In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD...In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.展开更多
Volunteer teams provide valuable support after large-scale disasters.However,excessive volunteer participation poses challenges for formal operations.Therefore,an appropriate decision-making method is required to quic...Volunteer teams provide valuable support after large-scale disasters.However,excessive volunteer participation poses challenges for formal operations.Therefore,an appropriate decision-making method is required to quickly determine the number of volunteers required after a disaster.This study proposes a data-driven decision-making(D^(3)M)method for typhoon disaster volunteerism that can effectively predict the number of volunteers required.Disaster data from actual cases were gathered,analyzed,and preprocessed to prepare the model.Feature selection,D^(3)M model training and optimization,and model validation were performed to fine-tune the volunteer participant predictions.Using data from an actual typhoon in the Philippines,the rationality and efficacy of the method were verified through a comparative analysis of the experimental results.The proposed method learns from disaster-event data to quickly predict the number of volunteers needed,such that it not only reasonably allocates volunteers to assist professional teams in rescue but also avoids secondary problems caused by an overwhelming response.展开更多
The rapid development of artificial intelligence technology has provided an opportunity to reshape the teaching ecosystem in open education.This article focuses on the concept of“adaptive learning”,in the context of ...The rapid development of artificial intelligence technology has provided an opportunity to reshape the teaching ecosystem in open education.This article focuses on the concept of“adaptive learning”,in the context of the artificial intelligence era,and explores the systematic reform of open education teaching models.The researchfirst constructed an integrated learning framework that combines cognitive diagnosis,dynamic paths,resource push,immediate feedback,and emotional support.Through data-driven and teacher experience collaboration,it realizes large-scale personalized teaching.Secondly,based on the teaching practice of public courses in multiple universities,the article collected and analyzed the entire process behavior data of learners,used deep models to dynamically optimize teaching strategies,and established an interpretable and iterative teaching loop.On this basis,the research focuses on educational equity and the mechanism of human-computer collaboration,ensuring that while technology is empowered,the dominant position of teachers and the warmth of the learning community are maintained.Through qualitative interviews and teaching observations,the article found that adaptive learning significantly enhanced the initiative,satisfaction,and knowledge transfer ability of learners,forming a new classroom culture that integrates online and offline elements and reshapes the roles of teachers and students.The research conclusion states that in the open education teaching reform of the artificial intelligence era,it should be driven by data intelligence,centered on learners,and based on educational equity,promoting the transformation from“standardized supply”to“precise services”,providing replicable models and sustainable paths for building a lifelong learning society.展开更多
This literature review primarily aims to explore and synthesise the previous studies in simulation education research conducted over the past five years related to the effects of simulation training on the self-effica...This literature review primarily aims to explore and synthesise the previous studies in simulation education research conducted over the past five years related to the effects of simulation training on the self-efficacy of undergraduate pre-registration nursing students. The second aim of this study is to explore additional outcome variables that were examined in the previous studies. Five electronic databases were searched systematically. These databases were MEDLINE, CINAHL Plus, Scopus, Embase and PsycINFO. The PICO model was employed to identify the search terms, with a thesaurus being used to provide synonyms. Reference lists of relevant articles were examined and hand searches of journals were also undertaken. The quality of each study was assessed using the Simulation Research Rubric (SRR). A total of 11 studies were included. All studies explored the impact of simulation education on undergraduate pre-registration nursing. Six studies explored nursing students’ competence and performance and two papers examined their critical thinking. Problem solving, learning motivation, communication skills and knowledge acquisition were examined once. The majority of studies indicated that simulation training has a positive impact on pre-registration nursing students’ self-efficacy and other outcome variables. Furthermore, the study results indicate that simulation training is more dependable than traditional training, and students were extremely satisfied with the simulation training. However, most of the studies included in this review had several gaps, including study design, sample size and dissimilarities between the scales used. Further research with large samples, reliable and valid instruments, and outcomes measures (such as critical thinking and transferability of skills) is required to provide better insight into the effectiveness of simulation in undergraduate nursing education. .展开更多
Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on ...Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.展开更多
This paper investigates the role of technology in optimizing college English education management systems.By examining the integration of digital tools and pedagogical strategies,it explores how institutions can enhan...This paper investigates the role of technology in optimizing college English education management systems.By examining the integration of digital tools and pedagogical strategies,it explores how institutions can enhance learning outcomes and streamline administrative processes.Through case studies and theoretical frameworks,the paper proposes innovative approaches to leverage technology for efficient and learner-centered English education management.展开更多
文摘In the rapidly evolving technological landscape,state-owned enterprises(SOEs)encounter significant challenges in sustaining their competitiveness through efficient R&D management.Integrated Product Development(IPD),with its emphasis on cross-functional teamwork,concurrent engineering,and data-driven decision-making,has been widely recognized for enhancing R&D efficiency and product quality.However,the unique characteristics of SOEs pose challenges to the effective implementation of IPD.The advancement of big data and artificial intelligence technologies offers new opportunities for optimizing IPD R&D management through data-driven decision-making models.This paper constructs and validates a data-driven decision-making model tailored to the IPD R&D management of SOEs.By integrating data mining,machine learning,and other advanced analytical techniques,the model serves as a scientific and efficient decision-making tool.It aids SOEs in optimizing R&D resource allocation,shortening product development cycles,reducing R&D costs,and improving product quality and innovation.Moreover,this study contributes to a deeper theoretical understanding of the value of data-driven decision-making in the context of IPD.
