In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is foun...In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.展开更多
After conducting a critical survey of the different categories of existing heat exchangers,the results of several experiments about the behaviour of a two-phase current in an open channel are reported.The results conf...After conducting a critical survey of the different categories of existing heat exchangers,the results of several experiments about the behaviour of a two-phase current in an open channel are reported.The results confirm the complexity of the problems induced in heat exchangers by flow maldistribution,especially when two-phase flows are considered in multi-channel systems.It is shown that severe misalignment of heat exchangers can lead to a loss of economic performance of more than 25%.Improper distribution of fluid flow causes longer fluid coils to form,and the liquid cochlea can eventually occupy a large space,thereby reducing heat transfer and disrupting the considered biphasic system.The use of a small diameter distribution pipe with properly spaced outlet holes seems to be a promising approach to fix many of these issues.It is found that the current distribution in the channels,in addition to the header pressure distribution,also depends on factors such as the geometry and the initial flow regime in the header.展开更多
Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-...Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.展开更多
A theoretical and experimental study of the thermal decomposition of nitroguanidine(NQ) has been carried out. Various thermolysis channels were studied by quantum chemistry methods at the CCSD(or DLPNO-CCSD) level usi...A theoretical and experimental study of the thermal decomposition of nitroguanidine(NQ) has been carried out. Various thermolysis channels were studied by quantum chemistry methods at the CCSD(or DLPNO-CCSD) level using the aug-cc-pVDZ basis set. It is shown that the lowest activation enthalpies(170-180kJ/mol) are characteristic of the reactions of NO2abstraction from the initial NQ and the reaction channel with the transfer of oxygen from the nitro group to carbon in the limiting stage. Additionally, the thermolysis of NQ was studied experimentally in a nonisothermal mode with heating rates from 1 to 10K/min. In these experiments, the weight loss of the sample, thermal effects, and mass spectra of the products were recorded. An analysis of the experimental data confirmed the results of a theoretical study of the mechanism of thermal decomposition of NQ. The main thermolysis products are N2O, HNCO, NH3, and NO2, which fully corresponds to quantum chemical calculations.展开更多
The economics’ ecological modernization approach based on the input-output balance model is considered in the paper. The government measures on Russian Federation’s transition to green economy on reducing of greenho...The economics’ ecological modernization approach based on the input-output balance model is considered in the paper. The government measures on Russian Federation’s transition to green economy on reducing of greenhouse gas emissions are analyzed. In an article for green economy evaluation, including greenhouse gas emissions per capita and per unit of GDP, the indicator of the production environmental costs is proposed to include. The authors suppose adding and to modernize the Leontief-Ford model of input-output balance by economic evaluation of the environment pollution effects. This model is proposed to consider macro-economic assessment of environmental damage, health deterioration due to environmental pollution, as well as the use costs of the environmentally friendly technologies, the environmental and energy innovations’ implementation, climate change. The proposed modified model of environmentally oriented input-output balance can be used in the economic compensation system implementation on natural capital use and ecosystem services’ consuming in countries and their regions. The modified Leontief-Ford model proposed in the paper can be used for green economy development calculating, for example in Russia during the development of measures for the environment and economic development. Also this paper opens discussions for opportunities of the further possible integration of the theoretical models for environment protection decision-making.展开更多
The scientific proof is the highest type of the rational proof. The mankind is looking for the best technology of the reasonable demonstration. What is a proof？. What is a scientific proof？. Philosophical investigat...The scientific proof is the highest type of the rational proof. The mankind is looking for the best technology of the reasonable demonstration. What is a proof？. What is a scientific proof？. Philosophical investigations of proofs have the long history. Philosophy is exploring physics, mathematics, astronomy, biology, history, and so on. Science demands strict proofs. Science uses the specific methods as the optimum technologies for the achievement of the truth. Strictness of the proof depends on the aim algorithm： the distribution of the functions between parts of the proof. The beginning stage, the middle parts, and the ending stage are the unit of the proof. Philosophy can make the correct model of the scientific proof only! Science and its methodology develop and the growth of knowledge has not the finish. The rational ideals improve continually. Science is looking for the criterion of the demonstrative strictness. The adaptation algorithm of the scientific method is the best technology for the achievement of the truth.展开更多
