Smart device industry allows developers and designers to embed different sensors, processors, and memories in small-size electronic devices. Sensors are added to enhance the usability of these devices and improve the quality of experience through data collection and analysis. However, with the era of big data and machine learning, sensors’ data may be processed by different techniques to infer various hidden information. The extracted information may be beneficial to device users, developers, and designers to enhance the management, operation, and development of these devices. However, the extracted information may be used to compromise the security and the privacy of humans in the era of Internet of Everything (IoE). In this work, we attempt to review the process of inferring meaningful data from smart devices’ sensors, especially, smartphones. In addition, different useful machine learning applications based on smartphones’ sensors data are shown. Moreover, different side channel attacks utilizing the same sensors and the same machine learning algorithms are overviewed.

A genetic algorithm (GA) contains a number of genetic operators that can be tweaked to improve the performance of specific implementations. Parent selection, crossover, and mutation are examples of these operators. One of the most important operations in GA is selection. The performance of GA in addressing the single-objective wireless sensor network stability period extension problem using various parent selection methods is evaluated and compared. In this paper, six GA selection operators are used: roulette wheel, linear rank, exponential rank, stochastic universal sampling, tournament, and truncation. According to the simulation results, the truncation selection operator is the most efficient operator in terms of extending the network stability period and improving reliability. The truncation operator outperforms other selection operators, most notably the well-known roulette wheel operator, by increasing the stability period by $25.8\%$ and data throughput by $26.86\%$. Furthermore, the truncation selection operator outperforms other selection operators in terms of the network residual energy after each protocol round.

Coronavirus (COVID-19) pandemics has spread rapidly around the world, sending millions of people into quarantine and lockdown. Based on that, the management of higher educational institutions had to use Internet technology in teaching as the only option to continue all the academic activities across all higher education institution worldwide. This paper aims to investigate how engineering students are coping with the e-learning methods adopted by Al-Zaytoonah University of Jordan during the pandemic. A total of 470 have participated in this study from the faculty of engineering and technology at Al-Zaytoonah University of Jordan. From the findings of the survey, it was clear that students are satisfied with the online teaching and learning instituted by Al-Zaytoonah University of Jordan, despite few identified challenges.

Temperature perception is a highly nonlinear phenomenon with faster rates of change being perceived at much lower thresholds than slower rates. This paper presents a method that takes advantage of this nonlinear characteristic to generate a perception of continuous cooling even though the average temperature is not changing. The method uses multiple thermal actuators so that a few are cooling quickly while the rest of the actuators are heating slowly. The slowly-heating actuators are below the perceptual threshold temperature change and hence are not perceived, while the quickly-cooling actuators are above the perceptual temperature change, hence are perceived. As a result, a feeling of decreasing temperature was elicited, when in fact, there was no net change in the temperature of the skin. Three sets of judiciously designed experiments were conducted in this study, investigating the effects of actuator sizes, forearm measurement locations, patterns of actuator layout, and various heating/cooling time cycles. Our results showed that 19 out 21 participants perceived the continuous cooling effect as hypothesized. Our research indicates that the measurement location, heating/cooling cycle times, and arrangement of the actuators affect the perception of continuous cooling.

The cooling of PV models is an important process that enhances the generated electricity from these models, especially in hot areas. In this work, a new, active cooling algorithm is proposed based on active fan cooling and an artificial neural network, which is named the artificial dynamic neural network Fan cooling algorithm (DNNFC). The proposed system attaches five fans to the back of a PV model. Subsequently, only two fans work at any given time to circulate the air under the PV model in order to cool it down. Five different patterns of working fans have been experimented with in this work. To select the optimal pattern for any given time, a back propagation neural network model was trained. The algorithm is a dynamic algorithm since it re-trains the model with new recorded surface temperatures over time. In this way, the model automatically adapts to any weather and environmental conditions. The model was trained with an indoor dataset and tested with an outdoor dataset. An accuracy of more than 97% has been recorded, with a mean square error of approximately 0.02.