Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. However, while considerable research has been conducted in industrial and academic ...settings, the complexity of milling processes continues to complicate the implementation of TCM. This paper presents a review of the state-of-the-art methods employed for conducting TCM in milling processes. The review includes three key components: (1) sensors, (2) feature extraction, and (3) monitoring models for the categorization of cutting tool states in the decision-making process. In addition, the primary strengths and weaknesses of current practices are presented for these three components. Finally, this paper concludes with a list of recommendations for future research.
Tool fault diagnosis in numerical control (NC) machines plays a significant role in ensuring manufacturing quality. Tool condition monitoring (TCM) based on multisensors can provide more information ...related to tool condition, but it can also increase the risk that effective information is overwhelmed by redundant information. Thus, the method of obtaining the most effective feature information from multisensor signals is currently a hot topic. However, most of the current feature selection methods take into account the correlation between the feature parameters and the tool state and do not analyze the influence of feature parameters on prediction accuracy. In this paper, a multisensor global feature extraction method for TCM in the milling process is researched. Several statistical parameters in the time, frequency, and time⁻frequency (Wavelet packet transform) domains of multiple sensors are selected as an alternative parameter set. The monitoring model is executed by a Kernel-based extreme learning Machine (KELM), and a modified genetic algorithm (GA) is applied in order to search the optimal parameter combinations in a two-objective optimization model to achieve the highest prediction precision. The experimental results show that the proposed method outperforms the Pearson's correlation coefficient (PCC) based, minimal redundancy and maximal relevance (mRMR) based, and Principal component analysis (PCA)-based feature selection methods.
Both electroencephalography and functional magnetic resonance imaging studies have revealed enhanced neural responses to perceived pain in same-race than other-race individuals. However, it remains ...unclear how neural responses in the sensorimotor, cognitive, and affective subsystems vary dynamically in the first few hundreds of milliseconds to generate racial ingroup favoritism in empathy for pain. We recorded magnetoencephalography signals to pain and neutral expressions of Asian and white faces from Chinese adults during judgments of racial identity of each face. We found that pain compared to neutral expressions of same-race faces induced early increased alpha oscillations in the precuneus/parietal cortices followed by increased alpha-band oscillations in the left anterior insula and temporoparietal junction. Pain compared to neutral expressions of other-race faces, however, induced early suppression of alpha-band oscillations in the bilateral sensorimotor cortices and left insular cortex. Moreover, decreased functional connectivity between the left sensorimotor cortex and left anterior insula predicted reduced subjective feelings of other-race suffering. Our results unraveled distinct patterns of modulations of neural dynamics of sensorimotor, affective, and cognitive components of empathy by interracial relationships between an observer and a target person, which provide possible brain mechanisms for understanding racial ingroup favoritism in social behavior.
Accurate tool condition monitoring (TCM) is essential for the development of fully automated milling processes. This is typically accomplished using indirect TCM methods that synthesize the ...information collected from one or more sensors to estimate tool condition based on machine learning approaches. Among the many sensor types available for conducting TCM, motor current sensors offer numerous advantages, in that they are inexpensive, easily installed, and have no effect on the milling process. Accordingly, this study proposes a new TCM method employing a few appropriate current sensor signal features based on the time, frequency, and time - frequency domains of the signals and an advanced monitoring model based on an improved kernel extreme learning machine (KELM). The selected multi-domain features are strongly correlated with tool wear condition and overcome the loss of useful information related to tool condition when employing a single domain. The improved KELM employs a two-layer network structure and an angle kernel function that includes no hyperparameter, which overcome the drawbacks of KELM in terms of the difficulty of learning the features of complex nonlinear data and avoiding the need for preselecting the kernel function and its hyperparameter. The performance of the proposed method is verified by its application to the benchmark NASA milling dataset and separate TCM experiments in comparison with existing TCM methods. The results indicate that the proposed TCM method achieves excellent monitoring performance using only a few key signal features of current sensors.
