For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean...For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.展开更多
Most existing studies of consumer search behaviour focus on page-level analysis,and some scholars start to examine the effect of refinement tools and characteristics in terms of products.However,it still remains undev...Most existing studies of consumer search behaviour focus on page-level analysis,and some scholars start to examine the effect of refinement tools and characteristics in terms of products.However,it still remains undeveloped on the product-level.To fill this gap,we reproduced the consumer shopping process in accordance with the topology of the Taobao platform from where we collected the clickstream data.We modelled consumers’sequential decision-making behaviour based on the taxonomy with Bayesian approach and found that not all the refinement tools are utilised for optimising decisions by users and it’s surprising that there exists no significant impact of all sorting tools.Besides,consumers are highly concerned with the characteristics of products.On the basis of the findings,platform function announcement and platform design suggestions were provided for improving platform functionality and optimising consumer decision-making,which also points out the direction of future research.展开更多
Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed wel...Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction.展开更多
Dear Editor , Artificial intelligence (AI) is the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, deci...Dear Editor , Artificial intelligence (AI) is the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. There are some knowledge and thinking tasks that humans cannot perform as perfectly as they wish to or should be able to. These tasks are closely related to security and responsibility. A multitude of cognitive distortions have been well explored1 and present opportunities to use AI for powerful assistance in thinking tasks. The core of the Industrial Revolution 4.0 is the adoption of AI methods. This revolution has affected all aspects of human activities and medicine is one example. AI systems can usually include formal algorithms for subtasks that can be solved using logic, for example, a decision tree. The task solution process moves from logic point to logic point similar to a train on a railway. These algorithms are fast and have the ability to explain.展开更多
Background:Previous studies have surveyed golf courses to determine nitrogen(N)fertilizer application rates on golf courses,but no previous studies have attempted to quantify how efficiently golf courses use nitrogen....Background:Previous studies have surveyed golf courses to determine nitrogen(N)fertilizer application rates on golf courses,but no previous studies have attempted to quantify how efficiently golf courses use nitrogen.Methods:This study tests the ability of the growth potential(GP)N Requirement model as a benchmarking tool to predict a target level of N use on 76 golf courses in 5 regions of the US(Midwest,Northeast,East Texas,Florida,Northwest)and 3 countries in Europe(Denmark,Norway,UK).Results:The ratio of the golf course-wide N application rate to the GP N requirement prediction(termed the nitrogen efficiency score or NES)was 0.27,indicating that golf courses used 73%less N than predicted by the model.As such,the GP N Requirement model needs to be recalibrated to predict N use on golf courses.This was achieved by adjusting the Nmax coefficient in the model.N rates on golf courses were widely variable both within and across regions.All regions had a coefficient of variation in N rates of 0.46 or greater.Conclusions:The high variation in N rates,which is largely unexplained by climate,economic factors,grass type,and soil type,may be indicative of inefficient N use in golf course management.展开更多
Highway agencies have been using many of the elements of asset management with the support of various decision-making tools.To determine the most effective investment strategy with scarce resources,the integration,and...Highway agencies have been using many of the elements of asset management with the support of various decision-making tools.To determine the most effective investment strategy with scarce resources,the integration,and hence better utilization,of existing tools and practices across asset classes is generally lacking.This paper applies data envelopment analysis(DEA)to benchmark different highway investment scenarios using existing data or data readily available through existing models.Three asset types,pavements,bridges,and traffic signage,are investigated.Asset investment analysis results from the Highway Economic Requirements System State Version(HERS-ST)application,the PONTIS bridge management system software,and purpose-built traffic signage spreadsheet are obtained to capture the changes of performance measures under various budget scenarios and are further used as the inputs for the DEA process to benchmark investment scenarios for each individual asset.Subsequently,the performance measures and budget levels are assembled in the Asset Manager-NT software,whose results are input into DEA to benchmark cross-assets resource allocation scenarios.Planning for the management of highway network is addressed via case studies in a systematic manner that recognizes the tradeoffs among different funding periods and objectives such as preserving existing investments,safety,roughness and user costs.This study has established a preliminary implementable framework of highway asset management by linking DEA approach and current widely used decision-making tools for more efficient investments within and cross assets,and better understand of the tradeoffs,costs and consequences of various asset management decisions.展开更多
