This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a nat...This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases,targeting the rapidly increasing number of adults in the country with diabetes.In the second example,the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge.Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted,illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows.In the first example,the choice was made to use the system in a semi‐automated(vs.fully automated)mode as this was assessed to be more cost‐effective,though still offering substantial productivity improvement.In the second example,machine learning algorithm design and model execution trade-offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy.The article concludes with several lessons learned related to deploying AI systems within healthcare settings,and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.展开更多
Energy security is the association between national security and the availability of natural resources for energy consumption.Access to cheaper energy has become essential to the functioning of modern economies.Howeve...Energy security is the association between national security and the availability of natural resources for energy consumption.Access to cheaper energy has become essential to the functioning of modern economies.However,the uneven distribution of energy supplies among countries has led to significant challenges.International energy relations have contributed to the globalization of the world,leading to energy security and energy vulnerability at the same time.The first article in Science Direct for energy security appeared in 1993 with authors from USA.Gradually the number of articles and their origin increases.In 2024 we observe a boom of these publications-30(not only in Science Direct)and one planned for 2025.The recent article explores these publications and sorts them by origin and content.The content is distributed in five topics:computer technologies impact,artificial intelligence applications,environmental management and energy security,economics and policy for energy security,and emerging technologies for energy security.Since the share of AI usage is relatively small the author suggests scenarios for AI agents’application in various situations concerning environmental decisions,failures,cyber-attacks,performance,and pricing.展开更多
It’s a pleasure to be here and speak about the industrial copilot and generative AI and the changing applications.As you all know,generative AI has arrived since a few years ago,we have generative AI not only in the ...It’s a pleasure to be here and speak about the industrial copilot and generative AI and the changing applications.As you all know,generative AI has arrived since a few years ago,we have generative AI not only in the consumer world,but also in the industrial world.Siemens is very active in the industrial space.We need to make AI real,because at the end in industry manufacturing,you are producing parts in the real world.So we need to make sure that AI and its applications can interact and comply with the real world.展开更多
The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, withincreasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promisingres...The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, withincreasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promisingresults in research. The growing interest comes from decision makers of both the industry and policydomains, searching for applications to increase companies’ profitability, raise efficiency and facilitate theenergy transition. This paper aims to provide a novel three-dimensional (3D) indicator for AI applicationsin the energy sector, based on their respective maturity level, regulatory risks and potential benefits. Casestudies are used to exemplify the application of the 3D indicator, showcasing how the developed frameworkcan be used to filter promising AI applications eligible for governmental funding or business development.In addition, the 3D indicator is used to rank AI applications considering different stakeholder preferences(risk-avoidance, profit-seeking, balanced). These results allow AI applications to be better categorised in theface of rapidly emerging national and intergovernmental AI strategies and regulations that constrain the useof AI applications in critical infrastructures.展开更多
The purpose of this study is to look at the impact of accounting information systems on the economy.The study has been directed based on the analytical and theoretical.It observed a total of 500 respondents.To run the...The purpose of this study is to look at the impact of accounting information systems on the economy.The study has been directed based on the analytical and theoretical.It observed a total of 500 respondents.To run the research and to get informative results,this paper used primary data.It uses the Chi squire test,ANOVA tests,and Multinomial Logistic tests for analyzing the results.It calculates the data with the help of IBM statistical packages for social science(SPSS).This paper assumes that AIS is beneficial for Bangladeshi organizations,which contributes to the economic development of Bangladesh.However,it finally shows that this system has a gap between what accounting information systems are&what should be.This paper suggests that an organization may get potential benefits through the implementation of AIS in Bangladesh.It also will be benefited stakeholders from implying it.The paper conducts based on the listed financial organizations of Bangladesh.This is the main limitation of this study.It is the first work in Bangladesh based on my knowledge.It provides accurate information to all stakeholders that help them to the right decision.It will also help to improve the economic development of Bangladesh.展开更多
Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material use.This transition from traditional construction meth...Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material use.This transition from traditional construction methods not only accelerates building processes but also enhances working efficiencies globally.Despite its widespread adoption,the performance of industrialized building manufacturing(IBM)can still be optimized,particularly in enhancing time efficiency and reducing costs.This paper explores the integration of Artificial Intelligence(AI)and robotics at IBM to improve efficiency,cost-effectiveness,and material use in off-site assembly.Through a narrative literature review,this study systematically categorizes AI-based Robots(AIRs)applications into four critical stages—Cognition,Communication,Control,and Collab-oration and Coordination,and then investigates their appli-cation in the factory assembly process for industrialized buildings,which is structured into distinct stages:compo-nent preparation,sub-assembly,main assembly,finishing tasks,and quality control.Each stage,from positioning components to the integration of larger modules and subsequent quality inspection,often involves robots or human-robot collaboration to enhance precision and effi-ciency.By examining research from 2014 to 2024,the review highlights the significant improvements AI-based robots have introduced to the construction sector,identifies existing challenges,and outlines future research directions.This comprehensive analysis aims to establish more effi-cient,precise,and tailored construction processes,paving the way for advanced IBM.展开更多
Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI development.Explainable AI(XAI)techniques with various methods and tools have been deve...Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI development.Explainable AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human understanding.However,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI practice.We first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of XAI.In addition,a taxonomy of XAI methods is developed for visual computing with analysis of method properties.Holding clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in practice.And we check the availability of open-source XAI tools for stakeholders'use.This survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.展开更多
In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously,the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these...In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously,the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these days.The first and foremost task is to predict the energy consumption based on the available data.This study investigates the integration of artificial neural networks in smart home technology to improve energy usage prediction and efficiency,without compromising the comfort of occupants.A dynamic model based on an artificial neural network model is designed in this study which artificially controls light,heating process,and cooling to cut down energy wastage.Energy consumption data of 114 single-family apartments was collected between 2014 and 2016.Energy consumption is predicted by the current model with an accuracy of up to 99.9%for energy usage patterns,which helps to optimize resource management in real time.A robust modeling approach i.e.multi-layer perceptron networks was implemented along with energy usage data.Seventy percent of the data is used for training the neural networks,and the rest is used for testing and validation purposes.The current defined model shows a significant improvement in prediction accuracy of energy usage and efficiency when compared to state-of-the-art models.Metrics such as R-values and mean square error are employed to check accuracy.These results show the essential role of artificial intelligence in improving energy management for smart buildings,with potential benefits including significant energy usage and loss management to help improve sustainable living.展开更多
In July 2017,the Chinese government issued a guideline on developing artificial intelligence（AI）,namely,the‘New-Generation Artificial Intelligence Development Plan’,through 2030 to the public,setting a goal of bec...In July 2017,the Chinese government issued a guideline on developing artificial intelligence（AI）,namely,the‘New-Generation Artificial Intelligence Development Plan’,through 2030 to the public,setting a goal of becoming a global innovation center in this field by 2030.According to the development plan,breakthroughs should be made in basic theories of AI in terms of big data intelligence.展开更多
The Scheil-Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science.Despite the prevalent use of the Calculation of Phase Diagrams(CALPHAD)method,its computational ...The Scheil-Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science.Despite the prevalent use of the Calculation of Phase Diagrams(CALPHAD)method,its computational intensity and time are limiting the simulation efficiency.Recently,Artificial Intelligence has emerged as a potent tool in materials science,offering robust and reliable predictive modeling capabilities.This study introduces an ensemble-based method that has the potential to enhance the prediction of the partitioning coefficient(k)in the Scheil equation by inputting various alloy compositions.The findings demonstrate that this approach can predict the temperature and solid fraction at the eutectic temperature with an accuracy exceeding 90%,while the accuracy for k prediction surpasses 70%.Additionally,a case study on a commercial alloy revealed that the model's predictions are within a 5℃deviation from experimental results,and the predicted solid fraction at the eutectic temperature is within a 15%difference of the values obtained from the CALPHAD model.展开更多
文摘This article explains how two AI systems have been incorporated into the everyday operations of two Singapore public healthcare nation‐wide screening programs.The first example is embedded within the setting of a national level population health screening program for diabetes related eye diseases,targeting the rapidly increasing number of adults in the country with diabetes.In the second example,the AI assisted screening is done shortly after a person is admitted to one of the public hospitals to identify which inpatients—especially which elderly patients with complex conditions—have a high risk of being readmitted as an inpatient multiple times in the months following discharge.Ways in which healthcare needs and the clinical operations context influenced the approach to designing or deploying the AI systems are highlighted,illustrating the multiplicity of factors that shape the requirements for successful large‐scale deployments of AI systems that are deeply embedded within clinical workflows.In the first example,the choice was made to use the system in a semi‐automated(vs.fully automated)mode as this was assessed to be more cost‐effective,though still offering substantial productivity improvement.In the second example,machine learning algorithm design and model execution trade-offs were made that prioritized key aspects of patient engagement and inclusion over higher levels of predictive accuracy.The article concludes with several lessons learned related to deploying AI systems within healthcare settings,and also lists several other AI efforts already in deployment and in the pipeline for Singapore's public healthcare system.
