Brain age has emerged as a critical biomarker for assessing neurodevelopmental health and aging trajectories, demonstrating significant potential in early detection and monitoring of neurological and psychiatric disorders. While existing brain age prediction models predominantly rely on structural MRI (sMRI) due to their rich anatomical detail and predictive accuracy, resting-state functional MRI (rs-fMRI)—which captures dynamic brain connectivity—remains underutilized despite offering complementary insights. This study proposes a highly accurate and generalizable brain age prediction framework that effectively fuses sMRI and rs-fMRI modalities. Specifically, we integrate a Squeeze-and-Excitation Transformer for structural feature extraction with a Graph Frequency Recurrent Network for modeling functional dynamics. Our hybrid model achieves a MAE of 1.31 years and Pearson's R of 0.976 on the ABIDE I dataset while generalizing effectively across independent datasets, thus demonstrating the utility of multimodal fusion for robust brain age estimation.

Artificial intelligence course has been required to take for compulsory education students in China. However, not all teachers and schools are fully prepared and ready. This is partially because of the lack of adequate teaching and learning resources, which requires a major expenditure of time and effort for schools and teachers to design and develop. To meet the challenge of lacking appropriate resources in teaching and learning AI from grade 1 to grade 9, we developed AI knowledge structure and instructional resources based on Chinese national curriculum for information science and technology. Our comprehensive AI syllabus contains 90 core concepts, 63 learning indicators, and 27 teaching and learning resources, which have been implemented. The resources have been taken as model courses in teacher training programs and an exemplary course has been implemented in primary schools that verified the effectiveness of our resources.

In response to the trend that artificial intelligence (AI) is becoming the main driver for social and economic development, enhancing the readiness of learners in AI is significant and important. The state council and the ministry of education of China put AI education for K-12 schools on a high priority in order to foster local AI talents and reduce educational disparities. However, the AI knowledge and technical skills are still limited for not only students but also the school teachers. Furthermore, many local schools in China, especially in the rural areas, are lack of the necessary software and hardware for teaching AI. Hence, we designed and implemented a structured series of AI courses, built on an online block-based visual programming platform. The AI courses are free and easily accessible for all. We have conducted the experimental classes in a local school and collected the results. The results show that the learners in general gained significant learning progress on AI knowledge comprehension, aroused strong interests in AI, and increased the degree of satisfaction towards the course. Especially, our practices significantly increased computational thinking of the students who were initially staying at a lower level.

In order to ensure good power quality, the grid-connected inverter mostly uses phase-locked loop (PLL) to maintain synchronization with large power grid. But the PLL will cause a negative-impact on the stability of grid-connected inverter under weak grids. The paper analyzes the equivalent impedance of the PLL under the weak grid firstly. Then the paper proposed a self-synchronization strategy using DC bus voltage and reactive power control to synchronize the frequency and amplitude with the grid-connected inverter instead of the applying PLL. Applying the proposed method to the on/off grid switching condition, a pre-synchronization control strategy based on virtual power is proposed to achieve seamless switching of the grid-connected inverter. At last, the designed self-synchronization control strategy and pre-synchronization strategy are verified on the Plecs simulation platform.

With the rapid development of mobile games, power consumption and battery life of mobile platforms become a crucial problem, therefore, saving power for display which consumes a lot of power without decreasing the user experience becomes necessary. Content-centric techniques are most commonly used to save display power. However, they cannot be applied to closed-source games as they need to modify the game images. In this paper, we explore a backlight power model and propose a novel method to make a trade-off between the game image quality and display power. Different from content-centric methods, based on the proposed trade-off model, we propose a backlight dimming algorithm which maintains the user game experience using game-state information and saves display power for mobile games without modifying any game image. We implement the proposed trade-off model and backlight dimming policy in a contemporary mobile platform where the evaluation results show that, maintaining a specific game image quality level, our policy can save system power up to 10.43% compared with the static policy without decreasing games' performances.

Contemporary mobile platforms employ embedded graphic processing units (GPUs) for graphics-intensive games, and dynamic voltage and frequency scaling (DVFS) policies are used to save energy without sacrificing quality. However, current GPU DVFS policies result in unnecessary power waste due to defective workload estimations of embedded GPUs during game play. In this paper, we propose the Frame-Complexity Model (FCM), a fine-grained estimation of the GPU workload in a game frame, to quantify the GPU workload with the real runtime demand for GPU computing resources of a game frame. In FCM, three constituents of a game frame (i.e., structure, textures and computation) are quantified without modification of mobile games. Preliminary experiments show that, compared with the default policy, the FCM-directed GPU DVFS policy can reduce more power consumption of games (11.3% to 25.8%) with good Quality of Service (QoS).

Since game applications have spilled over on the modern mobile platforms equipped with Multiprocessor Systemon-Chips and highlighted the power consumption and battery life problem of these platforms, reducing the game power for mobile devices becomes meaningful. The design of independent CPU-GPU power managements in contemporary platforms results in power consumption waste due to the failure of consideration of CPU-GPU interaction and game workload behaviors. Through analyzing the Application-Operating System (APP-OS) interaction and CPU-GPU interaction, we extract the system-call information and OpenGL API information to characterize the game workload in a low-complexity way. In this paper, based on identifying the game workload behavior and performance bottleneck, we propose a behavior-aware integrated CPU-GPU power management approach for mobile games. We also implement our power saving policy in the real platform, where the evaluation results show that our behavior-aware policy can significantly reduce power and improve game performance. Our policy provides 18% and 5% higher power-efficiency on average compared with the current policy used in our platform and the state-of-the-art policy respectively.

The rapid development of mobile games highlights the power consumption problem in the mobile platform. Most of the power saving techniques use the prediction-based dynamic voltage frequency scaling (DVFS) scheme. However, the prediction could be inaccurate resulting from the frequent interactions of user when playing games. We have observed that frame rate is near-linear to CPU frequency, but there is a bottleneck, frame rate will not increase as CPU frequency increases when CPU frequency reaches this threshold. Moreover, previous research has shown that utilizing the information of game state can reduce the influence of game interactive characterization to DVFS policy. We explore a method to automatically detect the game state. We propose the Automatic Frame Rate-Based DVFS policy, which can learn the threshold of frame rate online and utilize the information of game state and frame rate to scale the frequency without prediction. Our evaluation result shows that, compared with the prediction-based Android default Interactive DVFS policy, our policy saves more power in all the testing games. Up to 15.2% more power can be saved by Automatic Frame Rate-Based DVFS policy.