Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different ...Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.展开更多
The current predominant self-review mechanism by policy-making bodies suffers from deficiencies such as insufficient motivations, limited review capabilities, and weak external supervision. Third-party assessment, cha...The current predominant self-review mechanism by policy-making bodies suffers from deficiencies such as insufficient motivations, limited review capabilities, and weak external supervision. Third-party assessment, characterized by independence and specialization, is designed to mitigate these shortcomings. However, the implementation of third-party assessment faces challenges too. This paper intends to improve the third-party assessment system and to realize the legislative purpose of the system. Based on social research, discussions and exchanges with relevant parties, and the existing research results, this paper analyzes the challenges and possible optimization measures for the third-party assessment. The challenges include repulsion from policy-making bodies, insufficient independence of assessment bodies, disparity of assessment quality, and limited application of assessment outcomes. Possible optimization measures include promoting fair competition culture, increasing the acceptance of third-party assessment from policy-making bodies, enhancing the quality of third-party assessment, clarifying the relationship between policy-making bodies and assessment bodies, ensuring the independence of third-party assessments, and promoting the application of assessment results.展开更多
Quantitative data analysis in single-molecule localization microscopy(SMLM)is crucial for studying cellular functions at the biomolecular level.In the past decade,several quantitative methods were developed for analyz...Quantitative data analysis in single-molecule localization microscopy(SMLM)is crucial for studying cellular functions at the biomolecular level.In the past decade,several quantitative methods were developed for analyzing SMLM data;however,imaging artifacts in SMLM experiments reduce the accuracy of these methods,and these methods were seldom designed as user-friendly tools.Researchers are now trying to overcome these di±culties by developing easyto-use SMLM data analysis software for certain image analysis tasks.But,this kind of software did not pay su±cient attention to the impact of imaging artifacts on the analysis accuracy,and usually contained only one type of analysis task.Therefore,users are still facing di±culties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs.In this paper,we report an ImageJ plug-in called DecodeSTORM,which not only has a simple GUI for human–computer interaction,but also combines artifact correction with several quantitative analysis methods.DecodeSTORM includes format conversion,channel registration,artifact correction(drift correction and localization¯ltering),quantitative analysis(segmentation and clustering,spatial distribution statistics and colocalization)and visualization.Importantly,these data analysis methods can be combined freely,thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods.We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in,which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.展开更多
With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaboratio...With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.展开更多
China should prioritize the establishment and enhancement of a third-party funding system.It should actively refine the existing arbitration rules,addressing any loopholes in the current regulatory framework.Comprehen...China should prioritize the establishment and enhancement of a third-party funding system.It should actively refine the existing arbitration rules,addressing any loopholes in the current regulatory framework.Comprehensive measures should be implemented to regulate third-party funding,aligning with international trends.This is crucial not only to safeguard the foreign investment of the Chinese government and enterprises but also to position China as a globally influential arbitration center.展开更多
Through the analysis on the meanings and features as well as the ad- vantages of the third-party logistics for agricultural products, the quantization index system for the selection of third-party logistics providers ...Through the analysis on the meanings and features as well as the ad- vantages of the third-party logistics for agricultural products, the quantization index system for the selection of third-party logistics providers for agricultural products was constructed based on the system comprehensive evaluation theory. Analytic hierar- chy process （AHP） was used to determine the weight of the index system of each level, and AHP and fuzzy comprehensive evaluation method were used to determine the selection steps for the third-party logistics providers for agricultural products. The method was proved to be scientific and reasonable through calculation examples.展开更多
The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks c...The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).展开更多
