Fault diagnosis method for energy storage mechanism of high voltage circuit breaker based on CNN characteristic matrix constructed by sound-vibration signal

Aiming at the problem that some traditional high voltage circuit breaker fault diagnosis methods were over-dependent on subjective experience, the accuracy was not very high and the generalization ability was poor, a fault diagnosis method for energy storage mechanism of high voltage circuit breaker, which based on Convolutional Neural Network (CNN) characteristic matrix constructed by sound-vibration signal ,was proposed. In this paper, firstly, the morphological filtering was used for background noise cancellation of sound signal, and the time scale alignment method based on kurtosis and envelope similarity were proposed to ensure the synchronism of the sound-vibration signal. Secondly, the Pearson correlation coefficient was used to construct two-dimensional image characteristic matrix for the expanded sound-vibration signal. Finally, the characteristic matrix was trained by utilizing CNN. Local Response Normalization (LRN) and core function decorrelation were utilized to improve the structure of CNN model, which reduced the bad impact of large data fluctuation of energy storage process on the diagnostic accuracy of circuit breaker energy storage mechanism. Compared with the traditional method, the proposed method has obvious advantages, whose total accurate rate up to 98.2 % and generalization performance is excellent

Fault Diagnosis of Oil Pumping Machine Retarder Based on Sound Texture-Vibration Entropy Characteristics and Gray Wolf Optimization-Support Vector Machine

In order to diagnose the retarder faults of oil pumping machine accurately in complex environments and improve the generalization of the algorithm, a GWO-SVM fault diagnosis algorithm based on the combination of sound texture and vibration entropy characteristics was proposed. Firstly, the acquired sound signal was purified by band-pass filter, then generalized S-transform was developed to extract the box dimension, directivity, and contrast ratio, which reflect the characteristics of time-frequency spectrum, to construct three-dimensional texture eigenvectors. Secondly, the K parameter of variational mode decomposition (VMD) was reasonably selected by the energy method, and then the vibration signal was decomposed to get modal components, and the permutation entropy was obtained from modal components. Finally, joint eigenvectors were constructed and fed into SVM for learning. The gray wolf optimization (GWO) algorithm was used to optimize the parameters of the SVM model based on mixed kernel function, which reduces the impact of sensor frequency response, environmental noise, and load fluctuation disturbance on the accuracy of retarder fault diagnosis. The results showed that the GWO-SVM fault diagnosis method, which is based on the combination of sound texture and vibration entropy characteristics, makes full use of the complementary advantages of signal frequency band. And the overall diagnostic accuracy for the experimental samples reaches 100%, which has good generalization ability

Genome-wide identification of CAMTA gene family members in Medicago truncatula and their expression during root nodule symbiosis and hormone treatments

Calmodulin-binding transcription activators (CAMTAs) are well-characterized calmodulin-binding transcription factors in the plant kingdom. Previous work shows that CAMTAs play important roles in various biological processes including disease resistance, herbivore attack response and abiotic stress tolerance. However, studies that address the function of CAMTAs during the establishment of symbiosis between legumes and rhizobia are still lacking. This study undertook comprehensive identification and analysis of CAMTA genes using the latest updated M. truncatula genome. All the MtCAMTA genes were expressed in a tissues-specific manner and were responsive to environmental stress-related hormones. The expression profiling of MtCAMTA genes during the early phase of Sinorhizobium meliloti infection was also analyzed. Our data showed that the expression of most MtCAMTA genes was suppressed in roots by S. meliloti infection. The responsiveness of MtCAMTAs to S. meliloti infection indicated that they may function as calcium-regulated transcription factors in the early nodulation signaling pathway. In addition, bioinformatics analysis showed that CAMTA binding sites existed in the promoter regions of various early rhizobial infection response genes, suggesting possible MtCAMTAs-regulated downstream candidate genes during the early phase of S. meliloti infection. Taken together, these results provide basic information about MtCAMTAs in the model legume M. truncatula, and the involvement of MtCAMTAs in nodule organogenesis. This information furthers our understanding of MtCAMTA protein functions in M. truncatula and opens new avenues for continued research

Effects of an Individualized mHealth-Based Intervention on Health Behavior Change and Cardiovascular Risk Among People With Metabolic Syndrome Based on the Behavior Change Wheel: Quasi-Experimental Study

BackgroundMetabolic syndrome (MetS) is a common public health challenge. Health-promoting behaviors such as diet and physical activity are central to preventing and controlling MetS. However, the adoption of diet and physical activity behaviors has always been challenging. An individualized mobile health (mHealth)–based intervention using the Behavior Change Wheel is promising in promoting health behavior change and reducing atherosclerotic cardiovascular disease (ASCVD) risk. However, the effects of this intervention are not well understood among people with MetS in mainland China. ObjectiveWe aimed to evaluate the effects of the individualized mHealth-based intervention using the Behavior Change Wheel on behavior change and ASCVD risk in people with MetS. MethodsWe conducted a quasi-experimental, nonrandomized study. Individuals with MetS were recruited from the health promotion center of a tertiary hospital in Zhejiang province, China. The study involved 138 adults with MetS, comprising a control group of 69 participants and an intervention group of 69 participants. All participants received health education regarding diet and physical activity. The intervention group additionally received a 12-week individualized intervention through a WeChat mini program and a telephone follow-up in the sixth week of the intervention. Primary outcomes included diet, physical activity behaviors, and ASCVD risk. Secondary outcomes included diet self-efficacy, physical activity self-efficacy, knowledge of MetS, quality of life, and the quality and efficiency of health management services. The Mann-Whitney U test and Wilcoxon signed rank test were primarily used for data analysis. Data analysis was conducted based on the intention-to-treat principle using SPSS (version 25.0; IBM Corp). ResultsBaseline characteristics did not differ between the 2 groups. Compared with the control group, participants in the intervention group showed statistically significant improvements in diet behavior, physical activity behavior, diet self-efficacy, physical activity self-efficacy, knowledge of MetS, physical health, and mental health after a 12-week intervention (P=.04, P=.001, P=.04, P=.04, P=.001, P=.04, P=.04, and P<.05). The intervention group demonstrated a statistically significant improvement in outcomes from pre- to postintervention evaluations (P<.001, P=.03, P<.001, P=.04, P<.001, P<.001, and P<.001). The intervention also led to enhanced health management services and quality. ConclusionsThe individualized mHealth-based intervention using the Behavior Change Wheel was effective in promoting diet and physical activity behaviors in patients with MetS. Nurses and other health care professionals may incorporate the intervention into their health promotion programs

