In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convoluti...In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.展开更多
Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and...Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.展开更多
The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely u...The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.展开更多
In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of disti...In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.展开更多
Liver transplantation(LT)provides a life-saving option for cirrhotic patients with complications and hepatocellular carcinoma.Despite the increasing number of liver transplants performed each year,the number of LT can...Liver transplantation(LT)provides a life-saving option for cirrhotic patients with complications and hepatocellular carcinoma.Despite the increasing number of liver transplants performed each year,the number of LT candidates on the waitlist remains unchanged due to an imbalance between donor organ supply and the demand which increases the waitlist time and mortality.Living donor liver transplant had a great role in increasing the donor pool and shortened waitlist time for LT candidates.Nevertheless,further strategies can be implemented to increase the pool of potential donors in deceased donor LT,such as reducing the rate of organ discards.Utilizing hepatitis C virus(HCV)seropositive liver grafts is one of the expanded donor organ criteria.A yearly increase of hundreds of transplants is anticipated as a result of maximizing the utilization of HCV-positive organs for HCV-negative recipients.Direct-acting antiviral therapy's efficacy has revolutionized the treatment of HCV infection and the use of HCV-seropositive donors in transplantation.The American Society of Transplantation advises against performing transplants from HCV-infected liver donors(D+)into HCV-negative recipient(R-)unless under Institutional Review Board-approved study rules and with full informed consent of the knowledge gaps associated with such transplants.Proper selection of patients to be transplanted with HCV-infected grafts and confirming their access to direct-acting antivirals if needed is im-portant.National and international consensuses are needed to regulate this process to ensure the maximum benefit and the least adverse events.展开更多
The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic ...The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic emissions, noise pollution, and on the quality of life, e.g. parking problem, traffic congestion, and increase in the number of crashes and accidents. Transport demand management plays a very critical role in achieving greenhouse gas emission reduction targets. This study demonstrates that car pooling (CP) is an effective strategy to reduce transport volumes, transportation costs and related hill externalities in agreement with EU programs of emissions reduction targets. This paper presents an original approach to solve the CP problem. It is based on hierarchical clustering models, which have been adopted by an original decision support system (DSS). The DSS helps mobility managers to generate the pools and to design feasible paths for shared vehicles. A significant case studies and obtained results by the application of the proposed models are illustrated. They demonstrate the effectiveness of the approach and the supporting decisions tool.展开更多
The cultivar Ganoderma lucidum Hunong 5 was obtained using cross-breeding. Hunong 5 has high commercial value due to its high polysaccharide and triterpene content, This is the first report of using a DNA pooling meth...The cultivar Ganoderma lucidum Hunong 5 was obtained using cross-breeding. Hunong 5 has high commercial value due to its high polysaccharide and triterpene content, This is the first report of using a DNA pooling method to develop a stable sequence characterized amplified region （SCAR） marker for rapid identification of the G. lucidum Hunong 5 cultivar. The SCAR marker was developed by first generating and sequencing a distinctive inter simple sequence repeat （ISSR） fragment （882 bp） from G. lucidum Hunong 5 cultivar. A stable SCAR primer pair GLH5F/GLH5R were obtained to identify the cultivar and the SCAR marker is a DNA fragment of 773 bp.展开更多
In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of ...In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.展开更多
文摘In convolutional neural networks,pooling methods are used to reduce both the size of the data and the number of parameters after the convolution of the models.These methods reduce the computational amount of convolutional neural networks,making the neural network more efficient.Maximum pooling,average pooling,and minimum pooling methods are generally used in convolutional neural networks.However,these pooling methods are not suitable for all datasets used in neural network applications.In this study,a new pooling approach to the literature is proposed to increase the efficiency and success rates of convolutional neural networks.This method,which we call MAM(Maximum Average Minimum)pooling,is more interactive than other traditional maximum pooling,average pooling,and minimum pooling methods and reduces data loss by calculating the more appropriate pixel value.The proposed MAM pooling method increases the performance of the neural network by calculating the optimal value during the training of convolutional neural networks.To determine the success accuracy of the proposed MAM pooling method and compare it with other traditional pooling methods,training was carried out on the LeNet-5 model using CIFAR-10,CIFAR-100,and MNIST datasets.According to the results obtained,the proposed MAM pooling method performed better than the maximum pooling,average pooling,and minimum pooling methods in all pool sizes on three different datasets.
文摘Pooling,unpooling/specialization,and discretionary task completion are typical operational strategies in queueing systems that arise in healthcare,call centers,and online sales.These strategies may have advantages and disadvantages in different operational environments.This paper uses the M/M/1 and M/M/2 queues to study the impact of pooling,specialization,and discretionary task completion on the average queue length.Closed-form solutions for the average M/M/2 queue length are derived.Computational examples illustrate how the average queue length changes with the strength of pooling,specialization,and discretionary task completion.Finally,several conjectures are made in the paper.
基金supported by the Guangdong Basic and Applied Basic Research Foundation(2023A1515011244).