Sepsis is a systemic inflammatory response syndrome resulting from the invasion of the human body by bacteria and other pathogenic microorganisms. One of its most prevalent complications is acute ...lung injury, which places a significant medical burden on numerous countries and regions due to its high morbidity and mortality rates. MicroRNA (miRNA) plays a critical role in the body's inflammatory response and immune regulation. Recent studies have focused on miR-21-5p in the context of acute lung injury, but its role appears to vary in different models of this condition. In the LPS-induced acute injury model of A549 cells, there is differential expression, but the specific mechanism remains unclear. Therefore, our aim is to investigate the changes in the expression of miR-21-5p and SLC16A10 in a type II alveolar epithelial cell injury model induced by LPS and explore the therapeutic effects of their targeted regulation. A549 cells were directly stimulated with 10 µg/ml of LPS to construct a model of LPS-induced cell injury. Cells were collected at different time points and the expression of interleukin 1 beta (IL-1β), tumor necrosis factor-α (TNF-α) and miR-21-5p were measured by RT-qPCR and western blot. Then miR-21-5p mimic transfection was used to up-regulate the expression of miR-21-5p in A549 cells and the expression of IL-1β and TNF-α in each group of cells was measured by RT-qPCR and western blot. The miRDB, TargetScan, miRWalk, Starbase, Tarbase and miR Tarbase databases were used to predict the miR-21-5p target genes and simultaneously, the DisGeNet database was used to search the sepsis-related gene groups. The intersection of the two groups was taken as the core gene. Luciferase reporter assay further verified SLC16A10 as the core gene with miR-21-5p. The expression of miR-21-5p and SLC16A10 were regulated by transfection or inhibitors in A549 cells with or without LPS stimulation. And then the expression of IL-1β and TNF-α in A549 cells was tested by RT-qPCR and western blot in different groups, clarifying the role of miR-21-5p-SLC16A10 axis in LPS-induced inflammatory injury in A549 cells. (1) IL-1β and TNF-α mRNA and protein expression significantly increased at 6, 12, and 24 h after LPS stimulation as well as the miR-21-5p expression compared with the control group (P < 0.05). (2) After overexpression of miR-21-5p in A549 cells, the expression of IL-1β and TNF-α was significantly reduced after LPS stimulation, suggesting that miR-21-5p has a protection against LPS-induced injury. (3) The core gene set, comprising 51 target genes of miR-21-5p intersecting with the 1448 sepsis-related genes, was identified. This set includes SLC16A10, TNPO1, STAT3, PIK3R1, and FASLG. Following a literature review, SLC16A10 was selected as the ultimate target gene. Dual luciferase assay results confirmed that SLC16A10 is indeed a target gene of miR-21-5p. (4) Knocking down SLC16A10 expression by siRNA significantly reduced the expression of IL-1β and TNF-α in A549 cells after LPS treatment (P < 0.05). (5) miR-21-5p inhibitor increased the expression levels of IL-1β and TNF-α in A549 cells after LPS stimulation (P < 0.05). In comparison to cells solely transfected with miR-21-5p inhibitor, co-transfection of miR-21-5p inhibitor and si-SLC6A10 significantly reduced the expression of IL-1β and TNF-α (P < 0.05). MiR-21-5p plays a protective role in LPS-induced acute inflammatory injury of A549 cells. By targeting SLC16A10, it effectively mitigates the inflammatory response in A549 cells induced by LPS. Furthermore, SLC16A10 holds promise as a potential target for the treatment of acute lung injury.
Microchannel reactors are critical in biological plus energy-related applications and require meticulous design of hundreds-to-thousands of fluid flow channels. Such systems commonly comprise ...intricate space-filling microstructures to control the fluid flow distribution for the reaction process. Traditional flow channel design schemes are intuition-based or utilize analytical rule-based optimization strategies that are oversimplified for large-scale domains of arbitrary geometry. Here, a gradient-based optimization method is proposed, where effective porous media and fluid velocity vector design information is exploited and linked to explicit microchannel parameterizations. Reaction-diffusion equations are then utilized to generate space-filling Turing pattern microchannel flow structures from the porous media field. With this computationally efficient and broadly applicable technique, precise control of fluid flow distribution is demonstrated across large numbers (on the order of hundreds) of microchannels.
Microreactors are widely used in energy storage and conversion systems, where fluid flow fields play a significant role in reaction-fluid performance. While conventional and bio-inspired flow fields ...have been investigated in literature, they are often limited to a forward design framework. Forward-designed flow fields depend on an initial layout selection with limited design freedom. In this paper, an inverse design and dehomogenization framework is proposed to discover innovative microreactor flow field designs. A gradient-based method is applied to optimize the spatially varying orientation of anisotropic porous media. A dehomogenization method is adopted to recover the optimized porous media performance by way of intricate microchannel structures in a post-processing step. Through a numerical example, we find trade-offs between the reaction performance and fluid flow performance for multiple optimized microreactor flow fields. Parallel flow fields are confirmed as low pressure drop designs. Branching flow fields with primary and secondary flow paths are discovered for enhanced reaction performance. Optimized designs with balanced reaction-fluid performance can also be obtained with input design requirements. The three-dimensional (3D) performance of each flow field design is verified by additional numerical simulation. The optimized flow fields are shown to outperform conventional parallel and serpentine benchmark designs. The findings of the paper will be useful in novel reactor flow field designs for enhanced performance in biomedical, pharmaceutical, and energy applications.