The technology of knowledge base remote design of the smart fuzzy controllers with the application of the"Soft/quantum computing optimizer"toolkit software developed.The possibility of the transmission...The technology of knowledge base remote design of the smart fuzzy controllers with the application of the"Soft/quantum computing optimizer"toolkit software developed.The possibility of the transmission and communication the knowledge base using remote connection to the control object considered.Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies.Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk.As examples,two different models of robots described(mobile manipulator and(“cart-pole”system)inverted pendulum).A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented.The ability to connect and work with a physical model of control object without using than mathematical model demonstrated.The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers.It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge.Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA(at the first stage for the cooling system of superconducted magnets)is discussed.The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft/quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems.The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line.展开更多
In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing t...In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.展开更多
Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless ...Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.展开更多
The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of...The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.展开更多
Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt bat...Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.展开更多
Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in th...Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic situations.To handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified configurations.Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server.Data deduplication also saves network bandwidth.In this paper,a new cloud-based big data security technique utilizing dual encryption is proposed.The clustering model is utilized to analyze the Deduplication process hash function.Multi kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption procedure.The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is used.This security process involving dual encryption with the optimization model develops the productivity mechanism.In this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques.The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.展开更多
Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a...Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.展开更多
Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information processing.The recent advancements in multimedia sensors demand the Quality of Servic...Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information processing.The recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain standards.The restrictions and requirements in QoS management completely depend upon the nature of target application.Some of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and throughput.In this scenario,clustering and routing are considered as the most effective techniques to meet the demands of QoS.Since they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be implemented.The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)algorithm.The proposed QoSCRSI technique performs two-level clustering and proficient routing.Initially,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the network.In order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node counts.The experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.展开更多
Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield...Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.展开更多
Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directl...Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directly in these big data streams.At the same time,streaming data from several applications results in two major problems such as class imbalance and concept drift.The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming Data.The presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and classification.MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is employed.Besides,Statistical Test of Equal Proportions(STEPD),a CDD technique is also utilized.Finally,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for classification.In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried out.The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam dataset.An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model.The proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.展开更多
Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The ...Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality.The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing environment.The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques.The current research article presents a new Oppositional Glowworm Swarm Optimization(OGSO)algorithmbased clustering with Deep Neural Network(DNN)called OGSO-DNN model for distributed healthcare systems.The OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT devices.The selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare diagnosis.An extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among students.The proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%,specificity of 95.076%,the accuracy of 95.764%and F-score value of 96.888%.展开更多
