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A pilot survey on antimicrobial activity and diversity of soil-derived actinobacteria from different depths in Gudzhirganskoe saline lake in Eastern Siberia

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Journal of Oceanology and Limnology 2023年第4期41卷 1307-1320页

作者： Qin YANG Puyu GUO Elena Y.ABIDUEvA Elena v.LAvRENTYEvA irina M.LISEvICH Ilya A.OSTERMAN Petr v.SERGIEv Shaowei LIU Chunmei XUE Chenghang SUN Department of Microbial Chemistry Institute of Medicinal BiotechnologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100050China Beijing Key Laboratory of Antimicrobial Agents Institute of Medicinal BiotechnologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijing 100050China College of Life Sciences Jiamusi UniversityJiamusi 154000China Institute of General and Experimental Biology Siberian BranchRussian Academy of SciencesUlan-Ude 670047Russia Center of Life Sciences Skolkovo Institute of Science and TechnologyMoscow 101135Russia Department of Chemistry A.N.Belozersky Institute of Physico-Chemical BiologyLomonosov Moscow State UniversityMoscow 101135Russia

To explore pharmaceutical actinobacteria from salty environments in Siberia,8 soil samples were collected from different depths of Gudzhirganskoe saline lake,the Republic of Buryatia,***,635 actinobacterial strains affiliated with 21 genera in 12 families of 7 orders were obtained by culture-dependent *** predominant genus was Streptomyces(74.5%),followed by Microbacterium(4.9%),Agromyces(3.5%),Nocardiopsis(3.0%),and Kitasatospora(2.5%).Seven strains showed relatively low 16 S rRNA similarities(validly described ***-seven strains were selected as representatives to evaluate their antimicrobial activities and underlying mechanisms by paper-disk diffusion method and a double fluorescent protein reporter system(pDualrep 2),*** sp.S 6 b 3-1 was highlighted due to its strong inhibitory activities against Gram-positive bacteria and underlying antibacterial mechanism by inducing the SOS response in the reporter ***-1,(R)-6-[1-(benzoyloxy)ethyl]-phenazine-1-carboxylic acid,an analogue of saphenamycin was finally isolated from the culture broth of Streptomyces *** study preliminarily explored the actinobacterial diversity of soil samples collected in the highly alkaline and saline lake Gudzhirganskoe by culture-dependent approach and their capacity to produce antibiotics for the first time,which revealed that it deserves to make more efforts to discover new actinobacterial species and potential new antibiotics from the saline lake in Eastern Siberia.

关键词： Gudzhirganskoe saline lake actinobacteria diversity antimicrobial activities phenazine antibiotic

Deep Learning Based Intelligent and Sustainable Smart Healthcare Application in Cloud-Centric IoT

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Computers, Materials & Continua 2021年第2期66卷 1987-2003页

作者： K.v.Praveen P.M.Joe Prathap S.Dhanasekaran I.S.Hephzi Punithavathi P.Duraipandy irina v.Pustokhina Denis A.Pustokhin Department of Information Technology St.Peters College of Engineering and TechnologyChennai600054India Department of Information Technology RMD Engineering CollegeChennai601206India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Computer Science and Engineering Sphoorthy Engineering CollegeHyderabad501510India Department of Electrical and Electronics Engineering J B Institute of Engineering and TechnologyHyderabad500075India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

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 *** healthcare sector becomes advantageous and offers different ways to manage patient information in order to improve healthcare service *** futuristic sustainable computing solutions in e-healthcare applications depend upon Internet of Things(IoT)in cloud computing *** 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 *** 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 *** OGSO algorithm was applied in this study to select the Cluster Heads(CHs)from the available IoT *** selected CHs transmit the data to cloud server,which then executes DNN-based classification process for healthcare *** 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 *** 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%.

