The use of Artificial intelligence(AI)has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine.It leverages digital data and computational hardware for diverse applications,including ...The use of Artificial intelligence(AI)has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine.It leverages digital data and computational hardware for diverse applications,including diagnosis,prognosis,and treatment responses in gastrointestinal and hepatic conditions.AI has had an impact in diagnostic techniques,particularly endoscopy,ultrasound,and histopathology.AI encompasses machine learning,natural language processing,and robotics,with machine learning being central.This involves sophisticated algorithms capable of managing complex datasets,far surpassing traditional statistical methods.These algorithms,both supervised and unsupervised,are integral for interpreting large datasets.In liver diseases,AI's non-invasive diagnostic applications,particularly in non-alcoholic fatty liver disease,and its role in characterizing hepatic lesions is promising.AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma.AI enhances lesion identification during endoscopy,showing potential in the diagnosis and management of early-stage esophageal carcinoma.In peptic ulcer disease,AI technologies influence patient management strategies.AI is useful in colonoscopy,particularly in detecting smaller colonic polyps.However,its applicability in nonacademic settings requires further validation.Addressing these issues is vital for harnessing the potential of AI.In conclusion,while AI offers transformative possibilities in gastroenterology,careful integration and balancing of technical possibilities with ethical and practical application,is essential for optimal use.展开更多
Balancing time,cost,and quality is crucial in intelligent manufacturing.However,finding the optimal value of production parameters is a challengingnon-deterministic polynomial(NP)-hard problem.In the actual production...Balancing time,cost,and quality is crucial in intelligent manufacturing.However,finding the optimal value of production parameters is a challengingnon-deterministic polynomial(NP)-hard problem.In the actual production process,the production process has the characteristics of multi-stage parallel.Therefore,aiming at the difficult problem of multi-stage nonlinear production process optimization,this paper proposes a workflow optimization algorithm based on virtualization and nonlinear production quality under time constraints(T-OVQT).The algorithm proposed in this paper first abstracts the actual production process into a virtual workflow model,which is divided into three layers:The bottom production process collection layer,the middle layer of service node partial order composition layer,and the high level of virtual node collection layer.Then,the virtual technology is used to reconstruct the node set and divide the task interval.The optimal solution is obtained through inverse iterative normalization and forward scheduling,and the global optimal solution is obtained by algorithm integration.Experimental results demonstrate that this algorithm better meets actual production requirements than the traditional minimum critical path(MCP)algorithm.展开更多
The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow,so the main research direction of modern work-flow technology is how to assure ...The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow,so the main research direction of modern work-flow technology is how to assure the dynamic balance between the factors.Based on the work-flow technology,restraining the completion time,and analyzing the deficiency of traditional minimum critical path algorithm,a virtual iterative reduction algorithm(VIRA)was proposed,which can improve production accuracy effectively with time constrain.The VIRA with simplification as the core abstracts a virtual task that can predigest the process by combining the complex structures which are cyclic or parallel,finally,by using the virtual task and the other task in the process which is the iterative reduction strategy,determines a path which can make the production accuracy and completion time more balanced than the minimum critical path algorithm.The deadline,the number of tasks,and the number of cyclic structures were used as the factors affecting the performance of the algorithm,changing the influence factors can improve the performance of the algorithm effectively through the analysis of detailed data.Consequently,comparison experiments proved the feasibility of the VIRA.展开更多
文摘The use of Artificial intelligence(AI)has evolved from its mid-20th century origins to playing a pivotal tool in modern medicine.It leverages digital data and computational hardware for diverse applications,including diagnosis,prognosis,and treatment responses in gastrointestinal and hepatic conditions.AI has had an impact in diagnostic techniques,particularly endoscopy,ultrasound,and histopathology.AI encompasses machine learning,natural language processing,and robotics,with machine learning being central.This involves sophisticated algorithms capable of managing complex datasets,far surpassing traditional statistical methods.These algorithms,both supervised and unsupervised,are integral for interpreting large datasets.In liver diseases,AI's non-invasive diagnostic applications,particularly in non-alcoholic fatty liver disease,and its role in characterizing hepatic lesions is promising.AI aids in distinguishing between normal and cirrhotic livers and improves the accuracy of lesion characterization and prognostication of hepatocellular carcinoma.AI enhances lesion identification during endoscopy,showing potential in the diagnosis and management of early-stage esophageal carcinoma.In peptic ulcer disease,AI technologies influence patient management strategies.AI is useful in colonoscopy,particularly in detecting smaller colonic polyps.However,its applicability in nonacademic settings requires further validation.Addressing these issues is vital for harnessing the potential of AI.In conclusion,while AI offers transformative possibilities in gastroenterology,careful integration and balancing of technical possibilities with ethical and practical application,is essential for optimal use.
基金supported by Heilongjiang Provincial Natural Science Foundation of China(LH2021F030)。
文摘Balancing time,cost,and quality is crucial in intelligent manufacturing.However,finding the optimal value of production parameters is a challengingnon-deterministic polynomial(NP)-hard problem.In the actual production process,the production process has the characteristics of multi-stage parallel.Therefore,aiming at the difficult problem of multi-stage nonlinear production process optimization,this paper proposes a workflow optimization algorithm based on virtualization and nonlinear production quality under time constraints(T-OVQT).The algorithm proposed in this paper first abstracts the actual production process into a virtual workflow model,which is divided into three layers:The bottom production process collection layer,the middle layer of service node partial order composition layer,and the high level of virtual node collection layer.Then,the virtual technology is used to reconstruct the node set and divide the task interval.The optimal solution is obtained through inverse iterative normalization and forward scheduling,and the global optimal solution is obtained by algorithm integration.Experimental results demonstrate that this algorithm better meets actual production requirements than the traditional minimum critical path(MCP)algorithm.
基金supported by the Heilongjiang Provincial Natural Science Foundation of China(LH2021F030)。
文摘The factors like production accuracy and completion time are the determinants of the optimal scheduling of the complex products work-flow,so the main research direction of modern work-flow technology is how to assure the dynamic balance between the factors.Based on the work-flow technology,restraining the completion time,and analyzing the deficiency of traditional minimum critical path algorithm,a virtual iterative reduction algorithm(VIRA)was proposed,which can improve production accuracy effectively with time constrain.The VIRA with simplification as the core abstracts a virtual task that can predigest the process by combining the complex structures which are cyclic or parallel,finally,by using the virtual task and the other task in the process which is the iterative reduction strategy,determines a path which can make the production accuracy and completion time more balanced than the minimum critical path algorithm.The deadline,the number of tasks,and the number of cyclic structures were used as the factors affecting the performance of the algorithm,changing the influence factors can improve the performance of the algorithm effectively through the analysis of detailed data.Consequently,comparison experiments proved the feasibility of the VIRA.
