Utilizing social virtual reality robot (V2R) for music education to children with high-functioning autism

Virtual Reality (VR) technology is a growing technology that has been used in various fields of psychology, education, and therapy. One group of potential users of VR are children with autism who need education and have poor social interactions; this technology could help them improve their social skills through real-world simulation. In this study, we evaluated the feasibility of conducting virtual music education programs with automatic assessment system for children with autism at treatment/research centers without the need to purchase a robot, resulting in the possibility of offering schedules on a larger scale and at a lower cost. Intervention sessions were conducted for five children with high-functioning autism ranging in age from 6 to 8 years old during 20 weeks which includes a baseline session, a pre-test, training sessions, a post-test, and a follow-up test. Each music education sessions involved teaching different notes and pieces of music according to the child’s cooperation, accuracy, and skill level utilizing virtual reality robots and virtual musical instruments. Actually, by analysis of psychological tests, and questionnaires conducted by a psychologist, we observe slight improvements in cognitive skills because of the ceiling effect. Nevertheless, the effectiveness of the proposed method was proved by conducting statistical analysis on the child’s performance data during the music education sessions which were obtained by using both video coding and the proposed automatic assessment system. Consequently, a general upward trend in the musical ability of participants was shown to occur in these sessions, which warrants future studies in this field

A fault-tolerant cascaded switched-capacitor multilevel inverter for domestic applications in smart grids

Cascaded multilevel inverters (MLIs) generate an output voltage using series-connected power modules that employ standard configurations of low-voltage components. Each module may employ one or more switched capacitors to double or quadruple its input voltage. The higher number of switched capacitors and semiconductor switches in MLIs compared to conventional two-level inverters has led to concerns about overall system reliability. A fault-tolerant design can mitigate this reliability issue. If one part of the system fails, the MLI can continue its planned operation at a reduced level rather than the entire system failing, which makes the fault tolerance of the MLI particularly important. In this paper, a novel fault location technique is presented that leads to a significant reduction in fault location detection time based on the reliability priority of the components of the proposed fault-tolerant switched capacitor cascaded MLI (CSCMLI). The main contribution of this paper is to reduce the number of MLI switches under fault conditions while operating at lower levels. The fault-tolerant inverter requires fewer switches at higher reliability, and the comparison with similar MLIs shows a faster dynamic response of fault detection and reduced fault location detection time. The experimental results confirm the effectiveness of the presented methods applied in the CSCMLI. Also, all experimental data including processor code, schematic, PCB, and video of CSCMLI operation are attached. © 2013 IEEE

Exploring the interaction of quercetin-3-O-sophoroside with SARS-CoV-2 main proteins by theoretical studies: A probable prelude to control some variants of coronavirus including Delta

The aim of this study was to investigate the mechanism of interaction between quercetin-3-O-sophoroside and different SARS-CoV-2’s proteins which can bring some useful details about the control of different variants of coronavirus including the recent case, Delta. The chemical structure of the quercetin-3-O-sophoroside was first optimized. Docking studies were performed by CoV disease-2019 (COVID-19) Docking Server. Afterwards, the molecular dynamic study was done using High Throughput Molecular Dynamics (HTMD) tool. The results showed a remarkable stability of the quercetin-3-O-sophoroside based on the calculated parameters. Docking outcomes revealed that the highest affinity of quercetin-3-O-sophoroside was related to the RdRp with RNA. Molecular dynamic studies showed that the target E protein tends to be destabilized in the presence of quercetin-3-O-sophoroside. Based on these results, quercetin-3-O-sophoroside can show promising inhibitory effects on the binding site of the different receptors and may be considered as effective inhibitor of the entry and proliferation of the SARS-CoV-2 and its different variants. Finally, it should be noted, although this paper does not directly deal with the exploring the interaction of main proteins of SARS-CoV-2 Delta variant with quercetin-3-O-sophoroside, at the time of writing, no direct theoretical investigation was reported on the interaction of ligands with the main proteins of Delta variant. Therefore, the present data may provide useful information for designing some theoretical studies in the future for studying the control of SARS-CoV-2 variants due to possible structural similarity between proteins of different variants

