The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively ...The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.展开更多
Objective: To evaluate the organizational model of a perinatal network and its relevance in a resource-limited country. Methodology: This was a mixed prospective qualitative and quantitative study conducted over a 2-y...Objective: To evaluate the organizational model of a perinatal network and its relevance in a resource-limited country. Methodology: This was a mixed prospective qualitative and quantitative study conducted over a 2-year period, from January 1, 2022, to December 31, 2023. This study took place in Senegal, a country with limited resources and a weakness of hyperspecialized medical technical resources. There was no policy for the management of fetal malformations. The qualitative part was carried out through overt participant observation. The human resources and the organization of the perinatal network were described. For the quantitative part, all fetuses managed during the study period were included. The studied parameters related to neonatal care and outcomes. Qualitative variables were described using dispersion parameters, and quantitative variables were described using proportions. Results: The perinatal network includes several specialists across six hospitals. Of these hospitals, only one provided emergency pediatric surgery. The network included highly specialized human resources in prenatal diagnosis, congenital heart defects, pediatric surgery, anesthesia, and other medical specialties in perinatology. Advanced ultrasound was centralized by an obstetrician. The team decided on the follow-up methods, timing, and mode of delivery. The newborn was immediately transferred to the appropriate specialty. Over the 2-year period, 201 fetuses were managed. The rate of cesarean delivery was 76.3%. Neonatal mortality was 51.4%. Discussion: Centralizing care improves the quality of prenatal diagnosis and management of congenital defects. Mortality remains high when emergency surgery is not well available. This mortality is also due to the lack of a single center offering all perinatal care and so, the transfer of newborns. The cesarean rate increases due to underlying conditions and organizational factors. Conclusion: Public policies should prioritize the centralization of care for congenital disorders to reduce the costs of disability and mortality.展开更多
This paper proposes an efficient strategy for resource utilization in Elastic Optical Networks (EONs) to minimize spectrum fragmentation and reduce connection blocking probability during Routing and Spectrum Allocatio...This paper proposes an efficient strategy for resource utilization in Elastic Optical Networks (EONs) to minimize spectrum fragmentation and reduce connection blocking probability during Routing and Spectrum Allocation (RSA). The proposed method, Dynamic Threshold-Based Routing and Spectrum Allocation with Fragmentation Awareness (DT-RSAF), integrates rerouting and spectrum defragmentation as needed. By leveraging Yen’s shortest path algorithm, DT-RSAF enhances resource utilization while ensuring improved service continuity. A dynamic threshold mechanism enables the algorithm to adapt to varying network conditions, while its fragmentation awareness effectively mitigates spectrum fragmentation. Simulation results on NSFNET and COST 239 topologies demonstrate that DT-RSAF significantly outperforms methods such as K-Shortest Path Routing and Spectrum Allocation (KSP-RSA), Load Balanced and Fragmentation-Aware (LBFA), and the Invasive Weed Optimization-based RSA (IWO-RSA). Under heavy traffic, DT-RSAF reduces the blocking probability by up to 15% and achieves lower Bandwidth Fragmentation Ratios (BFR), ranging from 74% to 75%, compared to 77% - 80% for KSP-RSA, 75% - 77% for LBFA, and approximately 76% for IWO-RSA. DT-RSAF also demonstrated reasonable computation times compared to KSP-RSA, LBFA, and IWO-RSA. On a small-sized network, its computation time was 8710 times faster than that of Integer Linear Programming (ILP) on the same network topology. Additionally, it achieved a similar execution time to LBFA and outperformed IWO-RSA in terms of efficiency. These results highlight DT-RSAF’s capability to maintain large contiguous frequency blocks, making it highly effective for accommodating high-bandwidth requests in EONs while maintaining reasonable execution times.展开更多
为了充分利用特征间的高阶交互以提升点击率预测模型的预测精度,提出了一种基于图神经网络和注意力的点击率预测模型VBGA (vector-wise and bit-wise interaction model based on GNN and attention),该模型借助图神经网络和注意力机制...为了充分利用特征间的高阶交互以提升点击率预测模型的预测精度,提出了一种基于图神经网络和注意力的点击率预测模型VBGA (vector-wise and bit-wise interaction model based on GNN and attention),该模型借助图神经网络和注意力机制,为每个特征分别学习一个细粒度的权重,并将这种细粒度的特征权重输入到向量级交互层和元素级交互层联合预测点击率.VBGA模型主要由向量级交互层和元素级交互层构成,其中向量级交互层采用有向图来构建向量级的特征交互,实现无重复的显式特征交互,在减少计算量的同时,还可以实现更高阶的特征交叉,以获得更准确的预测精度.此外,本文还提出了一种交叉网络用于构建元素级特征交互.在Criteo和Avazu数据集上,与其他几种最先进的点击率预测模型进行了比较,实验结果表明,VBGA可以获得良好的预测结果.展开更多
Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dep...Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.展开更多
文摘The distribution networks sometimes suffer from excessive losses and voltage violations in densely populated areas. The aim of the present study is to improve the performance of a distribution network by successively applying mono-capacitor positioning, multiple positioning and reconfiguration processes using GA-based algorithms implemented in a Matlab environment. From the diagnostic study of this network, it was observed that a minimum voltage of 0.90 pu induces a voltage deviation of 5.26%, followed by active and reactive losses of 425.08 kW and 435.09 kVAR, respectively. Single placement with the NSGAII resulted in the placement of a 3000 kVAR capacitor at node 128, which proved to be the invariably neuralgic point. Multiple placements resulted in a 21.55% reduction in losses and a 0.74% regression in voltage profile performance. After topology optimization, the loss profile improved by 65.08% and the voltage profile improved by 1.05%. Genetic algorithms are efficient and effective tools for improving the performance of distribution networks, whose degradation is often dynamic due to the natural variability of loads.
