In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,...In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,focusing on its influence on learning outcomes and student engagement.The research uses a quantitative approach,utilizing surveys and academic performance data to evaluate two main objectives:(1)the impact of AI teaching methods on academic performance compared to traditional instruction;(2)the level of student engagement and satisfaction with AI-based learning tools.The study sample includes undergraduate students from multiple universities in China,allowing for a diverse representation of various disciplines.Data will be collected through standardized tests,questionnaires,and academic records,ensuring the reliability and validity of the results.The findings will provide insights into the potential advantages and challenges of AI integration in higher education and inform future strategies for adopting AI in Chinese classrooms.By exploring both the academic and practical aspects of AI-driven education,this research aims to contribute valuable knowledge to the growing field of AI in education,particularly in the context of Chinese higher education.The results are expected to have implications for educators,policymakers,and AI developers interested in enhancing the effectiveness of educational technologies.展开更多
This study investigates the integration of Artificial Intelligence(AI)technologies—particularly natural language processing and machine learning—into qualitative research(QR)workflows.Our research demonstrates that ...This study investigates the integration of Artificial Intelligence(AI)technologies—particularly natural language processing and machine learning—into qualitative research(QR)workflows.Our research demonstrates that AI can streamline data collection,coding,theme identification,and visualization,significantly improving both speed and accuracy compared to traditional manual methods.Notably,our experimental and numerical results provide a comprehensive analysis of AI’s effect on efficiency,accuracy,and usability across various QR tasks.By presenting and discussing studies on some AI&generative AI models,we contribute to the ongoing scholarly discussion on the role of AI in QR exploring its potential benefits,challenges,and limitations.We highlight the growing use of AI-powered qualitative data analysis tools such as ATLAS.ti,Quirkos,and NVivo for automating coding and data interpretation.Our analysis indicates that while AI tools fromleading companies(e.g.,OpenAI’s GPT-4,Google’s T5,Meta’s RoBERTa)can enhance efficiency and depth in QR,code-focused models and general-purpose proprietary language models often do not align with qualitative needs.Additionally,certain proprietary and open-source models(e.g.,DeepSeek,OLMo)are less prevalent in QR due to specialization gaps or adoption lags,whereas task-specific,transparent models,such as BERT for classification,T5 for text generation and summarization,and BLOOM for multilingual analysis,remain preferable for coding and thematic analysis due to their reproducibility and adaptability.We discuss key stages where AI has made a significant impact,including data collection and pre-processing,advanced text and sentiment analysis,simulation and modeling,improved objectivity and consistency.The benefits of integrating AI into QR,along with corresponding adaptations in research methodologies,are also presented.Noteworthy applications and techniques—including The AI Scientist,Carl,AI co-scientist,augmented physics,and explainable AI(XAI)—further illustrate the diverse potential of AI in research and the challenges to academic norms.Despite AI advancements,challenges persist.AI struggles with contextually nuanced data such as sarcasm,tone,and cultural context,and its reliance on training datasets raises ethical concerns regarding privacy,consent,and bias.Ultimately,we advocate for a hybrid approach where AI augments rather than replaces traditional qualitativemethods,anticipating that ongoing AI advancements will enable more sophisticated,collaborative research practices that effectively combine machine capabilities with human expertise.This trend is underpinned and exemplified by applications like AI co-scientist,augmented physics.展开更多
Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variabil...Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.展开更多
The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potentia...The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.展开更多
Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects.The primary challenge is to integrate diverse pharmacophores within a singl...Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects.The primary challenge is to integrate diverse pharmacophores within a single-molecule framework.To address this,we introduced DeepSA,a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine,a wellknown local anesthetic.DeepSA integrates deep neural networks into metaheuristics,effectively constraining molecular space during compound generation.This framework employs a sophisticated objective function that accounts for scaffold preservation,anti-inflammatory properties,and covalent constraints.Through a sequence of local editing to navigate the molecular space,DeepSA successfully identified AT-17,a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models.Mechanistic insights into AT-17 revealed its dual mode of action:selective inhibition of NaV1.7 and 1.8 channels,contributing to its prolonged local anesthetic effects,and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway.These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery,particularly within stringent chemical constraints.展开更多
