为探索补充维生素D(VD)对于NAFLD患者肠道菌群的影响。本研究共纳入29例NAFLD患者,采用随机分组法分为对照组(只接受内科治疗,n=15)和VD组(在内科治疗的基础上,补充维生素D_(2),n=14)。根据中国药典的剂量标准,对VD组NAFLD患者一次性给...为探索补充维生素D(VD)对于NAFLD患者肠道菌群的影响。本研究共纳入29例NAFLD患者,采用随机分组法分为对照组(只接受内科治疗,n=15)和VD组(在内科治疗的基础上,补充维生素D_(2),n=14)。根据中国药典的剂量标准,对VD组NAFLD患者一次性给予60万国际单位VD_(2),肌肉注射。30天后测定患者血清生化指标,采集患者粪便,进行16S r RNA测序分析,比较治疗前后肠道菌群在多样性、丰度的变化,以及肠道菌群与生化指标的相关性。研究结果表明:补充VD之后,NAFLD患者血清丙氨酸氨基转移酶(ALT)水平显著降低(P<0.001),肠道菌群多样性无显著变化(P>0.05),但毛螺菌科丰度显著降低(P<0.01),丹毒丝菌科(P<0.01)、丹毒丝菌科未命名属(P<0.05)丰度显著升高;毛螺菌科相对丰度水平与血清甘油三酯(TG)(P<0.05)呈显著正相关,丹毒丝菌科(P<0.01)和丹毒丝菌科未命名属(P<0.05)与血清25(OH)D水平呈显著正相关,而与ALT、天门冬氨酸氨基转移酶(AST)呈显著负相关(ALT:P<0.000 1;AST:P<0.05);补充VD能够显著降低血清ALT水平,对NAFLD患者肝损伤有一定改善作用;VD能显著降低患者肠道菌群毛螺菌科的丰度,显著提高丹毒丝菌科和丹毒丝菌科未命名属的丰度,进而降低血清TG、ALT和AST水平。展开更多
Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to pro...Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffi c. Therefore, a novel Qo S-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a Qo S-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.展开更多
Facial recognition has become the most common identity authentication technologies. However, problems such as uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are ...Facial recognition has become the most common identity authentication technologies. However, problems such as uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are a few pieces of research on face liveness detection under occlusion conditions. This paper designs a face recognition technique suitable for different degrees of facial occlusion, which employs the facial datasets of near-infrared(NIR) images and visible(VIS) light images to examine the single-modality detection accuracy rate(experimental control group) and the corresponding high-dimensional features through the residual network(ResNet). Based on the idea of data fusion, we propose two feature fusion methods. The two methods extract and fuse the data of one and two convolutional layers from two single-modality detectors respectively. The fusion of high-dimensional features apply a new ResNet to get the dual-modality detection accuracy. And then, a new ResNet is applied to test the accuracy of dual-modality detection. The experimental results show that the dual-modality face liveness detection model improves face live detection accuracy and robustness compared with the single-modality. The fusion of two-layer features from the single-modality detector can also improve face detection accuracy by utilizing the above-mentioned dual-modality detector, and it doesn’t increase the algorithm’s complexity.展开更多
With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtuali...With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization(NFV) technology with cloud computing and mobile edge computing(MEC), an NFV-enabled cloud-and-edge-collaborative IoT(CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain(SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping(DQLBM) algorithm as well as an energy-based topology adjustment(EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.展开更多
Federated learning(FL)is a promising technique to build a power generation model in photovoltaic(PV)scenarios.However,due to the heterogeneity of power generation data,there is a problem of slow convergence of the glo...Federated learning(FL)is a promising technique to build a power generation model in photovoltaic(PV)scenarios.However,due to the heterogeneity of power generation data,there is a problem of slow convergence of the global model or even deviation from the optimal solution during model training.Therefore,to improve the prediction accuracy and accelerate the model convergence speed,this paper proposes a model functional blocking and differentiated scheduling mechanism under personalized FL framework for intermittent PV power generation.Firstly,cluster analysis is conducted according to longitude,latitude,and altitude to form a model collaborative training region(MCTR).Then,based on the constructed MCTRs,a personalized FL model training method is proposed.This method is based on a combination of global shared convolutional neural network(CNN)model and local personalized long short term memory(LSTM)model,where CNN model block is responsible for feature extraction and LSTM model block is responsible for prediction.It adopts synchronous aggregation for global shared CNN and asynchronous aggregation for personalized LSTM.Furthermore,the FL server performs block scheduling of the CNN-LSTM models and aggregates them based on the regional membership which can provide differentiated and accurate prediction models with different power generation patterns.The simulation results show that the proposed algorithm has the highest accuracy of 85.1%and the best performance on mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE),with 0.1105,0.1224 and 0.4383 respectively.展开更多
文摘为探索补充维生素D(VD)对于NAFLD患者肠道菌群的影响。本研究共纳入29例NAFLD患者,采用随机分组法分为对照组(只接受内科治疗,n=15)和VD组(在内科治疗的基础上,补充维生素D_(2),n=14)。根据中国药典的剂量标准,对VD组NAFLD患者一次性给予60万国际单位VD_(2),肌肉注射。30天后测定患者血清生化指标,采集患者粪便,进行16S r RNA测序分析,比较治疗前后肠道菌群在多样性、丰度的变化,以及肠道菌群与生化指标的相关性。研究结果表明:补充VD之后,NAFLD患者血清丙氨酸氨基转移酶(ALT)水平显著降低(P<0.001),肠道菌群多样性无显著变化(P>0.05),但毛螺菌科丰度显著降低(P<0.01),丹毒丝菌科(P<0.01)、丹毒丝菌科未命名属(P<0.05)丰度显著升高;毛螺菌科相对丰度水平与血清甘油三酯(TG)(P<0.05)呈显著正相关,丹毒丝菌科(P<0.01)和丹毒丝菌科未命名属(P<0.05)与血清25(OH)D水平呈显著正相关,而与ALT、天门冬氨酸氨基转移酶(AST)呈显著负相关(ALT:P<0.000 1;AST:P<0.05);补充VD能够显著降低血清ALT水平,对NAFLD患者肝损伤有一定改善作用;VD能显著降低患者肠道菌群毛螺菌科的丰度,显著提高丹毒丝菌科和丹毒丝菌科未命名属的丰度,进而降低血清TG、ALT和AST水平。
基金supported by the State Grid Technology Project of China(SGIT0000 KJJS1500008)
