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Wear is an inevitable problem in abrasive belt grinding, and the material removal rate decreases with continuous wear of the abrasive belt. This indicates that the grinding control force is affected by two dynamic factors, namely the actual material removal and abrasive belt wear state. To obtain an accurate force-control model to achieve uniform material removal, a new method for online monitoring of abrasive belt material removal rates and their corresponding wear statuses is proposed herein using only the grinding sound signals. By performing material removal rate and abrasive belt wear experiments, the grinding sound signals during processing are obtained. The wear states of the abrasive belt are quantified using the newly defined gray-mean values of the topographical images of the belt into different levels. The grinding sound signals are quantitatively described via the statistical features of their sound wavelet signals. The statistical features related to material removal rates or belt wear states are selected on the basis of the Pearson correlation coefficients. The prediction models for material removal rate and wear levels of the abrasive based on the selected features are then established using the LightGBM learning algorithm. Experimental datasets are used to train and validate the established model. The test results show that the evaluation parameters of the prediction model of the material removal rate are all within 5%. Further, the accuracy of the wear levels of the abrasive belt can exceed 91%. Compared with other prediction models, the new LightGBM models exhibit superiority in terms of time factor without loss of accuracy of the model. It is thus proved that the proposed method can provide a good basis for monitoring the material removal rate and belt wear in the abrasive belt grinding process.

Increased nitrogen deposition (N factor) and changes in precipitation patterns (W factor) can greatly impact soil microbial communities in tropical/subtropical forests. Although knowledge about the effects of a single factor on soil microbial communities is growing rapidly, little is understood about the interactive effects of these two environmental change factors. In this study, we investigated the responses of soil bacterial and fungal communities to the short-term simulated environmental changes (nitrogen addition, precipitation seasonality change, and their combination) in a subtropical forest in South China. The interaction between N and W factors was detected significant for affecting some soil physicochemical properties (such as pH, soil water, and NO 3 - contents). Fungi were more susceptible to treatment than bacteria in a variety of community traits (alpha, beta diversity, and network topological features). The N and W factors act antagonistically to affect fungal alpha diversity, and the interaction effect was detected significant for the dry season. The topological features of the meta-community (containing both bacteria and fungi) network overrode the alpha and beta diversity of bacterial or fungal communities in explaining the variation of soil enzyme activities. The associations between Ascomycota fungi and Gammaproteobacteria or Alphaproteobacteria might be important in mediating the inter-kingdom interactions. In summary, our results suggested that fungal communities were more sensitive to N and W factors (and their interaction) than bacterial communities, and the treatments’ effects were more prominent in the dry season, which may have great consequences in soil processes and ecosystem functions in subtropical forests.

Accurately predicting the material removal rate (MRR) in belt grinding is challenging because of the randomly distributed multiple cutting edges, flexible contact, and continuous wear of the abrasive grains, undermining the ability to achieve the expected machining requirements for belt grinding using the planned parameters. With the development of sensing technology, big data, and intelligent algorithms, online identification methods for material removal through sensing signals have gained traction. A vision-based material removal monitoring method in the belt grinding process was investigated by adopting the gene expression programming (GEP) algorithm. First, the relationship between the grinding parameters and MRR was investigated through a series of experiments. Second, methods of image shooting distance calibration and automatic image segmentation were established. Furthermore, the definition and quantification method of 11 features related to the color, texture, and energy of spark images are described, based on which the features are extracted. Then, the optimal feature subset was determined by analyzing the fluctuation degree and correlation with MRR by computing the coefficient of variation of the features and Pearson’s coefficient of features and MRR, respectively. Finally, a continuous function model including the selected features was obtained using the GEP method. The predicted results and testing time were compared with those of other methods such as LightGBM, convolutional neural network (CNN), support vector regression (SVR), and BP neural network. The results show that the MRR prediction model based on the GEP algorithm can obtain explicit function expressions and is highly effective in predicting accuracy and test time, which is of utmost significance for accurate and efficient acquisition of MRR data online.

