Li Auto’s Beijing factory is a highly advanced,intelligent,and green-oriented factory.EVERY minute,one MEGA,Li Auto’s flagship multi-purpose vehicle(MPV)priced at over RMB 500,000,rolls off its production line.New-e...Li Auto’s Beijing factory is a highly advanced,intelligent,and green-oriented factory.EVERY minute,one MEGA,Li Auto’s flagship multi-purpose vehicle(MPV)priced at over RMB 500,000,rolls off its production line.New-energy vehicles(NEVs)epitomize China’s upgrade towards premium,intelligent,and green manufacturing.展开更多
When you think of Dongguan,chances are you picture humming factories and endless assembly lines.For decades,this city in southern China was known as the“world's workshop,”churning out everything from sneakers to...When you think of Dongguan,chances are you picture humming factories and endless assembly lines.For decades,this city in southern China was known as the“world's workshop,”churning out everything from sneakers to smartphones.展开更多
Characterized by robotics,automation,the Internet of Things,and other cutting-edge technologies,intelligent manufacturing has been at the forefront of industrial development in China.A green,low-carbon,and intelligent...Characterized by robotics,automation,the Internet of Things,and other cutting-edge technologies,intelligent manufacturing has been at the forefront of industrial development in China.A green,low-carbon,and intelligent air conditioner factory has been certified as an exceptional smart factory in Jinwan District,Zhuhai City,south China’s Guangdong Province.展开更多
Rice(Oryza sativa L.)plays a pivotal role in global food security,yet its breeding is constrained by its long generation time and seasonality.To enhance rice breeding efficiency and meet future food demands,we have de...Rice(Oryza sativa L.)plays a pivotal role in global food security,yet its breeding is constrained by its long generation time and seasonality.To enhance rice breeding efficiency and meet future food demands,we have developed a vertical hydroponic breeding system integrated with light-emitting diodes(LEDs)light-ing in a closed plant factory(PF),which significantly accelerates rice growth and generation advance-ment.The results show that indica rice can be harvested as early as after 63 days of cultivation,a 50%reduction compared with field cultivation,enabling the annual harvesting of 5-6 generations within the PF.A hyperspectral imaging(HSI)system and attenuated total reflectance infrared(ATR-IR)spec-troscopy were further employed to characterize the chemical composition of the PF-and field-cultivated rice.Metabolomics analysis with ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)and gas chromatography-mass spectrometry(GC-MS)revealed that,com-pared with the field-cultivated rice,the PF-cultivated rice exhibited an up-regulation of total phenolic acids along with 68 non-volatile and 19 volatile metabolites,such as isovitexin,succinic acid,and methylillicinone F.Overall,this study reveals the unique metabolic profile of PF-cultivated rice and high-lights the potential of PFs to accelerate the breeding of crops such as rice,offering an innovative agricul-tural strategy to support food security in the face of global population growth and climate change.展开更多
LD Data Factory以“4D连续帧真值”为核心,首创“数据采集-标注-质检-评价”闭环,具备AI自动标注、遮挡下目标追踪、跨帧一致性校验、感知性能KPI分析等核心能力。该系统基于多模态融合与时序建模原理,显著提升数据构建效率与质量稳定...LD Data Factory以“4D连续帧真值”为核心,首创“数据采集-标注-质检-评价”闭环,具备AI自动标注、遮挡下目标追踪、跨帧一致性校验、感知性能KPI分析等核心能力。该系统基于多模态融合与时序建模原理,显著提升数据构建效率与质量稳定性,支持SaaS与私有化部署,满足不同客户数据安全与交付需求。人工标注成本降低90%,效率提升3-4倍,已量产交付5500万帧高质4D数据。展开更多
China’s investment is creating life-changing opportunities for Rwandans In a significant development in the bilateral eco-nomic partnership,Chinese investors injected some$460 million into Rwanda’s economy in 2024.T...China’s investment is creating life-changing opportunities for Rwandans In a significant development in the bilateral eco-nomic partnership,Chinese investors injected some$460 million into Rwanda’s economy in 2024.This accounted for 14.1 percent of all registered foreign capital in Rwanda,making China the country’s leading foreign investor.According to a report from the Rwanda Development Board(RDB).展开更多
