Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower ...Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.展开更多
The use of management accounting tools in enterprises to reduce costs and increase efficiency is not only to improve the quality of enterprise management, but also to apply this new approach to cost-cutting and effici...The use of management accounting tools in enterprises to reduce costs and increase efficiency is not only to improve the quality of enterprise management, but also to apply this new approach to cost-cutting and efficiency-enhancing management through cost-cutting and efficiency-enhancing methods to improve the efficiency of enterprises and achieve customer satisfaction. Therefore, we should do a good job in financial management innovation, master the corresponding methods in the construction of management accounting tool system, and solve the actual problems, so as to ensure the smooth development of enterprise management accounting. In addition, to seriously study the laws of our country, we should take the enterprise itself as the starting point, strengthen the understanding of the knowledge related to management accounting, and finally realize the maximization of the interests of the enterprise.展开更多
At present, human resources management is no longer a traditional system, but is developing towards the allocation and management of funds. This document analyses the functional modules of human resources planning and...At present, human resources management is no longer a traditional system, but is developing towards the allocation and management of funds. This document analyses the functional modules of human resources planning and welfare management. Develop human resources development plans and corporate employee plans to improve efficiency, ensure a stable business environment and competent and necessary human resources to achieve organizational goals, including personal interests;In this way, the company's demand for points of sale is consistent with the ownership of future employees. In theory, if the labor supply-demand ratio is the same as the 1:1 ratio, then there may be a small ratio that better reflects the production efficiency.展开更多
Reverse Time Migration(RTM)Surface Ofset Gathers(SOGs)are demonstrated to deliver more superior residual dip information than ray-based approaches.It appears more powerful in complex geological settings,such as salt a...Reverse Time Migration(RTM)Surface Ofset Gathers(SOGs)are demonstrated to deliver more superior residual dip information than ray-based approaches.It appears more powerful in complex geological settings,such as salt areas.Still,the computational cost of constructing RTM SOGs is a big challenge in applying it to 3D feld data.To tackle this challenge,we propose a novel method using dips of local events as a guide for RTM gather interpolation.The residual-dip information of the SOGs is created by connecting local events from depth-domain to time-domain via ray tracing.The proposed method is validated by a synthetic experiment and a feld example.It mitigates the computational cost by an order of magnitude while producing comparable results as fully computed RTM SOGs.展开更多
For years,the development and de-ployment of advanced AI models were dominated by a few tech giants and well-funded institutions.The exorbitant costs of training and running these models have reate a high bar-rier to ...For years,the development and de-ployment of advanced AI models were dominated by a few tech giants and well-funded institutions.The exorbitant costs of training and running these models have reate a high bar-rier to entry,sidelining smaller enterprises and individuals.In reducing operational costs by 90 percent,DeepSeek-R1,released by a Chinese startup in January,has substantially lowered this barrier.Hailed as the“DeepSeek Moment,”it has brought AI out of elite research labs into the hands of the masses.展开更多
High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine ...High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.展开更多
Aspergillus niger is an efficient cell factory for organic acids production,particularly l-malic acid,through genetic manipulation.However,the traditional method of collecting A.niger spores for inoculation is labor-i...Aspergillus niger is an efficient cell factory for organic acids production,particularly l-malic acid,through genetic manipulation.However,the traditional method of collecting A.niger spores for inoculation is labor-intensive and resource-consuming.In our study,we used the CRISPR-Cas9 system to replace the promoter of brlA,a key gene in Aspergillus conidiation,with a xylose-inducible promoter xylP in l-malic acid-producing A.niger strain RG0095,generating strain brlAxylP.When induced with xylose in submerged liquid culture,brlAxylP exhibited significant upregulation of conidiation-related genes.This induction allowed us to easily collect an abundance of brlAxylP spores(>7.1×106/mL)in liquid xylose medium.Significantly,the submerged conidiation approach preserves the substantial potential of A.niger as a foundational cellular platform for the biosynthesis of organic acids,including but not limited to l-malic acid.In summary,our study offers a simplified submerged conidiation strategy to streamline the preparation stage and reduce labor and material costs for industrial organic acid production using Aspergillus species.展开更多
Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detec...Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.展开更多
文摘Reducing cost of service is an important goal for resource discovery and interaction technologies. The shortcomings of transhipment-method and hibernation-method are to increase holistic cost of service and to slower resource discovery respectively. To overcome these shortcomings, a context-aware computing-based method is developed. This method, firstly, analyzes the courses of devices using resource discovery and interaction technologies to identify some types of context related to reducing cost of service, then, chooses effective methods such as stopping broadcast and hibernation to reduce cost of service according to information supplied by the context but not the transhipment-method’s simple hibernations. The results of experiments indicate that under the worst condition this method overcomes the shortcomings of transhipment-method, makes the “poor” devices hibernate longer than hibernation-method to reduce cost of service more effectively, and discovers resources faster than hibernation-method; under the best condition it is far better than hibernation-method in all aspects.
文摘The use of management accounting tools in enterprises to reduce costs and increase efficiency is not only to improve the quality of enterprise management, but also to apply this new approach to cost-cutting and efficiency-enhancing management through cost-cutting and efficiency-enhancing methods to improve the efficiency of enterprises and achieve customer satisfaction. Therefore, we should do a good job in financial management innovation, master the corresponding methods in the construction of management accounting tool system, and solve the actual problems, so as to ensure the smooth development of enterprise management accounting. In addition, to seriously study the laws of our country, we should take the enterprise itself as the starting point, strengthen the understanding of the knowledge related to management accounting, and finally realize the maximization of the interests of the enterprise.
