult challenges of energy catalysis due to the sluggish kinetics. Herein, the Ir-HxWO3 catalyst is readily synthesized and exhibits enhanced performance for neutral hydrogen evolution reaction. HxWO3 support is functioned as proton sponge to create a local acid-like microenvironment around Ir metal sites by spontaneous injection of protons to WO3, as evidenced by spectroscopy and electrochemical analysis. Rationalize revitalized lattice-hydrogen species located in the interface are coupled with Had atoms on metallic Ir surfaces via thermodynamically favorable Volmer-Tafel steps, and thereby a fast kinetics. Elaborated Ir-HxWO3 demonstrates acid-like activity with a low overpotential of 20 mV at 10 mA cm-2 and low Tafel slope of 28 mV dec-1, which are even comparable to those in acidic environment. The concept exemplified in this work offer the possibilities for tailoring local reaction microenvironment to regulate catalytic activity and pathway.

LRBmat: A novel gut microbial interaction and individual heterogeneity inference method for colorectal cancer.

The gut microbial community has been shown to play a significant role in various diseases, including colorectal cancer (CRC), which is a major public health concern worldwide. The accurate diagnosis and etiological analysis of CRC are crucial issues. Numerous methods have utilized gut microbiota to address these challenges; however, few have considered the complex interactions and individual heterogeneity of the gut microbiota, which are important issues in genetics and intestinal microbiology, particularly in high-dimensional cases. This paper presents a novel method called Binary matrix based on Logistic Regression (LRBmat) to address these concerns. The binary matrix in LRBmat can directly mitigate or eliminate the influence of heterogeneity, while also capturing information on gut microbial interactions with any order. LRBmat is highly adaptable and can be combined with any machine learning method to enhance its capabilities. The proposed method was evaluated using real CRC data and demonstrated superior classification performance compared to state-of-the-art methods. Furthermore, the association rules extracted from the binary matrix of the real data align well with biological properties and existing literature, thereby aiding in the etiological analysis of CRC.

Automatic block-wise genotype-phenotype association detection based on hidden Markov model.

BACKGROUND: For detecting genotype-phenotype association from case-control single nucleotide polymorphism (SNP) data, one class of methods relies on testing each genomic variant site individually. However, this approach ignores the tendency for associated variant sites to be spatially clustered instead of uniformly distributed along the genome. Therefore, a more recent class of methods looks for blocks of influential variant sites. Unfortunately, existing such methods either assume prior knowledge of the blocks, or rely on ad hoc moving windows. A principled method is needed to automatically detect genomic variant blocks which are associated with the phenotype. RESULTS: In this paper, we introduce an automatic block-wise Genome-Wide Association Study (GWAS) method based on Hidden Markov model. Using case-control SNP data as input, our method detects the number of blocks associated with the phenotype and the locations of the blocks. Correspondingly, the minor allele of each variate site will be classified as having negative influence, no influence or positive influence on the phenotype. We evaluated our method using both datasets simulated from our model and datasets from a block model different from ours, and compared the performance with other methods. These included both simple methods based on the Fisher's exact test, applied site-by-site, as well as more complex methods built into the recent Zoom-Focus Algorithm. Across all simulations, our method consistently outperformed the comparisons. CONCLUSIONS: With its demonstrated better performance, we expect our algorithm for detecting influential variant sites may help find more accurate signals across a wide range of case-control GWAS.

Oxygen Vacancy Promoted O2 Activation over Mesoporous Ni-Co Mixed Oxides for Aromatic Hydrocarbon Oxidation.