•An inverse design and dehomogenization method for discovering novel microreactor flow fields.•Spatially varying orientation optimization to design the optimal anisotropic porous media.•Bio-inspired Turing pattern dehomogenization to generate microchannels.•The optimized flow fields exhibit geometric patterns often seen in naturally occurring systems.•The optimized flow fields outperform conventional parallel and serpentine benchmark designs.
Summary Background The model of infectious disease prevention and control changed significantly in China after the outbreak in 2003 of severe acute respiratory syndrome (SARS), but trends and ...epidemiological features of infectious diseases are rarely studied. In this study, we aimed to assess specific incidence and mortality trends of 45 notifiable infectious diseases from 2004 to 2013 in China and to investigate the overall effectiveness of current prevention and control strategies. Methods Incidence and mortality data for 45 notifiable infectious diseases were extracted from a WChinese public health science data centre from 2004 to 2013, which covers 31 provinces in mainland China. We estimated the annual percentage change in incidence of each infectious disease using joinpoint regression. Findings Between January, 2004, and December, 2013, 54 984 661 cases of 45 infectious diseases were reported (average yearly incidence 417·98 per 100 000). The infectious diseases with the highest yearly incidence were hand, foot, and mouth disease (114·48 per 100 000), hepatitis B (81·57 per 100 000), and tuberculosis (80·33 per 100 000). 132 681 deaths were reported among the 54 984 661 cases (average yearly mortality 1·01 deaths per 100 000; average case fatality 2·4 per 1000). Overall yearly incidence of infectious disease was higher among males than females and was highest among children younger than 10 years. Overall yearly mortality was higher among males than females older than 20 years and highest among individuals older than 80 years. Average yearly incidence rose from 300·54 per 100 000 in 2004 to 483·63 per 100 000 in 2013 (annual percentage change 5·9%); hydatid disease (echinococcosis), hepatitis C, and syphilis showed the fastest growth. The overall increasing trend changed after 2009, and the annual percentage change in incidence of infectious disease in 2009–13 (2·3%) was significantly lower than in 2004–08 (6·2%). Interpretation Although the overall incidence of infectious diseases was increasing from 2004, the rate levelled off after 2009. Effective prevention and control strategies are needed for diseases with the highest incidence—including hand, foot, and mouth disease, hepatitis B, and tuberculosis—and those with the fastest rates of increase (including hydatid disease, hepatitis C, and syphilis). Funding Chinese Ministry of Science and Technology, National Natural Science Foundation (China).
Inflammatory bowel disease (IBD) is a global disease that is in increasing incidence. The gut, which contains the largest amount of lymphoid tissue in the human body, as well as a wide range of ...nervous system components, is integral in ensuring intestinal homeostasis and function. By interacting with gut microbiota, immune cells, and the enteric nervous system, the intestinal barrier, which is a solid barrier, protects the intestinal tract from the external environment, thereby maintaining homeostasis throughout the body. Destruction of the intestinal barrier is referred to as developing a "leaky gut," which causes a series of changes relating to the occurrence of IBD. Changes in the interactions between the intestinal barrier and gut microbiota are particularly crucial in the development of IBD. Exploring the leaky gut and its interaction with the gut microbiota, immune cells, and the neuroimmune system may help further explain the pathogenesis of IBD and provide potential therapeutic methods for future use.
Bearing, an importunate component of any rotary machinery, is jeopardized to its failure during its operation in tough working conditions. The condition monitoring of bearing, to avoid its unforeseen ...failure, is important for its smooth working. Bearing damage assessment is mostly done by selecting features from the vibration signals, which is usually, a time consuming process. Consequently, it becomes importunate for us to achieve full automation for the safety purpose and reduction in the maintenance cost of the machinery. Towards this omnifarious effort, a wavelet transformed based Deep Convolutional Neural Network (DCNN) is proposed for the automatic identification of defective components and damage assessment of bearing, which is achieved by, firstly, processing vibration signals using continuous wavelet transform to form 2D grey scale images of time-frequency representation. Secondly, DCNN is trained using images for learning of defects severity. Through convolution and pooling operation layers, high level features are automatically extracted from images itself. Thereafter, trained 2D grey images are applied to DCNN so that defect severity assessment can be accurately carried out. The overall accuracy achieved using the proposed method is 100%.
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