文摘Objective: To evaluate the organizational model of a perinatal network and its relevance in a resource-limited country. Methodology: This was a mixed prospective qualitative and quantitative study conducted over a 2-year period, from January 1, 2022, to December 31, 2023. This study took place in Senegal, a country with limited resources and a weakness of hyperspecialized medical technical resources. There was no policy for the management of fetal malformations. The qualitative part was carried out through overt participant observation. The human resources and the organization of the perinatal network were described. For the quantitative part, all fetuses managed during the study period were included. The studied parameters related to neonatal care and outcomes. Qualitative variables were described using dispersion parameters, and quantitative variables were described using proportions. Results: The perinatal network includes several specialists across six hospitals. Of these hospitals, only one provided emergency pediatric surgery. The network included highly specialized human resources in prenatal diagnosis, congenital heart defects, pediatric surgery, anesthesia, and other medical specialties in perinatology. Advanced ultrasound was centralized by an obstetrician. The team decided on the follow-up methods, timing, and mode of delivery. The newborn was immediately transferred to the appropriate specialty. Over the 2-year period, 201 fetuses were managed. The rate of cesarean delivery was 76.3%. Neonatal mortality was 51.4%. Discussion: Centralizing care improves the quality of prenatal diagnosis and management of congenital defects. Mortality remains high when emergency surgery is not well available. This mortality is also due to the lack of a single center offering all perinatal care and so, the transfer of newborns. The cesarean rate increases due to underlying conditions and organizational factors. Conclusion: Public policies should prioritize the centralization of care for congenital disorders to reduce the costs of disability and mortality.
文摘This paper proposes an efficient strategy for resource utilization in Elastic Optical Networks (EONs) to minimize spectrum fragmentation and reduce connection blocking probability during Routing and Spectrum Allocation (RSA). The proposed method, Dynamic Threshold-Based Routing and Spectrum Allocation with Fragmentation Awareness (DT-RSAF), integrates rerouting and spectrum defragmentation as needed. By leveraging Yen’s shortest path algorithm, DT-RSAF enhances resource utilization while ensuring improved service continuity. A dynamic threshold mechanism enables the algorithm to adapt to varying network conditions, while its fragmentation awareness effectively mitigates spectrum fragmentation. Simulation results on NSFNET and COST 239 topologies demonstrate that DT-RSAF significantly outperforms methods such as K-Shortest Path Routing and Spectrum Allocation (KSP-RSA), Load Balanced and Fragmentation-Aware (LBFA), and the Invasive Weed Optimization-based RSA (IWO-RSA). Under heavy traffic, DT-RSAF reduces the blocking probability by up to 15% and achieves lower Bandwidth Fragmentation Ratios (BFR), ranging from 74% to 75%, compared to 77% - 80% for KSP-RSA, 75% - 77% for LBFA, and approximately 76% for IWO-RSA. DT-RSAF also demonstrated reasonable computation times compared to KSP-RSA, LBFA, and IWO-RSA. On a small-sized network, its computation time was 8710 times faster than that of Integer Linear Programming (ILP) on the same network topology. Additionally, it achieved a similar execution time to LBFA and outperformed IWO-RSA in terms of efficiency. These results highlight DT-RSAF’s capability to maintain large contiguous frequency blocks, making it highly effective for accommodating high-bandwidth requests in EONs while maintaining reasonable execution times.
文摘为了充分利用特征间的高阶交互以提升点击率预测模型的预测精度,提出了一种基于图神经网络和注意力的点击率预测模型VBGA (vector-wise and bit-wise interaction model based on GNN and attention),该模型借助图神经网络和注意力机制,为每个特征分别学习一个细粒度的权重,并将这种细粒度的特征权重输入到向量级交互层和元素级交互层联合预测点击率.VBGA模型主要由向量级交互层和元素级交互层构成,其中向量级交互层采用有向图来构建向量级的特征交互,实现无重复的显式特征交互,在减少计算量的同时,还可以实现更高阶的特征交叉,以获得更准确的预测精度.此外,本文还提出了一种交叉网络用于构建元素级特征交互.在Criteo和Avazu数据集上,与其他几种最先进的点击率预测模型进行了比较,实验结果表明,VBGA可以获得良好的预测结果.
文摘Aspect-oriented sentiment analysis is a meticulous sentiment analysis task that aims to analyse the sentiment polarity of specific aspects. Most of the current research builds graph convolutional networks based on dependent syntactic trees, which improves the classification performance of the models to some extent. However, the technical limitations of dependent syntactic trees can introduce considerable noise into the model. Meanwhile, it is difficult for a single graph convolutional network to aggregate both semantic and syntactic structural information of nodes, which affects the final sentence classification. To cope with the above problems, this paper proposes a bi-channel graph convolutional network model. The model introduces a phrase structure tree and transforms it into a hierarchical phrase matrix. The adjacency matrix of the dependent syntactic tree and the hierarchical phrase matrix are combined as the initial matrix of the graph convolutional network to enhance the syntactic information. The semantic information feature representations of the sentences are obtained by the graph convolutional network with a multi-head attention mechanism and fused to achieve complementary learning of dual-channel features. Experimental results show that the model performs well and improves the accuracy of sentiment classification on three public benchmark datasets, namely Rest14, Lap14 and Twitter.
