Research on parallel control of CMAC and PD based on U model

In this paper, the nonlinear U model with time-varying coefficients is investigated and the transformation of the nonlinear model is accomplished by the Newton iterative algorithm. Based on the nonlinear U model, a control algorithm with cerebellar model articulation controller and proportional derivative (PD) in parallel is proposed. The algorithm learns online through a neural network while optimizing the output of the PD, which ultimately enables the actual output of the system to track up to the desired output. Considering that the nonlinear object has the characteristic of rapid change with time, the article improves the PD algorithm to nonlinear PD control algorithm to complete the design of the system. The algorithm automatically adjusts the weights according to the error magnitude to complete the controller parameter adjustment, thus reducing the error of the system. The simulation results show that the nonlinear PD algorithm is better than the PD algorithm, meanwhile, the tracking speed and control precision of the system are improved

Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark

Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task.Comment: WSDM 202

Using Interpretable Machine Learning to Predict Maternal and Fetal Outcomes

Most pregnancies and births result in a good outcome, but complications are not uncommon and when they do occur, they can be associated with serious implications for mothers and babies. Predictive modeling has the potential to improve outcomes through better understanding of risk factors, heightened surveillance, and more timely and appropriate interventions, thereby helping obstetricians deliver better care. For three types of complications we identify and study the most important risk factors using Explainable Boosting Machine (EBM), a glass box model, in order to gain intelligibility: (i) Severe Maternal Morbidity (SMM), (ii) shoulder dystocia, and (iii) preterm preeclampsia. While using the interpretability of EBM's to reveal surprising insights into the features contributing to risk, our experiments show EBMs match the accuracy of other black-box ML methods such as deep neural nets and random forests.Comment: DSHealth at SIGKDD 2022, 5 pages, 3 figure

Analysis of Flow Behavior for Acid Fracturing Wells in Fractured-Vuggy Carbonate Reservoirs

This study develops a mathematical model for transient flow analysis of acid fracturing wells in fractured-vuggy carbonate reservoirs. This model considers a composite system with the inner region containing finite number of artificial fractures and wormholes and the outer region showing a triple-porosity medium. Both analytical and numerical solutions are derived in this work, and the comparison between two solutions verifies the model accurately. Flow behavior is analyzed thoroughly by examining the standard log-log type curves. Flow in this composite system can be divided into six or eight main flow regimes comprehensively. Three or two characteristic V-shaped segments can be observed on pressure derivative curves. Each V-shaped segment corresponds to a specific flow regime. One or two of the V-shaped segments may be absent in particular cases. Effects of interregional diffusivity ratio and interregional conductivity ratio on transient responses are strong in the early-flow period. The shape and position of type curves are also influenced by interporosity coefficients, storativity ratios, and reservoir radius significantly. Finally, we show the differences between our model and the similar model with single fracture or without acid fracturing and further investigate the pseudo-skin factor caused by acid fracturing

Porcine epidemic diarrhea virus causes diarrhea by activating EGFR to regulates NHE3 activity and mobility on plasma membrane

As part of the genus Enteropathogenic Coronaviruses, Porcine Epidemic Diarrhea Virus (PEDV) is an important cause of early diarrhea and death in piglets, and one of the most difficult swine diseases to prevent and control in the pig industry. Previously, we found that PEDV can block Na+ absorption and induce diarrhea in piglets by inhibiting the activity of the sodium-hydrogen ion transporter NHE3 in pig intestinal epithelial cells, but the mechanism needs to be further explored. The epidermal growth factor receptor (EGFR) has been proved to be one of the co-receptors involved in many viral infections and a key protein involved in the regulation of NHE3 activity in response to various pathological stimuli. Based on this, our study used porcine intestinal epithelial cells (IPEC-J2) as an infection model to investigate the role of EGFR in regulating NHE3 activity after PEDV infection. The results showed that EGFR mediated viral invasion by interacting with PEDV S1, and activated EGFR regulated the downstream EGFR/ERK signaling pathway, resulting in decreased expression of NHE3 and reduced NHE3 mobility at the plasma membrane, which ultimately led to decreased NHE3 activity. The low level of NHE3 expression in intestinal epithelial cells may be a key factor leading to PEDV-induced diarrhea in newborn piglets. This study reveals the importance of EGFR in the regulation of NHE3 activity by PEDV and provides new targets and clues for the prevention and treatment of PEDV-induced diarrhea in piglets

Mechanism of Lactiplantibacillus plantarum regulating Ca2+ affecting the replication of PEDV in small intestinal epithelial cells

Porcine epidemic diarrhea virus (PEDV) mainly invades the small intestine and promotes an inflammatory response, eventually leading to severe diarrhea, vomiting, dehydration, and even death of piglets, which seriously threatens the economic development of pig farming. In recent years, researchers have found that probiotics can improve the intestinal microenvironment and reduce diarrhea. At the same time, certain probiotics have been shown to have antiviral effects; however, their mechanisms are different. Herein, we aimed to investigate the inhibitory effect of Lactiplantibacillus plantarum supernatant (LP-1S) on PEDV and its mechanism. We used IPEC-J2 cells as a model to assess the inhibitory effect of LP-1S on PEDV and to further investigate the relationship between LP-1S, Ca2+, and PEDV. The results showed that a divalent cation chelating agent (EGTA) and calcium channel inhibitors (Bepridil hydrochloride and BAPTA-acetoxymethylate) could inhibit PEDV proliferation while effectively reducing the intracellular Ca2+ concentration. Furthermore, LP-1S could reduce PEDV-induced loss of calcium channel proteins (TRPV6 and PMCA1b), alleviate intracellular Ca2+ accumulation caused by PEDV infection, and promote the balance of intra- and extracellular Ca2+ concentrations, thereby inhibiting PEDV proliferation. In summary, we found that LP-1S has potential therapeutic value against PEDV, which is realized by modulating Ca2+. This provides a potential new drug to treat PEDV infection