文摘Cognitive radio sensor network is applied to facilitate network monitoring and management, and achieves high spectrum efficiencies in smart grid. However, the conventional traffic scheduling mechanisms are hard to provide guaranteed quality of service for the secondary users. It is because that they ignore the influence of diverse transition requirements in heterogeneous traffi c. Therefore, a novel Qo S-aware packet scheduling mechanism is proposed to improve transmission quality for secondary users. In this mechanism, a Qo S-based prioritization model is established to address data classification firstly. And then, channel quality and the effect of channel switch are integrated into priority-based packet scheduling mechanism. At last, the simulation is implemented with MATLAB and OPNET. The results show that the proposed scheduling mechanism improves the transmission quality of high-priority secondary users and increase the whole system utilization by 10%.
基金supported by the Science and Technology Project of State Grid Corporation of China(SGHEXT00YJJS1900050)。
文摘Facial recognition has become the most common identity authentication technologies. However, problems such as uneven light and occluded faces have increased the hardness of liveness detection. Nevertheless, there are a few pieces of research on face liveness detection under occlusion conditions. This paper designs a face recognition technique suitable for different degrees of facial occlusion, which employs the facial datasets of near-infrared(NIR) images and visible(VIS) light images to examine the single-modality detection accuracy rate(experimental control group) and the corresponding high-dimensional features through the residual network(ResNet). Based on the idea of data fusion, we propose two feature fusion methods. The two methods extract and fuse the data of one and two convolutional layers from two single-modality detectors respectively. The fusion of high-dimensional features apply a new ResNet to get the dual-modality detection accuracy. And then, a new ResNet is applied to test the accuracy of dual-modality detection. The experimental results show that the dual-modality face liveness detection model improves face live detection accuracy and robustness compared with the single-modality. The fusion of two-layer features from the single-modality detector can also improve face detection accuracy by utilizing the above-mentioned dual-modality detector, and it doesn’t increase the algorithm’s complexity.
基金supported by the Science and Technology Project of State Grid Corporation of China(SGLNXT00GCJS2000160)。
文摘With the rapid development of Internet of thing(IoT) technology, it has become a challenge to deal with the increasing number and diverse requirements of IoT services. By combining burgeoning network function virtualization(NFV) technology with cloud computing and mobile edge computing(MEC), an NFV-enabled cloud-and-edge-collaborative IoT(CECIoT) architecture can efficiently provide flexible service for IoT traffic in the form of a service function chain(SFC) by jointly utilizing edge and cloud resources. In this promising architecture, a difficult issue is how to balance the consumption of resource and energy in SFC mapping. To overcome this challenge, an intelligent energy-and-resource-balanced SFC mapping scheme is designed in this paper. It takes the comprehensive deployment consumption as the optimization goal, and applies a deep Q-learning(DQL)-based SFC mapping(DQLBM) algorithm as well as an energy-based topology adjustment(EBTA) strategy to make efficient use of the limited network resources, while satisfying the delay requirement of users. Simulation results show that the proposed scheme can decrease service delay, as well as energy and resource consumption.
基金supported by the Science and Technology Project of State Grid Corporation of China(5108-202218280A-2-394-XG)。
文摘Federated learning(FL)is a promising technique to build a power generation model in photovoltaic(PV)scenarios.However,due to the heterogeneity of power generation data,there is a problem of slow convergence of the global model or even deviation from the optimal solution during model training.Therefore,to improve the prediction accuracy and accelerate the model convergence speed,this paper proposes a model functional blocking and differentiated scheduling mechanism under personalized FL framework for intermittent PV power generation.Firstly,cluster analysis is conducted according to longitude,latitude,and altitude to form a model collaborative training region(MCTR).Then,based on the constructed MCTRs,a personalized FL model training method is proposed.This method is based on a combination of global shared convolutional neural network(CNN)model and local personalized long short term memory(LSTM)model,where CNN model block is responsible for feature extraction and LSTM model block is responsible for prediction.It adopts synchronous aggregation for global shared CNN and asynchronous aggregation for personalized LSTM.Furthermore,the FL server performs block scheduling of the CNN-LSTM models and aggregates them based on the regional membership which can provide differentiated and accurate prediction models with different power generation patterns.The simulation results show that the proposed algorithm has the highest accuracy of 85.1%and the best performance on mean absolute error(MAE),root mean square error(RMSE)and mean absolute percentage error(MAPE),with 0.1105,0.1224 and 0.4383 respectively.