This research paper analyzes revenue trends in e-commerce,a sector with an annual sales volume of more than 340 billion dollars.The article evaluates,despite a scarcity of data,the effects on e-commerce development of...This research paper analyzes revenue trends in e-commerce,a sector with an annual sales volume of more than 340 billion dollars.The article evaluates,despite a scarcity of data,the effects on e-commerce development of the ubiquitous lockdowns and restriction measures introduced by most countries during the pandemic period.The analysis covers monthly data from January 1996 to February 2021.The research paper analyzes relative changes in the original time series through the autocorrelation function.The objects of this analysis are Amazon and Alibaba,as they are benchmarks in the e-commerce industry.This paper tests the shock effect on the e-commerce companies Alibaba in China and Amazon in the USA,concluding that it is weaker for companies with small market capitalizations.As a result,the effect on estimated e-trade volume in the USA was approximately 35%in 2020.Another evaluation considers fuzzy decision-making methodology.For this purpose,balanced scorecard-based open financial innovation models for the e-commerce industry are weighted with multistepwise weight assessment ratio analysis based on q-rung orthopair fuzzy sets and the golden cut.Within this framework,a detailed analysis of competitors should be made.The paper proves that this situation positively affects the development of successful financial innovation models for the e-commerce industry.Therefore,it may be possible to attract greater attention from e-commerce companies for these financial innovation products.展开更多
Dear editor,As government restrictions put in place to slow the acceleration of the coronavirus disease-2019(COVID-19)pandemic start to ease,many people,including elite athletes,will begin to return back to their norm...Dear editor,As government restrictions put in place to slow the acceleration of the coronavirus disease-2019(COVID-19)pandemic start to ease,many people,including elite athletes,will begin to return back to their normal daily activities.Although the majority of risk factors for severe COVID-19-hypertension,respiratory system disease.展开更多
Garlic(Allium sativum) is a widely known medicinal plant, potential of which remains to be fully evaluated. Its wide-range beneficial effects appear to be relevant for treatment and prevention of atherosclerosis and r...Garlic(Allium sativum) is a widely known medicinal plant, potential of which remains to be fully evaluated. Its wide-range beneficial effects appear to be relevant for treatment and prevention of atherosclerosis and related diseases. It is generally believed that garlic-based preparations are able to improve lipid profile in humans, inhibit cholesterol biosynthesis, suppress low density lipoprotein oxidation, modulate blood pressure, suppress platelet aggregation, lower plasma fibrinogen level and increase fibrinolytic activity, thus providing clinically relevant cardioprotective and anti-atherosclerotic effects. It is important to assess the level of evidence available for different protective effects of garlic and to understand the underlying mechanisms. This information will allow adequate integration of garlic-based preparations to clinical practice. In this review, we discuss the mechanisms of anti-atherosclerotic effects of garlic preparations, focusing on antihyperlipidemic, hypotensive, anti-platelet and direct anti-atherosclerotic activities of the medicinal plant. We also provide an overview of available meta-analyses and a number of clinical trials that assess the beneficial effects of garlic.展开更多
文摘In present digital era,an exponential increase in Internet of Things(IoT)devices poses several design issues for business concerning security and privacy.Earlier studies indicate that the blockchain technology is found to be a significant solution to resolve the challenges of data security exist in IoT.In this view,this paper presents a new privacy-preserving Secure Ant Colony optimization with Multi Kernel Support Vector Machine(ACOMKSVM)with Elliptical Curve cryptosystem(ECC)for secure and reliable IoT data sharing.This program uses blockchain to ensure protection and integrity of some data while it has the technology to create secure ACOMKSVM training algorithms in partial views of IoT data,collected from various data providers.Then,ECC is used to create effective and accurate privacy that protects ACOMKSVM secure learning process.In this study,the authors deployed blockchain technique to create a secure and reliable data exchange platform across multiple data providers,where IoT data is encrypted and recorded in a distributed ledger.The security analysis showed that the specific data ensures confidentiality of critical data from each data provider and protects the parameters of the ACOMKSVM model for data analysts.To examine the performance of the proposed method,it is tested against two benchmark dataset such as Breast Cancer Wisconsin Data Set(BCWD)and Heart Disease Data Set(HDD)from UCI AI repository.The simulation outcome indicated that the ACOMKSVM model has outperformed all the compared methods under several aspects.
文摘After conducting a critical survey of the different categories of existing heat exchangers,the results of several experiments about the behaviour of a two-phase current in an open channel are reported.The results confirm the complexity of the problems induced in heat exchangers by flow maldistribution,especially when two-phase flows are considered in multi-channel systems.It is shown that severe misalignment of heat exchangers can lead to a loss of economic performance of more than 25%.Improper distribution of fluid flow causes longer fluid coils to form,and the liquid cochlea can eventually occupy a large space,thereby reducing heat transfer and disrupting the considered biphasic system.The use of a small diameter distribution pipe with properly spaced outlet holes seems to be a promising approach to fix many of these issues.It is found that the current distribution in the channels,in addition to the header pressure distribution,also depends on factors such as the geometry and the initial flow regime in the header.
