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关键词： AI-enabled   Simulation approach   Learning strategies   Innovation breakthrough   Innovation performance   C63   C88   O32   O36  

摘要： With the rapid development of AI technology, individual learning strategies are undergoing profound changes. This study aims to investigate how AI-enabled individual learning strategies impact scientific innovation breakthrough. Addressing the limitations of existing research methods in dealing with this issue, we developed a simulation approach named AI-EEM and validated the theory that the trade-off between exploration and exploitation is crucial for innovation breakthrough using actual publication data from scientists. The results indicate that the AI-EEM model can effectively identify the optimal strategies at different levels of exploration and exploitation. Moreover, this study analyzes the interaction between AI technology and individual learning strategies on the degree of scientific innovation breakthroughs. We find that AI-enabled individual learning strategies may reshape the traditional balance between exploration and exploitation. Specifically, as the level of exploration increases, there is a significant linear positive correlation with the degree of innovation breakthroughs. However, as the level of exploitation increases, the enhancing effect of AI-enabled individual learning strategies on innovation breakthrough diminishes. Therefore, we recommend that scientific researchers prioritize exploratory learning when leveraging AI technology to enhance innovation, in order to fully utilize the opportunities provided by AI and achieve a higher degree of innovation breakthroughs.

关键词： Latches   Inverters   Electronic mail   Feeds   Transistors   Transient analysis   Logic gates   Delays   Very large scale integration   Computer science   Circuit robustness   radiation hardness   single-event transient   soft error   triple node upset  

摘要： In nanoscale CMOS technology, harsh radiations in the environment can now easily cause soft errors, e.g., single-event transients (SETs) and triple node upsets (TNUs), severely affecting the reliability of space applications. In this article, TNUs tolerant and SET-pulses filtered latch (TUTPFL) with low power is proposed, which comprises from four input-stage C-elements (CEs), four inverters, and three output-stage CEs. The CEs' delay differential enables the TUTPFL latch to effectively filter SET-pulse, while the CEs' multilevel error-interception property enables the TUTPFL latch to tolerate any possible TNU. The results of HSPICE-based simulations and FPGA-based emulations demonstrate the TNU tolerance and SET filterability of the TUTPFL latch. Meanwhile, compared to the alternative radiation-hardened latches, the TUTPFL latch reduces power dissipation by roughly 20.43% on average.

关键词： technology   preschool   prediction   computer science   education  

摘要： Featured Application Machine learning algorithms applied to accelerometer data can enhance physical activity and sleep pattern assessment in preschool children, enabling more accurate and automated monitoring for educational and health *** This systematic review evaluated machine learning applications for analysing physical activity and behaviour in preschool children using accelerometer data. Following the PRISMA guidelines, we systematically searched PubMed, FECYT, and ProQuest Central databases. Fourteen studies implementing machine learning approaches for preschool accelerometry data were identified and assessed using the MINORS scale. Studies focused on two primary domains: physical activity analysis (n = 10) and sleep monitoring (n = 4). The ActiGraph GT3X+ was predominantly used, with placement varying between the hip and wrist. Random Forest algorithms proved most effective, achieving accuracy rates up to 86.4% in activity classification and 96.2% in sleep prediction. Sampling frequencies (0.25-100 Hz) and epoch lengths (1-60 s) varied considerably across studies. Machine learning applications show promising results for preschool physical activity assessment. However, small sample sizes and methodological inconsistencies limit generalizability. Future research should prioritise larger cohorts, explore multiple sensor integrations, and develop standardised protocols to enhance practical applications.

关键词： Trapped ions  

摘要： AlphaFold has achieved groundbreaking advancements in protein structure prediction, exerting profound influence across biology, medicine, and drug discovery. However, its reliance on multiple sequence alignment (MSA) is inherently time-consuming due to the NP-hard nature of constructing MSAs. Quantum computing emerges as a promising alternative, compared to classical computers, offering the potentials for exponential speedup and improved accuracy on such complex optimization challenges. This work bridges the gap between quantum computing and MSA task efficiently and successfully, where we compared classical and quantum computational scaling as the number of qubits increases, and assessed the role of quantum entanglement in model performance. Furthermore, we proposed an innovative hybrid query encoding approach hyQUBO to avoid redundancy, and thereby the quantum resources significantly reduced to a scaling of O(NL). Additionally, coupling of VQE and the quenched CVaR scheme was utilized to enhance the robustness and convergence. The integration of multiple strategies facilitates the robust deployment of the quantum algorithm from idealized simulators (on CPU and GPU) to real-world, noisy quantum devices (HYQ-A37). To the best of our knowledge, our work represented the largest-scale implementation of digital simulation using up to 16 qubits on a trapped-ion quantum computer for life science problem, which achieved state of the art performance in both simulation and experimental results. Our work paves the way towards large-scale simulations of life science tasks on real quantum processors. Copyright © 2025, The Authors. All rights reserved.

关键词： 大语言模型   地方高校   计算机专业人才  

摘要： 大语言模型技术的发展推动着教育领域的变革。计算机专业与大语言模型技术紧密相连,传统人才培养模式面临严峻考验。地方高校作为服务地方发展的重要力量,如何主动适应大语言模型技术成为一项紧迫课题。文章分析了产业界对计算机专业人才需求的变化,列举了可行对策,为地方高校计算机专业人才培养提供了有益参考。

关键词： 融媒体   课程思政   计算机   双创人才培养  

摘要： 双创人才培养是国家战略发展的需要,课程思政是新时代教育发展对高等学校的根本要求。本研究在马克思关于人的全面发展的理论、加德纳的多元智力理论、创造教育理论及思想政治教育相关理论的指导下,利用融媒体技术,探究将隐性思政教育渗透计算机专业双创人才培养的教学模式和实践策略,以切实提高应用型高校计算机专业学生的创新创业能力,推进思想政治教育的深入开展,促进学生综合素质的全面发展。

关键词： 双体实训   人才培养模式   应用型高校   高质量就业   计算机专业  

摘要： 近10年来,受互联网大潮的影响,高校广泛开设计算机专业,毕业生增多。但随着人工智能普及,行业用人需求波动,且国内院校人才培养模式与市场脱节,致使该专业毕业生就业难。本研究以重庆移通学院计算机专业为例,对比分析近5年国内外高校计算机相关专业人才培养特征,对“双体实训”人才培养模式展开实践探索,旨在借鉴国内外经验,探析就业难题,寻找计算机专业人才高质量就业的有效路径,为推动计算机专业教育发展与提升就业质量提供有益参考。

关键词： hi-Fashion   智能服装设计   生成式AI  

摘要： 基于生成式AI的智能服装设计系统作者:胡蓓佳(本科生)指导教师:柴春雷设计说明:在传统服装产业中,设计流程往往环节繁复,沟通成本高,从创意构想到市场呈现周期漫长,严重制约了企业的响应速度与创新能力。面对这一行业痛点,我们尝试以生成式人工智能为突破口,打造一套真正面向服装企业实战需求的智能化设计系统--hi-Fashion。