The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Pro...The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Progressive pruning is an oil palm pruning method in which pruning is done at the same time as fresh fruit bunch harvesting. This study evaluated the impact of progressive pruning on leaf miner population in oil palm and how these two factors (leaf miner and progressive pruning) affect the yield of oil palm at the Benso Oil Palm Plantation Public listed company (BOPP. Plc). Five distinct blocks in the plantation were selected for observations on fronds at various ranks (33, 25, or 17) based on the degree of defoliation by counting the number of pests on leaflets at different phases of insect development. Fronds from selected plots were sampled in a Completely Randomized Design (CRD). The size of plots used for the study ranged between 19 to 45 hectares. A minimum of 78 fronds were evenly cut from each block for pest count depending on the block size. Secondary data on annual yields of fresh fruit bunches before and after the introduction of progressive pruning were also obtained from BOPP. Plc records from 2011-2020. The results from the analyzed data on leaf miner index before and after the introduction of progressive pruning showed that progressive pruning has, to a high extent (64% to 36%), reduced leaf miner populations in the plantation. Paired t-test on fresh fruit bunch yield has also revealed a significant (p < 0.001) increase in annual fresh fruit bunch yield due to progressive pruning. A regression analysis, however, revealed a lower rate of yield loss (3.05 to 2.70 tonnes) to leaf miner infestation after the introduction of progressive pruning. The study recommends progressive pruning as a key cultural practice for improving crop yields in leaf miner prone plantations.展开更多
Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pru...Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pruning residues that are generated in cities in the Neotropics.Species such as Licania tomentosa,Azadirachta indica,Ficus benjamina,Terminalia catappa,Leucaena leucocephala,Prosopis juliflora and Pithecellobium dulce were selected because they have been previously studied and showed potential for thermal energy generation.These materials were calcined in an oxidizing atmosphere and characterized by X-ray diffraction and fluorescence,scanning electron microscopy with microchemistry,BET surface area,thermal gravimetric analysis,and differential scanning calorimetry.The pH and apparent density were also established.The results show high basicity materials(average pH 10),a behavior associated with the presence of chemical elements such as calcium,potassium,magnesium,chlorine,phosphorus,and sulfur.Structurally,these materials have a very significant amorphous fraction(between 49%and 74.5%),the dominant crystalline phases are calcite,arcanite,sylvite,and hydroxyapatite.These ashes have low surface area and do not exceed 13 m^(2)/g.Two characteristic morphological aspects were observed in these ashes:a morphology of rounded grains where silicon content is highlighted,and lamellar morphologies where the presence of chlorine is highlighted.Thermally,these ashes show four significant mass loss events(400℃,430℃,680℃,and 920℃),causing mass losses that vary between 25%and 40%.Through this study,it was possible to establish that,from a chemical point of view,these ashes are less dangerous in comparison with those of a mineral coal that was used as a reference.However,they require additional treatments for their disposal due to their high basicity.Because of their composition,these ashes have the potential to be used in the ceramic and cement industries,and in the manufacture of fertilizers.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
Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intellig...Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.展开更多
Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these...Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.展开更多
果园环境下柑橘的快速准确检测是自主采摘机器人作业的关键.针对现有的模型过于冗余、检测速度与精度不平衡等问题,提出一种轻量型果园环境果实检测方法.在YOLOv4算法的基础上引入焦点损失函数(Focal Loss)来提高模型在二分类检测任务...果园环境下柑橘的快速准确检测是自主采摘机器人作业的关键.针对现有的模型过于冗余、检测速度与精度不平衡等问题,提出一种轻量型果园环境果实检测方法.在YOLOv4算法的基础上引入焦点损失函数(Focal Loss)来提高模型在二分类检测任务中的负样本挖掘能力,并针对模型参数冗余等问题提出一种优化的模型剪枝方法.试验结果表明:提出的方法在果园环境中柑橘果实数据集检测得到的平均精度均值(mean average precision,M_(AP))达到94.22%,相较于YOLOv4模型提高了1.18%,模型参数减小了95.22%,模型尺寸为原来的4.84%,检测速度为原来的4.03倍.展开更多
文摘The oil palm leaf miner, Coelaenomenodera lameensis, is currently the most destructive pest of oil palm in Ghana and other African oil palm growing countries, causing significant losses in fresh fruit bunch yield. Progressive pruning is an oil palm pruning method in which pruning is done at the same time as fresh fruit bunch harvesting. This study evaluated the impact of progressive pruning on leaf miner population in oil palm and how these two factors (leaf miner and progressive pruning) affect the yield of oil palm at the Benso Oil Palm Plantation Public listed company (BOPP. Plc). Five distinct blocks in the plantation were selected for observations on fronds at various ranks (33, 25, or 17) based on the degree of defoliation by counting the number of pests on leaflets at different phases of insect development. Fronds from selected plots were sampled in a Completely Randomized Design (CRD). The size of plots used for the study ranged between 19 to 45 hectares. A minimum of 78 fronds were evenly cut from each block for pest count depending on the block size. Secondary data on annual yields of fresh fruit bunches before and after the introduction of progressive pruning were also obtained from BOPP. Plc records from 2011-2020. The results from the analyzed data on leaf miner index before and after the introduction of progressive pruning showed that progressive pruning has, to a high extent (64% to 36%), reduced leaf miner populations in the plantation. Paired t-test on fresh fruit bunch yield has also revealed a significant (p < 0.001) increase in annual fresh fruit bunch yield due to progressive pruning. A regression analysis, however, revealed a lower rate of yield loss (3.05 to 2.70 tonnes) to leaf miner infestation after the introduction of progressive pruning. The study recommends progressive pruning as a key cultural practice for improving crop yields in leaf miner prone plantations.