Artificial Intelligence(AI)promises solutions to the challenges raised by the digitalization of power grids and their assets.Decision-making,forecasting and even operational optimization of grids and assets are just s...Artificial Intelligence(AI)promises solutions to the challenges raised by the digitalization of power grids and their assets.Decision-making,forecasting and even operational optimization of grids and assets are just some of the solutions that AI algorithms can provide to operators,utilities and vendors.Nevertheless,barriers such as access to quality datasets,interpretability,repeatability,and availability of computational resources currently limit the extent of practical AI implementations.At the same time,Digital Twins(DTs)are foreseen as platforms that can overcome these barriers,and also provide a new environment for the development of enhanced and more intelligent applications.In this manuscript,we review the published literature to determine the existing capabilities and implementation challenges of AI algorithms in power systems,and classify AI-based applications based on their time scale to reveal their temporal sensitivity.Furthermore,DT-based technologies are discussed,identifying the potentials to tackle current limitations of real-world AI applications as well as exploring the synergies between DTs and AI.By combining AI and DT,we outline multiple prospective use cases for AI-enhanced power grid and power asset DTs.Our review also identifies that the combination of AI-based solutions and DTs leverages new applications with the potential to fundamentally change multiple aspects of the power industry.展开更多
文摘In recent years,artificial intelligence(AI)has been increasingly integrated into educational settings worldwide.This study aims to explore the effectiveness of AI classroom teaching for Chinese undergraduate students,focusing on its influence on learning outcomes and student engagement.The research uses a quantitative approach,utilizing surveys and academic performance data to evaluate two main objectives:(1)the impact of AI teaching methods on academic performance compared to traditional instruction;(2)the level of student engagement and satisfaction with AI-based learning tools.The study sample includes undergraduate students from multiple universities in China,allowing for a diverse representation of various disciplines.Data will be collected through standardized tests,questionnaires,and academic records,ensuring the reliability and validity of the results.The findings will provide insights into the potential advantages and challenges of AI integration in higher education and inform future strategies for adopting AI in Chinese classrooms.By exploring both the academic and practical aspects of AI-driven education,this research aims to contribute valuable knowledge to the growing field of AI in education,particularly in the context of Chinese higher education.The results are expected to have implications for educators,policymakers,and AI developers interested in enhancing the effectiveness of educational technologies.
文摘This study investigates the integration of Artificial Intelligence(AI)technologies—particularly natural language processing and machine learning—into qualitative research(QR)workflows.Our research demonstrates that AI can streamline data collection,coding,theme identification,and visualization,significantly improving both speed and accuracy compared to traditional manual methods.Notably,our experimental and numerical results provide a comprehensive analysis of AI’s effect on efficiency,accuracy,and usability across various QR tasks.By presenting and discussing studies on some AI&generative AI models,we contribute to the ongoing scholarly discussion on the role of AI in QR exploring its potential benefits,challenges,and limitations.We highlight the growing use of AI-powered qualitative data analysis tools such as ATLAS.ti,Quirkos,and NVivo for automating coding and data interpretation.Our analysis indicates that while AI tools fromleading companies(e.g.,OpenAI’s GPT-4,Google’s T5,Meta’s RoBERTa)can enhance efficiency and depth in QR,code-focused models and general-purpose proprietary language models often do not align with qualitative needs.Additionally,certain proprietary and open-source models(e.g.,DeepSeek,OLMo)are less prevalent in QR due to specialization gaps or adoption lags,whereas task-specific,transparent models,such as BERT for classification,T5 for text generation and summarization,and BLOOM for multilingual analysis,remain preferable for coding and thematic analysis due to their reproducibility and adaptability.We discuss key stages where AI has made a significant impact,including data collection and pre-processing,advanced text and sentiment analysis,simulation and modeling,improved objectivity and consistency.The benefits of integrating AI into QR,along with corresponding adaptations in research methodologies,are also presented.Noteworthy applications and techniques—including The AI Scientist,Carl,AI co-scientist,augmented physics,and explainable AI(XAI)—further illustrate the diverse potential of AI in research and the challenges to academic norms.Despite AI advancements,challenges persist.AI struggles with contextually nuanced data such as sarcasm,tone,and cultural context,and its reliance on training datasets raises ethical concerns regarding privacy,consent,and bias.Ultimately,we advocate for a hybrid approach where AI augments rather than replaces traditional qualitativemethods,anticipating that ongoing AI advancements will enable more sophisticated,collaborative research practices that effectively combine machine capabilities with human expertise.This trend is underpinned and exemplified by applications like AI co-scientist,augmented physics.
