Quantum dot can be seen as an amazing nanotechnological discovery, including inorganic semiconducting nanodots as well as carbon nanodots, like graphene quantum dots. Unlike pristine graphene nanosheet having two dimensional nanostructure, graphene quantum dot is a zero dimensional nanoentity having superior aspect ratio, surface properties, edge effects, and quantum confinement characters. To enhance valuable physical properties and potential prospects of graphene quantum dots, various high-performance nanocomposite nanostructures have been developed using polymeric matrices. In this concern, noteworthy combinations of graphene quantum dots have been reported for a number of thermoplastic polymers, like polystyrene, polyurethane, poly(vinylidene fluoride), poly(methyl methacrylate), poly(vinyl alcohol), and so on. Due to nanostructural compatibility, dispersal, and interfacial aspects, thermoplastics/graphene quantum dot nanocomposites depicted unique microstructure and technically reliable electrical/thermal conductivity, mechanical/heat strength, and countless other physical properties. Precisely speaking, thermoplastic polymer/graphene quantum dot nanocomposites have been reported in the literature for momentous applications in electromagnetic interference shielding, memory devices, florescent diodes, solar cells photocatalysts for environmental remediation, florescent sensors, antibacterial, and bioimaging. To the point, this review article offers an all inclusive and valuable literature compilation of thermoplastic polymer/graphene quantum dot nanocomposites (including design, property, and applied aspects) for field scientists/researchers to carry out future investigations on further novel designs and valued property-performance attributes.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

This study addresses the critical issue of employee turnover intention within Malaysia’s manufacturing sector, focusing on the semiconductor industry, a pivotal component of the inclusive economy growth. The research aims to unveil the determinants of employee turnover intentions through a comprehensive analysis encompassing compensation, career development, work-life balance, and leadership style. Utilizing Herzberg’s Two-Factor Theory as a theoretical framework, the study hypothesizes that motivators (e.g., career development, recognition) and hygiene factors (e.g., compensation, working conditions) significantly influence employees’ intentions to leave. The quantitative research methodology employs a descriptive correlation design to investigate the relationships between the specified variables and turnover intention. Data was collected from executives and managers in northern Malaysia’s semiconductor industry, revealing that compensation, rewards, and work-life balance are significant predictors of turnover intention. At the same time, career development and transformational leadership style show no substantial impact. The findings suggest that manufacturing firms must reevaluate their compensation strategies, foster a conducive work-life balance, and consider a diverse workforce’s evolving needs and expectations to mitigate turnover rates. This study contributes to academic discourse by filling gaps in current literature and offers practical implications for industry stakeholders aiming to enhance employee retention and organizational competitiveness.

In this paper, we assess the results of experiment with different machine learning algorithms for the data classification on the basis of accuracy, precision, recall and F1-Score metrics. We collected metrics like Accuracy, F1-Score, Precision, and Recall: From the Neural Network model, it produced the highest Accuracy of 0.129526 also highest F1-Score of 0.118785, showing that it has the correct balance of precision and recall ratio that can pick up important patterns from the dataset. Random Forest was not much behind with an accuracy of 0.128119 and highest precision score of 0.118553 knit a great ability for handling relations in large dataset but with slightly lower recall in comparison with Neural Network. This ranked the Decision Tree model at number three with a 0.111792, Accuracy Score while its Recall score showed it can predict true positives better than Support Vector Machine (SVM), although it predicts more of the positives than it actually is a majority of the times. SVM ranked fourth, with accuracy of 0.095465 and F1-Score of 0.067861, the figure showing difficulty in classification of associated classes. Finally, the K-Neighbors model took the 6th place, with the predetermined accuracy of 0.065531 and the unsatisfactory results with the precision and recall indicating the problems of this algorithm in classification. We found out that Neural Networks and Random Forests are the best algorithms for this classification task, while K-Neighbors is far much inferior than the other classifiers.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

Entrepreneurship education plays a crucial role in improving college students' entrepreneurial skills. With the significant momentum gained by digital entrepreneurship, there is an urgent need for digital transformation in entrepreneurship education. However, entrepreneurship education digital transformation (EEDT) is developing in a rapid but fragmented manner, which requires more systematic guidance. This study aims to assess the current research themes and formulate a framework for entrepreneurship education digital transformation. The research employs a systematic literature review and a theory triangulation method. According to the review’s outcome, which focused on 56 articles published between 2018 and 2023, the researcher constructed a conceptual framework for entrepreneurship education digital transformation. To test the construct validity of the framework, the researcher modified it twice through theory triangulation, following the guidelines of the entrepreneurship education ecosystem theory and the education digital transformation framework. This study offers recommendations for research and practice in digital transformation of entrepreneurship education, encompassing a holistic strategy, new educational approaches, novel curriculum designs, and the enhancement of digital literacy among entrepreneurship teachers.