TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis

Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures. As intelligent manufacturing and data-driven approaches evolve, Deep Learning (DL) has emerged as a pivotal technique in fault diagnosis research, recognized for its ability to autonomously extract complex features. However, the practical application of current fault diagnosis methods is challenged by the complexity of industrial environments. This paper proposed the Temporal Denoise Convolutional Neural Network With Attention (TDANet), designed to improve fault diagnosis performance in noise environments. This model transforms one-dimensional signals into two-dimensional tensors based on their periodic properties, employing multi-scale 2D convolution kernels to extract signal information both within and across periods. This method enables effective identification of signal characteristics that vary over multiple time scales. The TDANet incorporates a Temporal Variable Denoise (TVD) module with residual connections and a Multi-head Attention Fusion (MAF) module, enhancing the saliency of information within noisy data and maintaining effective fault diagnosis performance. Evaluation on two datasets, CWRU (single sensor) and Real aircraft sensor fault (multiple sensors), demonstrates that the TDANet model significantly outperforms existing deep learning approaches in terms of diagnostic accuracy under noisy environments

Expression of ethylene biosynthetic and receptor genes in rose floral tissues during ethylene-enhanced flower opening

Ethylene production, as well as the expression of ethylene biosynthetic (Rh-ACS1–4 and Rh-ACO1) and receptor (Rh-ETR1–5) genes, was determined in five different floral tissues (sepals, petals, stamens, gynoecia, and receptacles) of cut rose (Rosa hybrida cv. Samantha upon treatment with ethylene or the ethylene inhibitor 1-methylcyclopropene (1-MCP). Ethylene-enhanced ethylene production occurred only in gynoecia, petals, and receptacles, with gynoecia showing the greatest enhancement in the early stage of ethylene treatment. However, 1-MCP did not suppress ethylene production in these three tissues. In sepals, ethylene production was highly decreased by ethylene treatment, and increased dramatically by 1-MCP. Ethylene production in stamens remained unchanged after ethylene or 1-MCP treatment. Induction of certain ethylene biosynthetic genes by ethylene in different floral tissues was positively correlated with the ethylene production, and this induction was also not suppressed by 1-MCP. The expression of Rh-ACS2 and Rh-ACS3 was quickly induced by ethylene in gynoecia, but neither Rh-ACS1 nor Rh-ACS4 was induced by ethylene in any of the five tissues. In addition, Rh-ACO1 was induced by ethylene in all floral tissues except sepals. The induced expression of ethylene receptor genes by ethylene was much faster in gynoecia than in petals, and the expression of Rh-ETR3 was strongly suppressed by 1-MCP in all floral tissues. These results indicate that ethylene biosynthesis in gynoecia is regulated developmentally, rather than autocatalytically. The response of rose flowers to ethylene occurs initially in gynoecia, and ethylene may regulate flower opening mainly through the Rh-ETR3 gene in gynoecia

Burden of child maltreatment in China:A systematic review

Objective To estimate the health and economic burdens of child maltreatment in China. Methods We did a systematic review for studies on child maltreatment in China using PubMed, Embase, PsycInfo, CINAHL-EBSCO, ERIC and the Chinese National Knowledge Infrastructure databases. We did meta-analyses of studies that met inclusion criteria to estimate the prevalence of child neglect and child physical, emotional and sexual abuse. We used data from the 2010 global burden of disease estimates to calculate disability-adjusted life-years (DALYs) lost as a result of child maltreatment. Findings From 68 studies we estimated that 26.6% of children under 18 years of age have suffered physical abuse, 19.6% emotional abuse, 8.7% sexual abuse and 26.0% neglect. We estimate that emotional abuse in childhood accounts for 26.3% of the DALYs lost because of mental disorders and 18.0% of those lost because of self-harm. Physical abuse in childhood accounts for 12.2% of DALYs lost because of depression, 17.0% of those lost to anxiety, 20.7% of those lost to problem drinking, 18.8% of those lost to illicit drug use and 18.3% of those lost to self-harm. The consequences of physical abuse of children costs China an estimated 0.84% of its gross domestic product – i.e. 50 billion United States dollars – in 2010. The corresponding losses attributable to emotional and sexual abuse in childhood were 0.47% and 0.39% of the gross domestic product, respectively. Conclusion In China, child maltreatment is common and associated with large economic losses because many maltreated children suffer substantial psychological distress and might adopt behaviours that increase their risk of chronic disease

Effect of the physical aging on the secondary

Dynamic mechanical relaxation processes, i.e., main (α) relaxation and secondary (β) relaxation, are important issues to understand mechanical deformation, atomic diffusion as well as glass transition phenomenon of metallic glasses. In current work, La68Ni15Al15Cu2 metallic glass was selected as a protocol glass system. Mechanical relaxation processes were probed by dynamic mechanical analysis. The effects of annealing at different temperatures were analyzed by Kohlrausch–Williams–Watts (KWW)-type equation. The Kohlrausch exponent βKWW reflects the deviation from a single Debye relaxation, indicating the fact that dynamics in metallic glass are actually heterogeneous originating from the structural heterogeneity. The effects of thermal treatments were also discussed, which provides a potential solution to tune the relaxation behaviors in metallic glasses

Lightweight Architecture for Elliptic Curve Scalar Multiplication over Prime Field

In this paper, we present a novel lightweight elliptic curve scalar multiplication architecture for random Weierstrass curves over prime field Fp. The elliptic curve scalar multiplication is executed in Jacobian coordinates based on the Montgomery ladder algorithm with (X,Y)-only common Z coordinate arithmetic. At the finite field operation level, the adder-based modular multiplier and modular divider are optimized by the pre-calculation method to reduce the critical path while maintaining low resource consumption. At the group operation level, the point addition and point doubling methods in (X,Y)-only common Z coordinate arithmetic are modified to improve computation parallelism. A compact scheduling method is presented to improve the architecture’s performance, which includes appropriate scheduling of finite field operations and specific register connections. Compared with existing works, our design is implemented on the FPGA platform without using DSPs or BRAMs for higher portability. It utilizes 6.4~6.5k slices in Kintex-7, Virtex-7, and ZYNQ FPGA and executes an elliptic curve scalar multiplication for a field size of 256-bit in 1.73 ms, 1.70 ms, and 1.80 ms, respectively. Additionally, our design is resistant to timing attacks, simple power analysis attacks, and safe-error attacks. This architecture outperforms most state-of-the-art lightweight designs in terms of area-time products