文摘The state of in situ stress is a crucial parameter in subsurface engineering,especially for critical projects like nuclear waste repository.As one of the two ISRM suggested methods,the overcoring(OC)method is widely used to estimate the full stress tensors in rocks by independent regression analysis of the data from each OC test.However,such customary independent analysis of individual OC tests,known as no pooling,is liable to yield unreliable test-specific stress estimates due to various uncertainty sources involved in the OC method.To address this problem,a practical and no-cost solution is considered by incorporating into OC data analysis additional information implied within adjacent OC tests,which are usually available in OC measurement campaigns.Hence,this paper presents a Bayesian partial pooling(hierarchical)model for combined analysis of adjacent OC tests.We performed five case studies using OC test data made at a nuclear waste repository research site of Sweden.The results demonstrate that partial pooling of adjacent OC tests indeed allows borrowing of information across adjacent tests,and yields improved stress tensor estimates with reduced uncertainties simultaneously for all individual tests than they are independently analysed as no pooling,particularly for those unreliable no pooling stress estimates.A further model comparison shows that the partial pooling model also gives better predictive performance,and thus confirms that the information borrowed across adjacent OC tests is relevant and effective.
文摘In order to enhance the performance of the CNN-based segmentation models for bone metastases, this study proposes a segmentation method that integrates dual-pooling, DAC, and RMP modules. The network consists of distinct feature encoding and decoding stages, with dual-pooling modules employed in encoding stages to maintain the background information needed for bone scintigrams diagnosis. Both the DAC and RMP modules are utilized in the bottleneck layer to address the multi-scale problem of metastatic lesions. Experimental evaluations on 306 clinical SPECT data have demonstrated that the proposed method showcases a substantial improvement in both DSC and Recall scores by 3.28% and 6.55% compared the baseline. Exhaustive case studies illustrate the superiority of the methodology.
文摘Liver transplantation(LT)provides a life-saving option for cirrhotic patients with complications and hepatocellular carcinoma.Despite the increasing number of liver transplants performed each year,the number of LT candidates on the waitlist remains unchanged due to an imbalance between donor organ supply and the demand which increases the waitlist time and mortality.Living donor liver transplant had a great role in increasing the donor pool and shortened waitlist time for LT candidates.Nevertheless,further strategies can be implemented to increase the pool of potential donors in deceased donor LT,such as reducing the rate of organ discards.Utilizing hepatitis C virus(HCV)seropositive liver grafts is one of the expanded donor organ criteria.A yearly increase of hundreds of transplants is anticipated as a result of maximizing the utilization of HCV-positive organs for HCV-negative recipients.Direct-acting antiviral therapy's efficacy has revolutionized the treatment of HCV infection and the use of HCV-seropositive donors in transplantation.The American Society of Transplantation advises against performing transplants from HCV-infected liver donors(D+)into HCV-negative recipient(R-)unless under Institutional Review Board-approved study rules and with full informed consent of the knowledge gaps associated with such transplants.Proper selection of patients to be transplanted with HCV-infected grafts and confirming their access to direct-acting antivirals if needed is im-portant.National and international consensuses are needed to regulate this process to ensure the maximum benefit and the least adverse events.
文摘The continuous increase of human mobility combined with a relevant use of private vehicles contributes to increase the ill effects of vehicle externalities on the environment, e.g. high levels of air pollution, toxic emissions, noise pollution, and on the quality of life, e.g. parking problem, traffic congestion, and increase in the number of crashes and accidents. Transport demand management plays a very critical role in achieving greenhouse gas emission reduction targets. This study demonstrates that car pooling (CP) is an effective strategy to reduce transport volumes, transportation costs and related hill externalities in agreement with EU programs of emissions reduction targets. This paper presents an original approach to solve the CP problem. It is based on hierarchical clustering models, which have been adopted by an original decision support system (DSS). The DSS helps mobility managers to generate the pools and to design feasible paths for shared vehicles. A significant case studies and obtained results by the application of the proposed models are illustrated. They demonstrate the effectiveness of the approach and the supporting decisions tool.
基金financially supported by the National Natural Science Foundation of China (31401933)the Shanghai Municipal Committee of Agriculture,China (G2014070107)
文摘The cultivar Ganoderma lucidum Hunong 5 was obtained using cross-breeding. Hunong 5 has high commercial value due to its high polysaccharide and triterpene content, This is the first report of using a DNA pooling method to develop a stable sequence characterized amplified region （SCAR） marker for rapid identification of the G. lucidum Hunong 5 cultivar. The SCAR marker was developed by first generating and sequencing a distinctive inter simple sequence repeat （ISSR） fragment （882 bp） from G. lucidum Hunong 5 cultivar. A stable SCAR primer pair GLH5F/GLH5R were obtained to identify the cultivar and the SCAR marker is a DNA fragment of 773 bp.
基金supported by the National Natural Science Foundation of China(71401052)the Fundamental Research Funds for the Central Universities(2019B19514)。
文摘In order to solve the problem that existing multivariate grey incidence models cannot be applied to time series on different scales, a new model is proposed based on spatial pyramid pooling.Firstly, local features of multivariate time series on different scales are pooled and aggregated by spatial pyramid pooling to construct n levels feature pooling matrices on the same scale. Secondly,Deng's multivariate grey incidence model is introduced to measure the degree of incidence between feature pooling matrices at each level. Thirdly, grey incidence degrees at each level are integrated into a global incidence degree. Finally, the performance of the proposed model is verified on two data sets compared with a variety of algorithms. The results illustrate that the proposed model is more effective and efficient than other similarity measure algorithms.
