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Neuromorphic machines are intriguing for building energy-efficient intelligent systems, where spiking neurons are pivotal components. Recently, memristive neurons with promising bio-plausibility have been developed, but with limited reliability, bulky capacitors or additional reset circuits. Here, we propose an anti-ferroelectric field-effect transistor neuron based on the inherent polarization and depolarization of Hf 0.2 Zr 0.8 O 2 anti-ferroelectric film to meet these challenges. The intrinsic accumulated polarization/spontaneous depolarization of Hf 0.2 Zr 0.8 O 2 films implements the integration/leaky behavior of neurons, avoiding external capacitors and reset circuits. Moreover, the anti-ferroelectric neuron exhibits low energy consumption (37 fJ/spike), high endurance (>10 12 ), high uniformity and high stability. We further construct a two-layer fully ferroelectric spiking neural networks that combines anti-ferroelectric neurons and ferroelectric synapses, achieving 96.8% recognition accuracy on the Modified National Institute of Standards and Technology dataset. This work opens the way to emulate neurons with anti-ferroelectric materials and provides a promising approach to building high-efficient neuromorphic hardware. The scalability of neuromorphic devices depends on the dismissal of capacitors and additional circuits. Here Liu et al. report an artificial neuron based on the polarization and depolarization of an anti-ferroelectric film, avoiding additional elements and reaching 37 fJ/spike of power consumption.

Background Accumulating evidence has hinted at a correlation between immune cells and post-viral fatigue syndrome (PVFS). However, it is still ambiguous whether these associations indicate a causal connection. Objective To elucidate the potential causal link between immune cells and PVFS, we performed a two-sample Mendelian randomization (MR) study. Methods We obtained summary data on PVFS cases (Ncase = 195) and controls (Ncontrol = 382,198) from the FinnGen consortium. Additionally, we retrieved comprehensive statistical information on 731 immune cell features. Our analysis encompassed both forward and reverse MR approaches. To ensure the reliability and validity of our findings, we conducted rigorous sensitivity analyses, addressing issues of robustness and heterogeneity. Result Our study presents compelling evidence of a probable causal link between immune cells and PVFS. Notably, we have pinpointed 28 distinct types of immune cell traits that potentially exhibit a causal association with PVFS. Among a pool of 7 31 immune cell traits, we identified 28 immune cell types that exhibited a potential causal association with PVFS. These included 9 B cells, 1 conventional dendritic cell (cDC), 1 maturation stage of T cell, 3 myeloid cells, 9 T, B, NK, and monocyte cells (TBNK), and 5 regulatory T cells (Treg). Conclusion Through genetic analyses, our study has unveiled profound causal connections between specific types of immune cells and PVFS, offering valuable guidance for forthcoming clinical investigations.

Patients with myocardial infarction (MI) have a high incidence of depression, which deteriorates the cardiac function and increases the risk of cardiovascular events. Shuangxinfang (Psycho-cardiology Formula, PCF) was proved to possess antidepressant and cardioprotective effects post MI. However, the compounds of PCF remain unidentified, and the pertinent mechanism is still not systematic. The purpose of this study is to determine the ingredients of PCF, further to probe the underlying mechanism for MI with depression. The compounds of PCF were qualitatively identified by LC-MS/MS. The optimal dosage for lavage with the PCF solution in rats was determined to be 1 mL/100 g/day for a duration of 5 days. We also detected the PCF components migrating to blood in the control and model rats. Then the targets of PCF compounds were searched on Swiss target database, and the targets of depression and MI were predicted on TTD, OMIM, GeneCards, DrugBank and PharmGkb database. All the targets were intersected to construct the Protein-Protein Interaction (PPI) network on Metascape platform and the herb-compound-target (HCT) network on Cytoscape, to identify the hub targets. GO and KEGG pathway enrichment analysis were conducted on DAVID platform. Molecular docking was modeled on AutoDock Vina software. There were 142 bioactive compounds from PCF acting on 270 targets in a synergistic way. And a total of seven components migrating to blood were identified, including Miltionone I, Neocryptotanshinone, Danshenxinkun A, Ferulic acid, Valerophenone, Vanillic acid and Senkyunolide D. Then SRC and MAPK3 were obtained as the hub proteins by degree value in PPI network, and P2RY12 was picked out as seed proteins ranked by scores from MCODES. Further analysis of biological process and signaling pathways also revealed the significance of ERK/MAPK. Statistical analyses (e.g., GO and KEGG pathway enrichment, PPI network analysis) demonstrated the significance of the identified targets and pathways ( < 0.05). Molecular docking results showed that the binding energies were all less than -5 kcal/mol. The stability of Neocryptotanshinone possessed the lowest binding energy to MAPK3. We identified PCF's bioactive compounds and predicted its therapeutic mechanism for MI with depression using LC-MS/MS and bioinformatics. Key targets SRC, MAPK3, and seed protein P2RY12 were crucial for PCF's cardio-neuroprotective effects. Neocryptotanshinone showed the strongest binding to MAPK3, suggesting it as a pivotal active ingredient. These findings offer new insights and targets for future research on PCF.

