This document outlines the advancements in AI- accelerated frame generation utilizing Neural Processing Units (NPU) in mobile devices. The integration of NPU technology enhances the processing efficiency of mobile graphics, enabling real-time frame generation that significantly improves video and image quality. By leveraging specialized hardware designed for AI computations, the system reduces latency and optimizes power consumption, making it ideal for demanding applications such as gaming and augmented reality. This paper discusses the underlying architecture of NPUs, their role in accelerating frame generation, and the potential impacts on user experience in mobile environments. The findings illustrate how NPU-driven solutions can transform mobile graphics, offering a more immersive and responsive experience while efficiently managing resources.

This research investigates the effects of drying on some selected vegetables, which are Telfaria occidentalis, Amaranthu scruentus, Talinum triangulare, and Crussocephalum biafrae. These vegetables were collected fresh, sliced into smaller sizes of 0.5 cm, and dried in a convective dryer at varying temperatures of 60.0 °C, 70.0 °C and 80.0 °C respectively, for a regulated fan speed of 1.50 ms‒1, 3.00 ms‒1 and 6.00 ms‒1, and for a drying period of 6 hours. It was discovered that the drying rate for fresh samples was 4.560 gmin‒1 for Talinum triangulare, 4.390 gmin‒1for Amaranthu scruentus, 4.580 gmin‒1 for Talinum triangulare, and 4.640 gmin‒1 for Crussocephalum biafrae at different controlled fan speeds and regulated temperatures when the mass of the vegetable samples at each drying time was compared to the mass of the final samples dried for 6 hours. The samples are considered completely dried when the drying time reaches a certain point, as indicated by the drying rate and moisture contents tending to zero. According to drying kinetics, the rate of moisture loss was extremely high during the first two hours of drying and then steadily decreased during the remaining drying duration. The rate at which moisture was removed from the vegetable samples after the drying process at varying regulated temperatures was noted to be in this trend: 80.0 °C > 70.0 °C > 60.0 °C and 6.0 ms‒1 > 3.0 ms‒1 > 1.5 ms‒1 for regulated fan speed. It can be stated here that the moisture contents has significant effects on the drying rate of the samples of vegetables investigated because the drying rate decreases as the regulated temperatures increase and the moisture contents decrease. The present investigation is useful in the agricultural engineering and food engineering industries.

The rapid growth of portable electronics and electric vehicles has intensified the global demand for high-performance energy storage devices with superior power density, energy density, and long cycle life. Among transition metal oxide-based electrode materials with potential for energy storage, we report the development of MnO2–V2O5 nanocomposite electrodes for supercapacitor applications. Pure MnO2 and V2O5 were successfully fabricated via a simple and economical sol–gel method, while (MnO2)x–(V2O5)1−x (x = 1, 0.75, 0.50, and 0) nanocomposites were fabricated through an ex situ method. Analytical techniques, including X-ray diffraction, scanning electron microscopy, Fourier transform infrared spectroscopy, and UV-visible spectroscopy, were employed to investigate the structural, morphological, and optical properties of the electrodes. Furthermore, the electrochemical properties were systematically analysed using cyclic voltammetry, galvanostatic charge–discharge measurements, and electrochemical impedance spectroscopy. The (MnO2)0.75–(V2O5)0.25 nanocomposite demonstrated a remarkable specific capacitance of 666 F/g at a current density of 0.5 A/g in 1 M KOH electrolyte. Additionally, the electrode material exhibited an energy density of 23 Wh/kg and a power density of 450 W/kg, while maintaining a capacitance retention of 95% after 1,500 cycles. The incorporation of V2O5 boosted the conductivity and significantly optimised the number of lattice defects. This work substantially reinforces the importance of metal oxide-based nanocomposites for future energy storage devices.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

Based on 898 English documents and 363 Chinese documents citing the Rising of Network Society, it studied that the knowledge contribution of citation content analysis and citation context analysis methods, and the knowledge contribution of Chinese and foreign quotations to human geography. The study found that “mobile space” is the most quoted theoretical view in domestic and foreign literature, and the proportion of domestic research is significantly higher than foreign research; the focus of domestic and foreign research focuses on the external spatial form and its transformation, while foreign research pays more attention on the internal spatial dynamics of network society and three types of knowledge contributions, reflecting the influence of “network social theory” on human geography. Among them, critical references reveal the shortcomings of “network social theory” point out the abstraction of “spatial duality” the importance of local space, and the limitations of research data, methods, and time background, which provides new enlightenment for the future application and innovation of “network social theory” in the field of human geography.