Effect of boron on falling of prunes (Prunus mume, Sieb, et Zucc) was studied by applying 50 g borateper tree into soil on December 15, 1993 (soil-B) and spraying leaves leves evenly twice with 1.5 g kg^-1 boratesolut...Effect of boron on falling of prunes (Prunus mume, Sieb, et Zucc) was studied by applying 50 g borateper tree into soil on December 15, 1993 (soil-B) and spraying leaves leves evenly twice with 1.5 g kg^-1 boratesolution on March 1 and 8, 1994 (spray-B) on the soil with 0.28 mg kg--1’ rapidly available B. Comparedwith no borate treatment (CK), B concentrations of leaves, short branches and flowers were higher and thepercentage of flower and fruit drop was lower in the treatments of soil-B and spray-B. B fertilizer increased Bconcentrations in flowers, leaves and short branches, promoted pollen germination, reduced the percentage offall of flowers and fruits of prunes, increased the percentage of fertile fruits, and thus increased yields of prunesby 46% and 34.3% in the treatments of soil-B and spray-B, respectively. It could be inferred preliminarilythat if B concentration of leaves was lower than 35 mg kg--1, the prunes should be fertilized with B. Themeasured leaves should be picked from branches (3-10 cm in length) germinating from the central sectionof a tree crown during the last ten days of May to the early days of June.展开更多
The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accur...The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.展开更多
Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study ai...Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study aims to highlight the challenges and peculiarities in the management of PBS in a resource-poor setting. Materials and Methods: This is a ten-year retrospective study conducted at the University of Port Harcourt Teaching Hospital. Ethical approval for the study was sought and gotten from the hospital’s ethical committee. The information gotten included history, duration of symptoms, examination findings, age of the patient, category of disease, and intraoperative findings. The data from the folders were collected and evaluated. Frequencies, percentages, the mean and standard deviation were used to summarize the data as appropriate. Results: Fifteen patients were included in the study. The hospital incidence of PBS was 112/100,000, twelve males and three females. The age range was from 1 day to 15 years, mean age was 14 months ± 2.3 months. Most patients presented between 3 months and 2 years and 11 months. Twelve patients had category three PBS and five patients had associated anomalies. Eleven male patients died after 5 years of follow-up from progressive renal deterioration. The female patient fared better than the males. Conclusion: PBS is rare, most patients with the condition present late. The most common cause of mortality was progressive renal deterioration.展开更多
Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to ...Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.展开更多
Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats...Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.展开更多
Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from s...Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio.展开更多
文摘Effect of boron on falling of prunes (Prunus mume, Sieb, et Zucc) was studied by applying 50 g borateper tree into soil on December 15, 1993 (soil-B) and spraying leaves leves evenly twice with 1.5 g kg^-1 boratesolution on March 1 and 8, 1994 (spray-B) on the soil with 0.28 mg kg--1’ rapidly available B. Comparedwith no borate treatment (CK), B concentrations of leaves, short branches and flowers were higher and thepercentage of flower and fruit drop was lower in the treatments of soil-B and spray-B. B fertilizer increased Bconcentrations in flowers, leaves and short branches, promoted pollen germination, reduced the percentage offall of flowers and fruits of prunes, increased the percentage of fertile fruits, and thus increased yields of prunesby 46% and 34.3% in the treatments of soil-B and spray-B, respectively. It could be inferred preliminarilythat if B concentration of leaves was lower than 35 mg kg--1, the prunes should be fertilized with B. Themeasured leaves should be picked from branches (3-10 cm in length) germinating from the central sectionof a tree crown during the last ten days of May to the early days of June.
文摘The development of intelligent algorithms for controlling autonomous mobile robots in real-time activities has increased dramatically in recent years.However,conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories.The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory(PPART)neural network for effectively managing the touring process of autonomous mobile robots in real-time.The proposed system is implemented using the AlphaBot platform,and the performance of the system is evaluated according to the obstacle prediction accuracy,path detection accuracy,time-lapse,tour length,and the overall accuracy of the system.The proposed system provide a very high obstacle prediction accuracy of 99.61%.Accordingly,the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.
