Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of product...Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of products and brands, and the platforms where they search for the product. In this research, I study the relationship between product sales and consumer characteristics, the relationship between product sales and product qualities, demand curve analysis, and the search friction effect for different platforms. I utilized data from a randomized field experiment involving more than 400 thousand customers and 30 thousand products on JD.com, one of the world’s largest online retailing platforms. There are two focuses of the research: 1) how different consumer characteristics affect sales;2) how to set price and possible search friction for different channels. I find that JD plus membership, education level and age have no significant relationship with product sales, and higher user level leads to higher sales. Sales are highly skewed, with very high numbers of products sold making up only a small percentage of the total. Consumers living in more industrialized cities have more purchasing power. Women and singles lead to higher spending. Also, the better the product performs, the more it sells. Moderate pricing can increase product sales. Based on the research results of search volume in different channels, it is suggested that it is better to focus on app sales. By knowing the results, producers can adjust target consumers for different products and do target advertisements in order to maximize the sales. Also, an appropriate price for a product is also crucial to a seller. By the way, knowing the search friction of different channels can help producers to rearrange platform layout so that search friction can be reduced and more potential deals may be made.展开更多
Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods...Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.展开更多
Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems i...Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems in interpreting education and practical teaching suggestions:(1)Corpus-based researches collect numerous audios to study typical mistakes made by interpreting learners,particularly pause and self-repair,and suggest interpreting teaching improve learners’ability to use language chunks and encourage students to interpret smoothly;(2)Questionnaire surveys help understand requirements for professional interpreters and how interpreting teaching meets market demands;(3)Teaching experiments last for one to two semesters,addressing issues like outdated teaching materials and modes,and show how teaching materials and modes integrate modern technology.But empirical researches need to build new corpora,professional interpreters’corpora and address problems that haven’t been adequately discussed.This paper is helpful for improving interpreting education in China and other countries and for making clear tasks to be fulfilled in empirical research on interpreting education.展开更多
Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether ...Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.展开更多
We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpr...We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.展开更多
The journal Chemical Research in Chinese Universities is a comprehensive academic journal in the field of chemistry,published bimonthly since 1984.The journal publishes research articles,letters/communications and rev...The journal Chemical Research in Chinese Universities is a comprehensive academic journal in the field of chemistry,published bimonthly since 1984.The journal publishes research articles,letters/communications and reviews written by faculty members,researchers and postgraduates in universities,colleges and research institutes all over China and overseas.It reports the latest and the most creative results of important fundamental research in all aspects of chemistry and of developments with significant consequences across sub-disciplines.This journal is sponsored by Jilin University and mandated by the Ministry of Education of P.R.China.展开更多
The journal Chemical Research in Chinese Universities is a comprehensive academic journal in the field of chemistry,published bimonthly since 1984.The journal publishes research articles,letters/communications and rev...The journal Chemical Research in Chinese Universities is a comprehensive academic journal in the field of chemistry,published bimonthly since 1984.The journal publishes research articles,letters/communications and reviews written by faculty members,researchers and postgraduates in universities,colleges and research institutes all over China and overseas.It reports the latest and the most creative results of important fundamental research in all aspects of chemistry and of developments with significant consequences across sub-disciplines.This journal is sponsored by Jilin University and mandated by the Ministry of Education of P.R.China.展开更多
Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orien...Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.展开更多
The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three ...The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system.Moreover,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug development.Network pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research model.It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions.This approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs.It is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug development.The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.展开更多
Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英...Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英文开放获取式高水平学术期刊,国际标准连续出版物号:ISSN 3050-8622。展开更多
Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligen...Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.展开更多
Background: In response to the limitations of logical empiricism, interpretivism emerged as a philosophical approach for developing nursing knowledge. This paper discusses interpretivist constructivism and its value t...Background: In response to the limitations of logical empiricism, interpretivism emerged as a philosophical approach for developing nursing knowledge. This paper discusses interpretivist constructivism and its value to qualitative nursing research. Methods: The paper synthesizes relevant literature on the importance of interpretivist constructivism in nursing research. It reviews the key elements of interpretivism, the principles of constructivism, the connection between the two approaches, the benefits and limitations of constructivism in nursing research, and the steps for conducting constructivist stroke nursing research. Results: Interpretivist constructivism emphasizes the importance of human experiences, interactions, and social contexts in knowledge development. It allows nurse researchers to adopt flexible, participant-driven approaches to explore and understand complex subjective human phenomena. This approach respects the unique perspectives and contexts of stakeholders, including patients, caregivers, healthcare professionals, and knowledge users. By following specific steps, constructivist researchers can improve the rigor, transparency, and validity of qualitative nursing research while reducing biases in interpreting the inherently subjective experiences of patients. Conclusion: A deeper understanding of the complexities of interpretivism and constructivism in qualitative research is essential. This paper provides a clear, comprehensive guide for effectively applying these approaches in qualitative nursing research.展开更多
Purpose:We aimed to measure the variation in researchers’knowledge and attitudes towards bibliometric indicators.The focus is on mapping the heterogeneity of this metric-wiseness within and between disciplines.Design...Purpose:We aimed to measure the variation in researchers’knowledge and attitudes towards bibliometric indicators.The focus is on mapping the heterogeneity of this metric-wiseness within and between disciplines.Design/methodology/approach:An exploratory survey is administered to researchers at the Sapienza University of Rome,one of Europe’s oldest and largest generalist universities.To measure metric-wiseness,we use attitude statements that are evaluated by a 5-point Likert scale.Moreover,we analyze documents of recent initiatives on assessment reform to shed light on how researchers’heterogeneous attitudes regarding and knowledge of bibliometric indicators are taken into account.Findings:We found great heterogeneity in researchers’metric-wiseness across scientific disciplines.In addition,within each discipline,we observed both supporters and critics of bibliometric indicators.From the document analysis,we found no reference to individual heterogeneity concerning researchers’metric wiseness.Research limitations:We used a self-selected sample of researchers from one Italian university as an exploratory case.Further research is needed to check the generalizability of our findings.Practical implications:To gain sufficient support for research evaluation practices,it is key to consider researchers’diverse attitudes towards indicators.Originality/value:We contribute to the current debate on reforming research assessment by providing a novel empirical measurement of researchers’knowledge and attitudes towards bibliometric indicators and discussing the importance of the obtained results for improving current research evaluation systems.展开更多
Reproducibility is a key aspect of the scientific method as it provides evidence for research claims. It is essential to promote openness, accessibility, and collaboration within the scientific community. This article...Reproducibility is a key aspect of the scientific method as it provides evidence for research claims. It is essential to promote openness, accessibility, and collaboration within the scientific community. This article aims to provide an introduction to best practices in reproducibility that are relevant to the transportation research community, to discuss issues and barriers to reproducibility, and to describe methods for addressing these issues. This article starts by discussing openness and transparency, then discusses several key best practices for reproducibility in transportation engineering, highlighting common methods and techniques, as well as the associated benefits. The paper concludes with a discussion of the key barriers to implementing reproducibility practices in transportation research and potential solutions. The barriers include existing culture and attitudes, data sensitivity, insufficient methodological detail, lack of code sharing, limited validation, additional time and research burden, and skill and knowledge gaps. Discussing each of these items provides an opportunity for the transportation research community to evolve to become one that embraces the openness and transparency of reproducibility.展开更多
The integration of artificial intelligence(AI)is fundamentally reshaping the scientific research,giving rise to a new era of discovery and innovation.This paper explores this transformative shift,introducing an innova...The integration of artificial intelligence(AI)is fundamentally reshaping the scientific research,giving rise to a new era of discovery and innovation.This paper explores this transformative shift,introducing an innovative concept of the“AI-Driven Research Ecosystem”,a dynamic and collaborative research environment.Within this ecosystem,we focus on the unification of human-AI collaboration models and the emerging new research thinking paradigms.We analyze the multifaceted roles of AI within the research lifecycle,spanning from a passive tool to an active assistant and autonomous participants,and categorize these interactions into distinct human-AI collaboration models.Furthermore,we examine how the pervasive involvement of AI necessitates an evolution in human research thinking,emphasizing the significant roles of critical,creative,and computational thinking.Through a review of existing literature and illustrative case studies,this paper provides a comprehensive overview of the AI-driven research ecosystem,highlighting its potential for transforming scientific research.Our findings advance the current understanding of AI’s multiple roles in research and underscore its capacity to revolutionize both knowledge discovery and collaborative innovation,paving the way for a more integrated and impactful research paradigm.展开更多
