Investigation of Silicon Nanoparticle-Polystyrene Hybrids

Current LED lights are created with quantum dots made of metals like selenium, tellurium, and cadmium which can be toxic. Silicon is used as a non-toxic substance and is the second most abundant element in the earth's crust. When silicon is prepared at a nanometer size, unique luminesce optical properties emerge that can be tuned using sized surface chemistry. Therefore, silicon nanoparticles can be used as an alternative emitter for LED lights. To produce hydride-terminated silicon nanoparticles we must synthesize the particles. Hydrogen silsesquioxane (HSQ) is processed at 1100 °C for one hour causing Si to cluster and form a SiO2 matrix, also known as the composite. The composite is then manually crushed in ethanol. The solution is further ground using glass beads, then filtered to get the composite powder. The final step is the HF etching. The hydride-terminated particles are then functionalized using three different methods to synthesize silicon nanoparticle-polystyrene hybrids, which determine the magnitude of luminosity and the quality of the hybrids. We spin coat each method and results were analyzed. Method 1 uses heat to functionalize hydride-terminated silicon nanoparticles with styrene. This process also causes styrene to attach to styrene to form a polystyrene chain. Method 1 gave a homogeneous mixture which yielded a consistent, bright and homogenous film. In method 2, dodecyl-terminated silicon nanoparticles are mixed with premade polystyrene. While this method gave better control of the amount of silicon nanoparticles inside the polymer hybrid, a homogeneous mixture was not created due to the different structures of polystyrene and dodecyl chains. Method 3 has dodecyl-terminated silicon with in-situ styrene polymerization. It generated a homogeneous mixture. The in-situ polymerization stabilizes the particles, allowing for brighter luminescence. Because of the stability and lower molecular weight, the mixture was easier to dissolve. We concluded that the different methods resulted in different polymer molecular weights and this created distinct properties between the polymer hybrids when spin-coating.   &nbsp

Automated Academic Advisory System Based on Students’ Emotional Intelligence: A Study of University of Nizwa, Sultanate of Oman

The COVID-19 pandemic has brought outward unparalleled difficulties in the field of education, emphasizing the need for unique solutions.   This study aimed to evaluate the determinants that affect the effective implementation of the automated student academic advisory system at the University of Nizwa of the Sultanate of Oman, with a particular emphasis on its influence on students' emotional intelligence during the pandemic   The research utilized the Unified Theory of Acceptance and Use of Technology (UTAUT) model to investigate how factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions influence students' behavioral intentions.   In addition, the study examined the behavioral intention effects on the use of the automated system, emotional intelligence effects and the experience of COVID-19 specifically influences the actual use of the system.   The study employed a case study methodology in combination with a quantitative survey method to gather data from 272 students and advisors at the University of Nizwa.   The collected data was analyzed using SmartPLS, a technique known as structural equation modeling.   The research provides useful information on the adoption of technology in an educational environment and its impact on student well-being and emotional intelligence.   The study found that only performance expectancy and facilitating condition factors had a substantial impact on behavioral intention, whereas effort expectancy and social influence did not.   Behavioral intention showed a positive correlation with actual usage, but performance expectancy, effort expectancy, and social influence did not directly influence the actual utilization.   Moreover, behavioral intention served as a mediator for the indirect impact of performance expectancy and facilitating conditions on actual usage. However, it did not mediate the indirect impact of social influence and effort expectancy on actual utilization.   Furthermore, the impact of COVID-19 and emotional intelligence did not influence the relationship between facilitating conditions and actual use.   These findings offer valuable information into understanding educational technology, particularly in situations of global crises, and offer practical recommendations for educators, legislators, and academic institutions seeking to enhance student participation, support, and general well-being. &nbsp

Investigating User Preferences towards Visualization Types with a Focus on Neuroticism and its Implications on Mental Health

This study delves into the importance of visualizing mental health data to attain a comprehensive understanding of psychological disorders through the means of data storytelling and information visualization. Utilizing the Five Factor Model, the article investigates the correlation between two prevalent mental health conditions, depression and anxiety, with a specific focus on the neuroticism trait. The researchers aim to underscore the potential of data-driven techniques in shaping mental health interventions and promoting a deeper comprehension of these conditions by exploring the association between neuroticism and various visualization approaches. To achieve this objective, the study employs quantitative research methods to support population association analysis, cause-and-effect analysis, and the assessment of the link between independent and dependent variables. The findings show that neuroticism has no relationship to any sort of visualization types, and this paper presented several explanations for the lack of a relationship between visualization type and neuroticism

Investigating User Preferences Towards Visualization Types with a Focus on Neuroticism and its Implications on Mental Health

This study delves into the importance of visualizing mental health data to attain a comprehensive understanding of psychological disorders through the means of data storytelling and information visualization. Utilizing the Five Factor Model, the article investigates the correlation between two prevalent mental health conditions, depression and anxiety, with a specific focus on the neuroticism trait. The researchers aim to underscore the potential of data-driven techniques in shaping mental health interventions and promoting a deeper comprehension of these conditions by exploring the association between neuroticism and various visualization approaches. To achieve this objective, the study employs quantitative research methods to support population association analysis, cause-and-effect analysis, and the assessment of the link between independent and dependent variables. The findings show that neuroticism has no relationship to any sort of visualization types, and this paper presented several explanations for the lack of a relationship between visualization type and neuroticism

Computational intelligence-based optimization methods for power quality and dynamic response enhancement of ac microgrids

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures