IoT Task Offloading in Edge Computing Using Non-Cooperative Game Theory for Healthcare Systems

In this paper,we present a comprehensive system model for Industrial Internet of Things(IIoT)networks empowered by Non-Orthogonal Multiple Access(NOMA)and Mobile Edge Computing(MEC)technologies.The network comprises essential components such as base stations,edge servers,and numerous IIoT devices characterized by limited energy and computing capacities.The central challenge addressed is the optimization of resource allocation and task distribution while adhering to stringent queueing delay constraints and minimizing overall energy consumption.The system operates in discrete time slots and employs a quasi-static approach,with a specific focus on the complexities of task partitioning and the management of constrained resources within the IIoT context.This study makes valuable contributions to the field by enhancing the understanding of resourceefficient management and task allocation,particularly relevant in real-time industrial applications.Experimental results indicate that our proposed algorithmsignificantly outperforms existing approaches,reducing queue backlog by 45.32% and 17.25% compared to SMRA and ACRA while achieving a 27.31% and 74.12% improvement in Qn O.Moreover,the algorithmeffectively balances complexity and network performance,as demonstratedwhen reducing the number of devices in each group(Ng)from 200 to 50,resulting in a 97.21% reduction in complexity with only a 7.35% increase in energy consumption.This research offers a practical solution for optimizing IIoT networks in real-time industrial settings.

A perspective review on the biosynthesis of plant-based secondary metabolites and their application as potent drugs

Many phytochemicals and their derived metabolites produced by plants are extensively employed in commercial goods,pharmaceutical products as well as in the environmental and medicalfields.However,these secondary metabolites obtained from plants are in low amounts,and it is difficult to synthesize them at the industrial level.Despite these challenges,they may be utilized for a variety of medicinal products that are either available in the market or are being researched and tested.Secondary metabolites are complex compounds that exhibit chirality.Further,under controlled conditions with elicitors,desired secondary metabolites may be produced from plant cell cultures.This review emphasizes the various aspects of secondary metabolites including their types,synthesis,and applications as medicinal products.The article aims to promote the use of plant secondary metabolites in the management and treatment of various diseases.

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Twitter is a radiant platform with a quick and effective technique to analyze users’perceptions of activities on social media.Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group.The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools.An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine(SVM).This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare,behaviour estimation,etc.In addition,the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive,negative and neutral tweets.In this work,we obligated Twitter Application Programming Interface(API)account and programmed in python for sentiment analysis approach for the computational measure of user’s perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor.To distinguish the results in terms of the performance evaluation,an error analysis investigates the features of various stakeholders comprising social media analytics researchers,Natural Language Processing(NLP)developers,engineering managers and experts involved to have a decision-making approach.

Holt-Winters Algorithm to Predict the Stock Value Using Recurrent Neural Network

Prediction of stock market value is highly risky because it is based on the concept of Time Series forecasting system that can be used for investments in a safe environment with minimized chances of loss.The proposed model uses a real time dataset offifteen Stocks as input into the system and based on the data,predicts or forecast future stock prices of different companies belonging to different sectors.The dataset includes approximatelyfifteen companies from different sectors and forecasts their results based on which the user can decide whether to invest in the particular company or not;the forecasting is done for the next quarter.Our model uses 3 main concepts for forecasting results.Thefirst one is for stocks that show periodic change throughout the season,the‘Holt-Winters Triple Exponential Smoothing’.3 basic things taken into conclusion by this algorithm are Base Level,Trend Level and Seasoning Factor.The value of all these are calculated by us and then decomposition of all these factors is done by the Holt-Winters Algorithm.The second concept is‘Recurrent Neural Network’.The specific model of recurrent neural network that is being used is Long-Short Term Memory and it’s the same as the Normal Neural Network,the only difference is that each intermediate cell is a memory cell and retails its value till the next feedback loop.The third concept is Recommendation System whichfilters and predict the rating based on the different factors.

Secured ECG Signal Transmission Using Optimized EGC with Chaotic Neural Network in WBSN

In wireless body sensor network(WBSN),the set of electrocardiogram(ECG)data which is collected from sensor nodes and transmitted to the server remotely supports the experts to monitor the health of a patient.While transmit-ting these collected data some adversaries may capture and misuse it due to the compromise of security.So,the major aim of this work is to enhance secure trans-mission of ECG signal in WBSN.To attain this goal,we present Pity Beetle Swarm Optimization Algorithm(PBOA)based Elliptic Galois Cryptography(EGC)with Chaotic Neural Network.To optimize the key generation process in Elliptic Curve Cryptography(ECC)over Galoisfield or EGC,private key is chosen optimally using PBOA algorithm.Then the encryption process is enhanced by presenting chaotic neural network which is used to generate chaotic sequences or cipher data.Results of this work show that the proposed cryptogra-phy algorithm attains better encryption time,decryption time,throughput and SNR than the conventional cryptography algorithms.

Redundant Transmission Control Algorithm for Information-Centric Vehicular IoT Networks

Vehicular Adhoc Networks(VANETs)enable vehicles to act as mobile nodes that can fetch,share,and disseminate information about vehicle safety,emergency events,warning messages,and passenger infotainment.However,the continuous dissemination of information fromvehicles and their one-hop neighbor nodes,Road Side Units(RSUs),and VANET infrastructures can lead to performance degradation of VANETs in the existing hostcentric IP-based network.Therefore,Information Centric Networks(ICN)are being explored as an alternative architecture for vehicular communication to achieve robust content distribution in highly mobile,dynamic,and errorprone domains.In ICN-based Vehicular-IoT networks,consumer mobility is implicitly supported,but producer mobility may result in redundant data transmission and caching inefficiency at intermediate vehicular nodes.This paper proposes an efficient redundant transmission control algorithm based on network coding to reduce data redundancy and accelerate the efficiency of information dissemination.The proposed protocol,called Network Cording Multiple Solutions Scheduling(NCMSS),is receiver-driven collaborative scheduling between requesters and information sources that uses a global parameter expectation deadline to effectively manage the transmission of encoded data packets and control the selection of information sources.Experimental results for the proposed NCMSS protocol is demonstrated to analyze the performance of ICN-vehicular-IoT networks in terms of caching,data retrieval delay,and end-to-end application throughput.The end-to-end throughput in proposed NCMSS is 22%higher(for 1024 byte data)than existing solutions whereas delay in NCMSS is reduced by 5%in comparison with existing solutions.

A Pattern Classification Model for Vowel Data Using Fuzzy Nearest Neighbor

Classification of the patterns is a crucial structure of research and applications. Using fuzzy set theory, classifying the patterns has become of great interest because of its ability to understand the parameters. One of the problemsobserved in the fuzzification of an unknown pattern is that importance is givenonly to the known patterns but not to their features. In contrast, features of thepatterns play an essential role when their respective patterns overlap. In this paper,an optimal fuzzy nearest neighbor model has been introduced in which a fuzzifi-cation process has been carried out for the unknown pattern using k nearest neighbor. With the help of the fuzzification process, the membership matrix has beenformed. In this membership matrix, fuzzification has been carried out of the features of the unknown pattern. Classification results are verified on a completelyllabelled Telugu vowel data set, and the accuracy is compared with the differentmodels and the fuzzy k nearest neighbor algorithm. The proposed model gives84.86% accuracy on 50% training data set and 89.35% accuracy on 80% trainingdata set. The proposed classifier learns well enough with a small amount of training data, resulting in an efficient and faster approach.

Harnessing Blockchain to Address Plasma Donation Network Challenges

Plasma therapy is an extensively used treatment for critically unwell patients.For this procedure,a legitimate plasma donor who can continue to supply plasma after healing is needed.However,significant dangers are associated with supply management,such as the ambiguous provenance of plasma and the spread of infected or subpar blood into medicinal fabrication.Also,from an ideological standpoint,less powerful people may be exploited throughout the contribution process.Moreover,there is a danger to the logistics system because there are now just some plasma shippers.This research intends to investigate the blockchain-based solution for blood plasma to facilitate authentic plasma transfer.Blockchain parameters,including electronic identification,chain code,and certified ledgers,have the potential to exert a substantial,profound influence on the distribution and implementation process of blood banks.To understand the practical ramifications of blockchain,the current study provides a proof of concept approach that aims to simulate the procedural code of modern plasma distribution ecosystems using a blockchain-based architecture.The agent-based modeling used in the testing and evaluation mimics the supply chain to assess the blockchain’s feasibility,advantages,and constraints for the plasma.

FSE2R:An Improved Collision-Avoidance-based Energy Efficient Route Selection Protocolin USN

The 3D Underwater Sensor Network(USNs)has become the most optimistic medium for tracking and monitoring underwater environment.Energy and collision are two most critical factors in USNs for both sparse and dense regions.Due to harsh ocean environment,it is a challenge to design a reliable energy efficient with collision free protocol.Diversity in link qualities may cause collision and frequent communication lead to energy loss;that effects the network performance.To overcome these challenges a novel protocol Forwarder Selection Energy Efficient Routing(FSE2R)is proposed.Our proposal’s key idea is based on computation of node distance from the sink,Residual Energy(RE)of each node and Signal to Interference Noise Ratio(SINR).The node distance from sink and RE is computed for reliable forwarder node selection and SINR is used for analysis of collision.The novel proposal compares with existing protocols like H2AB,DEEP,and E2LR to achieve Quality of Service(QoS)in terms of through-put,packet delivery ratio and energy consumption.The comparative analysis shows that FSE2R gives on an average 30%less energy consumption,24.62%better PDR and 48.31%less end-to-end delay compared to other protocols.

A Component Selection Framework of Cohesion and Coupling Metrics

Component-based software engineering is concerned with the develop-ment of software that can satisfy the customer prerequisites through reuse or inde-pendent development.Coupling and cohesion measurements are primarily used to analyse the better software design quality,increase the reliability and reduced system software complexity.The complexity measurement of cohesion and coupling component to analyze the relationship between the component module.In this paper,proposed the component selection framework of Hexa-oval optimization algorithm for selecting the suitable components from the repository.It measures the interface density modules of coupling and cohesion in a modular software sys-tem.This cohesion measurement has been taken into two parameters for analyz-ing the result of complexity,with the help of low cohesion and high cohesion.In coupling measures between the component of inside parameters and outside parameters.Thefinal process of coupling and cohesion,the measured values were used for the average calculation of components parameter.This paper measures the complexity of direct and indirect interaction among the component as well as the proposed algorithm selecting the optimal component for the repository.The better result is observed for high cohesion and low coupling in compo-nent-based software engineering.

Mechanical and wear behaviour of AZ91D magnesium matrix hybrid composite reinforced with boron carbide and graphite

In this experimental study,magnesium(AZ91D)based boron carbide(B4C)and graphite(Gr)particle reinforced hybrid composite materials were manufactured by stir casting.The tribological and mechanical properties of these composite materials were investigated.The results of the tests revealed that the graphite reinforced hybrid composites exhibited a lower wear loss compared to the unreinforced AZ91D alloy and AZ91D–B4C composites.It was found that with an increase in the B4C content,the wear resistance increased monotonically with hardness and ultimate tensile strength decreased.This study revealed that the addition of both a hard reinforcement(e.g.,B4C)and soft reinforcement(e.g.,graphite)significantly improves the wear resistance of magnesium composites.These entire results designate that the hybrid magnesium composites can be considered as an excellent material where high strength,ultimate tensile strength and wear-resistant components are of major importance,primarily in the aerospace and automotive engineering sectors.

Heat transfer and parametric studies of an encapsulated phase change material based cool thermal energy storage system

This work investigates the transient behaviour of a phase change material based cool thermal energy storage (CTES) system comprised of a cylindrical storage tank filled with encapsulated phase change materials (PCMs) in spherical container integrated with an ethylene glycol chiller plant. A simulation program was developed to evaluate the temperature histories of the heat transfer fluid (HTF) and the phase change material at any axial location during the charging period. The results of the model were validated by comparison with experimental results of temperature profiles of HTF and PCM. The model was also used to investigate the effect of porosity, Stanton number, Stefan number and Peclet number on CTES system performance. The results showed that increase in porosity contributes to a higher rate of energy storage. However, for a given geometry and heat transfer coefficient, the mass of PCM charged in the unit decreases as the increase in porosity. The St number as well as the Ste number is also influential in the performance of the unit. The model is a convenient and more suitable method to determine the heat transfer characteristics of CTES system. The results reported are much useful for designing CTES system.

Rhizosphere Bacteria for Biocontrol of Bacterial Blight and Growth Promotion of Rice

Several bacterial strains were isolated from different rhizospheres. Among these, strain PDY7 exhibited strong antibacterial activity against the rice bacterial blight （BB） pathogen Xanthomonas oryzae pv. oryzae （Xoo） by the laboratory dual plate assays. The antibacterial property of the strain PDY7 was further investigated for the production of 2,4-diacetylphloroglucinol （DAPG）, which amplified a characteristic of 629-bp DNA fragment by PCR-based screening method using phlD primers. The application of phlD positive strains was carefully evaluated for disease control and growth promotion of rice plants under field conditions. The selected strain PDY7 suppressed the rice BB by 58.83% and 51.88% under glass house and field conditions, respectively. In addition, the strain PDY7 showed significant two-fold increase in root length （18.08 cm）, shoot length （29.81 cm）, and grain yield （96.07 g）. Strain PDY7 promoted the growth of rice plants by production of indole-3-acetic acid （IAA）, which was determined by high performance liquid chromatography （HPLC） analysis. Our findings suggest that PDY7 belongs to the P. fluorescens group and can serve as potential biocontrol of BB as well as biofertilizer agent for growth promotion of rice.

过渡金属氢化物TMH(TM=Mo、TC、RU)的显微组织、电子特性和力学性能的第一性原理研究(英文)

通过基于广义梯度近似密度泛函理论的第一性原理研究过渡金属氢化物TMH(TM=Mo、TC、RU)的显微组织、电子特性和力学性能。在被研究的5个晶体结构中,立方结构比六方晶结构更稳定。预测了不同过渡金属氢化物在高压下的相结构转变,如:MoH中从ZB结构到WC结构的转变、在TcH中从NaCl结构到NiAs结构的转变以及在RuH中从NaCl结构到ZB结构再到NiAs结构的转变。弹性常数的计算表明:三种氢化物的力学性能在环境压力下是稳定的。

In vitro evaluation of free radical scavenging activity of Codariocalyx motorius root extract

Objective:To determine the phenolic content in Codariocalyx motorius root extract and to evaluate its antioxidant properties using various in vitro assay systems.Methods:The antioxidant activity was evaluated based on scavenging of 1,1—diphenyl—2—picrylhydrazyl,hydroxyl radicals,superoxide anions,nitric oxide,hydrogen peroxide,peroxvnilrile,reducing power and by inhibition oi lipid peroxidation which was estimated in terms of thiohurhituric acid reactive substances.Results:The root extract of the Codariocalyx motorius(C.motorius) exhibited potent total antioxidant activity that increased with increasing amount of extract concentration,which was compared with standard drug such as quercetin.butylaled hvdroxvloluene.tocopherol at different concentrations.The different concentrations of the extracts showed inhibition on lipid peroxidation.In addition,the extracts had effective reducing power,free radical scavenging, super oxide anion scavenging,nitric oxide scavenging,lipid peroxidation,and total phenolic content depending on concentration.High correlation between total phenolic contents and scavenging potential of different reactive oxygen species(r^2=0.83 1 -0.978) indicated the polyphenols as the main antioxidants.Conclusions:Codariocalyx motorius(C.motorius) root possess the highly active antioxidant substance which can be used for the treatment of oxidative stress-related diseases.

Benefciation studies on recovery and in-depth characterization of ilmenite from red sediments of badlands topography of Ganjam District,Odisha,India

This paper deals with the recovery of ilmenite mineral from red sediments of badlands topography and suggested flowsheet with material balance.The results of these investigations reveal that the red sediment samples contain 33.2%total heavy mineral,in which ilmenite mineral concentrate is 28.71%(by weight).The ilmenite concentrate recovered from red sediment sample by physical benefciation process,which included scrubbing,desliming,gravity concentration,magnetic and electrostatic separation,contains 99.41%grade with 97.3%recovery.The ilmenite mineral concentrate recovered from red sediments is also suitable for industrial applications.The characterization studies on ilmenite reveal that the TiO2percentage is marginally increasing from 46.69%to 47.86%with increasing magnetic intensity from0.46 to 1.55 T.

Nanofluid Applications in Future Automobiles: Comprehensive Review of Existing Data

In recent years fluids containing suspension of nanometer sized particles have been an active area of research due to their enhanced thermo physical properties over the base fluids like water,oil etc.Nanofluids possess immense potential applications to improve heat transfer and energy efficient in several areas including automobile,micro electronics,nuclear,space and power generation.Nowadays most of the researchers are trying to use the nanofluids in automobile for various applications such as coolant,fuel additives,lubricant,shock absorber and refrigerant.The goal of this paper is to create the awareness on the promise of nanofluids and the impact it will have on the future automotive industry.This paper also presents a comprehensive data of nanofluids application in automobile for various aspects.