Background: Depression is a common complication of cardiovascular disease, which deteriorates cardiac function. Shuangxinfang (psycho-cardiology formula, PCF) was reported to alleviate myocardial ischemia injury and improve depression-like behavior. Interestingly, our previous proteomics study predicted that the protein S100A9 appeared as an important target, and macrophage/microglial inflammation might be involved in the process of PCF improving depression induced by acute myocardial infarction (AMI). This study aims to validate the proteomics results. Methods: AMI rat models were established in vivo , followed by the administration of PCF or ABR-215757 (also named paquinimod, inhibiting S100A9 binding to TLR4) for 5 days. Forced swimming test (FST) and open field test (OFT) were applied to record depression-like behavior, and echocardiography was employed to evaluate cardiac function. Morphological changes of cardiomyocytes were assessed by HE staining and TUNEL staining on day 7 after cardiac surgery, as well as Masson trichrome staining on day 21. Hippocampal neurogenesis was determined by Nissl staining, while 5-hydroxytryptamine (5-HT), tryptophan/kynurenine ratio, and brain-derived neurotrophic factor (BDNF) in the hippocampus were analyzed as biochemical indicators of depression. We employed RT-qPCR, western blotting, and immunofluorescence to detect the expression of pathway-related genes and proteins. Myocardial and hippocampal expression of inflammatory factors were performed by ELISA. The activation of macrophage and microglia was assessed via immunoreaction using CD68 and Iba1, respectively. For in vitro confirmation, BV2 cells were primed with recombinant protein S100A9 and then treated with PCF serum or ferulic acid to determine alterations in microglial inflammation. Results: Rats in the AMI group showed heart function deterioration and depression-like behavior. Coronary ligation not only brought about myocardial inflammation, cell apoptosis, and fibrosis but also reduced the neurogenesis, elevated the tryptophan/kynurenine ratio, and decreased the content of 5-HT. PCF could ameliorate the pathological and phenotypic changes in the heart and brain and inhibit the expression of the S100A9 protein, the activation of the microglial cell, and the secretion of IL-1β and TNF-α raised by AMI. ABR-215757 showed therapeutic effect and molecular biological mechanisms similar to PCF. Treatment with PCF serum or ferulic acid in vitro was proved to efficiently block the hyperactivation of BV2 cells and increment of cytokine contents induced by recombinant protein S100A9. Conclusion: We identify S100A9 as a novel and potent regulator of inflammation in both the heart and brain. Macrophage/microglia inflammation mediated by S100A9 is considered a pivotal pathogenic in depression after AMI and a major pathway for the treatment of PCF, suggesting that PCF is a promising therapeutic candidate for psycho-cardiology disease.

This paper introduces a new evolutionary computing method inspired by the seed transmission process of garden balsam. Garden balsam, a beautiful and attractive flower, randomly ejects the seeds within a certain range by virtue of mechanical force originating from cracking of mature seed pods, which is different from natural expansion of most species of plants. The seeds scattered to suitable growth area will have greater reproductive capacity in the next generation, followed by iteration until the most suitable point for growth in a particular space is eventually found. This phenomenon can more intuitively show the process of searching the problem solution space in the optimization problem. The garden balsam optimization algorithm proposed in this paper incorporates two different types of search processes and has a mechanism to maintain population diversity. Through the optimization experiment on 24 constrained optimization problems, the results obtained by using this algorithm are compared with those of some known meta-heuristic search algorithms. The statistical analysis of the experimental results has been implemented by Friedman rank test and Holm–Sidak test. The comparison results verify the effectiveness of the algorithm.