The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the...The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.展开更多
Purpose: This research aims to evaluate the potential threats to patient privacy and confidentiality posed by mHealth applications on mobile devices. Methodology: A comprehensive literature review was conducted, selec...Purpose: This research aims to evaluate the potential threats to patient privacy and confidentiality posed by mHealth applications on mobile devices. Methodology: A comprehensive literature review was conducted, selecting eighty-eight articles published over the past fifteen years. The study assessed data gathering and storage practices, regulatory adherence, legal structures, consent procedures, user education, and strategies to mitigate risks. Results: The findings reveal significant advancements in technologies designed to safeguard privacy and facilitate the widespread use of mHealth apps. However, persistent ethical issues related to privacy remain largely unchanged despite these technological strides.展开更多
The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term ...The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).展开更多
综合考虑电池和SCR催化器在低温环境下的工作特性,针对低温下温度对Plug-in柴电混合动力汽车性能的影响,提出最短时间控制和燃油消耗最少问题。以SCR起燃温度和电池正常工作温度时间最短为优化目标,以电池温度、电池荷电状态(State of C...综合考虑电池和SCR催化器在低温环境下的工作特性,针对低温下温度对Plug-in柴电混合动力汽车性能的影响,提出最短时间控制和燃油消耗最少问题。以SCR起燃温度和电池正常工作温度时间最短为优化目标,以电池温度、电池荷电状态(State of Charge,SOC)和SCR催化器温度为状态变量,利用极小值原理求得最优控制策略。通过仿真对比规则控制策略,分析了在不同低温条件下基于庞特里亚金极小值原理(Pontryagin’s Minimum Principle,PMP)的最短时间和最少油耗与排放优化控制策略对整车油耗和排放的影响。展开更多
文摘Plug-in Hybrid Electric Vehicles(PHEVs)represent an innovative breed of transportation,harnessing diverse power sources for enhanced performance.Energy management strategies(EMSs)that coordinate and control different energy sources is a critical component of PHEV control technology,directly impacting overall vehicle performance.This study proposes an improved deep reinforcement learning(DRL)-based EMSthat optimizes realtime energy allocation and coordinates the operation of multiple power sources.Conventional DRL algorithms struggle to effectively explore all possible state-action combinations within high-dimensional state and action spaces.They often fail to strike an optimal balance between exploration and exploitation,and their assumption of a static environment limits their ability to adapt to changing conditions.Moreover,these algorithms suffer from low sample efficiency.Collectively,these factors contribute to convergence difficulties,low learning efficiency,and instability.To address these challenges,the Deep Deterministic Policy Gradient(DDPG)algorithm is enhanced using entropy regularization and a summation tree-based Prioritized Experience Replay(PER)method,aiming to improve exploration performance and learning efficiency from experience samples.Additionally,the correspondingMarkovDecision Process(MDP)is established.Finally,an EMSbased on the improvedDRLmodel is presented.Comparative simulation experiments are conducted against rule-based,optimization-based,andDRL-based EMSs.The proposed strategy exhibitsminimal deviation fromthe optimal solution obtained by the dynamic programming(DP)strategy that requires global information.In the typical driving scenarios based onWorld Light Vehicle Test Cycle(WLTC)and New European Driving Cycle(NEDC),the proposed method achieved a fuel consumption of 2698.65 g and an Equivalent Fuel Consumption(EFC)of 2696.77 g.Compared to the DP strategy baseline,the proposed method improved the fuel efficiency variances(FEV)by 18.13%,15.1%,and 8.37%over the Deep QNetwork(DQN),Double DRL(DDRL),and original DDPG methods,respectively.The observational outcomes demonstrate that the proposed EMS based on improved DRL framework possesses good real-time performance,stability,and reliability,effectively optimizing vehicle economy and fuel consumption.
文摘The current predominant self-review mechanism by policy-making bodies suffers from deficiencies such as insufficient motivations, limited review capabilities, and weak external supervision. Third-party assessment, characterized by independence and specialization, is designed to mitigate these shortcomings. However, the implementation of third-party assessment faces challenges too. This paper intends to improve the third-party assessment system and to realize the legislative purpose of the system. Based on social research, discussions and exchanges with relevant parties, and the existing research results, this paper analyzes the challenges and possible optimization measures for the third-party assessment. The challenges include repulsion from policy-making bodies, insufficient independence of assessment bodies, disparity of assessment quality, and limited application of assessment outcomes. Possible optimization measures include promoting fair competition culture, increasing the acceptance of third-party assessment from policy-making bodies, enhancing the quality of third-party assessment, clarifying the relationship between policy-making bodies and assessment bodies, ensuring the independence of third-party assessments, and promoting the application of assessment results.