关键词： IoT devices healthcare deep learning energy efficiency glowworm swarm optimization

An Intelligent Deep Learning Based Xception Model for Hyperspectral Image Analysis and Classification

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Computers, Materials & Continua 2021年第5期67卷 2393-2407页

作者： J.Banumathi A.Muthumari S.Dhanasekaran S.Rajasekaran irina v.Pustokhina Denis A.Pustokhin K.Shankar Department of Information Technology University College of EngineeringNagercoil629004India Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Computer Science and Engineering Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Electrical Engineering PSN College of EngineeringTirunelveli627152India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Due to the advancements in remote sensing technologies,the generation of hyperspectral imagery(HSI)gets significantly *** classification of HSI becomes a critical process in the domain of hyperspectral data *** massive availability of spectral and spatial details of HSI has offered a great opportunity to efficiently illustrate and recognize ground ***,deep learning(DL)models particularly,convolutional neural networks(CNNs)become useful for HSI classification owing to the effective feature representation and high *** this view,this paper introduces a new DL based Xception model for HSI analysis and classification,called Xcep-HSIC ***,the presented model utilizes a feature relation map learning(FRML)to identify the relationship among the hyperspectral features and explore many features for improved classifier ***,the DL based Xception model is applied as a feature extractor to derive a useful set of features from the FRML *** addition,kernel extreme learning machine(KELM)optimized by quantum-behaved particle swarm optimization(QPSO)is employed as a classification model,to identify the different set of class *** extensive set of simulations takes place on two benchmarks HSI dataset,namely Indian Pines and Pavia University *** obtained results ensured the effective performance of the XcepHSIC technique over the existing methods by attaining a maximum accuracy of 94.32%and 92.67%on the applied India Pines and Pavia University dataset respectively.

关键词： Hyperspectral imagery deep learning xception kernel extreme learning map parameter tuning

High Security for De-Duplicated Big Data Using Optimal SIMON Cipher

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Computers, Materials & Continua 2021年第5期67卷 1863-1879页

作者： A.Muthumari J.Banumathi S.Rajasekaran P.vijayakarthik K.Shankar irina v.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering University College of EngineeringRamanathapuram623513India Department of Information Technology University College of EngineeringNagercoil629004India Department of EEE PSN College of Engineering(Anna University)Tirunelveli627152India Department of Information Science and Engineering Sir M.Visvesvaraya Institute of TechnologyBangalore56215India Department of Computer Applications Alagappa UniversityKaraikudi63003India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Cloud computing offers internet location-based affordable,scalable,and independent *** 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 *** 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 *** deduplication model enables cloud users to efficiently manage their cloud storage space by getting rid of redundant data stored in the *** deduplication also saves network *** this paper,a new cloud-based big data security technique utilizing dual encryption is *** clustering model is utilized to analyze the Deduplication process hash *** kernel Fuzzy C means(MKFCM)was used which helps cluster the data stored in cloud,on the basis of confidence data encryption *** confidence finest data is implemented in homomorphic encryption data wherein the Optimal SIMON Cipher(OSC)technique is *** security process involving dual encryption with the optimization model develops the productivity *** this paper,the excellence of the technique was confirmed by comparing the proposed technique with other encryption and clustering *** results proved that the proposed technique achieved maximum accuracy and minimum encryption time.

关键词： Cloud security deduplication clustering optimization big data dual encryption

Photoluminescence of Ag-In-S/ZnS quantum dots: Excitation energy dependence and low-energy electronic structure

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Nano Research 2019年第7期12卷 1595-1603页

作者： irina v. Martynenko Anvar S. Baimuratov Florian Weigert Jose X. Soares Lorena Dhamo Philip Nickl Ilona Doerfel Jutta Pauli Ivan D. Rukhlenko Alexander v. Baranov Ute Resch-Genger Federal Institute for Materials Research and Testing (BAM) Unter den Eichen 8712205 BerlinGermany ITMO University 49 Kronverksky Pr.St. Petersburg197101Russia University of Porto Rua de Jorge Viterbo Ferreira2284050-313 PortoPortugal Institute of Photonics and Optical Sciences (IPOS) School of PhysicsThe University of SydneyCamperdown 2006NSWAustralia

