Towards Wind Energy-based Charging Stations: A Review of Optimization Methods

Due to the growing importance of renewable sources in sustainable energy systems, the strategic deployment of robust optimization techniques plays a crucial role in the design of Electric Vehicle Charging Stations (EVCSs). These stations need to smoothly incorporate renewable sources, ensuring optimal energy utilization. This study provides a comprehensive overview of the methodologies and approaches employed in the enhancement of wind energy based EVCSs. The aim is to discern the most efficacious techniques for optimizing charging stations. Researchers engage diverse strategies and methodologies in the realm of sizing and optimization, encompassing a spectrum of algorithmic implementations and software solutions. Evidently, each algorithm or software application bears distinctive merits and demerits. Singular reliance on a solitary algorithm or software for charging utility optimization is discerned to be potentially limiting. The investigation reveals that achieving better results in Electric Vehicle Charging Station (EVCS) optimization is facilitated by the collaborative use of multiple algorithms like GA, PSO, and ACO, among others, or software tools like Homer or RETScreen

Towards Wearable Augmented Reality in Healthcare: A Comparative Survey and Analysis of Head-Mounted Displays

Head-mounted displays (HMDs) have the potential to greatly impact the surgical field by maintaining sterile conditions in healthcare environments. Google Glass (GG) and Microsoft HoloLens (MH) are examples of optical HMDs. In this comparative survey related to wearable augmented reality (AR) technology in the medical field, we examine the current developments in wearable AR technology, as well as the medical aspects, with a specific emphasis on smart glasses and HoloLens. The authors searched recent articles (between 2017 and 2022) in the PubMed, Web of Science, Scopus, and ScienceDirect databases and a total of 37 relevant studies were considered for this analysis. The selected studies were divided into two main groups; 15 of the studies (around 41%) focused on smart glasses (e.g., Google Glass) and 22 (59%) focused on Microsoft HoloLens. Google Glass was used in various surgical specialities and preoperative settings, namely dermatology visits and nursing skill training. Moreover, Microsoft HoloLens was used in telepresence applications and holographic navigation of shoulder and gait impairment rehabilitation, among others. However, some limitations were associated with their use, such as low battery life, limited memory size, and possible ocular pain. Promising results were obtained by different studies regarding the feasibility, usability, and acceptability of using both Google Glass and Microsoft HoloLens in patient-centric settings as well as medical education and training. Further work and development of rigorous research designs are required to evaluate the efficacy and cost-effectiveness of wearable AR devices in the future

Empirical Analysis for Stock Price Prediction Using NARX Model with Exogenous Technical Indicators

The file attached to this record is the Publisher's final version. Open access article.Stock price prediction is one of the major challenges for investors who participate in the stock markets. Therefore, different methods have been explored by practitioners and academicians to predict stock price movement. Artificial intelligence models are one of the methods that attracted many researchers in the field of financial prediction in the stock market. This study investigates the prediction of the daily stock prices for Commerce International Merchant Bankers (CIMB) using technical indicators in a NARX neural network model. The methodology employs comprehensive parameter trails for different combinations of input variables and different neural network designs. The study seeks to investigate the optimal artificial neural networks (ANN) parameters and settings that enhance the performance of the NARX model. Therefore, extensive parameter trails were studied for various combinations of input variables and NARX neural network configurations. The proposed model is further enhanced by preprocessing and optimising the NARX model’s input and output parameters. The prediction performance is assessed based on the mean squared error (MSE), R-squared, and hit rate. The performance of the proposed model is compared with other models, and it is shown that the utilisation of technical indicators with the NARX neural network improves the accuracy of one-step-ahead prediction for CIMB stock in Malaysia. The performance of the proposed model is further improved by optimising the input data and neural network parameters. The improved prediction of stock prices could help investors increase their returns from investment in stock markets

Techno-economic analysis of a wind-energy-based charging station for electric vehicles in high-rise buildings in Malaysia

Renewable energy sources have become necessary for long-term energy sustainability due to the increased demand for electric cars and worrisome rises in carbon dioxide emissions from traditional energy sources. Furthermore, transportation is one of the sectors that uses the most energy on the planet, accounting for 24% of overall consumption. Fossil fuels are still the dominant energy source for balancing global demand/supply dynamics. Supporting laws and regulations have enhanced the first phase of environmentally friendly energy-resource consumption. This has spurred the development of new solutions that cut greenhouse-gas emissions and reduce the air pollution produced by internal combustion engines that are fuelled by fossil fuels. Wind energy is one of the clean energy sources that may be utilised for this purpose. Wind energy has been used to power electric-car-charging infrastructure, generally in a hybrid mode with another renewable source. This research examines the possibility of using wind energy as a standalone energy source to support electric-vehicle-charging infrastructure. Using data from Malacca, Malaysia, and HOMER software, the project will build and optimise a standalone wind-powered charging station. An RC-5K-A wind turbine coupled to a battery and converter is the appropriate choice for the system. The findings demonstrate that the turbine can produce 214,272 kWh per year at the cost of USD 0.081/kWh, confirming wind’s future feasibility as an energy-infrastructure support source