基金The authors acknowledge financial support from the National Social Science Foundation of China(Grant#:19BGL016).
文摘Volunteer teams provide valuable support after large-scale disasters.However,excessive volunteer participation poses challenges for formal operations.Therefore,an appropriate decision-making method is required to quickly determine the number of volunteers required after a disaster.This study proposes a data-driven decision-making(D^(3)M)method for typhoon disaster volunteerism that can effectively predict the number of volunteers required.Disaster data from actual cases were gathered,analyzed,and preprocessed to prepare the model.Feature selection,D^(3)M model training and optimization,and model validation were performed to fine-tune the volunteer participant predictions.Using data from an actual typhoon in the Philippines,the rationality and efficacy of the method were verified through a comparative analysis of the experimental results.The proposed method learns from disaster-event data to quickly predict the number of volunteers needed,such that it not only reasonably allocates volunteers to assist professional teams in rescue but also avoids secondary problems caused by an overwhelming response.
文摘The rapid development of artificial intelligence technology has provided an opportunity to reshape the teaching ecosystem in open education.This article focuses on the concept of“adaptive learning”,in the context of the artificial intelligence era,and explores the systematic reform of open education teaching models.The researchfirst constructed an integrated learning framework that combines cognitive diagnosis,dynamic paths,resource push,immediate feedback,and emotional support.Through data-driven and teacher experience collaboration,it realizes large-scale personalized teaching.Secondly,based on the teaching practice of public courses in multiple universities,the article collected and analyzed the entire process behavior data of learners,used deep models to dynamically optimize teaching strategies,and established an interpretable and iterative teaching loop.On this basis,the research focuses on educational equity and the mechanism of human-computer collaboration,ensuring that while technology is empowered,the dominant position of teachers and the warmth of the learning community are maintained.Through qualitative interviews and teaching observations,the article found that adaptive learning significantly enhanced the initiative,satisfaction,and knowledge transfer ability of learners,forming a new classroom culture that integrates online and offline elements and reshapes the roles of teachers and students.The research conclusion states that in the open education teaching reform of the artificial intelligence era,it should be driven by data intelligence,centered on learners,and based on educational equity,promoting the transformation from“standardized supply”to“precise services”,providing replicable models and sustainable paths for building a lifelong learning society.
文摘This literature review primarily aims to explore and synthesise the previous studies in simulation education research conducted over the past five years related to the effects of simulation training on the self-efficacy of undergraduate pre-registration nursing students. The second aim of this study is to explore additional outcome variables that were examined in the previous studies. Five electronic databases were searched systematically. These databases were MEDLINE, CINAHL Plus, Scopus, Embase and PsycINFO. The PICO model was employed to identify the search terms, with a thesaurus being used to provide synonyms. Reference lists of relevant articles were examined and hand searches of journals were also undertaken. The quality of each study was assessed using the Simulation Research Rubric (SRR). A total of 11 studies were included. All studies explored the impact of simulation education on undergraduate pre-registration nursing. Six studies explored nursing students’ competence and performance and two papers examined their critical thinking. Problem solving, learning motivation, communication skills and knowledge acquisition were examined once. The majority of studies indicated that simulation training has a positive impact on pre-registration nursing students’ self-efficacy and other outcome variables. Furthermore, the study results indicate that simulation training is more dependable than traditional training, and students were extremely satisfied with the simulation training. However, most of the studies included in this review had several gaps, including study design, sample size and dissimilarities between the scales used. Further research with large samples, reliable and valid instruments, and outcomes measures (such as critical thinking and transferability of skills) is required to provide better insight into the effectiveness of simulation in undergraduate nursing education. .
文摘Risk management is relevant for every project that which seeks to avoid and suppress unanticipated costs, basically calling for pre-emptive action. The current work proposes a new approach for handling risks based on predictive analytics and machine learning (ML) that can work in real-time to help avoid risks and increase project adaptability. The main research aim of the study is to ascertain risk presence in projects by using historical data from previous projects, focusing on important aspects such as time, task time, resources and project results. t-SNE technique applies feature engineering in the reduction of the dimensionality while preserving important structural properties. This process is analysed using measures including recall, F1-score, accuracy and precision measurements. The results demonstrate that the Gradient Boosting Machine (GBM) achieves an impressive 85% accuracy, 82% precision, 85% recall, and 80% F1-score, surpassing previous models. Additionally, predictive analytics achieves a resource utilisation efficiency of 85%, compared to 70% for traditional allocation methods, and a project cost reduction of 10%, double the 5% achieved by traditional approaches. Furthermore, the study indicates that while GBM excels in overall accuracy, Logistic Regression (LR) offers more favourable precision-recall trade-offs, highlighting the importance of model selection in project risk management.
文摘This paper investigates the role of technology in optimizing college English education management systems.By examining the integration of digital tools and pedagogical strategies,it explores how institutions can enhance learning outcomes and streamline administrative processes.Through case studies and theoretical frameworks,the paper proposes innovative approaches to leverage technology for efficient and learner-centered English education management.