Cd-free Ⅰ-Ⅲ-Ⅵ group semiconductor quantum dots (QDs) like Ag-ln-S and Cu-ln-S show unstructured absorption spectra with a pronounced Urbach tail,rendering the determination of their band gap energy (Eg) and the energy structure of the exciton ***,the origin of the broad photoluminescence (PL) band with lifetimes of several hundred nanoseconds is still *** encouraged us to study the excitation energy dependence (EED) of the PL maxima,PL spectral band widths,quantum yields (QYs),and decay kinetics of AlS/ZnS QDs of j different size,composition,and surface capping *** results were then correlated with the second derivatives of the corresponding absorption *** excellent match between the onset of changes in PL band position and spectral width with the minima found for the second derivatives of the absorption spectra underlines the potential of the EED approach for deriving Eg values of these ternary QDs from PL *** PL QY is,however,independent of excitation energy in the energy range *** the EED of the PL features of the AlS/ZnS QDs we could also derive a mechanism of the formation of the low-energy electronic *** was additionally confirmed by a comparison of the EED of PL data of as-synthesized and size-selected QD ensembles and the comparison of these PL data with PL spectra of single *** results indicate a strong contribution of intrinsic inhomogeneous PL broadening to the overall emission features of AlS/ZnS QDs originating from radiative transitions from a set of energy states of defects localized at different positions within the quantum dot volume,in addition to contributions from dimensional and chemical *** mechanism was confirmed by numerically modelling the absorption and PL energies with a simple mass approximation for spherical QDs and a modified donor-acceptor model,thereby utilizing the advantages of previously proposed PL mechanisms of ternary *** findings will

关键词： core/shell quantum dot silver indium sulfide defect photoluminescence photoluminescence quantum yield single-dot spectroscopy

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

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Computers, Materials & Continua 2022年第5期71卷 3785-3801页

作者： Ashit Kumar Dutta S.Srinivasan Bobbili Prasada Rao B.Hemalatha irina v.Pustokhina Denis A.Pustokhin Gyanendra Prasad Joshi Department of Computer Science and Information Systems College of Applied SciencesAlmaarefa UniversityRiyadh13713Kingdom of Saudi Arabia Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesSaveetha NagarThandalamChennai602105India Department of Electrical and Electronics Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Electronics and Communication Engineering Thirumalai Engineering CollegeKancheepuram631551India Department of Entrepreneurship and Logistics Plekhanov Russian University of Economics117997MoscowRussia Department of Logistics State University of Management109542MoscowRussia Department of Computer Science and Engineering Sejong UniversitySeoul05006Korea

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 *** 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 *** this view,this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing(EMOQoSCMR)Protocol for IoT assisted *** proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy,throughput,delay,and *** proposed model involves two stage processes namely clustering and ***,the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering(CEROAC)technique to select an optimal set of cluster heads(CHs)and construct ***,oppositional chaos game optimization based routing(OCGOR)technique is employed for the optimal set of routes in the IoT assisted *** proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy,distance to sink node,*** 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 *** performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

关键词： Internet of things wireless sensor networks clustering routing metaheuristics cluster head selection QoS parameters

A Novel Deep Neural Network for Intracranial Haemorrhage Detection and Classification

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Computers, Materials & Continua 2021年第9期68卷 2877-2893页

作者： D.venugopal T.Jayasankar Mohamed Yacin Sikkandar Mohamed Ibrahim Waly irina v.Pustokhina Denis A.Pustokhin K.Shankar Department of ECE KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Electronics and Communication Engineering University College of EngineeringBIT CampusAnna UniversityTiruchirappalli620024India Department of Medical Equipment Technology College of Applied Medical SciencesMajmaah UniversityAl Majmaah11952Saudi Arabia Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia Department of Computer Applications Alagappa UniversityKaraikudi630001India

Data fusion is one of the challenging issues,the healthcare sector is facing in the recent *** diagnosis from digital imagery and treatment are deemed to be the right *** Haemorrhage(ICH),a condition characterized by injury of blood vessels in brain tissues,is one of the important reasons for *** generated by X-rays and Computed Tomography(CT)are widely used for estimating the size and location of *** use manual planimetry,a time-consuming process for segmenting CT scan *** Learning(DL)is the most preferred method to increase the efficiency of diagnosing *** 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 *** proposed FFEDL-ICH model has four stages namely,preprocessing,image segmentation,feature extraction,and *** input image is first preprocessed using the Gaussian Filtering(GF)technique to remove ***,the Density-based Fuzzy C-Means(DFCM)algorithm is used to segment the ***,the Fusion-based Feature Extraction model is implemented with handcrafted feature(Local Binary Patterns)and deep features(Residual Network-152)to extract useful ***,Deep Neural Network(DNN)is implemented as a classification technique to differentiate multiple classes of *** researchers,in the current study,used benchmark Intracranial Haemorrhage dataset and simulated the FFEDL-ICH model to assess its diagnostic *** 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 *** future researches,the researcher recommends the performance improvement of FFEDL-ICH model using learning rate scheduling techniques for DNN.