Agastache rugosa,a medicinal plant known for its bioactive compounds,has gained attention for its pharmacological and commercial potential.This study aimed to optimize ethanol concentration to enhance growth and bioac...Agastache rugosa,a medicinal plant known for its bioactive compounds,has gained attention for its pharmacological and commercial potential.This study aimed to optimize ethanol concentration to enhance growth and bioactive compound production in A.rugosa cultivated in a controlled plant factory system.Ethanol treatments at 40 and 80 mM significantly promoted both vegetative and reproductive growth.Plants treated with these concentrations exhibited higher net photosynthetic rates(A)and intercellular CO_(2) concentration(Ci)compared to the untreated control,whereas stomatal conductance(gs)and transpiration rate(E)remained unaffected.Chlorophyll and carotenoid concentrations,and SPAD values,significantly increased with ethanol treatment.Total flavonoid and total phenolic contents as well as 2,2-diphenyl-1-picrylhydrazyl(DPPH)radical-scavenging activities were significantly higher in plants treated with ethanol than in the untreated control.Ethanol treatments led to a significant enhancement in the activities of antioxidant enzymes,including superoxide dismutase,peroxidase,and catalase.Furthermore,ethanol treatment elevated rosmarinic acid concentrations in roots and tilianin and acacetin levels in flowers.Collectively,ethanol at 40 and 80 mM effectively enhanced growth,photosynthesis,antioxidant defense,and bioactive compound production in A.rugosa cultivated in a plant factory.These findings provide valuable insights for improving cultivation of medicinal plants with high pharmaceutical and nutraceutical value.展开更多
Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessi...Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.展开更多
The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability an...The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.展开更多
As an approximate Goldstone boson with zero quantum number and zero standard model charge,the long-lived η meson exhibits the decay processes that offer a unique opportunity to explore physics beyond the standard mod...As an approximate Goldstone boson with zero quantum number and zero standard model charge,the long-lived η meson exhibits the decay processes that offer a unique opportunity to explore physics beyond the standard model and new sources of charge parity violation.Further,they facilitate the testing of the low-energy quantum chromodynamics theory and measurement of the fundamental parameters of light quarks.To pursue these goals,we propose a plan to construct a super ηfactory at HIAF high-energy terminal or at CiADS after its energy upgrade.The high-intensity proton beam at HIAF enables the production of many η samples,exceeding 1013events per year during the first stage,utilizing multiple layers of thin targets composed of light nuclei.This paper presents the physics goals,the first-version conceptual design of the spectrometer,and preliminary simulation results.展开更多
文摘Li Auto’s Beijing factory is a highly advanced,intelligent,and green-oriented factory.EVERY minute,one MEGA,Li Auto’s flagship multi-purpose vehicle(MPV)priced at over RMB 500,000,rolls off its production line.New-energy vehicles(NEVs)epitomize China’s upgrade towards premium,intelligent,and green manufacturing.
文摘When you think of Dongguan,chances are you picture humming factories and endless assembly lines.For decades,this city in southern China was known as the“world's workshop,”churning out everything from sneakers to smartphones.
文摘Characterized by robotics,automation,the Internet of Things,and other cutting-edge technologies,intelligent manufacturing has been at the forefront of industrial development in China.A green,low-carbon,and intelligent air conditioner factory has been certified as an exceptional smart factory in Jinwan District,Zhuhai City,south China’s Guangdong Province.
基金supported by the National Key Research and Development Program(2023YFF1001500)the Local Financial Funds of National Agricultural Science and Technology Center,Chengdu(NASC2022KR02,NASC2023TD08,NASC2021ST08,NASC2021PC04,NASC2022KR07,NASC2022KR06,and NASC2023ST04)+2 种基金the Agricultural Science and Technology Innova-tion Program(ASTIP-34-IUA-01,ASTIP-34-IUA-02,ASTIP-IUA-2023003,and ASTIP2024-34-IUA-09)the Central Public-interest Scientific Institution Basal Research Fund(Y2023YJ07 and SZ202403)the Sichuan Science and Technology Program(2023YFN003,2024NSFC1261,2023YFQ0100,and 2023ZYD0089).
