Digital Twins by Physical Education Teaching Practice in Visual Sensing Training System

The paper expects to improve the efficiency and intelligence of somatosensory recognition technology in the application of physical education teaching practice. Firstly, the combination of induction recognition technology and the Internet is used. Secondly, through the Kinect sensor, bone data are acquired. Finally, the hidden Markov model (HMM) is used to simulate the experimental data. On the simulation results, a gait recognition algorithm is proposed. The gait recognition algorithm is used to identify the motion behaviour, and the results are displayed in the Web (World Wide Web) end built by the cloud server. Meantime, in view of the existing problems in the practice of physical education, combined with the establishment and operation of the Digital Twins (DTs) system, the camera source recognition architecture is carried out since the twin network and the two network branches share weights. This paper analyses these problems since the application of somatosensory recognition technology and puts forward the improvement methods. For the single problem of equipment in physical education, this paper puts forward the monitoring and identification function of the cloud server. It is to transmit data through Hypertext Transfer Protocol (HTTP) and locate and collect data through a monitoring terminal. For the lack of comprehensiveness and balance of sports plans, this paper proposes a scientific training plan and process customization based on Body Mass Index (BMI), analyses real-time data in the cloud, and makes scientific customization plans according to different students’ physical conditions. Moreover, 25 participants are invited to carry out the exercise detection and analysis experiment, and the joint monitoring of their daily movements is tested. This process has completed the design of a feasible and accurate platform for information collection and processing, which is convenient for managers and educators to comprehensively and scientifically master and manage the physical level and training of college students. The proposed method improves the recognition rate of the camera source to some extent and has important exploration significance in the field of action recognition.

Music Recommender System Using ChatBot

In this era of technological advances, text-based music recommendations are much needed as they will help humans relieve stress with soothing music according to their moods. In this project, we have implemented a chatbot that recommends music based on the user's text tone. By analyzing the tone of the text expressed by the user, we can identify the mood. Once the mood is identified, the application will play songs in the form of a web page based on the user's choice as well as his current mood. In our proposed system, themain goal is to reliably determine a user's mood based on their text tone with an application that can be installed on the user's desktop. In today's world, human computer interaction (HCI) plays a crucial role, and the most popular concept in HCI is recognition of emotion from text. As part of this process, the frontal view of the user's text is used to determine the mood. The extraction of text tone from the user's text is anotherimportant aspect. We have used IBM Analyser to check the text tone of the user and to predict the mood based on the text of the user, and Last.FM API to recommend songs based on themood of the user. Keywords: Introduction, Product-Architecture, Tone Analyzer, Music Classification Based on Mood, Acoustic Analysis, Experiment, Future/Current Use, Importance, Background, Literature Survey, Methodology, Equations, Planning, Tools and Technology, Conclusion.

Analysis of Dynamic Characteristics for Machine Tools Based on Dynamic Stiffness Sensitivity

Dynamic parameters are the intermediate information of the entirety of machine dynamics. The differences between components have not been combined with the structural vibration in the cutting process, so it is difficult to directly represent the dynamic characteristics of the whole machine related to spatial position. This paper presents a method to identify sensitive parts according to the dynamic stiffness-sensitivity algorithm, which represents the dynamic characteristics of the whole machine tool. In this study, two experiments were carried out, the simulation verification experiment (dynamic experiment with variable stiffness) and modal analysis experiment (vibration test of five-axis gantry milling machine). The key modes of sensitive parts obtained by this method can represent the position-related dynamic characteristics of the whole machine. The characteristic obtained is that the inherent properties of machine-tool structure are independent of excitation. The method proposed in this paper can accurately represent the dynamic characteristics of the whole machine tool.

Optimization Design of Mix Proportion of Large Stone Permeable Mixture Based on Target Air Voids

Through theoretical analysis, this paper preliminarily puts forward the optimization design method of a mix proportion large stone permeable mixture based on target voidage. The optimized large stone permeable mixture is abbreviated as OLSPM (optimization large stone permeable mixture). On this basis, the asphalt content was verified by leakage analysis experiment, and the molding method was determined by comparing the volume parameter changes and the appearance of the specimen in the molding process of both a Marshall compaction test and rotary compaction test. The final experimental analysis results show that the asphalt content calculated by this method can meet the technical requirements of leakage loss. The rotary compaction method is the suitable molding method for indoor cylindrical specimens of OLSPM, and the voidage is used as the index to control the compac-tion times of OLSPM. Under the same voidage, OLSPM-25 has more fine aggregates and thus leads to a relatively large amount of asphalt. In addition, the content of 4.75–19 mm coarse aggregate in its coarse aggregate is also higher than that of LSPM-25.