Whether the oxygen vacancies of heterogeneous catalysts improve their catalytic activity or not has recently been the topic of intense debate in the oxidation of hydrocarbons. We designed an effective strategy to construct mesoporous Ni-Co mixed oxides via a ligand-assisted self-assembly approach. The surface oxygen vacancy concentrations of the mesoporous Ni-Co mixed oxide catalysts were regulated by changing the doping amount of Ni or the reduction method, and the relationship between oxygen vacancies and catalytic activity was studied. Controlled experiments and DFT calculations revealed that oxygen molecules were more favorably adsorbed and activated on oxygen vacancies to form active oxygen species. Increasing the oxygen vacancy concentration within a certain range can effectively enrich the active oxygen species, therefore improving the oxidation rate of ethylbenzene. The optimized mCo3O4-0.1NiO catalyst exhibited a remarkable catalytic activity for the solvent-free oxidation of ethylbenzene to acetophenone, typically including 68.0% conversion and 95.4% selectivity (20 mg mCo3O4-0.1NiO, 10 mL ethylbenzene, and 0.6 MPa O2).

Decoupling the electronic and geometric effects of Pt catalysts in selective hydrogenation reaction.

Decoupling the electronic and geometric effects has been a long cherished goal for heterogeneous catalysis due to their tangled relationship. Here, a novel orthogonal decomposition method is firstly proposed to settle this issue in p-chloronitrobenzene hydrogenation reaction on size- and shape-controlled Pt nanoparticles (NPs) carried on various supports. Results suggest Fermi levels of catalysts can be modulated by supports with varied work function (Wf). And the selectivity on Pt NPs of similar size and shape is linearly related with the Wf of support. Optimized Fermi levels of the catalysts with large Wf weaken the ability of Pt NPs to fill valence electrons into the antibonding orbital of C-Cl bond, finally suppressing the hydrodehalogenation side reaction. Foremost, the geometric effect is firstly spun off through orthogonal relation based on series of linear relationships over various sizes of Pt NPs reflecting the electronic effect. Moreover, separable nested double coordinate system is established to quantitatively evaluate the two effects.

Fe2 Dimers for Non-Polar Diatomic O2 Electroreduction.

Non-polar diatomic molecule activation is of great significance for catalysis. Despite the high atomic efficiency, the catalytic performance of single-atom catalysts is limited by insufficient receiving sites for diatomic molecule adsorption. Here, Fe2 dimers were successfully synthesized through precisely regulating the metal loading on metal-organic frameworks. The unique role of metal dimers in activating diatomic O2 molecules was explored. In alkaline electrolytes, the specific oxygen reduction reaction activity of Fe2 dimers was 7 times higher than that of Fe1 counterparts. The hydrogen atom transfer probes indicated a different activation mode for O2 on Fe1 and Fe2 dimers, respectively. Theoretical calculation results revealed that Fe2 dimers opened up a new reaction pathway by promoting the direct breaking of O=O bonds, thus avoiding the usual formation of *OOH intermediates, which helped explain the lower H2 O2 yield and higher specific activity.

Strong Oxide-Support Interaction over IrO2 /V2 O5 for Efficient pH-Universal Water Splitting.

Constructing strong oxide-support interaction (SOSI) is compelling for modulating the atomic configurations and electronic structures of supported catalysts. Herein, ultrafine iridium oxide nanoclusters (≈1 nm) are anchored on vanadium oxide support (IrO2 /V2 O5 ) via SOSI. The as made catalyst, with a unique distorted IrO2 structure, is discovered to significantly boost the performance for pH-universal oxygen evolution reaction (OER). Based on experimental results and theoretical calculations, the distorted IrO2 active sites with flexible redox states in IrO2 /V2 O5 server as electrophilic centers balance the adsorption of oxo-intermediates and effectively facilitate the process of OO coupling, eventually propelling the fast turnover of water oxidation. As a result, IrO2 /V2 O5 demonstrates not only ultralow overpotentials at 10 mA cm-2 (266 mV, pH = 0; 329 mV, pH = 7; 283 mV, pH = 14) for OER, but also high-performance overall water electrolysis over a broad pH range, with a potential of mere 1.50 V (pH = 0), 1.65 V (pH = 7) or 1.49 V (pH = 14) at 10 mA cm-2 . In addition, SOSI can simultaneously secure the distorted active sites and thus remarkably improving the catalytic stability, making it a promising strategy to develop high-performance catalytic systems.

