Jansenchandler1529

Piezoelectric nanogenerators (PENGs) and triboelectric nanogenerators (TENGs) are representative technologies that can harvest mechanical energy. In general, piezoelectric/triboelectric hybrid generators can harvest considerable energy with a limited input; however, PENGs and TENGs entail different requirements for harvesting energy. Specifically, PENGs produce a large output when a large mechanical strain is applied, and TENGs require a large surface area to produce a high power. Therefore, it is necessary to develop an innovative strategy in terms of the structural design to satisfy the requirements of both PENGs and TENGs. In this study, we developed a triangulated cylinder origami-based piezoelectric/triboelectric hybrid generator (TCO-HG) with an origami structure to enable effective energy harvesting. The proposed structure consists of a vertical contact-separation TENG on the surface of the triangulated cylinder, PENG on the inner hinge, and rotational TENG on the top substrate to harvest mechanical energy from each motion. Each generator could produce a separate electrical output with a single input. The TCO-HG could charge a 22 μF commercial capacitor and power 60 LEDs when operated.Motor competence and physical fitness are key components for the promotion of an active and healthy lifestyle. Poor motor competence and low physical fitness in children, therefore, are a major threat to future public health. Even though the assessment of physical fitness and motor competence per se does not enhance these entities, fitness tests can provide important information for intervention strategies. Fitness tests may also motivate children to become more active in order to increase their physical abilities. In the school-year 2016/17 the Upper Austrian government initiated the state-wide testing program "wie fit bist du" (how fit are you) in elementary schools, that examined cardiorespiratory fitness, muscular power, speed, agility, flexibility and object control skills along with the assessment of height and weight. Since the beginning of the program more than 18,000 children between 6 and 11 years of age participated in the school-based tests. The results show a significant increase in the prevalenctant contributor for the promotion of an active and healthy lifestyle.Objective To verify the effect of a multicomponent intervention with overweight/obese adolescents on physical fitness, body composition, and insulin biomarkers. Methods A quasi-experimental study with 37 adolescents, aged 10 to 17 years, of both sexes, overweight and obese, allocated in two groups (Intervention-IG Group, n = 17; Control-GC Group, n = 20). The IGs were submitted to a multicomponent intervention for 6 months (three weekly sessions) consisting of physical exercises (sports, functional circuit, recreational, and water activities) and nutritional and psychological guidance. Participants were assessed before and after intervention on body composition [body mass index (BMI), body fat, waist circumference, and waist-to-hip ratio (WHR)], physical fitness [cardiorespiratory fitness (CRF) and abdominal strength], and biomarkers of insulin (glucose, insulin, evaluation of the homeostasis model of insulin, and resistin resistance). The prevalence of responders in both groups was obtained according to the theoretical model applied in previous studies similar to this one to determine the cutoff points for response to intervention. Poisson regression was used to verify the difference in the prevalence ratio (PR) of the interviewees between the groups. AZD7545 inhibitor Results The responders' prevalence between groups CG and IG showed significant differences for body fat (CG = 30.0%; IG = 70.6%; PR = 1.396; p less then 0.001), WHR (CG = 30.0%; IG = 76.5%; PR = 1.730; p less then 0.001), and CRF (CG = 15.0%; IG = 52.5%; PR = 1.580; p less then 0.001). Conclusions A 6-month multicomponent intervention program improved certain body composition parameters and the CRF of overweight and obese adolescents but did not improve insulin biomarkers. Clinical Trial Registration Clinical Trials under Protocol ID 54985316.0.0000.5343.Data leakage can lead to severe issues for a company, including financial loss, damage of goodwill, reputation, lawsuits and loss of future sales. To prevent these problems, a company can use other mechanisms on top of traditional Access Control. These mechanisms include for instance Data Leak Prevention or Information Rights Management and can be referred as Transmission Control. However, such solutions can lack usability and can be intrusive for end-users employees. To have a better understanding of the perception and usage of such mechanisms within business infrastructures, we have conducted in this article an online survey on 150 employees. These employees come from different companies of different sizes and sectors of activity. The results show that whatever the size of the company or its sector of activity, security mechanisms such as access control and transmission control can be considered as quite intrusive and blocking for employees. Moreover, our survey also shows interesting results regarding more acceptable and user-friendly anti-data leakage mechanisms that could be used within companies.Network embedding that encodes structural information of graphs into a low-dimensional vector space has been proven to be essential for network analysis applications, including node classification and community detection. Although recent methods show promising performance for various applications, graph embedding still has some challenges; either the huge size of graphs may hinder a direct application of the existing network embedding method to them, or they suffer compromises in accuracy from locality and noise. In this paper, we propose a novel Network Embedding method, NECL, to generate embedding more efficiently or effectively. Our goal is to answer the following two questions 1) Does the network Compression significantly boost Learning? 2) Does network compression improve the quality of the representation? For these goals, first, we propose a novel graph compression method based on the neighborhood similarity that compresses the input graph to a smaller graph with incorporating local proximity of its vertices into super-nodes; second, we employ the compressed graph for network embedding instead of the original large graph to bring down the embedding cost and also to capture the global structure of the original graph; third, we refine the embeddings from the compressed graph to the original graph.