文摘Background: Prune belly syndrome (PBS) is a congenital anomaly that consists of a triad of abdominal wall defect, bilateral cryptorchidism, and urinary tract dilation. The disease is of varying severity. This study aims to highlight the challenges and peculiarities in the management of PBS in a resource-poor setting. Materials and Methods: This is a ten-year retrospective study conducted at the University of Port Harcourt Teaching Hospital. Ethical approval for the study was sought and gotten from the hospital’s ethical committee. The information gotten included history, duration of symptoms, examination findings, age of the patient, category of disease, and intraoperative findings. The data from the folders were collected and evaluated. Frequencies, percentages, the mean and standard deviation were used to summarize the data as appropriate. Results: Fifteen patients were included in the study. The hospital incidence of PBS was 112/100,000, twelve males and three females. The age range was from 1 day to 15 years, mean age was 14 months ± 2.3 months. Most patients presented between 3 months and 2 years and 11 months. Twelve patients had category three PBS and five patients had associated anomalies. Eleven male patients died after 5 years of follow-up from progressive renal deterioration. The female patient fared better than the males. Conclusion: PBS is rare, most patients with the condition present late. The most common cause of mortality was progressive renal deterioration.
基金supported in part by National Natural Science Foundation of China (61101114, 61671324) the Program for New Century Excellent Talents in University (NCET-12-0401)
文摘Forward-backward algorithm, used by watermark decoder for correcting non-binary synchronization errors, requires to traverse a very large scale trellis in order to achieve the proper posterior probability, leading to high computational complexity. In order to reduce the number of the states involved in the computation, an adaptive pruning method for the trellis is proposed. In this scheme, we prune the states which have the low forward-backward quantities below a carefully-chosen threshold. Thus, a wandering trellis with much less states is achieved, which contains most of the states with quite high probability. Simulation results reveal that, with the proper scaling factor, significant complexity reduction in the forward-backward algorithm is achieved at the expense of slight performance degradation.
文摘Arrhythmia beat classification is an active area of research in ECG based clinical decision support systems. In this paper, Pruned Fuzzy K-nearest neighbor (PFKNN) classifier is proposed to classify six types of beats present in the MIT-BIH Arrhythmia database. We have tested our classifier on ~ 103100 beats for six beat types present in the database. Fuzzy KNN (FKNN) can be implemented very easily but large number of training examples used for classification can be very time consuming and requires large storage space. Hence, we have proposed a time efficient Arif-Fayyaz pruning algorithm especially suitable for FKNN which can maintain good classification accuracy with appropriate retained ratio of training data. By using Arif-Fayyaz pruning algorithm with Fuzzy KNN, we have achieved a beat classification accuracy of 97% and geometric mean of sensitivity of 94.5% with only 19% of the total training examples. The accuracy and sensitivity is comparable to FKNN when all the training data is used. Principal Component Analysis is used to further reduce the dimension of feature space from eleven to six without compromising the accuracy and sensitivity. PFKNN was found to robust against noise present in the ECG data.
基金supported by the Natural Science Foundation of Jiangsu Province of China under Grant No.BK20222003the National Natural Science Foundation of China under Grant Nos.61972196,61832008,and 61832005the Collaborative Innovation Center of Novel Software Technology and Industrialization,and the Sino-German Institutes of Social Computing.
文摘Filter pruning is an important technique to compress convolutional neural networks(CNNs)to acquire light-weight high-performance model for practical deployment.However,the existing filter pruning methods suffer from sharp performance drops when the pruning ratio is large,probably due to the unrecoverable information loss caused by aggressive pruning.In this paper,we propose a dual attention based pruning approach called DualPrune to push the limit of network pruning at an ultra-high compression ratio.Firstly,it adopts a graph attention network(GAT)to automatically extract filter-level and layer-level features from CNNs based on the roles of their filters in the whole computation graph.Then the extracted comprehensive features are fed to a side-attention network,which generates sparse attention weights for individual filters to guide model pruning.To avoid layer collapse,the side-attention network adopts a side-path design to preserve the information flow going through the CNN model properly,which allows the CNN model to be pruned at a high compression ratio at initialization and trained from scratch afterward.Extensive experiments based on several well-known CNN models and real-world datasets show that the proposed DualPrune method outperforms the state-of-the-art methods with significant performance improvement,particularly for model compression at a high pruning ratio.