文摘Energy security is the association between national security and the availability of natural resources for energy consumption.Access to cheaper energy has become essential to the functioning of modern economies.However,the uneven distribution of energy supplies among countries has led to significant challenges.International energy relations have contributed to the globalization of the world,leading to energy security and energy vulnerability at the same time.The first article in Science Direct for energy security appeared in 1993 with authors from USA.Gradually the number of articles and their origin increases.In 2024 we observe a boom of these publications-30(not only in Science Direct)and one planned for 2025.The recent article explores these publications and sorts them by origin and content.The content is distributed in five topics:computer technologies impact,artificial intelligence applications,environmental management and energy security,economics and policy for energy security,and emerging technologies for energy security.Since the share of AI usage is relatively small the author suggests scenarios for AI agents’application in various situations concerning environmental decisions,failures,cyber-attacks,performance,and pricing.
文摘It’s a pleasure to be here and speak about the industrial copilot and generative AI and the changing applications.As you all know,generative AI has arrived since a few years ago,we have generative AI not only in the consumer world,but also in the industrial world.Siemens is very active in the industrial space.We need to make AI real,because at the end in industry manufacturing,you are producing parts in the real world.So we need to make sure that AI and its applications can interact and comply with the real world.
文摘The utilisation of Artificial Intelligence (AI) applications in the energy sector is gaining momentum, withincreasingly intensive search for suitable, high-quality and trustworthy solutions that displayed promisingresults in research. The growing interest comes from decision makers of both the industry and policydomains, searching for applications to increase companies’ profitability, raise efficiency and facilitate theenergy transition. This paper aims to provide a novel three-dimensional (3D) indicator for AI applicationsin the energy sector, based on their respective maturity level, regulatory risks and potential benefits. Casestudies are used to exemplify the application of the 3D indicator, showcasing how the developed frameworkcan be used to filter promising AI applications eligible for governmental funding or business development.In addition, the 3D indicator is used to rank AI applications considering different stakeholder preferences(risk-avoidance, profit-seeking, balanced). These results allow AI applications to be better categorised in theface of rapidly emerging national and intergovernmental AI strategies and regulations that constrain the useof AI applications in critical infrastructures.
文摘The purpose of this study is to look at the impact of accounting information systems on the economy.The study has been directed based on the analytical and theoretical.It observed a total of 500 respondents.To run the research and to get informative results,this paper used primary data.It uses the Chi squire test,ANOVA tests,and Multinomial Logistic tests for analyzing the results.It calculates the data with the help of IBM statistical packages for social science(SPSS).This paper assumes that AIS is beneficial for Bangladeshi organizations,which contributes to the economic development of Bangladesh.However,it finally shows that this system has a gap between what accounting information systems are&what should be.This paper suggests that an organization may get potential benefits through the implementation of AIS in Bangladesh.It also will be benefited stakeholders from implying it.The paper conducts based on the listed financial organizations of Bangladesh.This is the main limitation of this study.It is the first work in Bangladesh based on my knowledge.It provides accurate information to all stakeholders that help them to the right decision.It will also help to improve the economic development of Bangladesh.