With growing pressures on marine ecosystems and on marine space,an increasingly needed strategy to optimise the use of marine space is to co-locate synergic marine human uses in close spatial–temporal proximity while...With growing pressures on marine ecosystems and on marine space,an increasingly needed strategy to optimise the use of marine space is to co-locate synergic marine human uses in close spatial–temporal proximity while separating conflicting marine human uses.The ArcMap toolbox SEANERGY is a new,cross-sectoral spatial decision support tool(DST)that enables maritime spatial planners to consider synergies and conflicts between marine uses to support assessments of co-location options.Cross-sectoral approaches are important to reach more integrative maritime spatial planning(MSP)processes.As this article demonstrates through a Baltic Sea analysis,SEANERGY presents a crosssectoral use catalogue for MSP through enabling the tool users to answer important specific questions to spatially and/or numerically weight potential synergies/conflicts between marine uses.The article discusses to what degree such a cross-sectoral perspective can support integrative MSP processes.While MSP integrative challenges still exist,SEANERGY enables MSP processes to move towards developing shared goals and initiate discussions built on best available knowledge regarding potential use-use synergies and use-use conflicts for whole sea basins at once.展开更多
In an effort to maintain safety while satisfying growing air traffic demand,air navigation service providers are considering the inclusion of advisory systems to identify potential conflicts and propose resolution com...In an effort to maintain safety while satisfying growing air traffic demand,air navigation service providers are considering the inclusion of advisory systems to identify potential conflicts and propose resolution commands for the air traffic controller to verify and issue to aircraft.To understand the potential workload implications of introducing advisory conflict-detection and resolution tools,this paper examines a metric of controller taskload:how many resolution commands an air traffic controller issues under the guidance of an advisory system.Through a simulation study,the research presented here evaluates how the underlying protocol of a conflict-resolution tool affects the controller taskload(system demands)associated with the conflict-resolution process,and implicitly the controller workload(physical and psychological demands).Ultimately,evidence indicates that there is significant flexibility in the design of conflict-resolution algorithms supporting an advisory system.展开更多
Reservoirs play an important role in water management and are key elements for water supply.Monitoring is needed in order to guarantee the quantity and quality of stored water.However,this task is sometimes not easy.T...Reservoirs play an important role in water management and are key elements for water supply.Monitoring is needed in order to guarantee the quantity and quality of stored water.However,this task is sometimes not easy.The objective of this study was to develop a procedure for predicting volume of stored water with remote sensing in water bodies under Mediterranean climate conditions.To achieve this objective,multispectral Landsat 7 and 8 images(NASA)were analyzed for the following five reservoirs:La Serena,La Pedrera,Beniarrés,Cubillas and Negratín(Spain).Reservoirs water surface was computed with the spectral angle mapper(SAM)algorithm.After that,cross-validation regression models were computed in order to assess the capability of water surface estimations to predict stored water in each of the reservoirs.The statistical models were trained with Landsat 7 images and were validated by using Landsat 8 images.Our results suggest a good capability of water volume prediction from free satellite imagery derived from surface water estimations.Combining free remote sensing images and open source GIS algorithms can be a very useful tool for water management and an integrated and efficient way to control water storage,especially in low accessible sites.展开更多
基金Project supported by the National Natural Science Foundation of China(Nos.72071179 and 51805479)the Natural Science Foundation of Zhejiang Province(No.LY19E050019)the Ministry of Industry and Information Technology of China(No.Z135060009002)。
文摘For efficient use of value stream mapping(VSM)for multi-varieties and small batch production in a data-rich environment enabled by Industry 4.0 technologies,a systematic framework of VSM to rejuvenate traditional lean tools is proposed.It addresses the issue that traditional VSM requires intensive on-site investigation and replies on experience,which hinders decisionmaking efficiency in dynamic and complex environments.The proposed framework follows the data-information-knowledge hierarchy model,and demonstrates how data can be collected in a production workshop,processed into information,and then interpreted into knowledge.In this paper,the necessity and limitations of VSM in automated root cause analysis are first discussed,with a literature review on lean production tools,especially VSM and VSM-based decision making in Industry 4.0.An implementation case of a furniture manufacturer in China is presented,where decision tree algorithm was used for automated root cause analysis.The results indicate that automated VSM can make good use of production data to cater for multi-varieties and small batch production with timely on-site waste identification and analysis.The proposed framework is also suggested as a guideline to renew other lean tools for reliable and efficient decision-making.
