Tracking differentiator based back-stepping control for valve-controlled hydraulic actuator system

Back-stepping design method is widely used in high-performance tracking control tasks As is known to all, the controller based on back-stepping design will become complex as the model order increases, which is the so called “explosion of terms” problem. In this paper, a tracking differentiator (TD) based back-stepping controller is proposed to handle the “explosion of terms” problem. Instead of calculating the derivatives of intermediate control variables through tedious analytical expressions, for the proposed method, the tracking differentiator is embedded into each recursive procedure to generate the substitute derivative signal for every intermediate control variable. As a result, the complexity of implementation procedure of back-stepping controller is significantly reduced. The discrepancies between the derivative substitutes and the real derivatives are considered. And the effects on control performances caused by the discrepancies are analyzed. In addition to giving the theoretical results and the stability proofs with Lyapunov methods, the developed controller design method is evaluated through a series of experiments with a hydraulic robot arm position serve system. The control performance of the proposed controller is verified by the experiments results.</p

Quantitative Label-Free Proteomic Analysis of Milk Fat Globule Membrane in Donkey and Human Milk

Previous studies have found donkey milk (DM) has the similar compositions with human milk (HM) and could be used as a potential hypoallergenic replacement diet for babies suffering from cow's milk allergy. Milk fat globule membrane (MFGM) proteins are involved in many biological functions, behaving as important indicators of the nutritional quality of milk. In this study, we used label-free proteomics to quantify the differentially expressed MFGM proteins (DEP) between DM (in 4–5 months of lactation) and HM (in 6–8 months of lactation). In total, 293 DEP were found in these two groups. Gene Ontology (GO) enrichment analysis revealed that the majority of DEP participated in regulation of immune system process, membrane invagination and lymphocyte activation. Several significant Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were determined for the DEP, such as lysosome, galactose metabolism and peroxisome proliferator-activated receptor (PPAR) signaling pathway. Our study may provide valuable information in the composition of MFGM proteins in DM and HM, and expand our knowledge of different biological functions between DM and HM

Development and Application of Computer-Aided Innovative Learning Mode of Undergraduate Entrepreneurship

This paper gives the related definitions of online school-based mode of research and teaching, on the basis of which the detailed activities are designed, including online lecture preparation, lecture evaluation, collective discussion and subject-oriented collaboration, obtaining of online resources and displaying of results for research and teaching. After determining the forms of online activities of research and teaching, the procedures were further explored and discussed. In combination with its own characteristics of school-based online mode of teaching and research, the practical procedures of activities were given for future reference

Application of Improved SVM Image Segmentation Algorithm in Computer Tomography Image Analysis

Medical imaging is becoming increasingly important in clinical diagnosis. Ultrasound imaging, computed tomography, magnetic resonance imaging (MRI) and other new medical imaging technology greatly broadens the imaging diagnostic methods. Animal Computer Tomography (CT) imaging, as an animal model, is of great significance to guide the experimental research of clinical diagnosis, and the treatment of pet disease also has a pioneering significance. Image segmentation, as the basis of medical image processing and analysis, has played a vital role in clinical diagnosis and treatment from doctors. In this paper, the existing segmentation algorithm is improved based on the characteristics of CT images of animals. In this paper, we use the global optimization of the genetic algorithm to improve the traditional support vector machine classification algorithm. At the same time, the kernel function of the support vector machine algorithm is improved to promote the segmentation results. The experiments show that the algorithm in this paper has a better segmentation effect in the processing of CT images of animals

A syntactic dependency method for aspect-level sentiment classification by deep learning

Most aspect-level sentiment classification networks include the long short-term memory (LSTM) network, coupled with attention mechanism and memory module, is becoming widely applied in aspect-level sentiment classification. Although it has achieved good results, it cannot extract the global and local information of the context at the same time, and it is only based on the semantic relatedness between an aspect and its corresponding context words to model, while neglecting their syntactic dependencies. This paper proposes the aspect-level sentiment classification by combining convolutional neural network (CNN) and proximity-weighted convolution network (PWCN), as well as a new method to calculate the proximity weight. To obtain contextualized word vectors, corpora has been trained by the model of bidirectional encoder representations from transformers (BERT), which can be taken as text features. The CNN is able to extract sequence features from the text and to take the sequence information from the text into account. In addition, the PWCN can consider the syntactic dependencies inside the sentences. The BERT model also has the ability to model complex features of words, such as their syntactic and semantic changes in a linguistic context. Experiments conducted on the SemEval 2014 benchmark demonstrate compared to the well-established ones, the proposed approach had bigger effectiveness

Genetic Heterogeneity and Mutated PreS Analysis of Duck Hepatitis B Virus Recently Isolated from Ducks and Geese in China

In this study, we detected 12 duck and 11 goose flocks that were positive for duck hepatitis B virus (DHBV) using polymerase chain reaction and isolated 23 strains between 2020 and 2022 in China. The complete genomes of goose strains E200801 and E210501 shared the highest identity (99.9%), whereas those of strains Y220217 and E210526 shared the lowest identity (91.39%). The phylogenetic tree constructed based on the genome sequences of these strains and reference strains was classified into three major clusters: the Chinese branch DHBV-I, the Chinese branch DHBV-II, and the Western branch DHBV-III. Furthermore, the duck-origin strain Y200122 was clustered into a separate branch and was predicted to be a recombinant strain derived from DHBV-M32990 (belonging to the Chinese branch DHBV-I) and Y220201 (belonging to the Chinese branch DHBV-II). Additionally, preS protein analysis of the 23 DHBV strains revealed extensive mutation sites, almost half of which were of duck origin. All goose-origin DHBV contained the mutation site G133E, which is related to increased viral pathogenicity. These data are expected to promote further research on the epidemiology and evolution of DHBV. Continuing DHBV surveillance in poultry will enhance the understanding of the evolution of HBV