基金supported by the Technology Development Program of MSS(No.S3033853).
文摘Recently,computation offloading has become an effective method for overcoming the constraint of a mobile device(MD)using computationintensivemobile and offloading delay-sensitive application tasks to the remote cloud-based data center.Smart city benefitted from offloading to edge point.Consider a mobile edge computing(MEC)network in multiple regions.They comprise N MDs and many access points,in which everyMDhasM independent real-time tasks.This study designs a new Task Offloading and Resource Allocation in IoT-based MEC using Deep Learning with Seagull Optimization(TORA-DLSGO)algorithm.The proposed TORA-DLSGO technique addresses the resource management issue in the MEC server,which enables an optimum offloading decision to minimize the system cost.In addition,an objective function is derived based on minimizing energy consumption subject to the latency requirements and restricted resources.The TORA-DLSGO technique uses the deep belief network(DBN)model for optimum offloading decision-making.Finally,the SGO algorithm is used for the parameter tuning of the DBN model.The simulation results exemplify that the TORA-DLSGO technique outperformed the existing model in reducing client overhead in the MEC systems with a maximum reward of 0.8967.
基金performed in accordance with the state task,state registration numbers AAAA-A19-119101690058-9 and АААА-А21-121011990037-8
文摘A theoretical and experimental study of the thermal decomposition of nitroguanidine(NQ) has been carried out. Various thermolysis channels were studied by quantum chemistry methods at the CCSD(or DLPNO-CCSD) level using the aug-cc-pVDZ basis set. It is shown that the lowest activation enthalpies(170-180kJ/mol) are characteristic of the reactions of NO2abstraction from the initial NQ and the reaction channel with the transfer of oxygen from the nitro group to carbon in the limiting stage. Additionally, the thermolysis of NQ was studied experimentally in a nonisothermal mode with heating rates from 1 to 10K/min. In these experiments, the weight loss of the sample, thermal effects, and mass spectra of the products were recorded. An analysis of the experimental data confirmed the results of a theoretical study of the mechanism of thermal decomposition of NQ. The main thermolysis products are N2O, HNCO, NH3, and NO2, which fully corresponds to quantum chemical calculations.
文摘The economics’ ecological modernization approach based on the input-output balance model is considered in the paper. The government measures on Russian Federation’s transition to green economy on reducing of greenhouse gas emissions are analyzed. In an article for green economy evaluation, including greenhouse gas emissions per capita and per unit of GDP, the indicator of the production environmental costs is proposed to include. The authors suppose adding and to modernize the Leontief-Ford model of input-output balance by economic evaluation of the environment pollution effects. This model is proposed to consider macro-economic assessment of environmental damage, health deterioration due to environmental pollution, as well as the use costs of the environmentally friendly technologies, the environmental and energy innovations’ implementation, climate change. The proposed modified model of environmentally oriented input-output balance can be used in the economic compensation system implementation on natural capital use and ecosystem services’ consuming in countries and their regions. The modified Leontief-Ford model proposed in the paper can be used for green economy development calculating, for example in Russia during the development of measures for the environment and economic development. Also this paper opens discussions for opportunities of the further possible integration of the theoretical models for environment protection decision-making.
文摘The scientific proof is the highest type of the rational proof. The mankind is looking for the best technology of the reasonable demonstration. What is a proof？. What is a scientific proof？. Philosophical investigations of proofs have the long history. Philosophy is exploring physics, mathematics, astronomy, biology, history, and so on. Science demands strict proofs. Science uses the specific methods as the optimum technologies for the achievement of the truth. Strictness of the proof depends on the aim algorithm： the distribution of the functions between parts of the proof. The beginning stage, the middle parts, and the ending stage are the unit of the proof. Philosophy can make the correct model of the scientific proof only! Science and its methodology develop and the growth of knowledge has not the finish. The rational ideals improve continually. Science is looking for the criterion of the demonstrative strictness. The adaptation algorithm of the scientific method is the best technology for the achievement of the truth.
