Fault diagnosis of mechanical drives under non-stationary conditions based on manifold learning of kernel mapping

For the detection of mechanical faults under the operating conditions of varying speeds and loads (such as wind turbines, excavators or helicopters, etc.), a new method for extracting the low-dimensional embedding of vibration data sets of mechanical drives under variable operation conditions is proposed. The hypothesis is that the space spanned by a set of vibration signals can be captured in a varying condition, to a close approximation, by a low-dimensional, nonlinear manifold. This paper presents a method to learn such a low-dimensional manifold from a given data set. The embedding manifold generated by vibration signals can be constructed from the feature set of parameters. Taking the variable operation condition into consideration, the kernel mapping is also introduced to improve the identification of submanifolds in terms of the projection distance. With the kernel mapping, the manifold coordinates can accurately capture the differences of the varying operation conditions. Experimental vibration signals obtained from normal and chipped tooth fault of gearbox in varying operation conditions are analyzed in this study. Results show that the proposed method is superior in identifying fault patterns and effective for gearbox condition monitoring

Human-Robot Interaction of a Craniotomy Robot Based on Fuzzy Model Reference Learning Control

In this paper, we design a variable admittance controller and propose a variable admittance human-robot cooperative control method based on fuzzy model reference learning. The method is intended to improve the flexible adaptive capability of the robot to assist the surgeon in accomplishing different stages of the task during a craniotomy. First, the method establishes the autoregressive integrated moving average-Kalman filtering-blood pressure (ARIMA-Kalman-BP) model for the drag force prediction by taking the features of natural human arm motion as the reference model of fuzzy learning control, which solves the problem of the features of natural human arm motion being difficult to model. Then the tuning parameter rules for variable virtual damping and virtual mass of the fuzzy conductivity controller are trained by the learning mechanism. Subsequently, the variable conductivity control method based on the tuning of virtual damping and virtual mass parameters is developed by using the robot acceleration and the robot velocity as inputs, and the robot desired velocity and desired acceleration as outputs. The experimental results show that the method can meet the requirement of flexibility; the maximum error of human-machine cooperative velocity is 0.0014 m/s, and the maximum error of human-machine cooperative acceleration is lower than 0.0021 m/s2. Compared with the fuzzy control based on the variable admittance parameter alone, this method has better tracking velocity and acceleration

Several constructions of constant dimension code using equal-division method

Constant dimension codes (CDCs) have garnered significant attention in recent years, primarily owing to their crucial applications in random network coding. A central problem in the study of CDCs involves determining the maximum achievable cardinality, denoted as ${A_q(n, 2\delta, k)}$ for given parameters. In this paper, we propose a new approach to constructing CDCs based on the equal-division method, which we subsequently combine with existing optimal codes from prior literature. The resulting codes yield improved lower bounds for ${A_q(n, 2\delta, k)}$ compared to previously established results across certain parameters. Furthermore, we extend our approach by incorporating multiple constructions based on distinct ways of equal division. The newly constructed CDCs have larger cardinality under some parameters

The immunological landscape of CCL26High invasive oral squamous cell carcinoma

BackgroundOur previous study demonstrated that CCL26 secreted by cancer-associated fibroblasts (CAF) promoted the invasive phenotype of oral squamous cell carcinoma (OSCC), however, more comprehensive clinical expression patterns of CCL26 and its role in immunotherapy remains ambiguous.MethodsCCL26 levels in different cancer and normal tissues were analyzed and validated in 67 OSCC patients through immunohistochemical staining (IHC). The clinical spatial distribution pattern of CCL26 in tumor microenvironment was determined, and its clinical outcomes were investigated. We also determined the invasive phenotype of tumor cells with distinct CCL26 level and explored its immune checkpoint and immunocytes relevance by differentially expressed gene (DEG) analysis, GSEA, and GO analysis. We collected peripheral blood from 28 OSCC patients to assess the percentage and absolute number of lymphocytes by flow cytometry.ResultsCCL26 was upregulated in HNSC and preferentially high-expressed on CAFs and tumor cells in OSCC patients, which exhibits a trend toward decreased overall survival. CCL26high OSCC had a characteristic of tumor invasive phenotype with upregulated CLDN8/20 and reduced keratin KRT36, which was significantly associated with EMT markers (CDH1, CDH2, VIM, SNAI2). In addition, CCL26high OSCC was found to be associated with immunoglobulin mediated immune response, B cell mediated immunity et al. Indeed, immune checkpoint molecules (PD-L1, PD-L2, et al.) also decreased in CCL26high OSCC. However, CCL26 did not affect T/B/NK lymphocytes in peripheral blood of OSCC patients.ConclusionCCL26 could regulate Immune balance and promote invasiveness of OSCC, which gave a new insight into a potential immunotherapy strategy

Immune Protection Induced on Day 10 Following Administration of the 2009 A/H1N1 Pandemic Influenza Vaccine

