721 research outputs found
Feasibility Analysis of Non-electromagnetical Signals Collected via Thingsee Sensors for Indoor Positioning
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
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
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
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Devulcanization of Waste Rubber and Generation of Active Sites for Silica Reinforcement
Each year, hundreds of millions of tires are produced and ultimately disposed into nature. To address this serious environmental issue, devulcanization could be one of the sustainable solutions that still remains as one of the biggest challenges across the globe. In this work, sulfur-vulcanized natural rubber (NR) is mechanochemically devulcanized utilizing a silane-based tetrasulfide as a devulcanizing agent, and subsequently, silica (SiO2)-based rubber composites are prepared. This method not only breaks the sulfur–sulfur cross-links but also produces reactive poly(isoprene) chains to interact with silica. The silica natural rubber composites are prepared by replacing 30% fresh NR by devulcanized NR with varying contents of silica. The composites exhibit excellent mechanical properties, tear strength, abrasion resistance, and dynamic mechanical properties as compared with the fresh natural rubber silica composites. The tensile strength of devulcanized rubber-based silica composites is ∼20 MPa, and the maximum elongation strain is ∼921%. The devulcanized composites are studied in detail by chemical, mechanical, and morphological analyses. Thus, the value added by the devulcanized rubber could attract the attention of recycling community for its sustainable applications
Group investigation model to improve interpersonal skills
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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Poly(acrylonitrile-co-butadiene) as polymeric crosslinking accelerator for sulphur network formation
The major controlling factors that determine the various mechanical properties of an elastomer system are type of chemical crosslinking and crosslink density of the polymer network. In this study, a catalytic amount of acrylonitrile butadiene copolymer (NBR) was used as a co-accelerator for the curing of polybutadiene (BR) elastomer. After the addition of this copolymer along with other conventional sulphur ingredients in polybutadiene compounds, a clear and distinct effect on the curing and other physical characteristics was noticed. The crosslinking density of BR was increased, as evidenced by rheometric properties, solid-state NMR and swelling studies. The vulcanization kinetics study revealed a substantial lowering of the activation energy of the sulphur crosslinking process when acrylonitrile butadiene copolymer was used in the formulation. The compounds were also prepared in the presence of carbon black and silica, and it was found that in the carbon black filled system the catalytic effect of the NBR was eminent. The effect was not only reflected in the mechanical performance but also the low-temperature crystallization behavior of BR systems was altered. © 2020 The AuthorsMaterials science; Materials chemistry; Crosslinking accelerator; Sulphur network; Solid state NMR; Curing kinetics; Activation energy; Acrylonitrile butadiene; Polybutadiene; Low-temperature; Crystallization. © 2020 The Author
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