A Novel Anti-Noise Fault Diagnosis Approach for Rolling Bearings Based on Convolutional Neural Network Fusing Frequency Domain Feature Matching Algorithm.

The development of deep learning provides a new research method for fault diagnosis. However, in the industrial field, the labeled samples are insufficient and the noise interference is strong so that raw data obtained by the sensor are occupied with noise signal. It is difficult to recognize time-domain fault signals under the severe noise environment. In order to solve these problems, the convolutional neural network (CNN) fusing frequency domain feature matching algorithm (FDFM), called CNN-FDFM, is proposed in this paper. FDFM extracts key frequency features from signals in the frequency domain, which can maintain high accuracy in the case of strong noise and limited samples. CNN automatically extracts features from time-domain signals, and by using dropout to simulate noise input and increasing the size of the first-layer convolutional kernel, the anti-noise ability of the network is improved. Softmax with temperature parameter T and D-S evidence theory are used to fuse the two models. As FDFM and CNN can provide different diagnostic information in frequency domain, and time domain, respectively, the fused model CNN-FDFM achieves higher accuracy under severe noise environment. In the experiment, when a signal-to-noise ratio (SNR) drops to -10 dB, the diagnosis accuracy of CNN-FDFM still reaches 93.33%, higher than CNN's accuracy of 45.43%. Besides, when SNR is greater than -6 dB, the accuracy of CNN-FDFM is higher than 99%.

Application of the improved simple bedside method for emergency temporary pacemaker implantation suitable for primary hospitals.

The purpose of the research was to evaluate the safety and effectiveness of the X-ray-free improved simple bedside method for emergency temporary pacemaker implantation as well as the practicability of the method in primary hospitals. Patients [including those suffering from sick sinus syndrome and third-degree and advanced atrioventricular blockage (AVB)] who needed emergency temporary pacemaker implantation from July 2017 to August 2020 in Hunan Provincial People's Hospital were selected. They were stochastically divided into a research group (95 cases) treated with the improved simple bedside method and a control group (95 cases) with X-ray guidance. The ordinary bipolar electrodes were used in both groups. On this condition, the operation duration, the first-attempt success rate of electrodes, pacing threshold, success rate of the operation, the rate of electrode displacement, and complications in the two groups were separately calculated. The comparison results of the research group with the control group are shown as follows: operation time [(18 ± 5.91) min vs. (43 ± 2.99) min, P < 0.05], the first-attempt success rate of the electrode (97% vs. 98%, P > 0.05), pacing threshold [(0.97 ± 0.35) vs. (0.97 ± 0.32) V, P > 0.05], success rate of the operation (98.9% vs. 100%, P > 0.05), the rate of electrode displacement (8.4% vs. 7.3%, P > 0.05) and complications (3.2% vs. 2.1%, P > 0.05). The emergency temporary pacemaker implantation based on the improved simple bedside method is as safe and effective as the surgical method under X-ray guidance, and the operation is simpler and easier to learn and requires a shorter operating time, therefore, it is more suitable for use in emergency and primary hospitals.

Dynamic Modification of Palladium Catalysts with Chain Alkylamines for the Selective Hydrogenation of Alkynes.

Selective hydrogenation of alkynes plays a pivotal role in the field of chemical production but still suffers from restrained catalytic activity and low alkene selectivity. Herein, a dynamic modification strategy was utilized by preferentially attaching diethylenetriamine (DETA) to the surface of the support to modify the Pd catalyst. The DETA-modified Pd catalyst demonstrates unprecedented reactivity (14,412 h-1) and selectivity as high as 94% for the semihydrogenation of 2-methyl-3-butyn-2-ol at 35 °C, presenting a 36-fold higher reactivity than the Lindlar catalyst. Moreover, the yield exceeds 98.2% at full conversion under no solvent and organic adsorbate conditions, indicating the potential applications for industrial production. Systematic studies reveal that flexible DETA serves in a reversible "breathing pattern" for the molecular discrimination by constructing dynamic metal-support interaction (DMSI), enabling selective exclusion of alkenes from the Pd surface. DETA is competent to dynamically adjust the adsorption behaviors of reactants and effectively boost the intrinsic activity of the modified catalyst. Impressively, the DETA-modified Pd catalyst exhibits exceptional stability even after being recycled 20 times. This work sheds light on a novel and applicable method for the rational design of heterogeneous catalysts via DMSI.

