Monitoring and Source Tracing of Machining Error Based on Built-in Sensor Signal

AbstractOnline monitoring and source tracing of machining error is of great significance for ensuring machining quality and improving machining efficiency. For an open numerical controller, the built-in sensors signals can be captured through driver interface in machining process. These signals contain various information of machining conditions of machine tool. The capture and analysis of the built-in sensors signals can be used for the online monitoring and source tracing of machining error. In this paper, an novel approach is developed for machining error monitoring and source tracing based on built-in sensor signal analysis and multi-body system theory. A ball screw grinding process was monitored, and the analysis results show the validity of the approach

Structural analysis and insertion study reveal the ideal sites for surface displaying foreign peptides on a betanodavirus-like particle

Additional file 3: The averaged density distribution of the 3D reconstructions. The mass densities of the RBS are spherically averaged and plotted as a function of the particle radius. Below a radius of 115 Å is the density of enclosed RNA fragments (The RNA fragments do not belong to the virus genome, they are arbitrarily enclosed bacterial RNA). The density distribution between 115–150 Å and 150–190 Å are the capsid and the protrusion respectively. In the capsid shell, each subunit arranged in a “jerry-roll” structure results in that the capsid shell looks like two layers (two density peaks)

Seisimicity migration and the upper crustal structure in the Xinfengjiang Reservoir

The row data of earthquakes catalog in the Xinfengjiang Reservoir used in the paper "Seismicity migration and the upper crustal structure in the Xinfengjiang Reservoir" submitted to Seismological Research Letters</p

Study on Early Identification of Rainfall-Induced Accumulation Landslide Hazards in the Three Gorges Reservoir Area

The early identification of potential hazards is crucial for landslide early warning and prevention and is a key focus and challenging issue in landslide disaster research. The challenges of traditional investigation and identification methods include identifying potential hazards of landslides triggered by heavy rainfall and mapping areas susceptible to landslides based on rainfall conditions. This article focuses on the problem of early identification of rainfall-induced accumulation landslide hazards and an early identification method is proposed, which is “first identifying the accumulation that is prone to landslides and then determining the associated rainfall conditions”. This method is based on identifying the distribution and thickness of accumulation, analyzing the rainfall conditions that trigger landslides with varying characteristics, and establishing rainfall thresholds for landslides with different accumulation characteristics, ultimately aiming to achieve early identification of accumulation landslide hazards. In this study, we focus on the Zigui section of the Three Gorges Reservoir as study the area, and eight main factors that influence the distribution and thickness of accumulation are extracted from multi-source data, then the relative thickness information extraction model of accumulation is established by using the BP neural network method. The accumulation distribution and relative thickness map of the study area are generated, and the study area is divided into rocky area (less than 1 m), thin (1 to 5 m), medium (5 to 10 m), and thick area (thicker than 10 m) according to accumulation thickness. Rainfall is a significant trigger for landslide hazards. It increases the weight of the sliding mass and decreases the shear strength of soil and rock layers, thus contributing to landslide events. Data on 101 rainfall-induced accumulation landslides in the Three Gorges Reservoir area and rainfall data for the 10 days prior to each landslide event were collected. The critical rainfall thresholds corresponding to a 90% probability of landslide occurrence with different characteristics were determined using the I-D threshold curve method. Prediction maps of accumulation landslide hazards under various rainfall conditions were generated by analyzing the rainfall threshold for landslides in the Three Gorges Reservoir area, serving as a basis for early identification of rainfall-induced accumulation landslides in the region. The research provides a method for the early identification of landslides caused by heavy rainfall, delineating landslide hazards under different rainfall conditions, and providing a basis for scientific responses, work arrangements, and disaster prevention and mitigation of landslides caused by heavy rainfall

Relationship between HgbA1c and Myocardial Blood Flow Reserve in Patients with Type 2 Diabetes Mellitus: Noninvasive Assessment Using Real-Time Myocardial Perfusion Echocardiography

To study the relationship between glycosylated hemoglobin (HgbA1c) and myocardial perfusion in type 2 diabetes mellitus (T2DM) patients, we prospectively enrolled 24 patients with known or suspected coronary artery disease (CAD) who underwent adenosine stress by real-time myocardial perfusion echocardiography (RTMPE). HgbA1c was measured at time of RTMPE. Microbubble velocity (β min−1), myocardial blood flow (MBF, mL/min/g), and myocardial blood flow reserve (MBFR) were quantified. Quantitative MCE analysis was feasible in all patients (272/384 segments, 71%). Those with HgbA1c > 7.1% had significantly lower βreserve and MBFR than those with HgbA1c ≤ 7.1% (P 2 as normal, HgbA1c > 7.1% significantly increased the risk for abnormal MBFR, (adjusted odds ratio: 1.92, 95% CI: 1.12–3.35, P=0.02). Optimal glycemic control is associated with preservation of MBFR as determined by RTMPE, in T2DM patients at risk for CAD