文摘Industrialized buildings,characterized by off-site manufacturing and on-site installation,offer notable improvements in efficiency,cost-effectiveness,and material use.This transition from traditional construction methods not only accelerates building processes but also enhances working efficiencies globally.Despite its widespread adoption,the performance of industrialized building manufacturing(IBM)can still be optimized,particularly in enhancing time efficiency and reducing costs.This paper explores the integration of Artificial Intelligence(AI)and robotics at IBM to improve efficiency,cost-effectiveness,and material use in off-site assembly.Through a narrative literature review,this study systematically categorizes AI-based Robots(AIRs)applications into four critical stages—Cognition,Communication,Control,and Collab-oration and Coordination,and then investigates their appli-cation in the factory assembly process for industrialized buildings,which is structured into distinct stages:compo-nent preparation,sub-assembly,main assembly,finishing tasks,and quality control.Each stage,from positioning components to the integration of larger modules and subsequent quality inspection,often involves robots or human-robot collaboration to enhance precision and effi-ciency.By examining research from 2014 to 2024,the review highlights the significant improvements AI-based robots have introduced to the construction sector,identifies existing challenges,and outlines future research directions.This comprehensive analysis aims to establish more effi-cient,precise,and tailored construction processes,paving the way for advanced IBM.
基金supported by National Natural Science Foundation of China(Nos.61772111 and 72010107002).
文摘Human adoption of artificial intelligence(AI)technique is largely hampered because of the increasing complexity and opacity of AI development.Explainable AI(XAI)techniques with various methods and tools have been developed to bridge this gap between high-performance black-box AI models and human understanding.However,the current adoption of XAI technique stil lacks"human-centered"guidance for designing proper solutions to meet different stakeholders'needs in XAI practice.We first summarize a human-centered demand framework to categorize different stakeholders into five key roles with specific demands by reviewing existing research and then extract six commonly used human-centered XAI evaluation measures which are helpful for validating the effect of XAI.In addition,a taxonomy of XAI methods is developed for visual computing with analysis of method properties.Holding clearer human demands and XAI methods in mind,we take a medical image diagnosis scenario as an example to present an overview of how extant XAI approaches for visual computing fulfil stakeholders'human-centered demands in practice.And we check the availability of open-source XAI tools for stakeholders'use.This survey provides further guidance for matching diverse human demands with appropriate XAI methods or tools in specific applications with a summary of main challenges and future work toward human-centered XAI in practice.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R 500)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In designing a modern home with a focus on the comfort of residents and energy usage optimization simultaneously,the rise of the Internet of Things and its incorporation with sensor technology plays a vital role these days.The first and foremost task is to predict the energy consumption based on the available data.This study investigates the integration of artificial neural networks in smart home technology to improve energy usage prediction and efficiency,without compromising the comfort of occupants.A dynamic model based on an artificial neural network model is designed in this study which artificially controls light,heating process,and cooling to cut down energy wastage.Energy consumption data of 114 single-family apartments was collected between 2014 and 2016.Energy consumption is predicted by the current model with an accuracy of up to 99.9%for energy usage patterns,which helps to optimize resource management in real time.A robust modeling approach i.e.multi-layer perceptron networks was implemented along with energy usage data.Seventy percent of the data is used for training the neural networks,and the rest is used for testing and validation purposes.The current defined model shows a significant improvement in prediction accuracy of energy usage and efficiency when compared to state-of-the-art models.Metrics such as R-values and mean square error are employed to check accuracy.These results show the essential role of artificial intelligence in improving energy management for smart buildings,with potential benefits including significant energy usage and loss management to help improve sustainable living.
文摘In July 2017,the Chinese government issued a guideline on developing artificial intelligence（AI）,namely,the‘New-Generation Artificial Intelligence Development Plan’,through 2030 to the public,setting a goal of becoming a global innovation center in this field by 2030.According to the development plan,breakthroughs should be made in basic theories of AI in terms of big data intelligence.
基金funded by KK-Stiftelsen Smart Industry Sweden,with project number 2020-0044.
文摘The Scheil-Gulliver equation is essential for assessing solid fractions during alloy solidification in materials science.Despite the prevalent use of the Calculation of Phase Diagrams(CALPHAD)method,its computational intensity and time are limiting the simulation efficiency.Recently,Artificial Intelligence has emerged as a potent tool in materials science,offering robust and reliable predictive modeling capabilities.This study introduces an ensemble-based method that has the potential to enhance the prediction of the partitioning coefficient(k)in the Scheil equation by inputting various alloy compositions.The findings demonstrate that this approach can predict the temperature and solid fraction at the eutectic temperature with an accuracy exceeding 90%,while the accuracy for k prediction surpasses 70%.Additionally,a case study on a commercial alloy revealed that the model's predictions are within a 5℃deviation from experimental results,and the predicted solid fraction at the eutectic temperature is within a 15%difference of the values obtained from the CALPHAD model.