基金supported by the National Natural Science Foundation of China(82160345)Key research and development project of Hainan province(ZDYF2021GXJS017)+2 种基金Key Science and Technology Plan Project of Haikou(2021-016)the Start-up Fund from Hainan University(KYQD(ZR)-20022 and KYQD(ZR)-20077)the Student Innovation and Entrepreneurship Project of Biomedical Engineer-ing School,Hainan University(BMECF2D2021001).
文摘Quantitative data analysis in single-molecule localization microscopy(SMLM)is crucial for studying cellular functions at the biomolecular level.In the past decade,several quantitative methods were developed for analyzing SMLM data;however,imaging artifacts in SMLM experiments reduce the accuracy of these methods,and these methods were seldom designed as user-friendly tools.Researchers are now trying to overcome these di±culties by developing easyto-use SMLM data analysis software for certain image analysis tasks.But,this kind of software did not pay su±cient attention to the impact of imaging artifacts on the analysis accuracy,and usually contained only one type of analysis task.Therefore,users are still facing di±culties when they want to have the combined use of different types of analysis methods according to the characteristics of their data and their own needs.In this paper,we report an ImageJ plug-in called DecodeSTORM,which not only has a simple GUI for human–computer interaction,but also combines artifact correction with several quantitative analysis methods.DecodeSTORM includes format conversion,channel registration,artifact correction(drift correction and localization¯ltering),quantitative analysis(segmentation and clustering,spatial distribution statistics and colocalization)and visualization.Importantly,these data analysis methods can be combined freely,thus improving the accuracy of quantitative analysis and allowing users to have an optimal combination of methods.We believe DecodeSTORM is a user-friendly and powerful ImageJ plug-in,which provides an easy and accurate data analysis tool for adventurous biologists who are looking for new imaging tools for studying important questions in cell biology.
文摘With a view to adopting to the globalized business landscape,organizations rely on third-party business relationships to enhance their operations,expand their capabilities,and drive innovation.While these collaborations offer numerous benefits,they also introduce a range of risks that organizations must carefully mitigate.If the obligation to meet the regulatory requirements is added to the equation,mitigating the third-party risk related to data governance,becomes one of the biggest challenges.
基金National Social Science Fund project(23BGL052)Shandong Key R&D Program(Soft Science Project)(2023RKY03009)Qingdao Social Science Fund Project(QDSKL2301121)。
文摘China should prioritize the establishment and enhancement of a third-party funding system.It should actively refine the existing arbitration rules,addressing any loopholes in the current regulatory framework.Comprehensive measures should be implemented to regulate third-party funding,aligning with international trends.This is crucial not only to safeguard the foreign investment of the Chinese government and enterprises but also to position China as a globally influential arbitration center.
基金Supported by the Natural Science Foundation of Guangxi Province(2011GXNSFB018061)the High-grade Scientific Research(Cultivation)Program of Qinzhou University(2014PY-SJ03,2014PY-SJ01)~~
文摘Through the analysis on the meanings and features as well as the ad- vantages of the third-party logistics for agricultural products, the quantization index system for the selection of third-party logistics providers for agricultural products was constructed based on the system comprehensive evaluation theory. Analytic hierar- chy process （AHP） was used to determine the weight of the index system of each level, and AHP and fuzzy comprehensive evaluation method were used to determine the selection steps for the third-party logistics providers for agricultural products. The method was proved to be scientific and reasonable through calculation examples.
