Objective:To evaluate the antidiabetic and antioxidant potential of Emblica officinalis(E.officinalis)fruit on normal and type 2 diabetic rats.Methods:Type 2 diabetes was induced into the male Long-Evans rats.The rats...Objective:To evaluate the antidiabetic and antioxidant potential of Emblica officinalis(E.officinalis)fruit on normal and type 2 diabetic rats.Methods:Type 2 diabetes was induced into the male Long-Evans rats.The rats were divided into nine groups including control groups receiving water,type 2 diabetic controls,type 2 diabetic rats treated with glibenclamide(T2GT)and type 2diabetic rats treated with aqueous extract of fruit pulp of E.officinalis.They were fed orally for8 weeks with a single feeding.Blood was collected by cutting the tail tip on 0 and 28 days and by decapitation on 56 day.Packed red blood cells and serum were used for evaluating different biochemical parameters.Results:Four weeks administration of aqueous extract of E.officinalis improved oral glucose tolerance in type 2 rats and after 8 weeks it caused significant(P<0.007)reduction in fasting serum glucose level compared to 0 day.Triglycerides decreased by 14%but there was no significant change in serum ALT,creatinine,cholesterol and insulin level in any group.Furthermore,reduced erythrocyte malondialdehyde level showed no significant change(P<0.07)but reduced glutathione content was found to be increased significantly(P<0.05).Conclusions:The aqueous extract of E.officinalis has a promising antidiabetic and antioxidant properties and may be considered for further clinical studies in drug development.展开更多
Smart grid constrained optimal control is a complex issue due to the constant growth of grid complexity and the large volume of data available as input to smart device control.In this context,traditional centralized c...Smart grid constrained optimal control is a complex issue due to the constant growth of grid complexity and the large volume of data available as input to smart device control.In this context,traditional centralized control paradigms may suffer in terms of the timeliness of optimization results due to the volume of data to be processed and the delayed asynchronous nature of the data transmission.To address these limits of centralized control,this paper presents a coordinated,distributed algorithm based on distributed,local controllers and a central coordinator for exchanging summarized global state information.The proposed model for exchanging global state information is resistant to fluctuations caused by the inherent interdependence between local controllers,and is robust to delays in information exchange.In addition,the algorithm features iterative refinement of local state estimations that is able to improve local controller ability to operate within network constraints.Application of the proposed coordinated,distributed algorithm through simulation shows its effectiveness in optimizing a global goal within a complex distribution system operating under constraints,while ensuring network operation stability under varying levels of information exchange delay,and with a range of network sizes.展开更多
The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable e...The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.展开更多
基金Shahhag, Dhaka. Bangladesh, for providing technical moral ami financial support for this project
文摘Objective:To evaluate the antidiabetic and antioxidant potential of Emblica officinalis(E.officinalis)fruit on normal and type 2 diabetic rats.Methods:Type 2 diabetes was induced into the male Long-Evans rats.The rats were divided into nine groups including control groups receiving water,type 2 diabetic controls,type 2 diabetic rats treated with glibenclamide(T2GT)and type 2diabetic rats treated with aqueous extract of fruit pulp of E.officinalis.They were fed orally for8 weeks with a single feeding.Blood was collected by cutting the tail tip on 0 and 28 days and by decapitation on 56 day.Packed red blood cells and serum were used for evaluating different biochemical parameters.Results:Four weeks administration of aqueous extract of E.officinalis improved oral glucose tolerance in type 2 rats and after 8 weeks it caused significant(P<0.007)reduction in fasting serum glucose level compared to 0 day.Triglycerides decreased by 14%but there was no significant change in serum ALT,creatinine,cholesterol and insulin level in any group.Furthermore,reduced erythrocyte malondialdehyde level showed no significant change(P<0.07)but reduced glutathione content was found to be increased significantly(P<0.05).Conclusions:The aqueous extract of E.officinalis has a promising antidiabetic and antioxidant properties and may be considered for further clinical studies in drug development.
文摘Smart grid constrained optimal control is a complex issue due to the constant growth of grid complexity and the large volume of data available as input to smart device control.In this context,traditional centralized control paradigms may suffer in terms of the timeliness of optimization results due to the volume of data to be processed and the delayed asynchronous nature of the data transmission.To address these limits of centralized control,this paper presents a coordinated,distributed algorithm based on distributed,local controllers and a central coordinator for exchanging summarized global state information.The proposed model for exchanging global state information is resistant to fluctuations caused by the inherent interdependence between local controllers,and is robust to delays in information exchange.In addition,the algorithm features iterative refinement of local state estimations that is able to improve local controller ability to operate within network constraints.Application of the proposed coordinated,distributed algorithm through simulation shows its effectiveness in optimizing a global goal within a complex distribution system operating under constraints,while ensuring network operation stability under varying levels of information exchange delay,and with a range of network sizes.
文摘The advancements in distributed generation(DG)technologies such as solar panels have led to a widespread integration of renewable power generation in modern power systems.However,the intermittent nature of renewable energy poses new challenges to the network operational planning with underlying uncertainties.This paper proposes a novel probabilistic scheme for renewable solar power generation forecasting by addressing data and model parameter uncertainties using Bayesian bidirectional long short-term memory(BiLSTM)neural networks,while handling the high dimensionality in weight parameters using variational auto-encoders(VAE).The forecasting performance of the proposed method is evaluated using various deterministic and probabilistic evaluation metrics such as root-mean square error(RMSE),Pinball loss,etc.Furthermore,reconstruction error and computational time are also monitored to evaluate the dimensionality reduction using the VAE component.When compared with benchmark methods,the proposed method leads to significant improvements in weight reduction,i.e.,from 76,4224 to 2,022 number of weight parameters,quantifying to 97.35%improvement in weight parameters reduction and 37.93%improvement in computational time for 6 months of solar power generation data.