基金This work was supported by the National Natural Science Foundation of China[grant number 71671048,71901075]the MOE Layout Foundation of Humanities and Social Sciences the Natural Science Foundation of Guangdong Province[grant number 2020A151501507]Co-Construction Project of Philosophy and Social Science Planning Discipline in Guangdong Planning Office of Philosophy and Social Science[grant number GD18XGL37].
文摘Most existing studies of consumer search behaviour focus on page-level analysis,and some scholars start to examine the effect of refinement tools and characteristics in terms of products.However,it still remains undeveloped on the product-level.To fill this gap,we reproduced the consumer shopping process in accordance with the topology of the Taobao platform from where we collected the clickstream data.We modelled consumers’sequential decision-making behaviour based on the taxonomy with Bayesian approach and found that not all the refinement tools are utilised for optimising decisions by users and it’s surprising that there exists no significant impact of all sorting tools.Besides,consumers are highly concerned with the characteristics of products.On the basis of the findings,platform function announcement and platform design suggestions were provided for improving platform functionality and optimising consumer decision-making,which also points out the direction of future research.
文摘Background:Diabetes and hypertension are two of the commonest diseases in the world.As they unfavorably affect people of different age groups,they have become a cause of concern and must be predicted and diagnosed well in advance.Objective:This research aims to determine the effectiveness of artificial neural networks(ANNs)in predicting diabetes and blood pressure diseases and to point out the factors which have a high impact on these diseases.Sample:This work used two online datasets which consist of data collected from 768 individuals.We applied neural network algorithms to predict if the individuals have those two diseases based on some factors.Diabetes prediction is based on five factors:age,weight,fat-ratio,glucose,and insulin,while blood pressure prediction is based on six factors:age,weight,fat-ratio,blood pressure,alcohol,and smoking.Method:A model based on the Multi-Layer Perceptron Neural Network(MLP)was implemented.The inputs of the network were the factors for each disease,while the output was the prediction of the disease’s occurrence.The model performance was compared with other classifiers such as Support Vector Machine(SVM)and K-Nearest Neighbors(KNN).We used performance metrics measures to assess the accuracy and performance of MLP.Also,a tool was implemented to help diagnose the diseases and to understand the results.Result:The model predicted the two diseases with correct classification rate(CCR)of 77.6%for diabetes and 68.7%for hypertension.The results indicate that MLP correctly predicts the probability of being diseased or not,and the performance can be significantly increased compared with both SVM and KNN.This shows MLPs effectiveness in early disease prediction.
文摘Dear Editor , Artificial intelligence (AI) is the theory and development of computer systems that are able to perform tasks that normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages. There are some knowledge and thinking tasks that humans cannot perform as perfectly as they wish to or should be able to. These tasks are closely related to security and responsibility. A multitude of cognitive distortions have been well explored1 and present opportunities to use AI for powerful assistance in thinking tasks. The core of the Industrial Revolution 4.0 is the adoption of AI methods. This revolution has affected all aspects of human activities and medicine is one example. AI systems can usually include formal algorithms for subtasks that can be solved using logic, for example, a decision tree. The task solution process moves from logic point to logic point similar to a train on a railway. These algorithms are fast and have the ability to explain.
文摘Background:Previous studies have surveyed golf courses to determine nitrogen(N)fertilizer application rates on golf courses,but no previous studies have attempted to quantify how efficiently golf courses use nitrogen.Methods:This study tests the ability of the growth potential(GP)N Requirement model as a benchmarking tool to predict a target level of N use on 76 golf courses in 5 regions of the US(Midwest,Northeast,East Texas,Florida,Northwest)and 3 countries in Europe(Denmark,Norway,UK).Results:The ratio of the golf course-wide N application rate to the GP N requirement prediction(termed the nitrogen efficiency score or NES)was 0.27,indicating that golf courses used 73%less N than predicted by the model.As such,the GP N Requirement model needs to be recalibrated to predict N use on golf courses.This was achieved by adjusting the Nmax coefficient in the model.N rates on golf courses were widely variable both within and across regions.All regions had a coefficient of variation in N rates of 0.46 or greater.Conclusions:The high variation in N rates,which is largely unexplained by climate,economic factors,grass type,and soil type,may be indicative of inefficient N use in golf course management.