General Synthesis of Ordered Mesoporous Carbonaceous Hybrid Nanostructures with Molecularly Dispersed Polyoxometallates.

Hybrid nanomaterials with controlled dimensions, intriguing components and ordered structures have attracted significant attention in nanoscience and technology. Herein, we report a facile and green polyoxometallate (POM)-assisted hydrothermal carbonization strategy for synthesis of carbonaceous hybrid nanomaterials with molecularly dispersed POMs and ordered mesopores. By using various polyoxometallates such as ammonium phosphomolybdate, silicotungstic acid, and phosphotungstic acid, our approach can be generalized to synthesize ordered mesoporous hybrid nanostructures with diverse compositions and morphologies (nanosheet-assembled hierarchical architectures, nanospheres, and nanorods). Moreover, the ordered mesoporous nanosheet-assembled hierarchical hybrids with molecularly dispersed POMs exhibit remarkable catalytic activity toward the dehydration of tert-butanol with the high isobutene selectivity (100 %) and long-term catalytic durability (80 h).

Discovering Cancer-Related miRNAs from miRNA-Target Interactions by Support Vector Machines.

MicroRNAs (miRNAs) have been shown to be closely related to cancer progression. Traditional methods for discovering cancer-related miRNAs mostly require significant marginal differential expression, but some cancer-related miRNAs may be non-differentially or only weakly differentially expressed. Such miRNAs are called dark matters miRNAs (DM-miRNAs) and are targeted through the Pearson correlation change on miRNA-target interactions (MTIs), but the efficiency of their method heavily relies on restrictive assumptions. In this paper, a novel method was developed to discover DM-miRNAs using support vector machine (SVM) based on not only the miRNA expression data but also the expression of its regulating target. The application of the new method in breast and kidney cancer datasets found, respectively, 9 and 24 potential DM-miRNAs that cannot be detected by previous methods. Eight and 15 of the newly discovered miRNAs have been found to be associated with breast and kidney cancers, respectively, in existing literature. These results indicate that our new method is more effective in discovering cancer-related miRNAs.

Biomass-derived ordered mesoporous carbon nano-ellipsoid encapsulated metal nanoparticles inside: ideal nanoreactors for shape-selective catalysis.

Ordered mesoporous polymers and carbon with a nano-ellipsoid morphology were synthesized by hydrothermal carbonization of biomass derivatives. Their multifunctional features have been demonstrated and the ability of encapsulating metal nanoparticles (NPs) is presented. With the merits of porosity and anchored metal NPs, the hybrid nanocatalyst shows potential for shape-selective hydrogenation.

Selective Electrochemical Reduction of Nitrogen to Ammonia by Adjusting the Three-Phase Interface.

The electrochemical nitrogen reduction reaction (NRR) provides a sustainable and alternative avenue to the Haber-Bosch process for ammonia (NH3) synthesis. Despite the great efforts made on catalysts and electrolytes, unfortunately, current NRR suffers from low selectivity due to the overwhelming competition with the hydrogen evolution reaction (HER). Here, we present an adjusted three-phase interface to enhance nitrogen (N2) coverage on a catalyst surface and achieve a record-high Faradic efficiency (FE) up to 97% in aqueous solution. The almost entirely suppressed HER process combined with the enhanced NRR activity, benefiting from the efficient three-interface contact line, is responsible for the excellent selectivity toward NH3, as evidenced by the theoretical and experimental results. Our strategy also demonstrates the applicability to other catalysts that feature strong H adsorption ability, to boost the FE for NH3 synthesis above 90% and to improve the NRR activity by engineering the catalysts.