Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to r...Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.展开更多
Background:Hepatitis B virus(HBV)has affected over 300 million people worldwide which causes to induce mostly liver disease and liver cancer.It is a member of the family Hepadnaviridae which is a small DNA virus with ...Background:Hepatitis B virus(HBV)has affected over 300 million people worldwide which causes to induce mostly liver disease and liver cancer.It is a member of the family Hepadnaviridae which is a small DNA virus with unusual characters like retroviruses.Generally,hepatoprotective drugs provoke some side effects in human beings.For the reason,this study aims to identify alternative drug molecules from the natural source of medicinal plants with smaller quantity of side effects than those conventional drugs in treating HBV.Methods:We developed computational methods for calculating drug and target binding resemblance using the Maestro v10.2 of Schrodinger suite.The target and ligand molecules were obtained from recognized databases.Ligand molecules of 40 phytoconstituents were retrieved from variety of plants after we executed crucial analyses such as molecular docking and absorption,distribution,metabolism,and excretion(ADME)analysis.Results:In the docking analysis,the natural analogues repandusinic acid showed better docking scores of-14.768 with good binding contacts.The remaining bioactive molecules corilagin,furosin,nirurin,iso-quercetin and gallocatechin also showed better docking scores.Conclusions:This computational analysis reveals that repandusinic acid is a suitable drug candidate for HBV.Therefore,we recommend that this analogue is suitable in further exploration using in vitro studies.展开更多
Thermal overload relays are economic electromechanical protection devices which offers reliable protection for electric motors in the event of overload or phase failure. Presently there are two types of overload relay...Thermal overload relays are economic electromechanical protection devices which offers reliable protection for electric motors in the event of overload or phase failure. Presently there are two types of overload relays which depend on the temperature characteristics of metals to provide protection by tripping the circuit. These relays lack accuracy as they do not activate the trip circuit at any exact specified temperature. In this paper we introduce a new form of thermal over-load relay actuated by ferrofluid. The ferrofluid has a very accurate transition temperature known as curie temperature. It acts as a ferromagnetic material below the curie temperature and loses the property of ferromagnetism above this temperature. By using this property of the fluid we propose an alternative method for more accurate op-eration under overload condition. This relay finds application in the protection system of electrical machines. Thus, in this paper we present a novel and simple technique for protection against thermal overloading using ferrofluid.展开更多
The Internet of Things(IoT)is a heterogeneous information sharing and access platform that provides services in a pervasive manner.Task and computation offloading in the IoT helps to improve the response rate and the ...The Internet of Things(IoT)is a heterogeneous information sharing and access platform that provides services in a pervasive manner.Task and computation offloading in the IoT helps to improve the response rate and the availability of resources.Task offloading in a service-centric IoT environment mitigates the complexity in response delivery and request processing.In this paper,the state-based task offloading method(STOM)is introduced with a view to maximize the service response rate and reduce the response time of the varying request densities.The proposed method is designed using the Markov decision-making model to improve the rate of requests processed.By defining optimal states and filtering the actions based on the probability of response and request analysis,this method achieves less response time.Based on the defined states,request processing and resource allocations are performed to reduce the backlogs in handling multiple requests.The proposed method is verified for the response rate and time for the varying requests and processing servers through an experimental analysis.From the experimental analysis,the proposed method is found to improve response rate and reduce backlogs,response time,and offloading factor by 11.5%,20.19%,20.31%,and 8.85%,respectively.展开更多
La_2 O_3 doped(Na0.495 K0.455 Li0.05)(Nb0.95 Ta0.05)O_3 ceramics are prepared using modified milling process, and the influences of La2 O3 on ferroelectric behaviour, ageing characteristics, thermal stability, electri...La_2 O_3 doped(Na0.495 K0.455 Li0.05)(Nb0.95 Ta0.05)O_3 ceramics are prepared using modified milling process, and the influences of La2 O3 on ferroelectric behaviour, ageing characteristics, thermal stability, electrical stability, crystal structure, microstructure, dielectric and piezoelectric properties were reported. La_2 O_3 addition improved the ferroelectric characteristic substantially, and obtained remnant polarization(Pr) and maximum strain(Smax) around 34.3 C/cm^2 and 0.13% respectively.La_2 O_3 doped ceramics improved the thermal stability and were stable up to 180 ℃ compared to undoped ceramics(120 ℃). The Rietveld refinement along with the high-temperature X-ray diffraction studies suggested the presence of monoclinic phase in La doped compositions, which is responsible for their idiosyncratic behaviour. The maximum values were obtained around 179 pC/N and 0.385 for piezoelectric constant(d33) and electromechanical coupling factor(kp) respectively in La_2 O_3 doped samples(0.02 wt%), which also exhibited the lowest ageing rate and stable electrical fatigue behaviour.展开更多
文摘Because stress has such a powerful impact on human health,we must be able to identify it automatically in our everyday lives.The human activity recognition(HAR)system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions.Using the multimodal dataset DEAP(Database for Emotion Analysis using Physiological Signals),this paper presents deep learning(DL)technique for effectively detecting human stress.The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal.Based on visual and EEG(Electroencephalogram)data,this research aims to enhance the performance and extract the dominating characteristics of stress detection.For the stress identification test,we utilized the DEAP dataset,which included video and EEG data.We also demonstrate that combining video and EEG characteristics may increase overall performance,with the suggested stochastic features providing the most accurate results.In the first step,CNN(Convolutional Neural Network)extracts feature vectors from video frames and EEG data.Feature Level(FL)fusion that combines the features extracted from video and EEG data.We use XGBoost as our classifier model to predict stress,and we put it into action.The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree(DT),Random Forest(RF),AdaBoost,Linear Discriminant Analysis(LDA),and KNearest Neighborhood(KNN).When we compared our technique to existing state-of-the-art approaches,we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification.