There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful...There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.展开更多
This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recogni...This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.展开更多
In an era where artificial intelligence(AI)is permeating many aspects of scientific research activity,what science and technology(S&T)journals can do to uphold research integrity and secure scholarly quality?What ...In an era where artificial intelligence(AI)is permeating many aspects of scientific research activity,what science and technology(S&T)journals can do to uphold research integrity and secure scholarly quality?What kind of revolution is needed in such a rapidly drifting world to hold on to the essential value of science?展开更多
文摘Unlike consumers in the mall or supermarkets, online consumers are “intangible” and their purchasing behaviors are affected by multiple factors, including product pricing, promotion and discounts, quality of products and brands, and the platforms where they search for the product. In this research, I study the relationship between product sales and consumer characteristics, the relationship between product sales and product qualities, demand curve analysis, and the search friction effect for different platforms. I utilized data from a randomized field experiment involving more than 400 thousand customers and 30 thousand products on JD.com, one of the world’s largest online retailing platforms. There are two focuses of the research: 1) how different consumer characteristics affect sales;2) how to set price and possible search friction for different channels. I find that JD plus membership, education level and age have no significant relationship with product sales, and higher user level leads to higher sales. Sales are highly skewed, with very high numbers of products sold making up only a small percentage of the total. Consumers living in more industrialized cities have more purchasing power. Women and singles lead to higher spending. Also, the better the product performs, the more it sells. Moderate pricing can increase product sales. Based on the research results of search volume in different channels, it is suggested that it is better to focus on app sales. By knowing the results, producers can adjust target consumers for different products and do target advertisements in order to maximize the sales. Also, an appropriate price for a product is also crucial to a seller. By the way, knowing the search friction of different channels can help producers to rearrange platform layout so that search friction can be reduced and more potential deals may be made.
基金This paper is the research result of“Research on Innovation of Evidence-Based Teaching Paradigm in Vocational Education under the Background of New Quality Productivity”(2024JXQ176)the Shandong Province Artificial Intelligence Education Research Project(SDDJ202501035),which explores the application of artificial intelligence big models in student value-added evaluation from an evidence-based perspective。
文摘Based on the educational evaluation reform,this study explores the construction of an evidence-based value-added evaluation system based on data-driven,aiming to solve the limitations of traditional evaluation methods.The research adopts the method of combining theoretical analysis and practical application,and designs the evidence-based value-added evaluation framework,which includes the core elements of a multi-source heterogeneous data acquisition and processing system,a value-added evaluation agent based on a large model,and an evaluation implementation and application mechanism.Through empirical research verification,the evaluation system has remarkable effects in improving learning participation,promoting ability development,and supporting teaching decision-making,and provides a theoretical reference and practical path for educational evaluation reform in the new era.The research shows that the evidence-based value-added evaluation system based on data-driven can reflect students’actual progress more fairly and objectively by accurately measuring the difference in starting point and development range of students,and provide strong support for the realization of high-quality education development.
基金USST Construction Project of English-taught Courses for International Students in 2024Key Course Construction Project in Universities of Shanghai in 2024USST Teaching Achievement Award(postgraduate)Cultivation Project in 2024。
文摘Based on 1,003 articles about empirical research on interpreting teaching from 2002 to 2022 retrieved from China National Knowledge Internet,this paper extracts three main research methods,uncovering common problems in interpreting education and practical teaching suggestions:(1)Corpus-based researches collect numerous audios to study typical mistakes made by interpreting learners,particularly pause and self-repair,and suggest interpreting teaching improve learners’ability to use language chunks and encourage students to interpret smoothly;(2)Questionnaire surveys help understand requirements for professional interpreters and how interpreting teaching meets market demands;(3)Teaching experiments last for one to two semesters,addressing issues like outdated teaching materials and modes,and show how teaching materials and modes integrate modern technology.But empirical researches need to build new corpora,professional interpreters’corpora and address problems that haven’t been adequately discussed.This paper is helpful for improving interpreting education in China and other countries and for making clear tasks to be fulfilled in empirical research on interpreting education.