The effect of value innovation in the superior performance and sustainable growth of telecommunications sector : Mediation effect of customer satisfaction and loyalty

The telecommunications industry has recognized innovation as the key to growth and survival. Globalization, liberalization, and privatization, the terms most commonly associated with this sector, have resulted in fierce competition, making it more difficult for telecommunications firms to increase their market share and, as a result, their customer base, sales volume, and, ultimately, profits. The new success strategy is differentiation through innovation, with the aim of breaking out of the competition and creating an uncontested market. This can be accomplished by providing high-value, innovative services that result in customer satisfaction and promote customer loyalty. The primary goal of this study was to create and validate a conceptual model of value innovation and its impact on firm performance and long-term growth by examining the mediation effect of customer satisfaction and loyalty. The empirical analysis results were based on 304 respondents who completed a paper-based survey provided to employees of Yemeni mobile service providers using a convenience non-probability sampling technique. SmartPLS 3 was used to test the hypothesized relationships using partial least squares structural equation modeling (PLS-PM). As a result, the findings empirically validated the theoretical research model, confirming the importance of the value innovation approach to achieving company performance and long-term growth by promoting customer satisfaction and loyalty. Finally, we have provided a discussion of the study’s theoretical contributions, managerial implications, and future research directions

An Integrated Success Model of Internet of Things (IoT)-Based Services in Facilities Management for Public Sector

The rapid growth of the Internet of Things (IoT) has vigorously affected government by enhancing quality and efficiency of public services. However, the application of IoT-based services in public sectors is slow, despite its benefits to citizens. Research is needed to deepen understanding of the factors that influence the successful implementation of facilities management as the Internet-of-Things-based services in public sectors. An integrated model is developed and validated to extend the DeLone and McLean IS success model by including technology readiness and other identified factors which impact the use of facilities management of IoT-based services in public sectors from the perspective of employees. An online questionnaire was developed and distributed to employees from all local authorities throughout Malaysia, and 187 usable responses were collected. The partial least squares structural equation modelling approach was used to test the model, with 90.8% of the variance in IoT-based services, suggesting an acceptable model fit with seven out of nine hypotheses were supported. Thus, the empirical evidence exerts significant effects of technology readiness towards the success of IoT-based facility management in the public sector

Towards Detection of Interest Using Physiological Sensors

The positive effects of interest on different aspects, e.g., learning and education, economy, psychological well-being, and social relations, have been widely addressed by many psychological and physiological studies in the last two decades. While the psychological work has investigated this impact of interest theoretically, the physiological studies have focused more on the modulatory effects. However, some studies have addressed both sides of the effects. In this work, we conduct a comprehensive review of physiological studies on interest detection, from different perspectives carried out between 2003 and 2019. A lack of connection between the psychological and physiological studies was identified. Therefore, this paper aims to integrate the unique psychological and physiological aspects and characteristics of interest to form a base for future research by considering the pros and cons of the included studies. For example, considering the two types of interest (situational and individual) the detected interest in learning, gaming, and advertisement’s physiological experiments could be referring specifically to situational interest. Hence, bridging the gap between both physiological and psychological studies is essential for improving the research on interest. Furthermore, we propose several suggestions for future work direction

Review of Renewable Energy-Based Charging Infrastructure for Electric Vehicles

With the rise in the demand for electric vehicles, the need for a reliable charging infrastructure increases to accommodate the rapid public adoption of this type of transportation. Simultaneously, local electricity grids are being under pressure and require support from naturally abundant and inexpensive alternative energy sources such as wind and solar. This is why the world has recently witnessed the emergence of renewable energy-based charging stations that have received great acclaim. In this paper, we review studies related to this type of alternative energy charging infrastructure. We provide comprehensive research covering essential aspects in this field, including resources, potentiality, planning, control, and pricing. The study also includes studying and clarifying challenges facing this type of electric charging station and proposing suitable solutions for those challenges. The paper aims to provide the reader with an overview of charging electric vehicles through renewable energy and establishing the ground for further research in this vital field

User Engagement and Abandonment of mHealth: A Cross-Sectional Survey

Mobile health (mHealth) apps have great potential to improve health outcomes. Given that mHealth apps have become ubiquitous, there is limited focus on their abandonment. Data concerning crucial metrics, including reasons for adoption and discontinued use, are limited. This study aims to gain broad insights into utilization of mHealth and game-like features promoting user engagement. We conducted a cross-sectional survey of 209 mHealth users worldwide. The 17-item survey assessed sociodemographics, as well as the key motivators for mHealth uptake and discontinued use. Our findings show that sports and fitness activity tracking were the most common categories of health apps, with most users engaging with them at least several times a week. Interestingly, the most downloaded mHealth apps among younger adults include MyFitnessPal, Fitbit, Nike Run Club, and Samsung Health. Critical drivers of abandonment of mHealth apps were amotivation, loss of interest, and experimenting with different apps to identify the most suitable tool. Additionally, the financial cost of mHealth apps is crucial, with most participants advocating for free or more affordable apps. The study findings suggest that while many individuals utilize mHealth, several factors drive their abandonment. Moreover, data indicate that mHealth developers need to consider gamification strategies to sustain user commitment, as well as psychological variables, such as intrinsic motivation