文摘Rice(Oryza sativa L.)plays a pivotal role in global food security,yet its breeding is constrained by its long generation time and seasonality.To enhance rice breeding efficiency and meet future food demands,we have developed a vertical hydroponic breeding system integrated with light-emitting diodes(LEDs)light-ing in a closed plant factory(PF),which significantly accelerates rice growth and generation advance-ment.The results show that indica rice can be harvested as early as after 63 days of cultivation,a 50%reduction compared with field cultivation,enabling the annual harvesting of 5-6 generations within the PF.A hyperspectral imaging(HSI)system and attenuated total reflectance infrared(ATR-IR)spec-troscopy were further employed to characterize the chemical composition of the PF-and field-cultivated rice.Metabolomics analysis with ultra-performance liquid chromatography-tandem mass spectrometry(UPLC-MS/MS)and gas chromatography-mass spectrometry(GC-MS)revealed that,com-pared with the field-cultivated rice,the PF-cultivated rice exhibited an up-regulation of total phenolic acids along with 68 non-volatile and 19 volatile metabolites,such as isovitexin,succinic acid,and methylillicinone F.Overall,this study reveals the unique metabolic profile of PF-cultivated rice and high-lights the potential of PFs to accelerate the breeding of crops such as rice,offering an innovative agricul-tural strategy to support food security in the face of global population growth and climate change.
文摘LD Data Factory以“4D连续帧真值”为核心,首创“数据采集-标注-质检-评价”闭环,具备AI自动标注、遮挡下目标追踪、跨帧一致性校验、感知性能KPI分析等核心能力。该系统基于多模态融合与时序建模原理,显著提升数据构建效率与质量稳定性,支持SaaS与私有化部署,满足不同客户数据安全与交付需求。人工标注成本降低90%,效率提升3-4倍,已量产交付5500万帧高质4D数据。
文摘China’s investment is creating life-changing opportunities for Rwandans In a significant development in the bilateral eco-nomic partnership,Chinese investors injected some$460 million into Rwanda’s economy in 2024.This accounted for 14.1 percent of all registered foreign capital in Rwanda,making China the country’s leading foreign investor.According to a report from the Rwanda Development Board(RDB).
基金supported by Korea Institute of Energy Technology Evaluation and Planning(KETEP)grant funded by the Korea government(MOTIE)(20212020800050,Development and demonstration of rooftop greenhouse-building integrated system using distributed polygeneration).
文摘Agastache rugosa,a medicinal plant known for its bioactive compounds,has gained attention for its pharmacological and commercial potential.This study aimed to optimize ethanol concentration to enhance growth and bioactive compound production in A.rugosa cultivated in a controlled plant factory system.Ethanol treatments at 40 and 80 mM significantly promoted both vegetative and reproductive growth.Plants treated with these concentrations exhibited higher net photosynthetic rates(A)and intercellular CO_(2) concentration(Ci)compared to the untreated control,whereas stomatal conductance(gs)and transpiration rate(E)remained unaffected.Chlorophyll and carotenoid concentrations,and SPAD values,significantly increased with ethanol treatment.Total flavonoid and total phenolic contents as well as 2,2-diphenyl-1-picrylhydrazyl(DPPH)radical-scavenging activities were significantly higher in plants treated with ethanol than in the untreated control.Ethanol treatments led to a significant enhancement in the activities of antioxidant enzymes,including superoxide dismutase,peroxidase,and catalase.Furthermore,ethanol treatment elevated rosmarinic acid concentrations in roots and tilianin and acacetin levels in flowers.Collectively,ethanol at 40 and 80 mM effectively enhanced growth,photosynthesis,antioxidant defense,and bioactive compound production in A.rugosa cultivated in a plant factory.These findings provide valuable insights for improving cultivation of medicinal plants with high pharmaceutical and nutraceutical value.
