This study investigates the performance assessment of methanol and water as working fluid in a solar-powered vapour absorption refrigeration system. This research clarifies the system’s performance across a spectrum of operating conditions. Furthermore, the HAP software was utilized to determine and scrutinize the cooling load, facilitating a comparative analysis between software-based results and theoretical calculations. To empirically substantiate the findings, this research investigates methanol-water as a superior refrigerant compared to traditional ammonia- water and LiBr-water systems. Through experimental analysis and its comparison with previous research, the methanol-water refrigeration system demonstrated higher cooling efficiency and better environmental compatibility. The system’s performance was evaluated under varying conditions, showing that methanol-water has a 1% higher coefficient of performance (COP) compared to ammonia-water systems, proving its superior effectiveness in solar-powered applications. This empirical model acts as a pivotal tool for understanding the dynamic relationship between methanol concentration (40%, 50%, 60%) and system performance. The results show that temperature of the evaporator (5–15 ℃), condenser (30 ℃–50 ℃), and absorber (25 ℃–50 ℃) are constant, the coefficient of performance (COP) increases with increase in generator temperature. Furthermore, increasing the evaporator temperature while keeping constant temperatures for the generator (70 ℃–100 ℃), condenser, and absorber improves the COP. The resulting data provides profound insights into optimizing refrigerant concentrations for improved efficiency.

Nigeria plays important roles in the overall socio-economic development of the entire African continent, including entrepreneurial activities. There is a less focus on the immersion of women and youths in playing participatory roles in digital entrepreneurship and digital technology innovation in order to boost the economic growth of the country. The primary objective of this study is to explore women and youths’ immersion, specifically in connection with digital entrepreneurship and digital technology innovation, for the purpose of fostering the growth of the economy. The methodology employed in this study is Critical Content Analysis (CCA) of cursory literature as an integral part of the qualitative method. The literature was sourced through different databases, such as library sources, journals, and the core collection of Web of Science (WOS), and the collections of studies used for analysis were between 2018 and 2023. The results demonstrated that small and medium enterprises (SMEs) play significant roles in digital entrepreneurship activities in the country. In addition, there are various entrepreneurship programmes in the country, such as the Youth Entrepreneurship Development Programme (YEDP), and there is awareness of the effectiveness and efficiency of digital entrepreneurship. In addition, the result further established that the use of digital technology is an important innovation for the success of digital entrepreneurship in the country. The study further indicated that five factors of women and youths’ immersion in entrepreneurship (perception and opportunities, business performance, digital adoption, skill acquisition, and enabling environment) can boost the growth of the economy in the country. In conclusion, the knowledge and skills of entrepreneurs are major drivers of wealth and job creations, with women and youths playing an active role in the overall entrepreneurship programmes. It is suggested that the stakeholders and actors in entrepreneurship should collaborate to foster the participation of women and youths in entrepreneurship programmes in the country.

As autonomous vehicles (AVs) revolutionize the global transportation landscape, their implications for emerging economies like Malaysia remain a subject of significant interest. This study delves into the multifaceted world of AV technology, focusing on Malaysia’s unique transportation challenges and opportunities. Through interviews with key stakeholders and experts, the research uncovers valuable insights into AV technology’s awareness, regulatory landscape, integration hurdles, potential benefits, and inclusivity impact in the Malaysian context. The study finds that while AVs hold the promise of improved road safety, reduced traffic congestion, and enhanced environmental sustainability, addressing challenges related to regulation, infrastructure, and public acceptance is imperative for successful integration. Additionally, AV technology has the potential to significantly enhance inclusivity in transportation, benefiting individuals with disabilities. The study underscores the need for holistic policy and infrastructure development to leverage the benefits of AV technology and pave the way for a sustainable and inclusive transportation future in Malaysia.

Surveys are one of the most important tasks to be executed to get valued information. One of the main problems is how the data about many different persons can be processed to give good information about their environment. Modelling environments through Artificial Neural Networks (ANNs) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in dealing with sets of data with some noise, but they are fundamentally surjective mathematical functions, and they aren’t able to give different results for the same input. So, if an ANN is trained using data where samples with the same input configuration has different outputs, which can be the case of survey data, it can be a major problem for the success of modelling the environment. The environment used to demonstrate the study is a strategic environment that is used to predict the impact of the applied strategies to an organization financial result, but the conclusions are not limited to this type of environment. Therefore, is necessary to adjust, eliminate invalid and inconsistent data. This permits one to maximize the probability of success and precision in modeling the desired environment. This study demonstrates, describes and evaluates each step of a process to prepare data for use, to improve the performance and precision of the ANNs used to obtain the model. This is, to improve the model quality. As a result of the studied process, it is possible to see a significant improvement both in the possibility of building a model as in its accuracy.