文摘The technology of knowledge base remote design of the smart fuzzy controllers with the application of the"Soft/quantum computing optimizer"toolkit software developed.The possibility of the transmission and communication the knowledge base using remote connection to the control object considered.Transmission and communication of the fuzzy controller’s knowledge bases implemented through the remote connection with the control object in the online mode apply the Bluetooth or WiFi technologies.Remote transmission of knowledge bases allows designing many different built-in intelligent controllers to implement a variety of control strategies under conditions of uncertainty and risk.As examples,two different models of robots described(mobile manipulator and(“cart-pole”system)inverted pendulum).A comparison of the control quality between fuzzy controllers and quantum fuzzy controller in various control modes is presented.The ability to connect and work with a physical model of control object without using than mathematical model demonstrated.The implemented technology of knowledge base design sharing in a swarm of intelligent robots with quantum controllers.It allows to achieve the goal of control and to gain additional knowledge by creating a new quantum hidden information source based on the synergetic effect of combining knowledge.Development and implementation of intelligent robust controller’s prototype for the intelligent quantum control system of mega-science project NICA(at the first stage for the cooling system of superconducted magnets)is discussed.The results of the experiments demonstrate the possibility of the ensured achievement of the control goal of a group of robots using soft/quantum computing technologies in the design of knowledge bases of smart fuzzy controllers in quantum intelligent control systems.The developed software toolkit allows to design and setup complex ill-defined and weakly formalized technical systems on line.
文摘In current days,the domain of Internet of Things(IoT)and Wireless Sensor Networks(WSN)are combined for enhancing the sensor related data transmission in the forthcoming networking applications.Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks.In this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted WSN.The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and lifetime.The proposed model involves two stage processes namely clustering and routing.Firstly,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct clusters.Besides,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted WSN.The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,etc.The proposed EMOQoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH,PSO-ECHS,E-OEERP,and iCSHS methods have resulted in a lesser NAN of 2,10,42,and 51 rounds.The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.
文摘Nowadays,healthcare applications necessitate maximum volume of medical data to be fed to help the physicians,academicians,pathologists,doctors and other healthcare professionals.Advancements in the domain of Wireless Sensor Networks(WSN)andMultimediaWireless Sensor Networks(MWSN)are tremendous.M-WMSN is an advanced form of conventional Wireless Sensor Networks(WSN)to networks that use multimedia devices.When compared with traditional WSN,the quantity of data transmission in M-WMSN is significantly high due to the presence of multimedia content.Hence,clustering techniques are deployed to achieve low amount of energy utilization.The current research work aims at introducing a new Density Based Clustering(DBC)technique to achieve energy efficiency inWMSN.The DBC technique is mainly employed for data collection in healthcare environment which primarily depends on three input parameters namely remaining energy level,distance,and node centrality.In addition,two static data collector points called Super Cluster Head(SCH)are placed,which collects the data from normal CHs and forwards it to the Base Station(BS)directly.SCH supports multi-hop data transmission that assists in effectively balancing the available energy.Adetailed simulation analysiswas conducted to showcase the superior performance of DBC technique and the results were examined under diverse aspects.The simulation outcomes concluded that the proposed DBC technique improved the network lifetime to a maximum of 16,500 rounds,which is significantly higher compared to existing methods.