In this paper, an automatic obstacle avoidance trajectory planning strategy is proposed for the simultaneous motion of multi-robots, which perform anthropomorphic skill operations in a large curved preformed three-dimensional (3D) weaving environment with multiple obstacles and limited space, to eliminate tedious manual calibration work of robot path in engineering. Firstly, an Adaptive Goal-guided Rapidly-exploring Random Trees (AGG-RRT) algorithm is proposed, combined with the robot obstacle avoidance strategy, to search the discrete position of the collision-free path of the end-effector gradually from the starting point to the ending point. Then the optimization of the path is completed by bidirectional pruning of redundant nodes and cubic non-uniform rational B-spline (NURBS) curve fitting. And finally, the robot trajectory is interpolated based on S-shaped acceleration/deceleration planning to ensure smooth robot joint motion. The simulation results demonstrate the superiority of the AGG-RRT algorithm over the basic RRT algorithm and related improved algorithms in terms of search time and success rate. The simulated experiments also achieve the smooth trajectory planning of multiple robotic arms with the synchronous obstacle avoidance motion, which shows that the AGG-RRT algorithm is successfully applied and the collision-free trajectory planning strategy is effective.

Automatic crankshaft production lines require high reliability and accuracy stability for the oscillating grinding machine. Crankshaft contour error represent the most intuitive data in production field selective inspection. If the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine can be found, it would be great significance for the reliability maintenance of the oscillating grinding machine. Firstly, a contour error decomposition method based on ensemble empirical mode decomposition (EEMD) is proposed. Secondly, according to the contour generating principle of the pin journal by oscillating grinding, a calculation method to obtain the effect of the axis position control error of the oscillating grinder on the contour error of the pin journal is proposed. Finally, through the grinding experiments, the error data are acquired and measured to calculate and decompose the contour error by using the proposed methods for obtaining the mapping relation between the crankshaft pin journal contour error and the axis position control error. The conclusions show that the proposed calculation and decomposition methods can obtain the mapping relation between the contour error components of the crankshaft pin journal and the axis position control error of the oscillating grinding machine, which can be used to predict the key functional component performance of the machine tool from the oscillating grinding workpiece contour error.

In response to the growing complexity of modern process manufacturing systems, this paper proposes a novel simulation framework named the Process–Equipment–In-Process State (PEI) simulation method, which introduces a unified and structured approach to modeling multi-stage industrial processes. Unlike conventional simulation approaches that rely on ad hoc or loosely organized modules, the PEI method decomposes the simulation system into three core and interoperable modules: Process Structure (P), Equipment Behavior (E), and In-Process State (I). This modular abstraction facilitates the decoupling of model logic. It also enables a structure-driven simulation execution mechanism. In this structure, the process topology governs task scheduling; equipment models translate control inputs into physical conditions; and state models simulate material evolution accordingly. A complete simulation case involving water mixing, heat exchange, and slurry transformation demonstrates the method’s capability to support traceable state evolution, logical task flow, and extensible model binding. The results demonstrate that the proposed method enables module decoupling, clear simulation pathways, and traceable state changes, providing effective support for structured modeling and behavioral evolution analysis in process manufacturing.

Noise source identification is the first key step to reduce the noise pressure level of the carpet tufting machine. For identifying the main noise sources of the carpet tufting machine, the single channel blind source separation (SCBSS) method is proposed to separate the acquired single channel noise, and the time-frequency signal analysis is applied to identify separated noise components. The SCBSS includes ensemble empirical mode decomposition (EEMD), improved Akaike information criterion (AIC) source number estimation and fast independent component analysis (FastICA). The separation method based on EEMD-AIC-FastICA not only overcomes traditional blind source separation problems that require enough test channel numbers, but also solves the problem that the number of virtual multichannel signals is unknown. Four independent components (ICs) are obtained after using the SCBSS. Combining the time-frequency analysis of the four ICs and the acquired vibration signals of six main components, the specific four noise sources can be identified. The four ICs correspond to the noise of needles, noise of hooks, noise of hook driven shaft, and noise of motor spindle, respectively.

The quality of composite preform has great influence on its mechanical properties. Aiming at the problems of difficulty in robot teaching and unstable braiding angle in the process of braiding three-dimensional complex component, a control method of robot is proposed. Firstly, the mandrel is discretized to ensure that the axis of each discrete mandrel is perpendicular to the braiding point plane, and the orientation and direction of the tool center are calculated. Then, the take-up speed of the robot is calculated, so that the self-adjustment of the braiding angle can be realized in the braiding process. The experimental results show that the control method can effectively reduce the braiding angle error of variable cross-section mandrel within 2°, and can improve the quality of composite products in actual production.