Semantic Representation of Robot Manipulation with Knowledge Graph

Autonomous indoor service robots are affected by multiple factors when they are directly involved in manipulation tasks in daily life, such as scenes, objects, and actions. It is of self-evident importance to properly parse these factors and interpret intentions according to human cognition and semantics. In this study, the design of a semantic representation framework based on a knowledge graph is presented, including (1) a multi-layer knowledge-representation model, (2) a multi-module knowledge-representation system, and (3) a method to extract manipulation knowledge from multiple sources of information. Moreover, with the aim of generating semantic representations of entities and relations in the knowledge base, a knowledge-graph-embedding method based on graph convolutional neural networks is proposed in order to provide high-precision predictions of factors in manipulation tasks. Through the prediction of action sequences via this embedding method, robots in real-world environments can be effectively guided by the knowledge framework to complete task planning and object-oriented transfer

Knowledge, Attitudes, and Practices Related to COVID-19 Among Malawi Adults: A Community-Based Survey

Introduction: It is well-recognized that containing COVID-19 successfully is determined by people’s prevention measures which are related to their knowledge, attitudes, and practices (KAP). This perception has attracted attention in low- and middle-income countries (LMIC) due to their fragile health systems and economies. The objective of this study was to understand how residents in Malawi perceived COVID-19, to determine the factors related to KAP. Methods: A semi-structured questionnaire was used for the data collection. A field-based survey was conducted among adult residents in Lilongwe, Malawi. Descriptive statistic, linear regression, the Chi-square test, and Pearson’s correlation statistics were used for data analysis. Results: A total of 580 questionnaires were involved. The mean knowledge, attitude, and practice (KAP) scores were 10 (SD = ±3, range: 3–19), 16 (SD = ±4, range: 5–25), and 2 (SD = ±1, range: 0–5), respectively. Lack of money and resources (39%) was the biggest challenge for people who practice prevention measures. Among the participants, the radio (70%) and friends/family (56%) were the main sources of information. A higher economic status was associated with better KAP. Conclusions: A low level of KAP was detected among the population. The people faced challenges regarding a lack of necessary preventive resources and formal information channels. The situation was worse considering vulnerable population who had low economic status. Further all-round health education is urgently needed along with providing adequate health supplies and ensuring proper information management

Recent progress of geophysical exploration in Earth's impact craters

Geophysical exploration plays an important role in detecting and studying impact structures. This article reviews the common geophysical features of Earth's impact craters, including their gravity, magnetic, electrical, and seismic characteristics. The most obvious geophysical feature of impact craters is the circular or ring-shaped negative gravity anomaly, which is mainly caused by rock fracture and brecciation resulting in lower rock density. Low magnetic anomalies with complex details are mainly due to impact melting reducing the magnetic susceptibility of rocks inside the crater and post-impact modification resulting in complex detailed features. High electrical conductivity is found in simple craters, while more complex craters have gradually increasing electrical conductivity from the central uplift to the marginal rim. The conductivity is dominated by the fracture extent and water content. Low seismic velocity is mainly due to the lower velocity of fractured breccia and fractures relative to the original rock. In addition, seismic reflection profiling has found that impact structures have distinct concave shapes.Internationally, there are abundant research on the geophysical exploration of impact craters. However, in China, confirmed impact craters are rare in number and lack related geophysical exploration. Summarizing the common geophysical characteristics of impact craters provides a basis for geophysical exploration of potential impact crater regions in China and offers material for popular science and public engagement purposes.There are two confirmed impact craters in China, the Xiuyan crater in Liaoning Province and the Yilan crater in Heilongjiang Province. Active seismic investigations had been conducted in Xiuyan crater, and revealed its relative velocity and attenuation structure. However, although several geological studies have been conducted, a comprehensive geophysical study of the newly discovered Yilan crater is still lacking. Recently, our group has conducted dense seismic nodes and distributed acoustic sensing in Yilan crater, the results of which will be reported in the near future