文摘The latest advancements in highway research domain and increase in the number of vehicles everyday led to wider exposure and attention towards the development of efficient Intelligent Transportation System(ITS).One of the popular research areas i.e.,Vehicle License Plate Recognition(VLPR)aims at determining the characters that exist in the license plate of the vehicles.The VLPR process is a difficult one due to the differences in viewpoint,shapes,colors,patterns,and non-uniform illumination at the time of capturing images.The current study develops a robust Deep Learning(DL)-based VLPR model using Squirrel Search Algorithm(SSA)-based Convolutional Neural Network(CNN),called the SSA-CNN model.The presented technique has a total of four major processes namely preprocessing,License Plate(LP)localization and detection,character segmentation,and recognition.Hough Transform(HT)is applied as a feature extractor and SSA-CNN algorithm is applied for character recognition in LP.The SSA-CNN method effectively recognizes the characters that exist in the segmented image by optimal tuning of CNN parameters.The HT-SSA-CNN model was experimentally validated using the Stanford Car,FZU Car,and HumAIn 2019 Challenge datasets.The experimentation outcome verified that the presented method was better under several aspects.The projected HT-SSA-CNN model implied the best performance with optimal overall accuracy of 0.983%.
文摘Wireless Sensor Network(WSN)comprises a massive number of arbitrarily placed sensor nodes that are linked wirelessly to monitor the physical parameters from the target region.As the nodes in WSN operate on inbuilt batteries,the energy depletion occurs after certain rounds of operation and thereby results in reduced network lifetime.To enhance energy efficiency and network longevity,clustering and routing techniques are commonly employed in WSN.This paper presents a novel black widow optimization(BWO)with improved ant colony optimization(IACO)algorithm(BWO-IACO)for cluster based routing in WSN.The proposed BWO-IACO algorithm involves BWO based clustering process to elect an optimal set of cluster heads(CHs).The BWO algorithm derives a fitness function(FF)using five input parameters like residual energy(RE),inter-cluster distance,intra-cluster distance,node degree(ND),and node centrality.In addition,IACO based routing process is involved for route selection in inter-cluster communication.The IACO algorithm incorporates the concepts of traditional ACO algorithm with krill herd algorithm(KHA).The IACO algorithm utilizes the energy factor to elect an optimal set of routes to BS in the network.The integration of BWO based clustering and IACO based routing techniques considerably helps to improve energy efficiency and network lifetime.The presented BWO-IACO algorithm has been simulated using MATLAB and the results are examined under varying aspects.A wide range of comparative analysis makes sure the betterment of the BWO-IACO algorithm over all the other compared techniques.
文摘Cloud computing offers internet location-based affordable,scalable,and independent services.Cloud computing is a promising and a cost-effective approach that supports big data analytics and advanced applications in the event of forced business continuity events,for instance,pandemic situations.To handle massive information,clusters of servers are required to assist the equipment which enables streamlining the widespread quantity of data,with elevated velocity and modified configurations.Data deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the server.Data deduplication also saves network bandwidth.In this paper,a new cloud-based big data security technique utilizing dual encryption is proposed.The clustering model is utilized to analyze the Deduplication process hash function.Multi kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption procedure.The confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is used.This security process involving dual encryption with the optimization model develops the productivity mechanism.In this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering techniques.The results proved that the proposed technique achieved maximum accuracy and minimum encryption time.
文摘Data fusion is one of the challenging issues,the healthcare sector is facing in the recent years.Proper diagnosis from digital imagery and treatment are deemed to be the right solution.Intracerebral Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for stroke.Images generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of hemorrhages.Radiologists use manual planimetry,a time-consuming process for segmenting CT scan images.Deep Learning(DL)is the most preferred method to increase the efficiency of diagnosing ICH.In this paper,the researcher presents a unique multi-modal data fusion-based feature extraction technique with Deep Learning(DL)model,abbreviated as FFE-DL for Intracranial Haemorrhage Detection and Classification,also known as FFEDL-ICH.The proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and classification.The input image is first preprocessed using the Gaussian Filtering(GF)technique to remove noise.Secondly,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the images.Furthermore,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful features.Finally,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of ICH.The researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic performance.The findings of the study revealed that the proposed FFEDL-ICH model has the ability to outperform existing models as there is a significant improvement in its performance.For future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.
