Smart Nutrient Deficiency Prediction System for Groundnut Leaf

Prediction of the nutrient deficiency range and control of it through application of an appropriate amount of fertiliser at all growth stages is critical to achieving a qualitative and quantitative yield.Distributing fertiliser in optimum amounts will protect the environment’s condition and human health risks.Early identification also prevents the disease’s occurrence in groundnut crops.A convo-lutional neural network is a computer vision algorithm that can be replaced in the place of human experts and laboratory methods to predict groundnut crop nitro-gen nutrient deficiency through image features.Since chlorophyll and nitrogen are proportionate to one another,the Smart Nutrient Deficiency Prediction System(SNDP)is proposed to detect and categorise the chlorophyll concentration range via which nitrogen concentration can be known.The model’sfirst part is to per-form preprocessing using Groundnut Leaf Image Preprocessing(GLIP).Then,in the second part,feature extraction using a convolution process with Non-negative ReLU(CNNR)is done,and then,in the third part,the extracted features areflat-tened and given to the dense layer(DL)layer.Next,the Maximum Margin clas-sifier(MMC)is deployed and takes the input from DL for the classification process tofind CCR.The dataset used in this work has no visible symptoms of a deficiency with three categories:low level(LL),beginning stage of low level(BSLL),and appropriate level(AL).This model could help to predict nitrogen deficiency before perceivable symptoms.The performance of the implemented model is analysed and compared with ImageNet pre-trained models.The result shows that the CNNR-MMC model obtained the highest training and validation accuracy of 99%and 95%,respectively,compared to existing pre-trained models.

Risk factors and predictive model of adrenocortical insuffi ciency in patients with traumatic brain injury

BACKGROUND:Neuroendocrine dysfunction after traumatic brain injury(TBI)has received increased attention due to its impact on the recovery of neural function.The purpose of this study is to investigate the incidence and risk factors of adrenocortical insuffi ciency(AI)after TBI to reveal independent predictors and build a prediction model of AI after TBI.METHODS:Enrolled patients were grouped into the AI and non-AI groups.Fourteen preset impact factors were recorded.Patients were regrouped according to each impact factor as a categorical variable.Univariate and multiple logistic regression analyses were performed to screen the related independent risk factors of AI after TBI and develop the predictive model.RESULTS:A total of 108 patients were recruited,of whom 34(31.5%)patients had AI.Nine factors(age,Glasgow Coma Scale[GCS]score on admission,mean arterial pressure[MAP],urinary volume,serum sodium level,cerebral hernia,frontal lobe contusion,diff use axonal injury[DAI],and skull base fracture)were probably related to AI after TBI.Three factors(urinary volume[X4],serum sodium level[X5],and DAI[X8])were independent variables,based on which a prediction model was developed(logit P=-3.552+2.583X4+2.235X5+2.269X8).CONCLUSIONS:The incidence of AI after TBI is high.Factors such as age,GCS score,MAP,urinary volume,serum sodium level,cerebral hernia,frontal lobe contusion,DAI,and skull base fracture are probably related to AI after TBI.Urinary volume,serum sodium level,and DAI are the independent predictors of AI after TBI.

Effect of sedation on short-term and long-term outcomes of critically ill patients with acute respiratory insufficiency

BACKGROUND: The present study aimed to determine the short-term and long-term outcomes of critically ill patients with acute respiratory insuffi ciency who had received sedation or no sedation.METHODS: The data of 91 patients who had received mechanical ventilation in the first 24 hours between November 2008 and October 2009 were retrospectively analyzed. These patients were divided into two groups: a sedation group(n=28) and a non-sedation group(n=63). The patients were also grouped in two groups: deep sedation group and daily interruption and /or light sedation group.RESULTS: Overall, the 91 patients who had received ventilation ≥48 hours were analyzed. Multivariate analysis demonstrated two independent risk factors for in-hospital death: sequential organ failure assessment score(P=0.019, RR 1.355, 95%CI 1.051–1.747, B=0.304, SE=0.130, Wald=50483) and sedation(P=0.041, RR 5.015, 95%CI 1.072–23.459, B=1.612, SE=0.787, Wald=4.195). Compared with the patients who had received no sedation, those who had received sedation had a longer duration of ventilation, a longer stay in intensive care unit and hospital, and an increased in-hospital mortality rate. The Kaplan-Meier method showed that patients who had received sedation had a lower 60-month survival rate than those who had received no sedation(76.7% vs. 88.9%, Log-rank test=3.630, P=0.057). Compared with the patients who had received deep sedation, those who had received daily interruption or light sedation showed a decreased in-hospital mortality rate(57.1% vs. 9.5%, P=0.008). The 60-month survival of the patients who had received deep sedation was signifi cantly lower than that of those who had daily interruption or light sedation(38.1% vs. 90.5%, Log-rank test=6.783, P=0.009).CONCLUSIONS: Sedation was associated with in-hospital death. The patients who had received sedation had a longer duration of ventilation, a longer stay in intensive care unit and in hospital, and an increased in-hospital mortality rate compared with the patients who did not receive sedation. Compared with daily interruption or light sedation, deep sedation increased the in-hospital mortality and decreased the 60-month survival for patients who had received sedation.

Input-output Efficiency of Elements of Urban Construction Land in Beijing: Empirical Analysis Based on Panel Data of Jurisdiction

Based on the Data Envelopment Analysis method,and by using CCR and BCC model,Super Efficiency model and Malmquist model guided by input efficiency,the input-output efficiency of elements of urban construction land in different jurisdictions of Beijing from 2005 to 2015 was studied.The results showed that there were obvious differences between input-output efficiency of elements of urban construction land in different jurisdictions of Beijing,among which the efficiency of the core area of capital,Yanqing District,Fangshan District and Huairou District was relatively high,while the efficiency of Daxing District,Fengtai District and Miyun District was relatively low.There was no obvious correlation between efficiency differentiation and location factors,which is mainly caused by whether the land use in each jurisdiction has scale effect,whether the technology is improved,whether the input is redundant and whether the output is insufficient.For the jurisdiction of inefficient land use,we should strengthen the consciousness of intensive land use,improve the technical level,appropriately reduce the redundancy of input elements,and pay attention to the output of social and ecological benefits.

Measuring effi ciency of listed tourism companies in China

This study seeks to evaluate the comparative productivity of 32 listed tourism companies which are the main suppliers of China tourism, using the popular methodology known as the data envelopment analysis(DEA). This study analyzes the productivity of listed tourism companies from business and region aspects based on the calculation of Malmquist index. The results show that(1) the overall productivity is non-effi cient(0.954);(2) the productivity of accommodation and catering is biggest, which shows the tourism develops quickly with supports from technology;(3) the productivity in western China is highest, where the economy and tourism attraction are better than other regions; and(4) the effi ciency differences among the listed tourism companies are not signifi cant, and they attribute to the scale effi-ciency, that is the input of the fi nance, resource, talents and policy.

Atomic tailoring-induced deficiency in tungsten oxides for high-performance energy-related devices

Nowadays,tungsten oxides,as a typical transition metal oxide,are widely and intensively investigated owing to their excellent material properties and device properties.Controlling oxygen defi ciency in tungsten oxides is typically the key to enhance their performances for a variety of critical technological applications.With a gradual increase of oxygen defi ciency,various non-stoichiometric tungsten oxides can be formed by re-adjustment of the atomic arrangement,which exhibits superior performances than their traditional stoichiometric counterparts.This review mainly focuses on the recent advances in oxygen-defi cient tungsten oxides from the point of atomic structures,including the forming mechanism of non-stoichiometric tungsten oxides and the superiority of these oxygen-defi cient tungsten oxides in energy-related devices.Finally,the challenge and perspective of oxygen-defi cient tungsten oxides are also discussed.

Overcoming power efficiency limitation of white fluorescence light-emitting diodes via multilevel-hydrogen-bond matrix

High power efficiency and low efficiency roll-off at practical luminance are two requirements for new-generation energy-saving lighting technologies,which are still bottlenecks of thermally activated delayed fluorescence(TADF)white organic light-emitting diodes(WOLED),despite the advantages of TADF materials and devices in low cost and high sustainability.Herein,we developed a spiro phosphine oxide host named SSOXSPO,which can form multiple and multidirectional intermolecular hydrogen bonds(IHB).The resulted multilevel IHB network integrates long-range ordered and short-range disordered alignments for suppressing triplet-polaron quenching(TPQ)and triplet-triplet annihilation(TTA).Electronic characteristics of SSOXSPO matrix are further regulated,leading to the optimal exciton allocation through balancing energy and charge transfer.As consequence,using SSOXSPO as host,the single-emissive-layer TADF WOLEDs realized the record performance,including ultralow operation voltage as∼4.0 V,power efficiency beyond fluorescent tube(70.1 lm W−1)and negligible external quantum efficiency roll-off(3%)at 1000 nits for indoor lighting.This work demonstrates that multiple interplays supported by host matrixes in TADF WOLEDs can facilitate the synergistic effects of TADF emitters on 100%exciton utilization.

Characterization of Flame Retardancy and Oil-Water Separation Capacity of Superhydrophobic Silylated Melamine Sponges

A silylated melamine sponge(SMS)was prepared by two simple steps,namely,immersion and dehydration of a melamine sponge coated with methyltrichlorosilane.The silylated structure of SMS was characterized by FT-IR(Fourier-transform infrared)spectroscopy,SEM(Scanning electron microscopy)and in terms of water contact angles.Its oil-water absorption and separation capacities were measured by FT-IR and UV-visible spectrophoto-metry.The experimental results have shown that oligomeric silanol covalently bonds by Si-N onto the surface of melamine sponge skeletons.SMS has shown superhydrophobicity with a water contact angle exceeding 150°±1°,a better separation efficiency with regard to diesel oil(by 99.31%(wt/wt%)in oil-water mixture and even up to 99.99%(wt/wt%)for diesel oil in its saturated aqueous solution.Moreover,SMS inherited the intrinsicflame retardancy of the melamine sponge.In general,SMS has shown superhydrophobicity,high porosity,excellent selectivity,remarkable recyclability,and better absorption capacity for various oils and organic solvents,and a high separation efficiency for oil in saturated aqueous solutions.

Diagnosis and management of pediatric adrenal insufficiency

Background:Adrenal insufficiency(AI)is a wellknown cause of potentially life-threatening disorders.Defects at each level of the hypothalamic-pituitary-adrenal axis can impair adrenal function,leading to varying degrees of glucocorticoid(GC)deficiency.Iatrogenic AI induced by exogenous GCs is the most common cause of AI.The criteria for the diagnosis and management of iatrogenic AI,neonatal AI,and critical illness-related corticosteroid insuffi ciency(CIRCI)are not clear.Data sources:We reviewed the recent original publications and classical data from the literature,as well as the clinical,diagnostic and management strategies of pediatric AI.Results:Practical points in the diagnosis and management of AI with an emphasis on iatrogenic AI,neonatal AI,and CIRCI are provided.Given the lack of sensitive and practical biochemical tests for diagnosis of subtle AI,GC treatment has to be tailored to highly suggestive clinical symptoms and signs.Treatment of adrenal crisis is well standardized and patients almost invariably respond well to therapy.It is mainly the delay in treatment that is responsible for mortality in adrenal crisis.Conclusion:Education of patients and health care professionals is mandatory for timely interventions for patients with adrenal crisis.

Study on the longitudinal capture efficiency of a CW heavy-ion Linac

Purpose The continuous-wave(CW)linear accelerator,SSC-Linac,has been put into operation successfully as the new injector of Separate Sector Cyclotron(SSC)for Heavy Ion Research Facility in Lanzhou(HIRFL).The SSC-Linac is operated at the frequency of 53.667 MHz,which is the four times of the SSC operating frequency.In order to improve the longitudinal capture efficiency and increase the beam current extracted from the SSC,an independent multi-harmonics buncher(MHB)operating at a fundamental frequency of 13.417 MHz had been designed and manufactured.Method This buncher adopts the dual-gap structure and sawtooth waveform is generated by multi-harmonics synthetized technology.Beam dynamics simulation with MHB was carried out and discussed in this paper.Furthermore,the beam commissioning using 84Kr14+had been performed on SSC-Linac.Results and conclusion The measured maximum capture efficiency 86.5%was obtained,which was well agreed with the simulation results.Due to the successful commissioning of MHB and the outstanding-performance of SSC-Linac,the extracted beam current from SSC has been improved one order of magnitude higher than before.

Review of preferentially selective lithium extraction from spent lithium batteries: Principle and performance

Lithium,as the lightest and lowest potential metal,is an ideal "battery metal" and the core strategic metal of the new energy industry revolution.Recovering lithium from spent lithium batteries(LIBs)has become one of the significant approaches to obtaining lithium resources.At present,the lithium extraction being generally placed at the last step of the spent LIBs recovery process has puzzles such as high acid consumption,low Li recovery purity and low recovery efficiency.Selective lithium extraction at the first step of the recovery process can effectively solve those puzzles.Since lithium leaching is a non-spontaneous reaction requiring additional energy to achieve,it is found that these methods can be divided into five ways according to the different types of energy driving the reaction occurring:(ⅰ)electric energy driving lithium extraction;(ⅱ) chemical energy driving lithium extraction;(ⅲ) mechanical energy driving lithium extraction;(ⅳ) thermal energy driving lithium extraction;(ⅴ) other energy driving lithium extraction.Through the analysis of the principle,reaction process and results of recovering lithium methods can provide a few directions for scholars’ subsequent research.It is necessary to speed up the exploration of the principle of these methods.It is expected that this study could provide a reference for the research on the selective lithium extraction.

Emergency department approach to monkeypox

BACKGROUND:Monkeypox(mpox) is a viral infection that is primarily endemic to countries in Africa,but large outbreaks outside of Africa have been historically rare.In June 2022,mpox began to spread across Europe and North America,causing the World Health Organization(WHO) to declare mpox a public health emergency of international concern.This article aims to review clinical presentation,diagnosis,and prevention and treatment strategies on mpox,providing the basic knowledge for prevention and control for emergency providers.METHODS:We conducted a review of the literature using PubMed and SCOPUS databases from their beginnings to the end of July 2023.The inclusion criteria were studies on adult patients focusing on emerging infections that described an approach to a public health emergency of international concern,systematic reviews,clinical guidelines,and retrospective studies.Studies that were not published in English were excluded.RESULTS:We included 50 studies in this review.The initial symptoms of mpox are non-specific:fever,malaise,myalgias,and sore throat.Rash,a common presentation of mpox,usually occurs 2–4 weeks after the prodrome,but the presence of lymphadenopathy may distinguish mpox from other infections from the Poxviridae family.Life-threatening complications such as pneumonia,sepsis,encephalitis,myocarditis,and death can occur.There are documented co-occurrences of human immunodeficiency virus(HIV) and other sexually transmitted infections that can worsen morbidity.CONCLUSION:The initial presentation of mpox is non-specific.The preferred treatment included tecovirimat in patients with severe illness or at high risk of developing severe disease and vaccination with two doses of JYNNEOS.However,careful history and physical examination can raise the clinicians’ suspicion and point toward a prompt diagnosis.There are diff erent modalities to prevent and treat mpox infection.

Investigating the Effects of Eichhornia Crassipes Biodiesel and Liquefied Petroleum Gas on the Performance and Emissions of a Dual-Fuel Engine

This study considers the effect of Eichhornia Crassipes Biodiesel(ECB)blends on the performances,combustion,and emission characteristics of a direct injection compression ignition engine operated in a dual-fuel mode(DFM)and equipped with an Exhaust gas recirculation technique(EGR).In particular,a single-cylinder,four-stroke,water-cooled diesel engine was utilized and four modes of fuel operation were considered:mode I,the engine operated with an ordinary diesel fuel;mode II,the engine operated with the addition of 2.4 L/min of lique-fied petroleum gas(LPG)and 20%EGR;mode III,20%ECB with 2.4 L/min LPG and 20%EGR;mode IV,40%ECB with 2.4 L/min LPG and 20%EGR.The operation conditions were constant engine speed(1500 rpm),var-iation of load(25%,50%,75%,and 100%),full load,with a compression ratio of 18,and a time injection of 23°BTDC(Before top died center).With regard to engine emissions,carbon dioxide(CO_(2)),carbon monoxide(CO),hydrocarbons(UHC),and nitrogen oxide(NOX)were measured using a gas analyzer.The smoke opacity was measured using an OPABOX smoke meter.By comparing the results related to the different modes with mode I at full load,the BTE(Brake thermal efficiency)increased by 20.17%,11.45%,and 12.66%with modes II,III,and IV,respectively.In comparison to the results for mode II,the BTE decreased due to the combustion of ECB blends by 7.26%and 6.24%for mode III and mode IV,respectively,at full load.In comparison to mode II,the Brake specific energy consumption(BSEC)increased with the ECB substitution.With ECB blends,there is a noticeable decrease in the CO,CO_(2),and UHC emissions at a partial load.Furthermore,the 20%ECB has no effect on CO emissions at full load.For modes II and IV,the CO_(2)increased by 33.33%and 19%,respectively,while the UHC emissions were reduced by 14.49%for mode III and 26.08%for mode IV.The smoke of mode III was lower by 7.21%,but for mode IV,it was higher by 12.37%.In addition,with mode III and mode IV,the NOx emissions increased by 30.50%and 18.80%,respectively.

Genetic-Chicken Swarm Algorithm for Minimizing Energy in Wireless Sensor Network

Wireless Sensor Network(WSN)technology is the real-time applica-tion that is growing rapidly as the result of smart environments.Battery power is one of the most significant resources in WSN.For enhancing a power factor,the clustering techniques are used.During the forward of data in WSN,more power is consumed.In the existing system,it works with Load Balanced Cluster-ing Method(LBCM)and provides the lifespan of the network with scalability and reliability.In the existing system,it does not deal with end-to-end delay and deliv-ery of packets.For overcoming these issues in WSN,the proposed Genetic Algo-rithm based on Chicken Swarm Optimization(GA-CSO)with Load Balanced Clustering Method(LBCM)is used.Genetic Algorithm generates chromosomes in an arbitrary method then the chromosomes values are calculated using Fitness Function.Chicken Swarm Optimization(CSO)helps to solve the complex opti-mization problems.Also,it consists of chickens,hens,and rooster.It divides the chicken into clusters.Load Balanced Clustering Method(LBCM)maintains the energy during communication among the sensor nodes and also it balances the load in the gateways.The proposed GA-CSO with LBCM improves the life-span of the network.Moreover,it minimizes the energy consumption and also bal-ances the load over the network.The proposed method outperforms by using the following metrics such as energy efficiency,ratio of packet delivery,throughput of the network,lifetime of the sensor nodes.Therefore,the evaluation result shows the energy efficiency that has achieved 83.56%and the delivery ratio of the packet has reached 99.12%.Also,it has attained linear standard deviation and reduced the end-to-end delay as 97.32 ms.

Energy Efficient Networks Using Ant Colony Optimization with Game Theory Clustering

Real-time applications based on Wireless Sensor Network(WSN)tech-nologies quickly lead to the growth of an intelligent environment.Sensor nodes play an essential role in distributing information from networking and its transfer to the sinks.The ability of dynamical technologies and related techniques to be aided by data collection and analysis across the Internet of Things(IoT)network is widely recognized.Sensor nodes are low-power devices with low power devices,storage,and quantitative processing capabilities.The existing system uses the Artificial Immune System-Particle Swarm Optimization method to mini-mize the energy and improve the network’s lifespan.In the proposed system,a hybrid Energy Efficient and Reliable Ant Colony Optimization(ACO)based on the Routing protocol(E-RARP)and game theory-based energy-efficient clus-tering algorithm(GEC)were used.E-RARP is a new Energy Efficient,and Reli-able ACO-based Routing Protocol for Wireless Sensor Networks.The suggested protocol provides communications dependability and high-quality channels of communication to improve energy.For wireless sensor networks,a game theo-ry-based energy-efficient clustering technique(GEC)is used,in which each sen-sor node is treated as a player on the team.The sensor node can choose beneficial methods for itself,determined by the length of idle playback time in the active phase,and then decide whether or not to rest.The proposed E-RARP-GEC improves the network’s lifetime and data transmission;it also takes a minimum amount of energy compared with the existing algorithms.

Chaotic Sandpiper Optimization Based Virtual Machine Scheduling for Cyber-Physical Systems

Recently,with the growth of cyber physical systems(CPS),several applications have begun to deploy in the CPS for connecting the cyber space with the physical scale effectively.Besides,the cloud computing(CC)enabled CPS offers huge processing and storage resources for CPS thatfinds helpful for a range of application areas.At the same time,with the massive development of applica-tions that exist in the CPS environment,the energy utilization of the cloud enabled CPS has gained significant interest.For improving the energy effective-ness of the CC platform,virtualization technologies have been employed for resource management and the applications are executed via virtual machines(VMs).Since effective scheduling of resources acts as an important role in the design of cloud enabled CPS,this paper focuses on the design of chaotic sandpi-per optimization based VM scheduling(CSPO-VMS)technique for energy effi-cient CPS.The CSPO-VMS technique is utilized for searching for the optimum VM migration solution and it helps to choose an effective scheduling strategy.The CSPO algorithm integrates the concepts of traditional SPO algorithm with the chaos theory,which substitutes the main parameter and combines it with the chaos.In order to improve the process of determining the global optimum solutions and convergence rate of the SPO algorithm,the chaotic concept is included in the SPO algorithm.The CSPO-VMS technique also derives afitness function to choose optimal scheduling strategy in the CPS environment.In order to demonstrate the enhanced performance of the CSPO-VMS technique,a wide range of simulations were carried out and the results are examined under varying aspects.The simulation results ensured the improved performance of the CSPO-VMS technique over the recent methods interms of different measures.

