721 research outputs found

    Feasibility Analysis of Non-electromagnetical Signals Collected via Thingsee Sensors for Indoor Positioning

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    Internet of Things (IoT) has significant impacts on wireless networking and communication technologies of modern times. Recently it has gained also attention in the field of indoor positioning and localization, both in research and industrial markets. IoT technologies enables access to the real time information about indoor environment which are collected through sensors. The sensor data is processed and analysed to understand the complexity of the indoor environment so that it can be used for making applications based on positioning. This thesis deals with some modern applications, challenges, key technologies and architectural overviews of Internet of Things including some recent works which were carried out based on electromagnetical and non-electromagnetical approaches. Then. a feasibility analysis is made for indoor positioning using non-electromagnetical sensor data which includes temperature, humidity, pressure and luminance. These sensors are also known as environmental sensors. An IoT development device named ‘Thingsee One’ was used where the environmental sensors were embedded in. The device was used for capturing environmental data from different locations inside a university building in Tampere, Finland. At first, Thingsee One device was configured for capturing temperature, humidity, pressure and luminance data from an indoor environment. Measurements were taken from different locations of the building, from first and second floor. Different times and weather condition were also taken into account during data capturing. Then the captured data has been analysed for identifying those positions through histograms and power maps. The results show that, the data captured by the sensors are highly dependent on time and weather which makes them rather inconsistent over the same position in different situations and time and therefore not likely candidates for positioning estimation

    A Systematic Review of Mobile Apps for Child Sexual Abuse Education: Limitations and Design Guidelines

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    The objectives of this study are understanding the requirements of a CSA education app, identifying the limitations of existing apps, and providing a guideline for better app design. An electronic search across three major app stores(Google Play, Apple, and Microsoft) is conducted and the selected apps are rated by three independent raters. Total 191 apps are found and finally, 14 apps are selected for review based on defined inclusion and exclusion criteria. An app rating scale for CSA education apps is devised by modifying existing scales and used to evaluate the selected 14 apps. Our rating scale evaluates essential features, criteria, and software quality characteristics that are necessary for CSA education apps, and determined their effectiveness for potential use as CSA education programs for children. The internal consistency of the rating scale and the inter and intra-rater reliability among the raters are also calculated. User comments from the app stores are collected and analyzed to understand their expectations and views. After analyzing the feasibility of reviewed apps, CSA app design considerations are proposed that highlight game-based teaching approaches. Evaluation results showed that most of the reviewed apps are not suitable for being used as CSA education programs. While a few may be able to teach children and parents individually, only the apps "Child Abuse Prevention" (rate 3.89 out of 5) and "Orbit Rescue" (rate 3.92 out of 5) could be deemed suitable for a school-based CSA education program. However, all those apps need to be improved both their software qualities and CSA-specific features for being considered as potential CSA education programs. This study provides the necessary knowledge to developers and individuals regarding essential features and software quality characteristics for designing and developing CSA education apps

    A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameters

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    The reason behind the remarkable properties of High-Entropy Alloys (HEAs) is rooted in the diverse phases and the crystal structures they contain. In the realm of material informatics, employing machine learning (ML) techniques to classify phases and crystal structures of HEAs has gained considerable significance. In this study, we assembled a new collection of 1345 HEAs with varying compositions to predict phases. Within this collection, there were 705 sets of data that were utilized to predict the crystal structures with the help of thermodynamics and electronic configuration. Our study introduces a methodical framework i.e., the Pearson correlation coefficient that helps in selecting the strongly co-related features to increase the prediction accuracy. This study employed five distinct boosting algorithms to predict phases and crystal structures, offering an enhanced guideline for improving the accuracy of these predictions. Among all these algorithms, XGBoost gives the highest accuracy of prediction (94.05%) for phases and LightGBM gives the highest accuracy of prediction of crystal structure of the phases (90.07%). The quantification of the influence exerted by parameters on the model's accuracy was conducted and a new approach was made to elucidate the contribution of individual parameters in the process of phase prediction and crystal structure prediction

    Group investigation model to improve interpersonal skills

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    This study aimed to prove the effectiveness of the application of the group investigation learning model in improving students' interpersonal skills. The sample of this study was 116 students, which was determined by a simple random sampling technique. This experimental research used pre-test post-test Control Group Design. Data were obtained by direct observation of the interpersonal skills of students during the learning process. Final observation score of interpersonal skills is 0.026 and the t value count greater than t table (2.272>1.980). Thus, there are differences in interpersonal skills between the experimental class and the control class. This means that the use of the group investigation model is effective in improving students' interpersonal skills
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