基金supported by the University of DelawareUniversity Transportation Center+1 种基金Delaware Center for TransportationDelaware Department of Transportation。
文摘Highway agencies have been using many of the elements of asset management with the support of various decision-making tools.To determine the most effective investment strategy with scarce resources,the integration,and hence better utilization,of existing tools and practices across asset classes is generally lacking.This paper applies data envelopment analysis(DEA)to benchmark different highway investment scenarios using existing data or data readily available through existing models.Three asset types,pavements,bridges,and traffic signage,are investigated.Asset investment analysis results from the Highway Economic Requirements System State Version(HERS-ST)application,the PONTIS bridge management system software,and purpose-built traffic signage spreadsheet are obtained to capture the changes of performance measures under various budget scenarios and are further used as the inputs for the DEA process to benchmark investment scenarios for each individual asset.Subsequently,the performance measures and budget levels are assembled in the Asset Manager-NT software,whose results are input into DEA to benchmark cross-assets resource allocation scenarios.Planning for the management of highway network is addressed via case studies in a systematic manner that recognizes the tradeoffs among different funding periods and objectives such as preserving existing investments,safety,roughness and user costs.This study has established a preliminary implementable framework of highway asset management by linking DEA approach and current widely used decision-making tools for more efficient investments within and cross assets,and better understand of the tradeoffs,costs and consequences of various asset management decisions.
基金supported by BONUS EEIG:[grant number 2017-06-19].
文摘With growing pressures on marine ecosystems and on marine space,an increasingly needed strategy to optimise the use of marine space is to co-locate synergic marine human uses in close spatial–temporal proximity while separating conflicting marine human uses.The ArcMap toolbox SEANERGY is a new,cross-sectoral spatial decision support tool(DST)that enables maritime spatial planners to consider synergies and conflicts between marine uses to support assessments of co-location options.Cross-sectoral approaches are important to reach more integrative maritime spatial planning(MSP)processes.As this article demonstrates through a Baltic Sea analysis,SEANERGY presents a crosssectoral use catalogue for MSP through enabling the tool users to answer important specific questions to spatially and/or numerically weight potential synergies/conflicts between marine uses.The article discusses to what degree such a cross-sectoral perspective can support integrative MSP processes.While MSP integrative challenges still exist,SEANERGY enables MSP processes to move towards developing shared goals and initiate discussions built on best available knowledge regarding potential use-use synergies and use-use conflicts for whole sea basins at once.
基金funded by NASA(No.NNX08AY52A)FAA Award(No.07-C-NEGIT)+1 种基金Amendment(Nos.005,010,020)Air Force Contract(No.FA9550-08-1-0375)。
文摘In an effort to maintain safety while satisfying growing air traffic demand,air navigation service providers are considering the inclusion of advisory systems to identify potential conflicts and propose resolution commands for the air traffic controller to verify and issue to aircraft.To understand the potential workload implications of introducing advisory conflict-detection and resolution tools,this paper examines a metric of controller taskload:how many resolution commands an air traffic controller issues under the guidance of an advisory system.Through a simulation study,the research presented here evaluates how the underlying protocol of a conflict-resolution tool affects the controller taskload(system demands)associated with the conflict-resolution process,and implicitly the controller workload(physical and psychological demands).Ultimately,evidence indicates that there is significant flexibility in the design of conflict-resolution algorithms supporting an advisory system.
文摘Reservoirs play an important role in water management and are key elements for water supply.Monitoring is needed in order to guarantee the quantity and quality of stored water.However,this task is sometimes not easy.The objective of this study was to develop a procedure for predicting volume of stored water with remote sensing in water bodies under Mediterranean climate conditions.To achieve this objective,multispectral Landsat 7 and 8 images(NASA)were analyzed for the following five reservoirs:La Serena,La Pedrera,Beniarrés,Cubillas and Negratín(Spain).Reservoirs water surface was computed with the spectral angle mapper(SAM)algorithm.After that,cross-validation regression models were computed in order to assess the capability of water surface estimations to predict stored water in each of the reservoirs.The statistical models were trained with Landsat 7 images and were validated by using Landsat 8 images.Our results suggest a good capability of water volume prediction from free satellite imagery derived from surface water estimations.Combining free remote sensing images and open source GIS algorithms can be a very useful tool for water management and an integrated and efficient way to control water storage,especially in low accessible sites.