Mapping local patterns of childhood overweight and wasting in low- and middle-income countries between 2000 and 2017

A double burden of malnutrition occurs when individuals, household members or communities experience both undernutrition and overweight. Here, we show geospatial estimates of overweight and wasting prevalence among children under 5 years of age in 105 low- and middle-income countries (LMICs) from 2000 to 2017 and aggregate these to policy-relevant administrative units. Wasting decreased overall across LMICs between 2000 and 2017, from 8.4% (62.3 (55.1–70.8) million) to 6.4% (58.3 (47.6–70.7) million), but is predicted to remain above the World Health Organization’s Global Nutrition Target of <5% in over half of LMICs by 2025. Prevalence of overweight increased from 5.2% (30 (22.8–38.5) million) in 2000 to 6.0% (55.5 (44.8–67.9) million) children aged under 5 years in 2017. Areas most affected by double burden of malnutrition were located in Indonesia, Thailand, southeastern China, Botswana, Cameroon and central Nigeria. Our estimates provide a new perspective to researchers, policy makers and public health agencies in their efforts to address this global childhood syndemic

Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

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Early Detection of the Advanced Persistent Threat Attack Using Performance Analysis of Deep Learning

One of the most common and critical destructive attacks on the victim system is the advanced persistent threat (APT)-attack. An APT attacker can achieve its hostile goal through obtaining information and gaining financial benefits from the infrastructure of a network. One of the solutions to detect a unanimous APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the system may crash due to the high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods of C5.0 decision tree, Bayesian network, and deep learning are used for the timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, a 10-fold cross-validation method is used to experiment with these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 95.64%, 88.37%, and 98.85%, respectively. Also, in terms of the critical criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network, and 6-layer deep learning models is obtained as 2.56, 10.47, and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false-negative rate, and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models.publishedVersio

Evaluation of the Functional Movement Screen (FMS) in Identifying Active Females Who are Prone to Injury. A Systematic Review

Background!#!The validity of the Functional Movement Screen (FMS) in identifying active females who are predisposed to injury has not been specifically reviewed. This study aims to synthesize the literature on the ability of the FMS to identify at-risk active females.!##!Methods!#!Six online databases, including PubMed, Medline, Web of Science, Science Direct, SPORTDiscus and Google Scholar, were searched for the period of April 2006 to September 2021. Out of the 61 potential references, 17 were reviewed in detail with respect to the inclusion criteria; ten were ultimately included. The risk of bias, applicability and level of the studies were then identified using the QUADAS-2 and a checklist for assessing methodological quality. The following data were obtained from the included studies: year of publication, title, study type, participants' demographic, sample size, FMS cutoff point, injury definition, statistical analyses used, FMS results and study level.!##!Results!#!Generally, the quality of eight studies was poor to moderate due to both small sample sizes and short follow-up periods. Except for a study on military members, all studies were carried out on team sports players. The overall bias of the studies was low, but there was an unclear amount of bias for participant selection. Two studies reported no predictive validity for the FMS, while three defended its predictive validity; the rest partially supported the FMS as a valid diagnostic tool. The reliability of the recommended cutoff point was confirmed, though cutoffs higher than 14 were significantly associated with the predictive ability of the FMS.!##!Conclusion!#!Although the FMS is reliable for clinical practice, and the current literature shows promise regarding the predictive ability of the FMS among active females, concerns remain regarding its validity in identifying at-risk females. Given the lack of clarity in the literature on the use of the FMS in females, further well-organized studies with larger sample sizes and longer monitoring periods are highly recommended. The sensitivity and specificity of the recommended cutoff of ≤ 14 has considerably decreased , and higher cutoff values should be applied to increase the FMS predictive ability. Level of evidence The level of evidence was determined to be 2b

Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

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