Classify Elderly Pain Severity from Open-Source Automatically Video Clip Facial Action Units Analysis. A Study from the Integrated Pain Artificial Intelligence Network (I-PAIN) Data Repository.

Background: It is increasingly interesting to monitor pain severity in elderly individuals by applying machine learning models. In previous studies, OpenFace© - a well-known automated facial analysis algorithm, was used to detect facial action units (FAUs) that initially need long hours of human coding. However, OpenFace© developed from the dataset that dominant young Caucasians who were illicit pain in the lab. Therefore, this study aims to evaluate the accuracy and feasibility of the model using data from OpenFace© to classify pain severity in elderly Asian patients in clinical settings.Methods: Data from 255 Thai individuals with chronic pain were collected at Chiang Mai Medical School Hospital. The phone camera recorded faces for 10 seconds at a 1-meter distance briefly after the patients provided self-rating pain severity. For those unable to self-rate, the video was recorded just after the move, which illicit pain. The trained assistant rated each video clip for the Pain Assessment in Advanced Dementia (PAINAD). The classification of pain severity was mild, moderate, or severe. OpenFace© process video clip into 18 FAUs. Five classification models were used, including logistic regression, multilayer perception, naïve Bayes, decision tree, k-nearest neighbors (KNN), and support vector machine (SVM). Results: Among the models that included only FAU described in the literature (FAUs 4, 6, 7, 9, 10, 25, 26, 27 and 45), multilayer perception yielded the highest accuracy of 50%. Among the machine learning selection features, the SVM model for FAU 1, 2, 4, 7, 9, 10, 12, 20, 25, 45, and gender yielded the best accuracy of 58%. Conclusion: Our open-source automatic video clip facial action unit analysis experiment was not robust for classifying elderly pain. Retraining facial action unit detection algorithms, enhancing frame selection strategies, and adding pain-related functions may improve the accuracy and feasibility of the model.

Geochemical and reactive transport modelling in R with the RedModRphree package

Advances in computing and experimental capabilities in the research of water-rock-interactions require geoscientists to routinely combine laboratory data and models to produce new knowledge. Data science is hence a more and more pervasive instrument for geochemists, which in turn demands flexible and easy to learn software adaptable to their specific needs. The GNU R language and programming environment has established itself as de facto standard language for statistics and machine learning, enjoying increasing diffusion in many applied scientific fields such as bioinformatics, chemometrics and ecological modelling. The availability of excellent third party extensions as well as its advanced graphical and numerical capabilities make R an ideal platform for comprehensive geochemical data analysis, experiment evaluation and modelling. We introduce the open source RedModRphree extension package, which leverages the R interface to the established PHREEQC geochemical simulator. The aim of RedModRphree is to provide the user with an easy-to-use, high-level interface to program algorithms involving geochemical models: parameter calibration, error and sensitivity analysis, thermodynamical database manipulation, up to CPU-intensive parallel coupled reactive transport models. Among the out-of-the-box features included in RedModRphree, we highlight the computation and visualization of Pourbaix (Eh-pH) diagrams using full speciation as computed by PHREEQC and the implementation of 1D advective reactive transport supporting the use of surrogate models replacing expensive equation-based calculations.

Prenatal and postnatal traffic pollution exposure, DNA methylation in Shank3 and MeCP2 promoter regions, H3K4me3 and H3K27me3 and sociability in rats’ offspring