文摘Purpose:Evaluating the quality of academic journal articles is a time consuming but critical task for national research evaluation exercises,appointments and promotion.It is therefore important to investigate whether Large Language Models(LLMs)can play a role in this process.Design/methodology/approach:This article assesses which ChatGPT inputs(full text without tables,figures,and references;title and abstract;title only)produce better quality score estimates,and the extent to which scores are affected by ChatGPT models and system prompts.Findings:The optimal input is the article title and abstract,with average ChatGPT scores based on these(30 iterations on a dataset of 51 papers)correlating at 0.67 with human scores,the highest ever reported.ChatGPT 4o is slightly better than 3.5-turbo(0.66),and 4o-mini(0.66).Research limitations:The data is a convenience sample of the work of a single author,it only includes one field,and the scores are self-evaluations.Practical implications:The results suggest that article full texts might confuse LLM research quality evaluations,even though complex system instructions for the task are more effective than simple ones.Thus,whilst abstracts contain insufficient information for a thorough assessment of rigour,they may contain strong pointers about originality and significance.Finally,linear regression can be used to convert the model scores into the human scale scores,which is 31%more accurate than guessing.Originality/value:This is the first systematic comparison of the impact of different prompts,parameters and inputs for ChatGPT research quality evaluations.
基金supported by National Key Research and Development Program (2019YFA0708301)National Natural Science Foundation of China (51974337)+2 种基金the Strategic Cooperation Projects of CNPC and CUPB (ZLZX2020-03)Science and Technology Innovation Fund of CNPC (2021DQ02-0403)Open Fund of Petroleum Exploration and Development Research Institute of CNPC (2022-KFKT-09)
文摘We propose an integrated method of data-driven and mechanism models for well logging formation evaluation,explicitly focusing on predicting reservoir parameters,such as porosity and water saturation.Accurately interpreting these parameters is crucial for effectively exploring and developing oil and gas.However,with the increasing complexity of geological conditions in this industry,there is a growing demand for improved accuracy in reservoir parameter prediction,leading to higher costs associated with manual interpretation.The conventional logging interpretation methods rely on empirical relationships between logging data and reservoir parameters,which suffer from low interpretation efficiency,intense subjectivity,and suitability for ideal conditions.The application of artificial intelligence in the interpretation of logging data provides a new solution to the problems existing in traditional methods.It is expected to improve the accuracy and efficiency of the interpretation.If large and high-quality datasets exist,data-driven models can reveal relationships of arbitrary complexity.Nevertheless,constructing sufficiently large logging datasets with reliable labels remains challenging,making it difficult to apply data-driven models effectively in logging data interpretation.Furthermore,data-driven models often act as“black boxes”without explaining their predictions or ensuring compliance with primary physical constraints.This paper proposes a machine learning method with strong physical constraints by integrating mechanism and data-driven models.Prior knowledge of logging data interpretation is embedded into machine learning regarding network structure,loss function,and optimization algorithm.We employ the Physically Informed Auto-Encoder(PIAE)to predict porosity and water saturation,which can be trained without labeled reservoir parameters using self-supervised learning techniques.This approach effectively achieves automated interpretation and facilitates generalization across diverse datasets.
文摘The journal Chemical Research in Chinese Universities is a comprehensive academic journal in the field of chemistry,published bimonthly since 1984.The journal publishes research articles,letters/communications and reviews written by faculty members,researchers and postgraduates in universities,colleges and research institutes all over China and overseas.It reports the latest and the most creative results of important fundamental research in all aspects of chemistry and of developments with significant consequences across sub-disciplines.This journal is sponsored by Jilin University and mandated by the Ministry of Education of P.R.China.
文摘The journal Chemical Research in Chinese Universities is a comprehensive academic journal in the field of chemistry,published bimonthly since 1984.The journal publishes research articles,letters/communications and reviews written by faculty members,researchers and postgraduates in universities,colleges and research institutes all over China and overseas.It reports the latest and the most creative results of important fundamental research in all aspects of chemistry and of developments with significant consequences across sub-disciplines.This journal is sponsored by Jilin University and mandated by the Ministry of Education of P.R.China.