文摘At present, human resources management is no longer a traditional system, but is developing towards the allocation and management of funds. This document analyses the functional modules of human resources planning and welfare management. Develop human resources development plans and corporate employee plans to improve efficiency, ensure a stable business environment and competent and necessary human resources to achieve organizational goals, including personal interests;In this way, the company's demand for points of sale is consistent with the ownership of future employees. In theory, if the labor supply-demand ratio is the same as the 1:1 ratio, then there may be a small ratio that better reflects the production efficiency.
基金This study is jointly supported by the National Key R&D Program of China(2017YFC1500303 and 2020YFA0710604)the Science Foundation of China University of Petroleum,Beijing(2462019YJRC007 and 2462020YXZZ047)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-05).
文摘Reverse Time Migration(RTM)Surface Ofset Gathers(SOGs)are demonstrated to deliver more superior residual dip information than ray-based approaches.It appears more powerful in complex geological settings,such as salt areas.Still,the computational cost of constructing RTM SOGs is a big challenge in applying it to 3D feld data.To tackle this challenge,we propose a novel method using dips of local events as a guide for RTM gather interpolation.The residual-dip information of the SOGs is created by connecting local events from depth-domain to time-domain via ray tracing.The proposed method is validated by a synthetic experiment and a feld example.It mitigates the computational cost by an order of magnitude while producing comparable results as fully computed RTM SOGs.
文摘For years,the development and de-ployment of advanced AI models were dominated by a few tech giants and well-funded institutions.The exorbitant costs of training and running these models have reate a high bar-rier to entry,sidelining smaller enterprises and individuals.In reducing operational costs by 90 percent,DeepSeek-R1,released by a Chinese startup in January,has substantially lowered this barrier.Hailed as the“DeepSeek Moment,”it has brought AI out of elite research labs into the hands of the masses.
基金supported by the JST CREST[grant number:JPMJCR16O2]and MEXT KAKENHI[grant number:JP22H02306].The funders had no role in the study design,data collection and analysis,decision to publish,or manuscript preparation.
文摘High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles.In this study,we developed models to estimate the phenotypes of biomass-related traits in soybean(Glycine max)using unmanned aerial vehicle(UAV)remote sensing and deep learning models.In 2018,a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions:drought and control.We used a convolutional neural network(CNN)as a model to estimate the phenotypic values of 5 conventional biomass-related traits:dry weight,main stem length,numbers of nodes and branches,and plant height.We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models.The accuracy of the developed models was assessed through 10-fold cross-validation,which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously.Deep learning enabled us to extract features that exhibited strong correlations with the output(i.e.,phenotypes of the target traits)and accurately estimate the values of the features from the input data.We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits.Furthermore,we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions.The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios.
基金This work was financially supported by the National Key Research and Development Program of China(2021YFC2104300)the National Natural Science Foundation of China(32200055 and 22378210)the Natural Science Foundation of Jiangsu Province(BK20202002).
文摘Aspergillus niger is an efficient cell factory for organic acids production,particularly l-malic acid,through genetic manipulation.However,the traditional method of collecting A.niger spores for inoculation is labor-intensive and resource-consuming.In our study,we used the CRISPR-Cas9 system to replace the promoter of brlA,a key gene in Aspergillus conidiation,with a xylose-inducible promoter xylP in l-malic acid-producing A.niger strain RG0095,generating strain brlAxylP.When induced with xylose in submerged liquid culture,brlAxylP exhibited significant upregulation of conidiation-related genes.This induction allowed us to easily collect an abundance of brlAxylP spores(>7.1×106/mL)in liquid xylose medium.Significantly,the submerged conidiation approach preserves the substantial potential of A.niger as a foundational cellular platform for the biosynthesis of organic acids,including but not limited to l-malic acid.In summary,our study offers a simplified submerged conidiation strategy to streamline the preparation stage and reduce labor and material costs for industrial organic acid production using Aspergillus species.
基金supported by the State Grid Research Project“Study on Intelligent Analysis Technology of Abnormal Power Data Quality based on Rule Mining” (5700-202119176A-0-0-00).
文摘Anomaly detection is an important problem in various research and application fields.Researchers design reliable schemes to provide solutions for effectively detecting anomaly points.Most of the existing anomaly detection schemes are unsupervised methods,such as anomaly detection methods based on density,distance and clustering.In total,unsupervised anomaly detection methods have many limitations.For example,they cannot be well combined with prior knowledge in some anomaly detection tasks.For some nonlinear anomaly detection tasks,the modeling is complex and faces dimensional disasters,which are greatly affected by noise.Sometimes it is difficult to find abnormal events that users are interested in,and users need to customize model parameters before detection.With the wide application of deep learning technology,it has a good modeling ability to solve linear and nonlinear data relationships,but the application of deep learning technology in the field of anomaly detection has many challenges.If we regard exceptions as a supervised problem,exceptions are a few,and we usually face the problem of too few labels.To obtain a model that performs well in the anomaly detection task,it requires a high initial training set.Therefore,to solve the above problems,this paper proposes a supervised learning method with manual participation.We introduce the integrated learning model and train a supervised anomaly detection model with strong stability and high accuracy through active learning technology.In addition,this paper adopts certain strategies to maximize the accuracy of anomaly detection and minimize the cost of manual labeling.In the experimental link,we will show that our method is better than some traditional anomaly detection algorithms.
