Analysis of temperature field for a surface-mounted and interior permanent magnet synchronous motor adopting magnetic-thermal coupling method

Aiming at obtaining high power density of surface-mounted and interior permanent magnet synchronous motor (SIPMSM), it is important to accurately calculate the temperature field distribution of SIPMSM, and a magnetic-thermal coupling method is proposed. The magnetic-thermal coupling mechanism is analyzed. The thermal network model and finite element model are built by this method, respectively. The effects of power frequency on iron losses and temperature fields are analyzed by the magnetic-thermal coupling finite element model under the condition of rated load, and the relationship between the load and temperature field is researched under the condition of the synchronous speed. In addition, the equivalent thermal network model is used to verify the magnetic-thermal coupling method. Then the temperatures of various nodes are obtained. The results show that there are advantages in both computational efficiency and accuracy for the proposed coupling method, which can be applied to other permanent magnet motors with complex structures

Curcumin Enhances Neurogenesis and Cognition in Aged Rats: Implications for Transcriptional Interactions Related to Growth and Synaptic Plasticity

Background: Curcumin has been demonstrated to have many neuroprotective properties, including improvement of cognition in humans and neurogenesis in animals, yet the mechanism of such effects remains unclear. Methodology: We assessed behavioural performance and hippocampal cell proliferation in aged rats after 6- and 12-week curcumin-fortified diets. Curcumin enhanced non-spatial and spatial memory, as well as dentate gyrate cell proliferation as compared to control diet rats. We also investigated underlying mechanistic pathways that might link curcumin treatment to increased cognition and neurogenesis via exon array analysis of cortical and hippocampal mRNA transcription. The results revealed a transcriptional network interaction of genes involved in neurotransmission, neuronal development, signal transduction, and metabolism in response to the curcumin treatment. Conclusions: The results suggest a neurogenesis- and cognition-enhancing potential of prolonged curcumin treatment i

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

Game-Theory-Based Task Offloading and Resource Scheduling in Cloud-Edge Collaborative Systems

Task offloading and resource allocation are the major elements of edge computing. A reasonable task offloading strategy and resource allocation scheme can reduce task processing time and save system energy consumption. Most of the current studies on the task migration of edge computing only consider the resource allocation between terminals and edge servers, ignoring the huge computing resources in the cloud center. In order to sufficiently utilize the cloud and edge server resources, we propose a coarse-grained task offloading strategy and intelligent resource matching scheme under Cloud-Edge collaboration. We consider the heterogeneity of mobile devices and inter-channel interference, and we establish the task offloading decision of multiple end-users as a game-theory-based task migration model with the objective of maximizing system utility. In addition, we propose an improved game-theory-based particle swarm optimization algorithm to obtain task offloading strategies. Experimental results show that the proposed scheme outperforms other schemes with respect to latency and energy consumption, and it scales well with increases in the number of mobile devices

Online Support Vector Machine with a Single Pass for Streaming Data

In this paper, we focus on training a support vector machine (SVM) online with a single pass over streaming data.Traditional batch-mode SVMs require previously prepared training data; these models may be unsuitable for streaming data circumstances. Online SVMs are effective tools for solving this problem by receiving data streams consistently and updating model weights accordingly. However, most online SVMs require multiple data passes before the updated weights converge to stable solutions, and may be unable to address high-rate data streams. This paper presents OSVM_SP, a new online SVM with a single pass over streaming data, and three budgeted versions to bound the space requirement with support vector removal principles. The experimental results obtained with five public datasets show that OSVM_SP outperforms most state-of-the-art single-pass online algorithms in terms of accuracy and is comparable to batch-mode SVMs. Furthermore, the proposed budgeted algorithms achieve comparable predictive performance with only 1/3 of the space requirement

Advances in the pathogenesis of Alzheimer’s disease: a re-evaluation of amyloid cascade hypothesis

Abstract Alzheimer’s disease (AD) is a common neurodegenerative disease characterized clinically by progressive deterioration of memory, and pathologically by histopathological changes including extracellular deposits of amyloid-beta (A-beta) peptides forming senile plaques (SP) and the intracellular neurofibrillary tangles (NFT) of hyperphosphorylated tau in the brain. This review focused on the new developments of amyloid cascade hypothesis with details on the production, metabolism and clearance of A-beta, and the key roles of some important A-beta-related genes in the pathological processes of AD. The most recent research advances in genetics, neuropathology and pathogenesis of the disease were also discussed.</p

Development of Deep Learning Methodology for Maize Seed Variety Recognition Based on Improved Swin Transformer

In order to solve the problems of high subjectivity, frequent error occurrence and easy damage of traditional corn seed identification methods, this paper combines deep learning with machine vision and the utilization of the basis of the Swin Transformer to improve maize seed recognition. The study was focused on feature attention and multi-scale feature fusion learning. Firstly, input the seed image into the network to obtain shallow features and deep features; secondly, a feature attention layer was introduced to give weights to different stages of features to strengthen and suppress; and finally, the shallow features and deep features were fused to construct multi-scale fusion features of corn seed images, and the seed images are divided into 19 varieties through a classifier. The experimental results showed that the average precision, recall and F1 values of the MFSwin Transformer model on the test set were 96.53%, 96.46%, and 96.47%, respectively, and the parameter memory is 12.83 M. Compared to other models, the MFSwin Transformer model achieved the highest classification accuracy results. Therefore, the neural network proposed in this paper can classify corn seeds accurately and efficiently, could meet the high-precision classification requirements of corn seed images, and provide a reference tool for seed identification

Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model

Corn is one of the main food crops in China, and its area ranks in the top three in the world. However, the corn leaf disease has seriously affected the yield and quality of corn. To quickly and accurately identify corn leaf diseases, taking timely and effective treatment to reduce the loss of corn yield. We proposed identifying corn leaf diseases using the Mobilenetv3 (CD-Mobilenetv3) model. Based on the Mobilenetv3 model, we replaced the model&rsquo;s cross-entropy loss function with a bias loss function to improve accuracy. Replaced the model&rsquo;s squeeze and excitation (SE) module with the efficient channel attention (ECA) module to reduce parameters. Introduced the cross-layer connections between Mobile modules to utilize features synthetically. Then we Introduced the dilated convolutions in the model to increase the receptive field. We integrated a hybrid open-source corn leaf disease dataset (CLDD). The test results on CLDD showed the accuracy reached 98.23%, the precision reached 98.26%, the recall reached 98.26%, and the F1 score reached 98.26%. The test results are improved compared to the classic deep learning (DL) models ResNet50, ResNet101, ShuffleNet_x2, VGG16, SqueezeNet, InceptionNetv3, etc. The loss value was 0.0285, and the parameters were lower than most contrasting models. The experimental results verified the validity of the CD-Mobilenetv3 model in the identification of corn leaf diseases. It provides adequate technical support for the timely control of corn leaf diseases

Identification Method of Corn Leaf Disease Based on Improved Mobilenetv3 Model

Corn is one of the main food crops in China, and its area ranks in the top three in the world. However, the corn leaf disease has seriously affected the yield and quality of corn. To quickly and accurately identify corn leaf diseases, taking timely and effective treatment to reduce the loss of corn yield. We proposed identifying corn leaf diseases using the Mobilenetv3 (CD-Mobilenetv3) model. Based on the Mobilenetv3 model, we replaced the model’s cross-entropy loss function with a bias loss function to improve accuracy. Replaced the model’s squeeze and excitation (SE) module with the efficient channel attention (ECA) module to reduce parameters. Introduced the cross-layer connections between Mobile modules to utilize features synthetically. Then we Introduced the dilated convolutions in the model to increase the receptive field. We integrated a hybrid open-source corn leaf disease dataset (CLDD). The test results on CLDD showed the accuracy reached 98.23%, the precision reached 98.26%, the recall reached 98.26%, and the F1 score reached 98.26%. The test results are improved compared to the classic deep learning (DL) models ResNet50, ResNet101, ShuffleNet_x2, VGG16, SqueezeNet, InceptionNetv3, etc. The loss value was 0.0285, and the parameters were lower than most contrasting models. The experimental results verified the validity of the CD-Mobilenetv3 model in the identification of corn leaf diseases. It provides adequate technical support for the timely control of corn leaf diseases

Clinical nursing mentors’ motivation, attitude, and practice for mentoring and factors associated with them

Abstract Objective To investigate the motivation, attitude, and practice toward mentoring and related factors among clinical nursing mentors. Methods This cross-sectional study included clinical nursing mentors from 30 hospitals in Zhejiang Province between August and September 2023. Demographic information, motivation, attitude, and practice were collected through a self-administered questionnaire. Results A total of 495 valid questionnaires were collected, and most of the participants were 30–39 years old (68.7%). Average motivation, attitude, and practice scores were 29 [26, 32] (possible range: 8–40), 87 (82, 94) (possible range: 22–110), and 41 (38, 45) (possible range: 11–55), respectively. Correlation analyses showed that the motivation scores were positively correlated with attitude scores (r = 0.498, P < 0.001) and practice scores (r = 0.408, P = 0.001), while attitude scores were positively correlated with practice scores (r = 0.554, P < 0.001). Multivariate logistic regression showed that intermediate and senior nursing mentors (OR = 0.638, 95% CI: [0.426–0.956], P = 0.030) and different hospitals (OR = 1.627, 95% CI: [1.054–2.511], P = 0.028) were independently associated with motivation. The hospital’s frequency of psychological care was a significant factor associated with nursing mentoring motivation, attitude, and practice. Participation in training (OR = 2.908, 95% CI: [1.430, 5.913], P = 0.003) and lower frequency of job evaluation in hospital (“Often”: OR = 0.416, 95% CI: [0.244–0.709], P = 0.001 and “Sometimes”: OR = 0.346, 95% CI: [0.184–0.650], P = 0.001) were independently associated with practice. Conclusion Clinical nursing mentors had adequate motivation, positive attitude, and proactive practice towards mentoring and associated factors. Clinical nursing mentorship should be enhanced by prioritizing mentor training, fostering a supportive environment with consistent psychological care, and promoting structured mentorship activities