基金Under the auspices of National Natural Science Foundation of China(No.42293270)。
文摘Throughout the contemporary Chinese history of geography,geographical engineering has consistently played a pivotal role as a fundamental scientific activity.It possesses its distinct ontological basis and value orientation,rendering it inseparable from being merely a derivative of geographical science or technology.This paper defines geographical engineering and introduces its development history through the lens of Chinese geographical engineering praxises.Furthermore,it is highlighted the logical and functional consistency between the theory of human-earth system and the praxis of geographical engineering.Six modern cases of geographical engineering projects are presented in detail to demonstrate the points and characteristics of different types of modern geographical engineering.Geographical engineering serves as an engine for promoting integrated geography research,and in response to the challenge posed by fragmented geographies,this paper advocates for an urgent revitalization of geographical engineering.The feasibility of revitalizing geographical engineering is guaranteed because it aligns with China’s national strategies.
基金supported by the National Medical Products Administration Commissioned Research Project (No.20211440216)the National Administration of Traditional Chinese Medicine Science and Technology Project (No.GZY-KJS-2024-03)+3 种基金the State Key Laboratory of Drug Regulatory Science Project (No.2023SKLDRS0104)the Basic Research Program Natural Science Fund-Frontier Leading Technology Basic Research Special Project of Jiangsu Province (No.BK20232014)the Programs Foundation for Leading Talents in National Administration of Traditional Chinese Medicine of China“Qihuang scholars”Projectthe Tianjin Administration for Market Regulation Science and Technology Key Projects (No.2022-W35)。
文摘The research and development of new traditional Chinese medicine(TCM)drugs have progressively established a novel system founded on the integration of TCM theory,human experience,and clinical trials(termed the“Three Combinations”).However,considering TCM's distinctive features of“syndrome differentiation and treatment”and“multicomponent formulations and complex mechanisms”,current TCM drug development faces challenges such as insufficient understanding of the material basis and the overall mechanism of action and an incomplete evidence chain system.Moreover,significant obstacles persist in gathering human experience data,evaluating clinical efficacy,and controlling the quality of active ingredients,which impede the innovation process in TCM drug development.Network pharmacology,centered on the“network targets”theory,transcends the limitations of the conventional“single target”reductionist research model.It emphasizes the comprehensive effects of disease or syndrome biological networks as targets to elucidate the overall regulatory mechanism of TCM prescriptions.This approach aligns with the holistic perspective of TCM,offering a novel method consistent with TCM's holistic view for investigating the complex mechanisms of TCM and developing new TCM drugs.It is internationally recognized as a“next-generation drug research model”.To advance the research of new tools,methods,and standards for TCM evaluation and to overcome fundamental,critical,and cutting-edge technical challenges in TCM regulation,this consensus aims to explore the characteristics,progress,challenges,applicable pathways,and specific applications of network pharmacology as a new theory,method,and tool in TCM drug development.The goal is to enhance the quality of TCM drug research and development and accelerate the efficiency of developing new TCM products.
文摘Autonomous Transporta tion Research(中文刊名《自主交通研究》,简称ATRes期刊)是由武汉理工大学主办,水路交通控制全国重点实验室、国家水运安全工程技术研究中心、交通信息与安全教育部工程研究中心等协办,科爱出版社出版发行的英文开放获取式高水平学术期刊,国际标准连续出版物号:ISSN 3050-8622。
文摘Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.
文摘Background: In response to the limitations of logical empiricism, interpretivism emerged as a philosophical approach for developing nursing knowledge. This paper discusses interpretivist constructivism and its value to qualitative nursing research. Methods: The paper synthesizes relevant literature on the importance of interpretivist constructivism in nursing research. It reviews the key elements of interpretivism, the principles of constructivism, the connection between the two approaches, the benefits and limitations of constructivism in nursing research, and the steps for conducting constructivist stroke nursing research. Results: Interpretivist constructivism emphasizes the importance of human experiences, interactions, and social contexts in knowledge development. It allows nurse researchers to adopt flexible, participant-driven approaches to explore and understand complex subjective human phenomena. This approach respects the unique perspectives and contexts of stakeholders, including patients, caregivers, healthcare professionals, and knowledge users. By following specific steps, constructivist researchers can improve the rigor, transparency, and validity of qualitative nursing research while reducing biases in interpreting the inherently subjective experiences of patients. Conclusion: A deeper understanding of the complexities of interpretivism and constructivism in qualitative research is essential. This paper provides a clear, comprehensive guide for effectively applying these approaches in qualitative nursing research.
基金supported by the Sapienza Universitàdi Roma Sapienza Awards no.6H15XNFS.