基金Ministry of Science,Technology and Innovation of Colombia through the“Fondo Francisco Joséde Caldas”National Financing Fund for Science,Technology and Innovation for the financing provided for the development of the project (Project 120885272102,Call 852 of 2019).
文摘Secondary lignocellulosic biomass has proved to be useful as an energy source through its oxidation by means of combustion processes.In accordance with the above,in this paper,we wanted to study the ash from urban pruning residues that are generated in cities in the Neotropics.Species such as Licania tomentosa,Azadirachta indica,Ficus benjamina,Terminalia catappa,Leucaena leucocephala,Prosopis juliflora and Pithecellobium dulce were selected because they have been previously studied and showed potential for thermal energy generation.These materials were calcined in an oxidizing atmosphere and characterized by X-ray diffraction and fluorescence,scanning electron microscopy with microchemistry,BET surface area,thermal gravimetric analysis,and differential scanning calorimetry.The pH and apparent density were also established.The results show high basicity materials(average pH 10),a behavior associated with the presence of chemical elements such as calcium,potassium,magnesium,chlorine,phosphorus,and sulfur.Structurally,these materials have a very significant amorphous fraction(between 49%and 74.5%),the dominant crystalline phases are calcite,arcanite,sylvite,and hydroxyapatite.These ashes have low surface area and do not exceed 13 m^(2)/g.Two characteristic morphological aspects were observed in these ashes:a morphology of rounded grains where silicon content is highlighted,and lamellar morphologies where the presence of chlorine is highlighted.Thermally,these ashes show four significant mass loss events(400℃,430℃,680℃,and 920℃),causing mass losses that vary between 25%and 40%.Through this study,it was possible to establish that,from a chemical point of view,these ashes are less dangerous in comparison with those of a mineral coal that was used as a reference.However,they require additional treatments for their disposal due to their high basicity.Because of their composition,these ashes have the potential to be used in the ceramic and cement industries,and in the manufacture of fertilizers.
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 by Key-Area Research and Development Program of Guangdong Province(2021B0101420002)the Major Key Project of PCL(PCL2021A09)+3 种基金National Natural Science Foundation of China(62072187)Guangdong Major Project of Basic and Applied Basic Research(2019B030302002)Guangdong Marine Economic Development Special Fund Project(GDNRC[2022]17)Guangzhou Development Zone Science and Technology(2021GH10,2020GH10).
文摘Nowadays,the rapid development of edge computing has driven an increasing number of deep learning applications deployed at the edge of the network,such as pedestrian and vehicle detection,to provide efficient intelligent services to mobile users.However,as the accuracy requirements continue to increase,the components of deep learning models for pedestrian and vehicle detection,such as YOLOv4,become more sophisticated and the computing resources required for model training are increasing dramatically,which in turn leads to significant challenges in achieving effective deployment on resource-constrained edge devices while ensuring the high accuracy performance.For addressing this challenge,a cloud-edge collaboration-based pedestrian and vehicle detection framework is proposed in this paper,which enables sufficient training of models by utilizing the abundant computing resources in the cloud,and then deploying the well-trained models on edge devices,thus reducing the computing resource requirements for model training on edge devices.Furthermore,to reduce the size of the model deployed on edge devices,an automatic pruning method combines the convolution layer and BN layer is proposed to compress the pedestrian and vehicle detection model size.Experimental results show that the framework proposed in this paper is able to deploy the pruned model on a real edge device,Jetson TX2,with 6.72 times higher FPS.Meanwhile,the channel pruning reduces the volume and the number of parameters to 96.77%for the model,and the computing amount is reduced to 81.37%.
基金supported by Institute of Information&communications Technology Planning&Evaluation(IITP)grant funded by the Korea government(MSIT)(No.2022-0-00377,Development of Intelligent Analysis and Classification Based Contents Class Categorization Technique to Prevent Imprudent Harmful Media Distribution).
文摘Automatic speech recognition(ASR)systems have emerged as indispensable tools across a wide spectrum of applications,ranging from transcription services to voice-activated assistants.To enhance the performance of these systems,it is important to deploy efficient models capable of adapting to diverse deployment conditions.In recent years,on-demand pruning methods have obtained significant attention within the ASR domain due to their adaptability in various deployment scenarios.However,these methods often confront substantial trade-offs,particularly in terms of unstable accuracy when reducing the model size.To address challenges,this study introduces two crucial empirical findings.Firstly,it proposes the incorporation of an online distillation mechanism during on-demand pruning training,which holds the promise of maintaining more consistent accuracy levels.Secondly,it proposes the utilization of the Mogrifier long short-term memory(LSTM)language model(LM),an advanced iteration of the conventional LSTM LM,as an effective alternative for pruning targets within the ASR framework.Through rigorous experimentation on the ASR system,employing the Mogrifier LSTM LM and training it using the suggested joint on-demand pruning and online distillation method,this study provides compelling evidence.The results exhibit that the proposed methods significantly outperform a benchmark model trained solely with on-demand pruning methods.Impressively,the proposed strategic configuration successfully reduces the parameter count by approximately 39%,all the while minimizing trade-offs.
文摘果园环境下柑橘的快速准确检测是自主采摘机器人作业的关键.针对现有的模型过于冗余、检测速度与精度不平衡等问题,提出一种轻量型果园环境果实检测方法.在YOLOv4算法的基础上引入焦点损失函数(Focal Loss)来提高模型在二分类检测任务中的负样本挖掘能力,并针对模型参数冗余等问题提出一种优化的模型剪枝方法.试验结果表明:提出的方法在果园环境中柑橘果实数据集检测得到的平均精度均值(mean average precision,M_(AP))达到94.22%,相较于YOLOv4模型提高了1.18%,模型参数减小了95.22%,模型尺寸为原来的4.84%,检测速度为原来的4.03倍.