Performance Analysis of a Chunk-Based Speech Emotion Recognition Model Using RNN

Recently,artificial-intelligence-based automatic customer response sys-tem has been widely used instead of customer service representatives.Therefore,it is important for automatic customer service to promptly recognize emotions in a customer’s voice to provide the appropriate service accordingly.Therefore,we analyzed the performance of the emotion recognition(ER)accuracy as a function of the simulation time using the proposed chunk-based speech ER(CSER)model.The proposed CSER model divides voice signals into 3-s long chunks to effi-ciently recognize characteristically inherent emotions in the customer’s voice.We evaluated the performance of the ER of voice signal chunks by applying four RNN techniques—long short-term memory(LSTM),bidirectional-LSTM,gated recurrent units(GRU),and bidirectional-GRU—to the proposed CSER model individually to assess its ER accuracy and time efficiency.The results reveal that GRU shows the best time efficiency in recognizing emotions from speech signals in terms of accuracy as a function of simulation time.

High Linear Voltage Gain in QZNC Through Synchronizing Switching Circuits

The solar powered systems require high step-up converter for efficient energy transfer.For this,quasi-impedance network converter has been introduced.The quasi-impedance network converter(QZNC)is of two types:type-1 and type-2 configuration.Both the type-1 and type-2 QZNC configurations have drooping voltage gain profile due to presence of high switching noise.To overcome this,a new quasi-impedance network converter synchronizing the switching circuit with low frequency noise has been proposed.In this paper,the proposed QZNC con-figuration utilizes the current controlling diode to prevent the output voltage drop.Thus,the suggested topology provides linear high voltage gain profile,low load voltage ripple,and reduced impedance network stress and device stress.There-fore,the efficiency of proposed QZNC has been improved.The topology descrip-tion,working principle,parameter design and comparison with traditional converters are illustrated.Andfinally,both simulation and practical results are presented to confirm the converter characteristics and performance.From the results,it has been found that the performance of the suggested topology is better as it achieves a higher efficiency of 81%and hence,it is suitable for high power applications.

Hybrid Optimisation with Black Hole Algorithm for Improving Network Lifespan

Wireless sensor networks(WSNs)are projected to have a wide range of applications in the future.The fundamental problem with WSN is that it has afinite lifespan.Clustering a network is a common strategy for increasing the life-time of WSNs and,as a result,allowing for faster data transmission.The cluster-ing algorithm’s goal is to select the best cluster head(CH).In the existing system,Hybrid grey wolf sunflower optimization algorithm(HGWSFO)and optimal clus-ter head selection method is used.It does not provide better competence and out-put in the network.Therefore,the proposed Hybrid Grey Wolf Ant Colony Optimisation(HGWACO)algorithm is used for reducing the energy utilization and enhances the lifespan of the network.Black hole method is used for selecting the cluster heads(CHs).The ant colony optimization(ACO)technique is used tofind the route among origin CH and destination.The open cache of nodes,trans-mission power,and proximity are used to improve the CH selection.The grey wolf optimisation(GWO)technique is the most recent and well-known optimiser module which deals with grey wolves’hunting activity(GWs).These GWs have the ability to track down and encircle food.The GWO method was inspired by this hunting habit.The proposed HGWACO improves the duration of the net-work,minimizes the power consumption,also it works with the large-scale net-works.The HGWACO method achieves 25.64%of residual energy,25.64%of alive nodes,40.65%of dead nodes also it enhances the lifetime of the network.

Modeling and Control of Parallel Hybrid Electric Vehicle Using Sea-Lion Optimization

This paper develops a parallel hybrid electric vehicle(PHEV)propor-tional integral controller with driving cycle.To improve fuel efficiency and reduce hazardous emissions in hybrid electric vehicles(HEVs)combine an electric motor(EM),a battery and an internal combustion engine(ICE).The electric motor assists the engine when accelerating,driving longer highways or climbing hills.This enables the use of a smaller,more efficient engine.It also makes use of the concept of regenerative braking to maximize energy efficiency.In a Hybrid Electric Vehicle(HEV),energy dissipated while braking is utilized to charge the battery.The proportional integral controller was used in this paper to analyze engine,motor performance and the New European Driving Cycle(NEDC)was used in the vehicle driving test using Matlab/Simulink.The proportional integral controllers were designed to track the desired vehicle speed and manage the vehi-cle’s energyflow.The Sea Lion Optimization(SLnO)methods were created to reduce fuel consumption in a parallel hybrid electric vehicle and the results were obtained for the New European Driving Cycle.