基金funded by Changzhou Science and Technology Project(No.CZ20230025)Postgraduate Research&Practice Innovation Program of Jiangsu Province(No.XSJCX23_36).
文摘Aiming at the problem that the existing algorithms for vehicle detection in smart factories are difficult to detect partial occlusion of vehicles,vulnerable to background interference,lack of global vision,and excessive suppression of real targets,which ultimately cause accuracy degradation.At the same time,to facilitate the subsequent positioning of vehicles in the factory,this paper proposes an improved YOLOv8 algorithm.Firstly,the RFCAConv module is combined to improve the original YOLOv8 backbone.Pay attention to the different features in the receptive field,and give priority to the spatial features of the receptive field to capture more vehicle feature information and solve the problem that the vehicle is partially occluded and difficult to detect.Secondly,the SFE module is added to the neck of v8,which improves the saliency of the target in the reasoning process and reduces the influence of background interference on vehicle detection.Finally,the head of the RT-DETR algorithm is used to replace the head in the original YOLOv8 algorithm,which avoids the excessive suppression of the real target while combining the context information.The experimental results show that compared with the original YOLOv8 algorithm,the detection accuracy of the improved YOLOv8 algorithm is improved by 4.6%on the self-made smart factory data set,and the detection speed also meets the real-time requirements of smart factory vehicle detection and subsequent vehicle positioning.
基金supported by National Natural Science Foundation of China under Grant No.62372110Fujian Provincial Natural Science of Foundation under Grants 2023J02008,2024H0009.
文摘The rapid advancement of Industry 4.0 has revolutionized manufacturing,shifting production from centralized control to decentralized,intelligent systems.Smart factories are now expected to achieve high adaptability and resource efficiency,particularly in mass customization scenarios where production schedules must accommodate dynamic and personalized demands.To address the challenges of dynamic task allocation,uncertainty,and realtime decision-making,this paper proposes Pathfinder,a deep reinforcement learning-based scheduling framework.Pathfinder models scheduling data through three key matrices:execution time(the time required for a job to complete),completion time(the actual time at which a job is finished),and efficiency(the performance of executing a single job).By leveraging neural networks,Pathfinder extracts essential features from these matrices,enabling intelligent decision-making in dynamic production environments.Unlike traditional approaches with fixed scheduling rules,Pathfinder dynamically selects from ten diverse scheduling rules,optimizing decisions based on real-time environmental conditions.To further enhance scheduling efficiency,a specialized reward function is designed to support dynamic task allocation and real-time adjustments.This function helps Pathfinder continuously refine its scheduling strategy,improving machine utilization and minimizing job completion times.Through reinforcement learning,Pathfinder adapts to evolving production demands,ensuring robust performance in real-world applications.Experimental results demonstrate that Pathfinder outperforms traditional scheduling approaches,offering improved coordination and efficiency in smart factories.By integrating deep reinforcement learning,adaptable scheduling strategies,and an innovative reward function,Pathfinder provides an effective solution to the growing challenges of multi-robot job scheduling in mass customization environments.
基金supported by the National Natural Science Foundation of China(Nos.12222512 and 12005266)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDB34030300).
文摘As an approximate Goldstone boson with zero quantum number and zero standard model charge,the long-lived η meson exhibits the decay processes that offer a unique opportunity to explore physics beyond the standard model and new sources of charge parity violation.Further,they facilitate the testing of the low-energy quantum chromodynamics theory and measurement of the fundamental parameters of light quarks.To pursue these goals,we propose a plan to construct a super ηfactory at HIAF high-energy terminal or at CiADS after its energy upgrade.The high-intensity proton beam at HIAF enables the production of many η samples,exceeding 1013events per year during the first stage,utilizing multiple layers of thin targets composed of light nuclei.This paper presents the physics goals,the first-version conceptual design of the spectrometer,and preliminary simulation results.