文摘The plug-in hybrid vehicles(PHEV)technology can effectively address the issues of poor dynamics and higher energy consumption commonly found in traditional mining dump trucks.Meanwhile,plug-in hybrid electric trucks can achieve excellent fuel economy through efficient energy management strategies(EMS).Therefore,a series hybrid system is constructed based on a 100-ton mining dump truck in this paper.And inspired by the dynamic programming(DP)algorithm,a predictive equivalent consumption minimization strategy(P-ECMS)based on the DP optimization result is proposed.Based on the optimal control manifold and the SOC reference trajectory obtained by the DP algorithm,the P-ECMS strategy performs real-time stage parameter optimization to obtain the optimal equivalent factor(EF).Finally,applying the equivalent consumption minimization strategy(ECMS)realizes real-time control.The simulation results show that the equivalent fuel consumption of the P-ECMS strategy under the experimentally collected mining cycle conditions is 150.8 L/100 km,which is 10.9%less than that of the common CDCS strategy(169.3 L/100 km),and achieves 99.47%of the fuel saving effect of the DP strategy(150 L/100 km).
文摘The North China Plain and the agricultural region are crossed by the Shanxi-Beijing natural gas pipeline.Resi-dents in the area use rototillers for planting and harvesting;however,the depth of the rototillers into the ground is greater than the depth of the pipeline,posing a significant threat to the safe operation of the pipeline.Therefore,it is of great significance to study the dynamic response of rotary tillers impacting pipelines to ensure the safe opera-tion of pipelines.This article focuses on the Shanxi-Beijing natural gas pipeline,utilizingfinite element simulation software to establish afinite element model for the interaction among the machinery,pipeline,and soil,and ana-lyzing the dynamic response of the pipeline.At the same time,a decision tree model is introduced to classify the damage of pipelines under different working conditions,and the boundary value and importance of each influen-cing factor on pipeline damage are derived.Considering the actual conditions in the hemp yam planting area,targeted management measures have been proposed to ensure the operational safety of the Shanxi-Beijing natural gas pipeline in this region.
文摘Purpose: This research aims to evaluate the potential threats to patient privacy and confidentiality posed by mHealth applications on mobile devices. Methodology: A comprehensive literature review was conducted, selecting eighty-eight articles published over the past fifteen years. The study assessed data gathering and storage practices, regulatory adherence, legal structures, consent procedures, user education, and strategies to mitigate risks. Results: The findings reveal significant advancements in technologies designed to safeguard privacy and facilitate the widespread use of mHealth apps. However, persistent ethical issues related to privacy remain largely unchanged despite these technological strides.
文摘The transportation sector is responsible for 25% of the total Carbon dioxide (CO2) emissions, whereas 60.6% of this sector represents small and medium passenger cars. However, as noted by the European Union Long-term strategy, there are two ways to reduce the amount of CO2 emissions in the transportation sector. The first way is characterized by creating more efficient vehicles. In contrast, the second way is characterized by changing the fuel used. The current study addressed the second way, changing the fuel type. The study examined the potential of battery electric vehicles (BEVs) as an alternative fuel type to reduce CO2 emissions in Hungarys transportation sector. The study used secondary data retrieved from Statista and stata.com to analyze the future trends of BEVs in Hungary. The results showed that the percentage of BEVs in Hungary in 2022 was 0.4% compared to the total number of registered passenger cars, which is 3.8 million. The simple exponential smoothing (SES) time series forecast revealed that the number of BEVs is expected to reach 84,192 in 2030, indicating a percentage increase of 2.21% in the next eight years. The study suggests that increasing the number of BEVs is necessary to address the negative impact of CO2 emissions on society. The Hungarian Ministry of Innovation and Technologys strategy to reduce the cost of BEVs may increase the percentage of BEVs by 10%, resulting in a potential average reduction of 76,957,600 g/km of CO2 compared to gasoline, diesel, hybrid electric vehicles (HEVs), and plug-in hybrid vehicles (PHEVs).
文摘综合考虑电池和SCR催化器在低温环境下的工作特性,针对低温下温度对Plug-in柴电混合动力汽车性能的影响,提出最短时间控制和燃油消耗最少问题。以SCR起燃温度和电池正常工作温度时间最短为优化目标,以电池温度、电池荷电状态(State of Charge,SOC)和SCR催化器温度为状态变量,利用极小值原理求得最优控制策略。通过仿真对比规则控制策略,分析了在不同低温条件下基于庞特里亚金极小值原理(Pontryagin’s Minimum Principle,PMP)的最短时间和最少油耗与排放优化控制策略对整车油耗和排放的影响。