Emission and performance analysis of a diesel engine burning cashew nut shell oil bio diesel mixed with hexanol

In this study, the effect of doping hexanol into biodiesel which is from neat cashew nut shell biodiesel oil on the emissions and the performance characteristics was studied in a constant speed diesel engine. The main purpose of this work is to reduce various emissions and also to improve the performance of the diesel engine when fueled with blends of hexanol and neat cashew nut shell biodiesel. Cashew nut shell oil is not edible, and hence it can be used as a viable alternative to diesel.Cashew nut shell biodiesel is prepared by conventional transesterification. Hexanol with 99.2% purity was employed as an oxygenated additive. Experimental studies were conducted by fueling diesel as a baseline and by fueling hexanol and neat cashew nut shell biodiesel mixture. A fuel comprising 10%（by volume） of hexanol and 90%（by volume） neat cashew nut shell biodiesel was referred to as CNSBD900 H100 and fuel comprising 20%（by volume） of hexanol and 80%（by volume）of neat cashew nut shell biodiesel was referred to as CNSBD800 H200. This study also investigated the possibility of using pure biofuel in an unmodified naturally aspirated diesel engine. The outcome of this study showed that adding hexanol at10% and 20%（by volume） to cashew nut shell biodiesel results in a reduction in emissions. In addition, a significant improvement in brake thermal efficiency and reduction in brake-specific fuel consumptions were achieved. Hence, it could be concluded that hexanol could be a viable and promising additive for improving the drawbacks of biodiesel when it was used to fuel an unmodified diesel engine.

Fabrication, characterization, in vitro drug release and glucose uptake activity of 14-deoxy,11, 12-didehydroandrographolide loaded polycaprolactone nanoparticles

Biodegradable polymer based novel drug delivery systems brought a considerable attention in enhancing the therapeutic efficacy and bioavailability of various drugs. 14-deoxy 11, 12-didehydro andrographolide(poorly water soluble compound) loaded polycaprolactone(nanoDDA) was synthesized using the solvent evaporation technique. Nano-DDA was characterized by scanning electron microscopy(SEM) and dynamic light scattering(DLS) studies. Fourier Transform InfraRed Spectroscopy(FTIR) was used to investigate the structural interaction between the drug and the polymer. Functional characterization of the formulation was determined using drug content, cellular uptake and in vitro drug release. 2-deoxy-D-[1-~3H] glucose uptake assay was carried out to assess the antidiabetic potential of nano-DDA in L6 myotubes.The nano-DDA displayed spherical shape with a smooth surface(252.898 nm diameter), zeta potential, encapsulation and loading efficiencies of -38.9 mV, 91.98 ± 0.13% and 15.09 ± 0.18% respectively. No structural alteration between the drug and the polymer was evidenced(FTIR analysis). Confocal microscopy studies with rhodamine 123 loaded polycaprolactone nanoparticles(Rh123-PCL NPs) revealed the internalization of Rh123-PCL NPs in a time dependent manner in L6 myoblasts. A dose dependent increase in glucose uptake was observed for nano-DDA with a maximal uptake of 108.54 ± 1.42% at 100 nM on L6 myotubes, thereby proving its anti-diabetic efficacy. A biphasic pattern of in vitro drug release demonstrated an initial burst release at 24 h followed by a sustained release for up to 11 days. To conclude,our results revealed that nano-DDA formulation can be a potent candidate for antidiabetic drug delivery.

Pulsed current and dual pulse gas metal arc welding of grade AISI: 310S austenitic stainless steel

The transverse shrinkage, mechanical and metallurgical properties of AISI: 310 S ASS weld joints prepared by P-GMAW and DP-GMAW processes were investigated. It was observed that the use of the DP-GMAW process improves the aforementioned characteristics in comparison to that of the P-GMAW process. The enhanced quality of weld joints obtained with DP-GMAW process is primarily due to the combined effect of pulsed current and thermal pulsation(low frequency pulse). During the thermal pulsation period, there is a fluctuation of wire feed rate,which results in the further increase in welding current and the decrease in arc voltage. Because of this synchronization between welding current and arc voltage during the period of low frequency pulse, the DP-GMAW deposit introduces comparatively more thermal shock compared to the P-GMAW deposit, thereby reducing the heat input and improves the properties of weld joints.