文摘Background:Hepatitis B virus(HBV)has affected over 300 million people worldwide which causes to induce mostly liver disease and liver cancer.It is a member of the family Hepadnaviridae which is a small DNA virus with unusual characters like retroviruses.Generally,hepatoprotective drugs provoke some side effects in human beings.For the reason,this study aims to identify alternative drug molecules from the natural source of medicinal plants with smaller quantity of side effects than those conventional drugs in treating HBV.Methods:We developed computational methods for calculating drug and target binding resemblance using the Maestro v10.2 of Schrodinger suite.The target and ligand molecules were obtained from recognized databases.Ligand molecules of 40 phytoconstituents were retrieved from variety of plants after we executed crucial analyses such as molecular docking and absorption,distribution,metabolism,and excretion(ADME)analysis.Results:In the docking analysis,the natural analogues repandusinic acid showed better docking scores of-14.768 with good binding contacts.The remaining bioactive molecules corilagin,furosin,nirurin,iso-quercetin and gallocatechin also showed better docking scores.Conclusions:This computational analysis reveals that repandusinic acid is a suitable drug candidate for HBV.Therefore,we recommend that this analogue is suitable in further exploration using in vitro studies.
文摘Thermal overload relays are economic electromechanical protection devices which offers reliable protection for electric motors in the event of overload or phase failure. Presently there are two types of overload relays which depend on the temperature characteristics of metals to provide protection by tripping the circuit. These relays lack accuracy as they do not activate the trip circuit at any exact specified temperature. In this paper we introduce a new form of thermal over-load relay actuated by ferrofluid. The ferrofluid has a very accurate transition temperature known as curie temperature. It acts as a ferromagnetic material below the curie temperature and loses the property of ferromagnetism above this temperature. By using this property of the fluid we propose an alternative method for more accurate op-eration under overload condition. This relay finds application in the protection system of electrical machines. Thus, in this paper we present a novel and simple technique for protection against thermal overloading using ferrofluid.
基金The partial APC is will be paid Durban University of Technology(DUT)University,South Africa.
文摘The Internet of Things(IoT)is a heterogeneous information sharing and access platform that provides services in a pervasive manner.Task and computation offloading in the IoT helps to improve the response rate and the availability of resources.Task offloading in a service-centric IoT environment mitigates the complexity in response delivery and request processing.In this paper,the state-based task offloading method(STOM)is introduced with a view to maximize the service response rate and reduce the response time of the varying request densities.The proposed method is designed using the Markov decision-making model to improve the rate of requests processed.By defining optimal states and filtering the actions based on the probability of response and request analysis,this method achieves less response time.Based on the defined states,request processing and resource allocations are performed to reduce the backlogs in handling multiple requests.The proposed method is verified for the response rate and time for the varying requests and processing servers through an experimental analysis.From the experimental analysis,the proposed method is found to improve response rate and reduce backlogs,response time,and offloading factor by 11.5%,20.19%,20.31%,and 8.85%,respectively.
文摘La_2 O_3 doped(Na0.495 K0.455 Li0.05)(Nb0.95 Ta0.05)O_3 ceramics are prepared using modified milling process, and the influences of La2 O3 on ferroelectric behaviour, ageing characteristics, thermal stability, electrical stability, crystal structure, microstructure, dielectric and piezoelectric properties were reported. La_2 O_3 addition improved the ferroelectric characteristic substantially, and obtained remnant polarization(Pr) and maximum strain(Smax) around 34.3 C/cm^2 and 0.13% respectively.La_2 O_3 doped ceramics improved the thermal stability and were stable up to 180 ℃ compared to undoped ceramics(120 ℃). The Rietveld refinement along with the high-temperature X-ray diffraction studies suggested the presence of monoclinic phase in La doped compositions, which is responsible for their idiosyncratic behaviour. The maximum values were obtained around 179 pC/N and 0.385 for piezoelectric constant(d33) and electromechanical coupling factor(kp) respectively in La_2 O_3 doped samples(0.02 wt%), which also exhibited the lowest ageing rate and stable electrical fatigue behaviour.