文摘Automated cartoon character recognition is crucial for applications in content indexing,filtering,and copyright protection,yet it faces a significant challenge in animated media due to high intra-class visual variability,where characters frequently alter their appearance.To address this problem,we introduce the novel Kral Sakir dataset,a public benchmark of 16,725 images specifically curated for the task of multi-label cartoon character classification under these varied conditions.This paper conducts a comprehensive benchmark study,evaluating the performance of state-of-the-art pretrained Convolutional Neural Networks(CNNs),including DenseNet,ResNet,and VGG,against a custom baseline model trained from scratch.Our experiments,evaluated using metrics of F1-Score,accuracy,and Area Under the ROC Curve(AUC),demonstrate that fine-tuning pretrained models is a highly effective strategy.The best-performing model,DenseNet121,achieved an F1-Score of 0.9890 and an accuracy of 0.9898,significantly outperforming our baseline CNN(F1-Score of 0.9545).The findings validate the power of transfer learning for this domain and establish a strong performance benchmark.The introduced dataset provides a valuable resource for future research into developing robust and accurate character recognition systems.
文摘The widespread adoption of QR codes has revolutionized various industries, streamlined transactions and improved inventory management. However, this increased reliance on QR code technology also exposes it to potential security risks that malicious actors can exploit. QR code Phishing, or “Quishing”, is a type of phishing attack that leverages QR codes to deceive individuals into visiting malicious websites or downloading harmful software. These attacks can be particularly effective due to the growing popularity and trust in QR codes. This paper examines the importance of enhancing the security of QR codes through the utilization of artificial intelligence (AI). The abstract investigates the integration of AI methods for identifying and mitigating security threats associated with QR code usage. By assessing the current state of QR code security and evaluating the effectiveness of AI-driven solutions, this research aims to propose comprehensive strategies for strengthening QR code technology’s resilience. The study contributes to discussions on secure data encoding and retrieval, providing valuable insights into the evolving synergy between QR codes and AI for the advancement of secure digital communication.
基金supported by the National Natural Science Foundation of China(82273784,China)the Research and Develop Program,West China Hospital of Stomatology Sichuan University(RD-03-202004,China)+3 种基金the 1.3.5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(ZYYC 21002,ZYGD23025,China)the Clinical Research Innovation Project,West China Hospital,Sichuan University(2019 HXCX006,China)the Science and Technology Major Project of Tibetan Autonomous Region of China(XZ202201ZD0001G,China)the Sichuan Science and Technology Program(2023 ZYD0168,China).
文摘Multifunctional therapeutics have emerged as a solution to the constraints imposed by drugs with singular or insufficient therapeutic effects.The primary challenge is to integrate diverse pharmacophores within a single-molecule framework.To address this,we introduced DeepSA,a novel edit-based generative framework that utilizes deep simulated annealing for the modification of articaine,a wellknown local anesthetic.DeepSA integrates deep neural networks into metaheuristics,effectively constraining molecular space during compound generation.This framework employs a sophisticated objective function that accounts for scaffold preservation,anti-inflammatory properties,and covalent constraints.Through a sequence of local editing to navigate the molecular space,DeepSA successfully identified AT-17,a derivative exhibiting potent analgesic properties and significant anti-inflammatory activity in various animal models.Mechanistic insights into AT-17 revealed its dual mode of action:selective inhibition of NaV1.7 and 1.8 channels,contributing to its prolonged local anesthetic effects,and suppression of inflammatory mediators via modulation of the NLRP3 inflammasome pathway.These findings not only highlight the efficacy of AT-17 as a multifunctional drug candidate but also highlight the potential of DeepSA in facilitating AI-enhanced drug discovery,particularly within stringent chemical constraints.
文摘Artificial Intelligence(AI)promises solutions to the challenges raised by the digitalization of power grids and their assets.Decision-making,forecasting and even operational optimization of grids and assets are just some of the solutions that AI algorithms can provide to operators,utilities and vendors.Nevertheless,barriers such as access to quality datasets,interpretability,repeatability,and availability of computational resources currently limit the extent of practical AI implementations.At the same time,Digital Twins(DTs)are foreseen as platforms that can overcome these barriers,and also provide a new environment for the development of enhanced and more intelligent applications.In this manuscript,we review the published literature to determine the existing capabilities and implementation challenges of AI algorithms in power systems,and classify AI-based applications based on their time scale to reveal their temporal sensitivity.Furthermore,DT-based technologies are discussed,identifying the potentials to tackle current limitations of real-world AI applications as well as exploring the synergies between DTs and AI.By combining AI and DT,we outline multiple prospective use cases for AI-enhanced power grid and power asset DTs.Our review also identifies that the combination of AI-based solutions and DTs leverages new applications with the potential to fundamentally change multiple aspects of the power industry.