文摘Wireless Sensor Networks(WSN)started gaining attention due to its wide application in the fields of data collection and information processing.The recent advancements in multimedia sensors demand the Quality of Service(QoS)be maintained up to certain standards.The restrictions and requirements in QoS management completely depend upon the nature of target application.Some of the major QoS parameters in WSN are energy efficiency,network lifetime,delay and throughput.In this scenario,clustering and routing are considered as the most effective techniques to meet the demands of QoS.Since they are treated as NP(Non-deterministic Polynomial-time)hard problem,Swarm Intelligence(SI)techniques can be implemented.The current research work introduces a new QoS aware Clustering and Routing-based technique using Swarm Intelligence(QoSCRSI)algorithm.The proposed QoSCRSI technique performs two-level clustering and proficient routing.Initially,the fuzzy is hybridized with Glowworm Swarm Optimization(GSO)-based clustering(HFGSOC)technique for optimal selection of Cluster Heads(CHs).Here,Quantum Salp Swarm optimization Algorithm(QSSA)-based routing technique(QSSAR)is utilized to select the possible routes in the network.In order to evaluate the performance of the proposed QoSCRSI technique,the authors conducted extensive simulation analysis with varying node counts.The experimental outcomes,obtained from the proposed QoSCRSI technique,apparently proved that the technique is better compared to other state-of-the-art techniques in terms of energy efficiency,network lifetime,overhead,throughput,and delay.
文摘Internet of Things(IoT)paves a new direction in the domain of smart farming and precision agriculture.Smart farming is an upgraded version of agriculture which is aimed at improving the cultivation practices and yield to a certain extent.In smart farming,IoT devices are linked among one another with new technologies to improve the agricultural practices.Smart farming makes use of IoT devices and contributes in effective decision making.Rice is the major food source in most of the countries.So,it becomes inevitable to detect rice plant diseases during early stages with the help of automated tools and IoT devices.The development and application of Deep Learning(DL)models in agriculture offers a way for early detection of rice diseases and increase the yield and profit.This study presents a new Convolutional Neural Network-based inception with ResNset v2 model and Optimal Weighted Extreme Learning Machine(CNNIR-OWELM)-based rice plant disease diagnosis and classification model in smart farming environment.The proposed CNNIR-OWELM method involves a set of IoT devices which capture the images of rice plants and transmit it to cloud server via internet.The CNNIROWELM method uses histogram segmentation technique to determine the affected regions in rice plant image.In addition,a DL-based inception with ResNet v2 model is engaged to extract the features.Besides,in OWELM,the Weighted Extreme Learning Machine(WELM),optimized by Flower Pollination Algorithm(FPA),is employed for classification purpose.The FPA is incorporated into WELM to determine the optimal parameters such as regularization coefficient C and kernelγ.The outcome of the presented model was validated against a benchmark image dataset and the results were compared with one another.The simulation results inferred that the presented model effectively diagnosed the disease with high sensitivity of 0.905,specificity of 0.961,and accuracy of 0.942.
文摘Big data streams started becoming ubiquitous in recent years,thanks to rapid generation of massive volumes of data by different applications.It is challenging to apply existing data mining tools and techniques directly in these big data streams.At the same time,streaming data from several applications results in two major problems such as class imbalance and concept drift.The current research paper presents a new Multi-Objective Metaheuristic Optimization-based Big Data Analytics with Concept Drift Detection(MOMBD-CDD)method on High-Dimensional Streaming Data.The presented MOMBD-CDD model has different operational stages such as pre-processing,CDD,and classification.MOMBD-CDD model overcomes class imbalance problem by Synthetic Minority Over-sampling Technique(SMOTE).In order to determine the oversampling rates and neighboring point values of SMOTE,Glowworm Swarm Optimization(GSO)algorithm is employed.Besides,Statistical Test of Equal Proportions(STEPD),a CDD technique is also utilized.Finally,Bidirectional Long Short-Term Memory(Bi-LSTM)model is applied for classification.In order to improve classification performance and to compute the optimum parameters for Bi-LSTM model,GSO-based hyperparameter tuning process is carried out.The performance of the presented model was evaluated using high dimensional benchmark streaming datasets namely intrusion detection(NSL KDDCup)dataset and ECUE spam dataset.An extensive experimental validation process confirmed the effective outcome of MOMBD-CDD model.The proposed model attained high accuracy of 97.45%and 94.23%on the applied KDDCup99 Dataset and ECUE Spam datasets respectively.
