9,673 research outputs found

    A MEMS electrostatic particle transportation system

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    We demonstrate here an electrostatic MEMS system capable of transporting particles 5-10μm in diameter in air. This system consists of 3-phase electrode arrays covered by insulators (Figs. 1, 2). Extensive testing of this system has been done using a variety of insulation materials (silicon nitride, photoresist, and Teflon), thickness (0- 12μm), particle sizes (1-10μm), particle materials (metal, glass, polystyrene, spores, etc), waveforms, frequencies, and voltages. Although previous literature [1-2] claimed it impractical to electrostatically transport particles with sizes 5-10μm due to complex surface forces, this effort actually shows it feasible (as high as 90% efficiency) with the optimal combination of insulation thickness, electrode geometry, and insulation material. Moreover, we suggest a qualitative theory for our particle transportation system which is consistent with our data and finite-element electrostatic simulations

    Educational Probe for Developing Online Education: A Case of Online Problem-Based Learning in Design Education in India

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    The COVID-19 pandemic brought challenges and opportunities for higher education and one of the important areas was online education. Especially in design field, Online Problem-Based Learning has emerged as a promising method. This paper explores the potential of online-PBL and how it can be developed through a prototype approach. An action research in Indian HEI shows insights regarding the potentiality of online-PBL and application of a prototype approach to educational development activities. A concept of educational probes was proposed as a method to design educational program. © The Author(s), 2022

    Assessment of sleep quality in school children of 6-12 years in COVID-19 pandemic

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    Background: Sleep disorders in children are one of the common disorders and their frequency has increased during the COVID-19 pandemic. The study aims to assess the quality of sleep and study the parameters of sleep in school children aged 6-12 years in pandemic with the help of children’s sleep habit questionnaire (CSHQ).Methods: A survey-based study was conducted from December 2020 to March 2021 using the data obtained from CSHQ. The study involved 498 school children, among which 244 were male participants and 254 were female participants. It involved students from schools of Rahata and Mumbai, Maharashtra.Results: The results of the study were withdrawn. Bedtime Resistance had mean value of 11.79±4.56, sleep onset delay had mean of 1.56±0.71. Average of sleep duration was 4.56±2.09. Sleep anxiety had mean of 7.48±3.1, night wakings had mean of 4.27±1.91. Mean of parasomnias was 10.1±4.46. Average of sleep disordered breathing was 4.09±1.86, for daytime sleepiness mean was 13.04±5.44 with significance of p˂0.0001.Conclusions: The study concluded that sleep time became lesser and bedtime became later in present scenario of COVID-19. The subscale items of CSHQ scale have increased values indicating towards altered sleep pattern. The total scoring of CSHQ for age group 6-9 years on average is higher than age group 10-12 years except “sleep onset delay” and “sleep duration”. Also, the total scores of female participants are higher as compared to male participants

    Loss Guided Activation for Action Recognition in Still Images

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    One significant problem of deep-learning based human action recognition is that it can be easily misled by the presence of irrelevant objects or backgrounds. Existing methods commonly address this problem by employing bounding boxes on the target humans as part of the input, in both training and testing stages. This requirement of bounding boxes as part of the input is needed to enable the methods to ignore irrelevant contexts and extract only human features. However, we consider this solution is inefficient, since the bounding boxes might not be available. Hence, instead of using a person bounding box as an input, we introduce a human-mask loss to automatically guide the activations of the feature maps to the target human who is performing the action, and hence suppress the activations of misleading contexts. We propose a multi-task deep learning method that jointly predicts the human action class and human location heatmap. Extensive experiments demonstrate our approach is more robust compared to the baseline methods under the presence of irrelevant misleading contexts. Our method achieves 94.06\% and 40.65\% (in terms of mAP) on Stanford40 and MPII dataset respectively, which are 3.14\% and 12.6\% relative improvements over the best results reported in the literature, and thus set new state-of-the-art results. Additionally, unlike some existing methods, we eliminate the requirement of using a person bounding box as an input during testing.Comment: Accepted to appear in ACCV 201

    On the instantaneous distribution of vertical velocity in the monsoon field and structure of the monsoon circulation

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