In the abrasive belt grinding process, actual material removal is an important parameter that affects its accuracy. At present, for obtaining the actual material removal, offline measurements are required to establish the mathematical prediction model. To improve the accuracy and efficiency of abrasive belt machine grinding, this paper proposes a novel method for monitoring material removal using multiple sensors and a two-dimensional (2D) convolutional neural network (2D-CNN) learning algorithm. In this method, features of multiple types (color, texture, and shape) are extracted from vision signals, and that of multiple domains (time, frequency, and time–frequency domain) are extracted from sound and tactile signals. These features are constructed into a 2D feature matrix as the input model, and the 2D-CNN prediction model is established between the multisensor features and the material removal rate of the abrasive belt grinding process. An experimental dataset is used to train and verify the established model. The results show that the proposed method can identify that sensor signals are sensitive to the material removal rate. After optimizing and tuning the model parameters, the coefficient of determination of the prediction results is as high as 94.5% and the root mean square error is 0.017. Therefore, the proposed method can be employed for the prediction of material removal rate for different belt specifications and different grinding parameters. Compared to traditional machine learning methods, this method can yield better training results without feature selection and optimization.

Sphere-pipe joints welding is widely used in industrial applications. This paper presents a robot welding approach for the sphere-pipe joints with swing and multi-layer planning. Firstly, various coordinate systems are used to describe the geometric relationship between weld seam and robot welding torch. The sphere-pipe intersecting curve welding process is basically uphill and downhill welding. Therefore, this paper establishes a description model of the welding torch attitude, which parameterizes the attitude description and automatically adjusts the torch attitude during the welding process according to the change of weld inclination angle. To overcome the negative effects of gravity, such as deepening of the molten pool and reduction of the weld width, this paper integrates the swing welding technology into trajectory planning and gives a solving algorithm for the welding torch swing curve. The swing welding also can reduce the number of weld pass. Therefore, multi-layer single-pass swing welding is an economical and efficient way for thicker weldments. In this paper, a multi-layer single-pass swing welding planning algorithm is proposed, which can automatically determine the height and swing amplitude of each welding layer. Finally, the industrial robot Puma560 is used to carry out experimental simulation, and the simulation results are used to verify the feasibility and accuracy of this approach.

Belt grinding is a material processing operation which is capable of producing parts to high dimensional accuracy, excellent finish surface, and surface integrity. Unlike turning or other metal cutting operations that use geometrically well-defined tools, belt grinding involves tool geometry and cutting actions that are not well defined. Therefore, it is quite difficult to obtain a comprehensive theoretical model to predict the grinding depth. As well known, the strain rate is very high in grinding with abrasive tools, which results in substantial heat produced in the shear zone. Sparks are produced when the hot chips thrown out during the process, get oxidized, and burn in the atmosphere. Spark is an inherent feature for most dry grinding process. An approach on material removal rate monitoring in belt grinding by spark field measurement is proposed. The size of the spark field is a visualized reflection of the number of chips instantaneous produced in grinding. Features of spark field related to the material removal rate are analyzed and quantified. With this method, the coupling between the grinding parameters is no need to be considered. Experimental results indicate that the changing of grinding parameters has a different impact on the feature values of the spark field. Feature values of the spark field, such as area, boundary, and density, show a tight correlation with the material removal rate. The resolution accuracy of spark features on the grinding depth is studied. The correct rate of the grinding depth identification can reach more than 95% for the area, and density features of the spark field with the resolution range greater than 10 μm. The analysis indicates that the proposed method is effective and easy-to-accomplish for material removal rate monitoring in belt grinding.

