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Context: In the children's education process, not only children but parents, educators, and teachers are using touchscreen technology-enabled contrivances for better quality of life. Every stakeholder has their perspective on the usage of touchscreen contrivances. A lot of moderators and critical factors affect this perception. Objective: To analyse the moderators and critical factors that affect early childhood education. Method: A comprehensive literature survey and focused group discussion members have finalized six moderators and ten critical factors that affect early childhood education. A hybrid fuzzy AHP-fuzzy TOPSIS has been used to rank identified critical factors. Fuzzy AHP is used to get the weights of the moderators and fuzzy TOPSIS used these weights to rank the critical factors. Results: The moderator 'children age' is having the highest normalized weight (0.211) and the 'screen size' is having the lowest normalized weight (0.106). The two most critical factors that ranked first and second are 'app features and content' and 'applied pedagogical approach' having closeness coefficients of 0.9713 and 0.9594. Potential future scope: The results of the study will help the teachers, parents, and educators to focus on the highly ranked factors which will directly or indirectly improve the children's education.

The probability of medical staff to get affected from COVID19 is much higher due to their working environment which is more exposed to infectious diseases. So, as a preventive measure the body temperature monitoring of medical staff at regular intervals is highly recommended. Infrared temperature sensing guns have proved its effectiveness and therefore such devices are used to monitor the body temperature. These devices are either used on hands or forehead. As a result, there are many issues in monitoring the temperature of frontline healthcare professionals. Firstly, these healthcare professionals keep wearing PPE (Personal Protective Equipment) kits during working hours and as a result it would be very difficult to monitor their body temperature. Secondly, these healthcare professionals also wear face shields and in such cases monitoring temperature by exposing forehead needs removal of face shield. Doing so after regular intervals is surely uncomfortable for healthcare professionals. To avoid such issues, this paper is disclosing a technologically advanced face shield equipped with sensors capable of monitoring body temperature instantly without the hassle of removing the face shield. This face shield is integrated with a built-in infrared temperature sensor. A total of 10 such face shields were printed and assembled within the university lab and then handed over to a group of ten members including faculty and students of nursing and health science department. This sequence was repeated four times and as a result 40 healthcare workers participated in the study. Thereafter, feedback analysis was conducted on questionnaire data and found a significant overall mean score of 4.59 out of 5 which indicates that the product is effective and worthy in every facet. Stress analysis is also performed in the simulated environment and found that the device can easily withstand the typically applied forces. The limitations of this product are difficulty in cleaning the product and comparatively high cost due to the deployment of electronic equipment. [ABSTRACT FROM AUTHOR]

The aim of this study is to discover which kind of scaffolding can effectively promote learning. The past studies have shown mixed results in this regard. The process in which a domain expert gives and withdraws support in order to make a novice learner complete the task is known as scaffolding. A total of four distinct scaffold combinations and four groups were made. This experimental study was repeated twice to cross verify the outcomes using computer based learning environment (CBLE). The CBLE was designed with intelligent web program in PHP and jQuery to evaluate the solutions submitted by the learners instantly. The CBLE acted as an intelligent feedback system. In the first study, it was found that there was a significant effect of different scaffolding treatments on the learning outcomes, F (3,76) = 5.762, p=.001. The result analysis involves multiple comparisons based on Tukey HSD test and indicated that the mean score for the indirect support and adaptive fading (M=4.45, SD=1.191) was considerably different than the others. Likewise, second study also found that there was a significant effect of different scaffold treatments on the learning outcome, F (3,76) = 4.258, p=.008. The Tukey HSD test applied during the second study indicated that the mean score for the indirect support and adaptive fading (M=4.55, SD=1.19) was again significantly different than the others. The present study additionally measured the flow state of all the four groups using Kruskal-Wallis H test and found that indirect support and adaptive fading group was significantly different than direct support and adapting fading group as well as direct support and gradual fading group in both the studies. [ABSTRACT FROM AUTHOR]

Background: Brain tumours represent a diagnostic challenge, especially in the imaging area, where the differentiation of normal and pathologic tissues should be precise. The use of up-to-date machine learning techniques would be of great help in terms of brain tumor identification accuracy from MRI data. Objective This research paper aims to check the efficiency of a federated learning method that joins two classifiers, such as convolutional neural networks (CNNs) and random forests (R.F.F.), with dual U-Net segmentation for federated learning. This procedure benefits the image identification task on preprocessed MRI scan pictures that have already been categorized., Methods: In addition to using a variety of datasets, federated learning was utilized to train the CNN-RF model while taking data privacy into account. The processed MRI images with Median, Gaussian, and Wiener filters are used to filter out the noise level and make the feature extraction process easy and efficient. The surgical part used a dual U-Net layout, and the performance assessment was based on precision, recall, F1-score, and accuracy., Results: The model achieved excellent classification performance on local datasets as CRPs were high, from 91.28% to 95.52% for macro, micro, and weighted averages. Throughout the process of federated averaging, the collective model outperformed by reaching 97% accuracy compared to those of 99%, which were subjected to different clients. The correctness of how data is used helps the federated averaging method convert individual model insights into a consistent global model while keeping all personal data private., Conclusion: The combined structure of the federated learning framework, CNN-RF hybrid model, and dual U-Net segmentation is a robust and privacypreserving approach for identifying MRI images from brain tumors. The results of the present study exhibited that the technique is promising in improving the quality of brain tumor categorization and provides a pathway for practical utilization in clinical settings., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)