文摘Recent developments in information technology can be attributed to the development of smart cities which act as a key enabler for next-generation intelligent systems to improve security,reliability,and efficiency.The healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service quality.The futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing environment.The energy consumed during data communication from IoT devices to cloud server is significantly high and it needs to be reduced with the help of clustering techniques.The current research article presents a new Oppositional Glowworm Swarm Optimization(OGSO)algorithmbased clustering with Deep Neural Network(DNN)called OGSO-DNN model for distributed healthcare systems.The OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT devices.The selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare diagnosis.An extensive simulation analysis was carried out utilizing a student perspective healthcare data generated from UCI repository and IoT devices to forecast the severity level of the disease among students.The proposed OGSO-DNN model outperformed previous methods by attaining the maximum average sensitivity of 96.956%,specificity of 95.076%,the accuracy of 95.764%and F-score value of 96.888%.
文摘This research paper analyzes revenue trends in e-commerce,a sector with an annual sales volume of more than 340 billion dollars.The article evaluates,despite a scarcity of data,the effects on e-commerce development of the ubiquitous lockdowns and restriction measures introduced by most countries during the pandemic period.The analysis covers monthly data from January 1996 to February 2021.The research paper analyzes relative changes in the original time series through the autocorrelation function.The objects of this analysis are Amazon and Alibaba,as they are benchmarks in the e-commerce industry.This paper tests the shock effect on the e-commerce companies Alibaba in China and Amazon in the USA,concluding that it is weaker for companies with small market capitalizations.As a result,the effect on estimated e-trade volume in the USA was approximately 35%in 2020.Another evaluation considers fuzzy decision-making methodology.For this purpose,balanced scorecard-based open financial innovation models for the e-commerce industry are weighted with multistepwise weight assessment ratio analysis based on q-rung orthopair fuzzy sets and the golden cut.Within this framework,a detailed analysis of competitors should be made.The paper proves that this situation positively affects the development of successful financial innovation models for the e-commerce industry.Therefore,it may be possible to attract greater attention from e-commerce companies for these financial innovation products.
文摘Dear editor,As government restrictions put in place to slow the acceleration of the coronavirus disease-2019(COVID-19)pandemic start to ease,many people,including elite athletes,will begin to return back to their normal daily activities.Although the majority of risk factors for severe COVID-19-hypertension,respiratory system disease.
基金supported by the Russian Science Foundation(No.19-15-00010)
文摘Garlic(Allium sativum) is a widely known medicinal plant, potential of which remains to be fully evaluated. Its wide-range beneficial effects appear to be relevant for treatment and prevention of atherosclerosis and related diseases. It is generally believed that garlic-based preparations are able to improve lipid profile in humans, inhibit cholesterol biosynthesis, suppress low density lipoprotein oxidation, modulate blood pressure, suppress platelet aggregation, lower plasma fibrinogen level and increase fibrinolytic activity, thus providing clinically relevant cardioprotective and anti-atherosclerotic effects. It is important to assess the level of evidence available for different protective effects of garlic and to understand the underlying mechanisms. This information will allow adequate integration of garlic-based preparations to clinical practice. In this review, we discuss the mechanisms of anti-atherosclerotic effects of garlic preparations, focusing on antihyperlipidemic, hypotensive, anti-platelet and direct anti-atherosclerotic activities of the medicinal plant. We also provide an overview of available meta-analyses and a number of clinical trials that assess the beneficial effects of garlic.