文摘Purpose:We aimed to measure the variation in researchers’knowledge and attitudes towards bibliometric indicators.The focus is on mapping the heterogeneity of this metric-wiseness within and between disciplines.Design/methodology/approach:An exploratory survey is administered to researchers at the Sapienza University of Rome,one of Europe’s oldest and largest generalist universities.To measure metric-wiseness,we use attitude statements that are evaluated by a 5-point Likert scale.Moreover,we analyze documents of recent initiatives on assessment reform to shed light on how researchers’heterogeneous attitudes regarding and knowledge of bibliometric indicators are taken into account.Findings:We found great heterogeneity in researchers’metric-wiseness across scientific disciplines.In addition,within each discipline,we observed both supporters and critics of bibliometric indicators.From the document analysis,we found no reference to individual heterogeneity concerning researchers’metric wiseness.Research limitations:We used a self-selected sample of researchers from one Italian university as an exploratory case.Further research is needed to check the generalizability of our findings.Practical implications:To gain sufficient support for research evaluation practices,it is key to consider researchers’diverse attitudes towards indicators.Originality/value:We contribute to the current debate on reforming research assessment by providing a novel empirical measurement of researchers’knowledge and attitudes towards bibliometric indicators and discussing the importance of the obtained results for improving current research evaluation systems.
文摘Reproducibility is a key aspect of the scientific method as it provides evidence for research claims. It is essential to promote openness, accessibility, and collaboration within the scientific community. This article aims to provide an introduction to best practices in reproducibility that are relevant to the transportation research community, to discuss issues and barriers to reproducibility, and to describe methods for addressing these issues. This article starts by discussing openness and transparency, then discusses several key best practices for reproducibility in transportation engineering, highlighting common methods and techniques, as well as the associated benefits. The paper concludes with a discussion of the key barriers to implementing reproducibility practices in transportation research and potential solutions. The barriers include existing culture and attitudes, data sensitivity, insufficient methodological detail, lack of code sharing, limited validation, additional time and research burden, and skill and knowledge gaps. Discussing each of these items provides an opportunity for the transportation research community to evolve to become one that embraces the openness and transparency of reproducibility.
基金funded by the General Program of the National Natural Science Foundation of China grant number 62277022.
文摘The integration of artificial intelligence(AI)is fundamentally reshaping the scientific research,giving rise to a new era of discovery and innovation.This paper explores this transformative shift,introducing an innovative concept of the“AI-Driven Research Ecosystem”,a dynamic and collaborative research environment.Within this ecosystem,we focus on the unification of human-AI collaboration models and the emerging new research thinking paradigms.We analyze the multifaceted roles of AI within the research lifecycle,spanning from a passive tool to an active assistant and autonomous participants,and categorize these interactions into distinct human-AI collaboration models.Furthermore,we examine how the pervasive involvement of AI necessitates an evolution in human research thinking,emphasizing the significant roles of critical,creative,and computational thinking.Through a review of existing literature and illustrative case studies,this paper provides a comprehensive overview of the AI-driven research ecosystem,highlighting its potential for transforming scientific research.Our findings advance the current understanding of AI’s multiple roles in research and underscore its capacity to revolutionize both knowledge discovery and collaborative innovation,paving the way for a more integrated and impactful research paradigm.
文摘There is a growing body of clinical research on the utility of synthetic data derivatives,an emerging research tool in medicine.In nephrology,clinicians can use machine learning and artificial intelligence as powerful aids in their clinical decision-making while also preserving patient privacy.This is especially important given the epidemiology of chronic kidney disease,renal oncology,and hypertension worldwide.However,there remains a need to create a framework for guidance regarding how to better utilize synthetic data as a practical application in this research.
文摘This editorial explores the transformative potential of artificial intelligence(AI)in identifying conflicts of interest(COIs)within academic and scientific research.By harnessing advanced data analysis,pattern recognition,and natural language processing techniques,AI offers innovative solutions for enhancing transparency and integrity in research.This editorial discusses how AI can automatically detect COIs,integrate data from various sources,and streamline reporting processes,thereby maintaining the credibility of scientific findings.
文摘In an era where artificial intelligence(AI)is permeating many aspects of scientific research activity,what science and technology(S&T)journals can do to uphold research integrity and secure scholarly quality?What kind of revolution is needed in such a rapidly drifting world to hold on to the essential value of science?
