Dynamics simulation-based deep residual neural networks to detect flexible shafting faults

Use of simulation data is necessary for training fault diagnostic models because there is an insufficient amount of fault data available for intricate supercritical flexible shafting. A hybrid dynamic modelling approach combining finite element and lumped mass techniques was used to construct dynamic models of the system in both normal and fault states. The simulation signals corresponding to each state were obtained through numerical calculations and subsequently compared with the existing literature to ensure the accuracy and validity of the dynamic model. By establishing this foundation, dependable training data can be acquired for fault diagnosis within a system. A deep residual neural network with a multi-scale convolution kernel (MSResNet) was used to conduct fault diagnosis of the flexible shafting. The efficacy of the suggested approach was substantiated through an experimental analysis. The outcomes of this research establish a theoretical foundation for fault diagnosis of flexible shafting in scenarios with an insufficient number of fault samples

Allelic Interactions among Pto-MIR475b and Its Four Target Genes Potentially Affect Growth and Wood Properties in Populus

MicroRNAs (miRNAs) play crucial roles in plant growth and development, but few studies have illuminated the allelic interactions among miRNAs and their targets in perennial plants. Here, we combined analysis of expression patterns and single-nucleotide polymorphism (SNP)-based association studies to explore the interactions between Pto-MIR475b and its four target genes (Pto-PPR1, Pto-PPR2, Pto-PPR3, and Pto-PPR4) in 435 unrelated individuals of Populus tomentosa. Expression patterns showed a significant negative correlation (r = -0.447 to -0.411, P < 0.01) between Pto-MIR475b and its four targets in eight tissues of P. tomentosa, suggesting that Pto-miR475b may negatively regulate the four targets. Single SNP-based association studies identified 93 significant associations (P < 0.01, Q < 0.1) representing associations of 80 unique SNPs in Pto-MIR475b and its four targets with nine traits, revealing their potential roles in tree growth and wood formation. Moreover, one common SNP in the precursor region significantly altered the secondary structure of the pre-Pto-miR475b and changed the expression level of Pto-MIR475b. Analysis of epistatic interactions identified 115 significant SNP–SNP associations (P < 0.01) representing 45 unique SNPs from Pto-MIR475b and its four targets for 10 traits, revealing that genetic interactions between Pto-MIR475b and its targets influence quantitative traits of perennial plants. Our study provided a feasible strategy to study population genetics in forest trees and enhanced our understanding of miRNAs by dissecting the allelic interactions between this miRNA and its targets in P. tomentosa

Distributed Beacon Drifting Detection for Localization in Unstable Environments

Localization is a fundamental research issue in wireless sensor networks (WSNs). In most existing localization schemes, several beacons are used to determine the locations of sensor nodes. These localization mechanisms are frequently based on an assumption that the locations of beacons are known. Nevertheless, for many WSN systems deployed in unstable environments, beacons may be moved unexpectedly; that is, beacons are drifting, and their location information will no longer be reliable. As a result, the accuracy of localization will be greatly affected. In this paper, we propose a distributed beacon drifting detection algorithm to locate those accidentally moved beacons. In the proposed algorithm, we designed both beacon self-scoring and beacon-to-beacon negotiation mechanisms to improve detection accuracy while keeping the algorithm lightweight. Experimental results show that the algorithm achieves its designed goals.</jats:p

Hyperleptinemia Contributes to Antipsychotic Drug-Associated Obesity and Metabolic Disorders

Despite their high degree of effectiveness in the management of psychiatric conditions, exposure to antipsychotic drugs, including olanzapine and risperidone, is frequently associated with substantial weight gain and the development of diabetes. Even before weight gain, a rapid rise in circulating leptin concentrations can be observed in most patients taking antipsychotic drugs. To date, the contribution of this hyperleptinemia to weight gain and metabolic deterioration has not been defined. Here, with an established mouse model that recapitulates antipsychotic drug-induced obesity and insulin resistance, we not only confirm that hyperleptinemia occurs before weight gain but also demonstrate that hyperleptinemia contributes directly to the development of obesity and associated metabolic disorders. By suppressing the rise in leptin through the use of a monoclonal leptin-neutralizing antibody, we effectively prevented weight gain, restored glucose tolerance, and preserved adipose tissue and liver function in antipsychotic drug-treated mice. Mechanistically, suppressing excess leptin resolved local tissue and systemic inflammation typically associated with antipsychotic drug treatment. We conclude that hyperleptinemia is a key contributor to antipsychotic drug-associated weight gain and metabolic deterioration. Leptin suppression may be an effective approach to reducing the undesirable side effects of antipsychotic drugs