Background Road traffic air pollution is linked with an increased risk of autistic spectrum disorder (ASD). The aim of this study is to assess the effect of exposure to prenatal or postnatal traffic-related air pollution combining concomitant noise pollution on ASD-related epigenetic and behavioral alternations on offspring. Methods A 2 × 2 factorial analysis experiment was designed. Wistar rats were exposed at different sites (L group: green space; H group: crossroads) and timings (E group: full gestation; P group: 21 days after birth) at the same time, and air pollutants of nitrogen dioxide (NO2) and fine particles (PM2.5) were meanwhile sampled. On postnatal day 25, brains from offspring of each group were extracted to determine the levels of DNA methylation in Shank3 (three parts: Shank3_01, Shank3_02, Shank3_03) and MeCP2 (two parts: MeCP2_01, MeCP2_02) promoter regions, H3K4me3 and H3K27me3 after three-chamber social test. Meanwhile, the Shank3 and MeCP2 levels were quantified. Results The concentrations of PM2.5 (L: 58.33 µg/m3; H: 88.33 µg/m3, P < 0.05) and NO2 (L: 52.76 µg/m3; H: 146.03 µg/m3, P < 0.01) as well as the intensity of noise pollution (L: 44.4 dB (A); H: 70.1 dB (A), P < 0.001) differed significantly from 18:00 to 19:00 between experimental sites. Traffic pollution exposure (P = 0.006) and neonatal exposure (P = 0.001) led to lower weight of male pups on PND25. Male rats under early-life exposure had increased levels of Shank3 (Shank3_02: timing P < 0.001; site P < 0.05, Shank3_03: timing P < 0.001) and MeCP2 (MeCP2_01: timing P < 0.001, MeCP2_02: timing P < 0.001) methylation and H3K4me3 (EL: 11.94 µg/mg; EH: 11.98; PL: 17.14; PH: 14.78, timing P < 0.05), and reduced levels of H3K27me3 (EL: 71.07 µg/mg; EH: 44.76; PL: 29.15; PH: 28.67, timing P < 0.001; site P < 0.05) in brain compared to those under prenatal exposure. There was, for female pups, a same pattern of Shank3 (Shank3_02: timing P < 0.001; site P < 0.05, Shank3_03: timing P < 0.001) and MeCP2 (MeCP2_01: timing P < 0.05, MeCP2_02: timing P < 0.001) methylation and H3K4me3 (EL: 11.27 µg/mg; EH: 11.55; PL: 16.11; PH: 15.44, timing P < 0.001), but the levels of H3K27me3 exhibited an inverse trend concerning exposure timing. Hypermethylation at the MeCP2 and Shank3 promoter was correlated with the less content of MeCP2 (female: EL: 32.23 ng/mg; EH: 29.58; PL: 25.01; PH: 23.03, timing P < 0.001; site P < 0.05; male: EL: 31.05 ng/mg; EH: 32.75; PL: 23.40; PH: 25.91, timing P < 0.001) and Shank3 (female: EL: 5.10 ng/mg; EH: 5.31; PL: 4.63; PH: 4.82, timing P < 0.001; male: EL: 5.40 ng/mg; EH: 5.48; PL: 4.82; PH: 4.87, timing P < 0.001). Rats with traffic pollution exposure showed aberrant sociability preference and social novelty, while those without it behaved normally. Conclusions Our findings suggest early life under environmental risks is a crucial window for epigenetic perturbations and then abnormalities in protein expression, and traffic pollution impairs behaviors either during pregnancy or after birth.

Dynamic Robustness of Open-Source Project Knowledge Collaborative Network Based on Opinion Leader Identification

A large amount of semantic content is generated during designer collaboration in open-source projects (OSPs). Based on the characteristics of knowledge collaboration behavior in OSPs, we constructed a directed, weighted, semantic-based knowledge collaborative network. Four social network analysis indexes were created to identify the key opinion leader nodes in the network using the entropy weight and TOPSIS method. Further, three degradation modes were designed for (1) the collaborative behavior of opinion leaders, (2) main knowledge dissemination behavior, and (3) main knowledge contribution behavior. Regarding the degradation model of the collaborative behavior of opinion leaders, we considered the propagation characteristics of opinion leaders to other nodes, and we created a susceptible–infected–removed (SIR) propagation model of the influence of opinion leaders’ behaviors. Finally, based on empirical data from the Local Motors open-source vehicle design community, a dynamic robustness analysis experiment was carried out. The results showed that the robustness of our constructed network varied for different degradation modes: the degradation of the opinion leaders’ collaborative behavior had the lowest robustness; this was followed by the main knowledge dissemination behavior and the main knowledge contribution behavior; the degradation of random behavior had the highest robustness. Our method revealed the influence of the degradation of collaborative behavior of different types of nodes on the robustness of the network. This could be used to formulate the management strategy of the open-source design community, thus promoting the stable development of OSPs.

Impact of Lightning Data Assimilation on Forecasts of a Leeward Slope Precipitation Event in the Western Margin of the Junggar Basin

A moderate precipitation event occurring in northern Xinjiang, a region with a continental climate with little rainfall, and in leeward slope areas influenced by topography is important but rarely studied. In this study, the performance of lightning data assimilation is evaluated in the short-term forecasting of a moderate precipitation event along the western margin of the Junggar Basin and eastern Jayer Mountain. Pseudo-water vapor observations driven by lightning data are assimilated in both single and cycling analysis experiments of the Weather Research and Forecast (WRF) three-dimensional variational (3DVAR) system. Lightning data assimilation yields a larger increment in the relative humidity in the analysis field at the observed lightning locations, and the largest increment is obtained in the cycling analysis experiment. Due to the increase in water vapor content in the analysis field, more suitable thermal and dynamic conditions for moderate precipitation are obtained on the leeward slope, and the ice-phase and raindrop particle contents increase in the forecast field. Lightning data assimilation significantly improves the short-term leeward slope moderate precipitation prediction along the western margin of the Junggar Basin and provides the best forecast skill in cycling analysis experiments.