BACKGROUND: The 2009 swine-origin influenza virus (S-OIV) H1N1 pandemic has caused more than 18,000 deaths worldwide. Vaccines against the 2009 A/H1N1 influenza virus are useful for preventing infection and controlling the pandemic. The kinetics of the immune response following vaccination with the 2009 A/H1N1 influenza vaccine need further investigation. METHODOLOGY/PRINCIPAL FINDINGS: 58 volunteers were vaccinated with a 2009 A/H1N1 pandemic influenza monovalent split-virus vaccine (15 µg, single-dose). The sera were collected before Day 0 (pre-vaccination) and on Days 3, 5, 10, 14, 21, 30, 45 and 60 post vaccination. Specific antibody responses induced by the vaccination were analyzed using hemagglutination inhibition (HI) assay and enzyme-linked immunosorbent assay (ELISA). After administration of the 2009 A/H1N1 influenza vaccine, specific and protective antibody response with a major subtype of IgG was sufficiently developed as early as Day 10 (seroprotection rate: 93%). This specific antibody response could maintain for at least 60 days without significant reduction. Antibody response induced by the 2009 A/H1N1 influenza vaccine could not render protection against seasonal H1N1 influenza (seroconversion rate: 3% on Day 21). However, volunteers with higher pre-existing seasonal influenza antibody levels (pre-vaccination HI titer ≥1∶40, Group 1) more easily developed a strong antibody protection effect against the 2009 A/H1N1 influenza vaccine as compared with those showing lower pre-existing seasonal influenza antibody levels (pre-vaccination HI titer <1∶40, Group 2). The titer of the specific antibody against the 2009 A/H1N1 influenza was much higher in Group 1 (geometric mean titer: 146 on Day 21) than that in Group 2 (geometric mean titer: 70 on Day 21). CONCLUSIONS/SIGNIFICANCE: Recipients could gain sufficient protection as early as 10 days after vaccine administration. The protection could last at least 60 days. Individuals with a stronger pre-existing seasonal influenza antibody response may have a relatively higher potential for developing a stronger humoral immune response after vaccination with the 2009 A/H1N1 pandemic influenza vaccine

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Automated Scoring of Translations with BERT Models: Chinese and English Language Case Study

With the wide application of artificial intelligence represented by deep learning in natural language-processing tasks, the automated scoring of translations has also advanced and improved. This study aims to determine if the BERT-assist system can reliably assess translation quality and identify high-quality translations for potential recognition. It takes the Han Suyin International Translation Contest as a case study, which is a large-scale and influential translation contest in China, with a history of over 30 years. The experimental results show that the BERT-assist system is a reliable second rater for massive translations in terms of translation quality, as it can effectively sift out high-quality translations with a reliability of r = 0.9 or higher. Thus, the automated translation scoring system based on BERT can satisfactorily predict the ranking of translations according to translation quality and sift out high-quality translations potentially shortlisted for prizes

Study on level-of-service for urban rail transit passages from the passenger perspective

With the acceleration of urbanization and the shortage of transportation resources in large cities, urban rail transit has gradually become the backbone of urban public transportation system. In order to improve the service level of urban rail transit, this paper studies the factors influencing the service level of subway station passages and the classification method based on the passenger-perception method. Through investigation and research, the passage service level evaluation indicators (width, walking time, per capita area, illumination intensity and guide signage) have been determined. Based on orthogonal experiment, 3ds max was used to set up 25 passage scenes with different parameters from passengers' perspective. A passenger satisfaction questionnaire was designed and distributed, the reliability and validity of the questionnaire were tested to ensure the validity of the data. And based on the factor analysis method, the factors affecting passenger perception are analyzed. A well-fitting model of the relationship between passenger scores and passage parameters is established, and the passenger scores are converted into service levels. Finally, a service level classification method is given, which can provide reference for the service level evaluation of existing stations and the design of physical attributes and environmental factors of new station passages

Study on level-of-service for urban rail transit passages from the passenger perspective

With the acceleration of urbanization and the shortage of transportation resources in large cities, urban rail transit has gradually become the backbone of urban public transportation system. In order to improve the service level of urban rail transit, this paper studies the factors influencing the service level of subway station passages and the classification method based on the passenger-perception method. Through investigation and research, the passage service level evaluation indicators (width, walking time, per capita area, illumination intensity and guide signage) have been determined. Based on orthogonal experiment, 3ds max was used to set up 25 passage scenes with different parameters from passengers' perspective. A passenger satisfaction questionnaire was designed and distributed, the reliability and validity of the questionnaire were tested to ensure the validity of the data. And based on the factor analysis method, the factors affecting passenger perception are analyzed. A well-fitting model of the relationship between passenger scores and passage parameters is established, and the passenger scores are converted into service levels. Finally, a service level classification method is given, which can provide reference for the service level evaluation of existing stations and the design of physical attributes and environmental factors of new station passages.</jats:p