Organ-specific metastasis is the primary cause of cancer patient death. The distant metastasis of tumor cells to specific organs depends on both the intrinsic characteristics of the tumor cells and extrinsic factors in their microenvironment. During an intermediate stage of metastasis, circulating tumor cells (CTCs) are released into the bloodstream from primary and metastatic tumors. CTCs harboring aggressive or metastatic features can extravasate to remote sites for continuous colonizing growth, leading to further lesions. In the past decade, numerous studies demonstrated that CTCs exhibited huge clinical value including predicting distant metastasis, assessing prognosis and monitoring treatment response et al. Furthermore, increasingly numerous experiments are dedicated to identifying the key molecules on or inside CTCs and exploring how they mediate CTC-related organ-specific metastasis. Based on the above molecules, more and more inhibitors are being developed to target CTCs and being utilized to completely clean CTCs, which should provide promising prospects to administer advanced tumor. Recently, the application of various nanomaterials and microfluidic technologies in CTCs enrichment technology has assisted to improve our deep insights into the phenotypic characteristics and biological functions of CTCs as a potential therapy target, which may pave the way for us to make practical clinical strategies. In the present review, we mainly focus on the role of CTCs being involved in targeted organ metastasis, especially the latest molecular mechanism research and clinical intervention strategies related to CTCs.

Anoikis resistance is a prerequisite for the successful development of osteosarcoma (OS) metastases, whether the expression of anoikis-related genes (ARGs) correlates with OS prognosis remains unclear. This study aimed to investigate the feasibility of using ARGs as prognostic tools for the risk stratification of OS. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases provided transcriptome information relevant to OS. The GeneCards database was used to identify ARGs. Differentially expressed ARGs (DEARGs) were identified by overlapping ARGs with common differentially expressed genes (DEGs) between OS and normal samples from the GSE16088, GSE19276, and GSE99671 datasets. Anoikis-related clusters of patients were obtained by consistent clustering, and gene set variation analysis (GSVA) of the different clusters was completed. Next, a risk model was created using Cox regression analyses. Risk scores and clinical features were assessed for independent prognostic values, and a nomogram model was constructed. Subsequently, a functional enrichment analysis of the high- and low-risk groups was performed. In addition, the immunological characteristics of OS samples were compared between the high- and low-risk groups, and their sensitivity to therapeutic agents was explored. Seven DEARGs between OS and normal samples were obtained by intersecting 501 ARGs with 68 common DEGs. and were significantly differentially expressed between both clusters ( <0.05) and were identified as prognosis-related genes. The risk model showed that the risk score and tumor metastasis were independent prognostic factors of patients with OS. A nomogram combining risk score and tumor metastasis effectively predicted the prognosis. In addition, patients in the high-risk group had low immune scores and high tumor purity. The levels of immune cell infiltration, expression of human leukocyte antigen (HLA) genes, immune response gene sets, and immune checkpoints were lower in the high-risk group than those in the low-risk group. The low-risk group was sensitive to the immune checkpoint PD-1 inhibitor, and the high-risk group exhibited lower inhibitory concentration values by 50% for 24 drugs, including AG.014699, AMG.706, and AZD6482. The prognostic stratification framework of patients with OS based on ARGs, such as and , may lead to more efficient clinical management.

The use of artificial intelligence within the healthcare sector is consistently growing. However, the majority of deep learning-based AI systems are of a black box nature, causing these systems to suffer from a lack of transparency and credibility. Due to the widespread adoption of medical imaging for diagnostic purposes, the healthcare industry frequently relies on methods that provide visual explanations, enhancing interpretability. Existing research has summarized and explored the usage of visual explanation methods in the healthcare domain, providing introductions to the methods that have been employed. However, existing reviews are frequently used for interpretable analysis in the medical field ignoring comprehensive reviews on Class Activation Mapping (CAM) methods because researchers typically categorize CAM under the broader umbrella of visual explanations without delving into specific applications in the healthcare sector. Therefore, this study primarily aims to analyze the specific applications of CAM-based deep explainable methods in the healthcare industry, following the PICO (Population, Intervention, Comparison, Outcome) framework. Specifically, we selected 45 articles for systematic review and comparative analysis from three databases—PubMed, Science Direct, and Web of Science—and then compared eight advanced CAM-based methods using five datasets to assist in method selection. Finally, we summarized current hotspots and future challenges in the application of CAM in the healthcare field.