Development of an individualized WeChat mini program-based intervention to increase adherence to dietary recommendations applying the behaviour change wheel among individuals with metabolic syndrome

AbstractBackground Metabolic Syndrome (MetS) is a serious public health issue. Dietary changes form the core of MetS treatment. The adherence to dietary recommendations is critical for reducing the severity of MetS components and preventing complications. However, the adherence to dietary recommendations was not adequate among adults with MetS. This study utilizes the Behaviour Change Wheel (BCW) to develop an individualized WeChat mini program-based behavioural change intervention aimed at strengthening adherence to dietary recommendations in people with MetS.Methods The BCW theory was used to design an individualized WeChat mini program-based behavioural change intervention. A descriptive qualitative study was conducted to identify the determinants of adherence to dietary recommendations in individuals with MetS. The study was conducted at the health promotion centre of a prominent general university hospital in Zhejiang, China. Subsequently, the intervention functions (IFs) and policy categories were selected following the identified determinants. Afterwards, behaviour change techniques (BCTs) were chosen to translate into potential intervention strategies, and the delivery mode was determined.Results Our study identified fifteen barriers to improve the adherence to dietary recommendations in this population. These were linked with six IFs: education, training, persuasion, enablement, modelling, and environmental restructuring. Then, twelve BCTs were linked with the IFs and fifteen barriers. The delivery mode was a WeChat mini program. After these actions, an individualized WeChat mini program-based behavioural change intervention was developed to enhance adherence to dietary recommendations for individuals with MetS.Conclusions The BCW theory helped scientifically and systematically develop an individualized WeChat mini program-based behavioural change intervention for individuals with MetS. In the future, our research team will refine and upgrade the WeChat mini program and then test the usability and effectiveness of the individualized WeChat mini program-based behavioural change intervention program

Comparative Metabolomics in <i>Glycine max</i> and <i>Glycine soja</i> under Salt Stress To Reveal the Phenotypes of Their Offspring

Metabolomics is developing as an important functional genomics tool for understanding plant systems’ response to genetic and environmental changes. Here, we characterized the metabolic changes of cultivated soybean C08 (<i>Glycine max</i> L. Merr) and wild soybean W05 (<i>Glycine soja</i> Sieb.et Zucc.) under salt stress using MS-based metabolomics, in order to reveal the phenotypes of their eight hybrid offspring (9H0086, 9H0124, 9H0391, 9H0736, 9H0380, 9H0400, 9H0434, and 9H0590). Total small molecule extracts of soybean seedling leaves were profiled by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–Fourier transform mass spectrometry (LC–FT/MS). We found that wild soybean contained higher amounts of disaccharides, sugar alcohols, and acetylated amino acids than cultivated soybean, but with lower amounts of monosaccharides, carboxylic acids, and unsaturated fatty acids. Further investigations demonstrated that the ability of soybean to tolerate salt was mainly based on synthesis of compatible solutes, induction of reactive oxygen species (ROS) scavengers, cell membrane modifications, and induction of plant hormones. On the basis of metabolic phenotype, the salt-tolerance abilities of 9H0086, 9H0124, 9H0391, 9H0736, 9H0380, 9H0400, 9H0434, and 9H0590 were discriminated. Our results demonstrated that MS-based metabolomics provides a fast and powerful approach to discriminate the salt-tolerance characteristics of soybeans

Comparative Metabolomics in <i>Glycine max</i> and <i>Glycine soja</i> under Salt Stress To Reveal the Phenotypes of Their Offspring

Metabolomics is developing as an important functional genomics tool for understanding plant systems’ response to genetic and environmental changes. Here, we characterized the metabolic changes of cultivated soybean C08 (<i>Glycine max</i> L. Merr) and wild soybean W05 (<i>Glycine soja</i> Sieb.et Zucc.) under salt stress using MS-based metabolomics, in order to reveal the phenotypes of their eight hybrid offspring (9H0086, 9H0124, 9H0391, 9H0736, 9H0380, 9H0400, 9H0434, and 9H0590). Total small molecule extracts of soybean seedling leaves were profiled by gas chromatography–mass spectrometry (GC–MS) and liquid chromatography–Fourier transform mass spectrometry (LC–FT/MS). We found that wild soybean contained higher amounts of disaccharides, sugar alcohols, and acetylated amino acids than cultivated soybean, but with lower amounts of monosaccharides, carboxylic acids, and unsaturated fatty acids. Further investigations demonstrated that the ability of soybean to tolerate salt was mainly based on synthesis of compatible solutes, induction of reactive oxygen species (ROS) scavengers, cell membrane modifications, and induction of plant hormones. On the basis of metabolic phenotype, the salt-tolerance abilities of 9H0086, 9H0124, 9H0391, 9H0736, 9H0380, 9H0400, 9H0434, and 9H0590 were discriminated. Our results demonstrated that MS-based metabolomics provides a fast and powerful approach to discriminate the salt-tolerance characteristics of soybeans