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
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
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
Node synchronization is essential for the stability of the Bitcoin network. Critics have raised doubts about the ability of a new node to quickly and efficiently synchronize with the Bitcoin network and alleviate the ...Node synchronization is essential for the stability of the Bitcoin network. Critics have raised doubts about the ability of a new node to quickly and efficiently synchronize with the Bitcoin network and alleviate the storage pressure from existing full nodes to stockpile new data. Basic pruning and other techniques have been explored to address these concerns but have been insufficient to reduce node synchronization delay and effectively suppress the growth of synchronized data. In this study, we propose SnapshotPrune, a novel pruning and synchronization protocol that achieves fast node bootstrapping in the Bitcoin blockchain. Real Bitcoin historical data are leveraged to measure the synchronization time and monitor the network traffic during node bootstrapping. The protocol requires data downloads that are 99.70% less than Bitcoin Core, 81% less than CoinPrune, and 60% less than SnapshotSave, thereby saving 97.23% of download time. Findings show that the proposed design enhances the storage efficiency and reduces the node synchronization delay compared with existing techniques. We hypothesize that the efficiency of this protocol increases with the block height.展开更多
Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used....Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used.Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint.In that paper,we present a novel visual analytics approach to interactively prune those networks and thus limit that issue.Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable.We assess the well-founded of our approach through two realistic case studies and a user study.For both of them,the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.展开更多
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.
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.
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 National Key Project of China(No.2020YFB1005700)the Natural Science Foundation of Shandong Province(No.ZR2021MF086)+3 种基金the National Key Research and Development Program of China(No.2021YFA1000600)the National Natural Science Foundation of China(Nos.62132018 and 62172117)the National Key Research and Development Program,the Young Scientist Scheme(No.2022YFB3102400)the National Key Research and Development Program of Guangdong Province(No.2020B0101090002).
文摘Node synchronization is essential for the stability of the Bitcoin network. Critics have raised doubts about the ability of a new node to quickly and efficiently synchronize with the Bitcoin network and alleviate the storage pressure from existing full nodes to stockpile new data. Basic pruning and other techniques have been explored to address these concerns but have been insufficient to reduce node synchronization delay and effectively suppress the growth of synchronized data. In this study, we propose SnapshotPrune, a novel pruning and synchronization protocol that achieves fast node bootstrapping in the Bitcoin blockchain. Real Bitcoin historical data are leveraged to measure the synchronization time and monitor the network traffic during node bootstrapping. The protocol requires data downloads that are 99.70% less than Bitcoin Core, 81% less than CoinPrune, and 60% less than SnapshotSave, thereby saving 97.23% of download time. Findings show that the proposed design enhances the storage efficiency and reduces the node synchronization delay compared with existing techniques. We hypothesize that the efficiency of this protocol increases with the block height.
基金We acknowledge the Nouvelle-Aquitaine Region,Bordeaux Métropole and SUEZ,le LyRE for mainly funding and supporting this work through the Convention N°AAPR2020-2019-8171810。
文摘Current deep learning approaches are cutting-edge methods for solving classification tasks.Arising transfer learning techniques allows applying large generic model to simple tasks whereas simpler models could be used.Large models raise the major problem of their memory consumption and processor usage and lead to a prohibitive ecological footprint.In that paper,we present a novel visual analytics approach to interactively prune those networks and thus limit that issue.Our technique leverages a novel sparkline matrix visualization technique as well as a novel local metric which evaluates the discriminatory power of a filter to guide the pruning process and make it interpretable.We assess the well-founded of our approach through two realistic case studies and a user study.For both of them,the interactive refinement of the model led to a significantly smaller model having similar prediction accuracy than the original one.