关键词： Intracerebral hemorrhage fusion model feature extraction deep features classification

AModified Search and Rescue Optimization Based Node Localization Technique inWSN

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Computers, Materials & Continua 2022年第1期70卷 1229-1245页

作者： Suma Sira Jacob K.Muthumayil M.Kavitha Lijo Jacob varghese M.Ilayaraja irina v.Pustokhina Denis A.Pustokhin Department of Computer Science and Engineering Christian College of Engineering and TechnologyOddanchatram624619India Department of Information Technology PSNA College of Engineering and TechnologyDindigul624622India Department of Electronics and Communication Engineering K.Ramakrishnan College of TechnologyTiruchirappalli621112India Department of Electrical and Electronics Engineering Christian College of Engineering and TechnologyOddanchatram624619India School of Computing Kalasalingam Academy of Research and EducationKrishnankoil626128India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

Wireless sensor network(WSN)is an emerging technology which find useful in several application areas such as healthcare,environmentalmonitoring,border surveillance,*** issues that exist in the designing of WSN are node localization,coverage,energy efficiency,security,and so *** spite of the issues,node localization is considered an important issue,which intends to calculate the coordinate points of unknown nodes with the assistance of *** efficiency of the WSN can be considerably influenced by the node localization ***,this paper presents a modified search and rescue optimization based node localization technique(MSRONLT)*** major aim of theMSRO-NLT technique is to determine the positioning of the unknown nodes in *** the traditional search and rescue optimization(SRO)algorithm suffers from the local optima problemwith an increase in number of iterations,MSRO algorithm is developed by the incorporation of chaotic maps to improvise the diversity of the *** application of the concept of chaotic map to the characteristics of the traditional SRO algorithm helps to achieve better exploration ability of the MSRO *** order to validate the effective node localization performance of the MSRO-NLT algorithm,a set of simulations were performed to highlight the supremacy of the presented model.A detailed comparative results analysis showcased the betterment of the MSRO-NLT technique over the other compared methods in terms of different measures.

关键词： Node localization WSN chaotic map search and rescue optimization algorithm localization error

AI Based Traffic Flow Prediction Model for Connected and Autonomous Electric vehicles

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Computers, Materials & Continua 2022年第2期70卷 3333-3347页

作者： P.Thamizhazhagan M.Sujatha S.Umadevi K.Priyadarshini velmurugan Subbiah Parvathy irina v.Pustokhina Denis A.Pustokhin Department of Electrical and Electronics Engineering University College of EngineeringPanruti607106India Department of Electronics and Communication Engineering Koneru Lakshmaiah Education FoundationVijayawada522502India Centre for Nanoelectronics and vLSI Design SENSEVIT UniversityChennai CampusChennai600127India Department of Electronics and Communication Engineering K.Ramakrishnan College of EngineeringTiruchirappalli621112India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and EducationKrishnankoil626126India Department of Entrepreneurship and Logistics Plekhanov Russian University of EconomicsMoscow117997Russia Department of Logistics State University of ManagementMoscow109542Russia

There is a paradigm shift happening in automotive industry towards electric vehicles as environment and sustainability issues gainedmomentum in the recent years among potential *** and Autonomous Electric vehicle(CAEv)technologies are fascinating the automakers and inducing them to manufacture connected autonomous vehicles with self-driving features such as autopilot and ***,Traffic Flow Prediction(TFP)is identified as a major issue in CAEv technologies which needs to be addressed with the help of Deep Learning(DL)*** this view,the current research paper presents an artificial intelligence-based parallel autoencoder for TFP,abbreviated as AIPAE-TFP model in *** presented model involves two major processes namely,feature engineering and *** feature engineering process,there are multiple stages involved such as feature construction,feature selection,and feature *** addition to the above,a Support vector Data Description(SvDD)model is also used in the filtration of anomaly points and smoothen the raw ***,AIPAE model is applied to determine the predictive values of traffic *** order to illustrate the proficiency of the model’s predictive outcomes,a set of simulations was performed and the results were investigated under distinct *** experimentation outcomes verified the effectual performance of the proposed AIPAE-TFP model over other methods.

关键词： Autonomous electric vehicle traffic flow predictive automation industry connected vehicles seep learning