This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path plannin...This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.展开更多
In recent years,volatile fatty acid(VFA)production through anaerobic fermentation of sewage sludge,instead of methane production,has been regarded as a high-value and promising roadmap for sludge stabilization and res...In recent years,volatile fatty acid(VFA)production through anaerobic fermentation of sewage sludge,instead of methane production,has been regarded as a high-value and promising roadmap for sludge stabilization and resource recovery.This review first presents the effects of some essential factors that influence VFA production and composition.In the second part,we present an extensive analysis of conventional pretreatment and co-fermentation strategies ultimately addressed to improving VFA production and composition.Also,the effectiveness of these approaches is summarized in terms of sludge degradation,hydrolysis rate,and VFA production and composition.According to published studies,it is concluded that some pretreatments such as alkaline and thermal pretreatment are the most effective ways to enhance VFA production from sewage sludge.The possible reasons for the improvement of VFA production by different methods are also discussed.Finally,this review also highlights several current technical challenges and opportunities in VFA production with spectrum control,and further related research is proposed.展开更多
Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved...Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.展开更多
This study attempts to quantify the influence of spatial configuration on the functional efficiency of mosque layouts in the early Ottoman period. The literature review consists of two parts. The first part is a theor...This study attempts to quantify the influence of spatial configuration on the functional efficiency of mosque layouts in the early Ottoman period. The literature review consists of two parts. The first part is a theoretical study of the relationship between spatial configuration and functional efficiency of mosque layouts. This part highlights the key syntactical characteristics and effect of spatial configuration on the level of functional efficiency using space syntax theory. The second part is an analytical comparative description of the changes and transfor- mations in the configurations of mosque layouts. The architectural styles are classified into six types based on layout designs. The main benchmarks and indicators involved in measuring the functional efficiency of mosque layouts are analyzed using the A-graph 2009 software program, which provides numerical results. This analysis compares entire samples of mosque layout designs for each category. The numerical results indicate the effect of spatial configurations and the functions of mosque layouts. This study shows that mosques with courtyard layouts are accessible, efficient, and flexible in terms of function because of their distinct syntactical and morphological spatial structures.展开更多
To research solar energy's efficiency and environmental benefits,the thermal efficiency,exergy,and entropy of solar collectors were calculated.The experiment involved two glass-topped collectors,fluid transfer tub...To research solar energy's efficiency and environmental benefits,the thermal efficiency,exergy,and entropy of solar collectors were calculated.The experiment involved two glass-topped collectors,fluid transfer tubes,and aluminum heat-absorbing plates.Glass wool insulation minimized heat loss.A 0.5% TiO_(2)/Water nanofluid was created using a mechanical and ultrasonic stirrer.Results showed that solar radiation increased thermal efficiency until midday,reaching 48.48% for water and 51.23% for the nanofluid.With increasing mass flow rates from 0.0045 kg/s to 0.02 kg/s,thermal efficiency improved from 16.26% to 47.37% for water and from 20.65% to 48.76% for the nanofluid.Filtered water provided 380 W and 395 W of energy in March and April,while the nanofluid increased it to 395 W and 415 W during these months.Mass flow generated energy,and the Reynolds number raised entropy.The noon exergy efficiency for nanofluids was 50%-55%,compared to 30% for water.At noon,the broken exergy measured 877.53 W for the nanofluid and 880.12 W for water.In Kirkuk,Iraq,the 0.5% TiO_(2)/Water nanofluid outperformed water in solar collectors.展开更多
Objective Pharmacopoeias regulate the manufacture of potentised pharmaceutical preparations used in different branches of complementary and integrative medicine.The physicochemical properties and biological activity o...Objective Pharmacopoeias regulate the manufacture of potentised pharmaceutical preparations used in different branches of complementary and integrative medicine.The physicochemical properties and biological activity of these preparations are often investigated in preclinical research,yet no guidelines for experimental research currently exist in this area.The present PrePoP guidelines aim to provide recommendations to promote high-quality,statistically sound,and reproducible preclinical research on potentised preparations.Methods Input was gathered from researchers nominated by the relevant scientific societies using a simplified Delphi consensus approach covering the most relevant aspects of basic research methodology in the field including appropriate controls,sample preparation and handling,and statistics.After three rounds of feedback,a consensus was finally reached on the most important aspects and considerations for conducting high-quality research on potentised preparations.Results We present a series of recommendations on a range of topics including experimental controls,system stability,blinding and randomisation,environmental influences,and procedures for the preparation of potentised samples and controls,and we address some specific challenges of this research field.Conclusion This expert consensus process resulted in a robust set of methodological guidelines for research on potentised preparations and provides a valuable framework that will inform and improve the quality of subsequent research in this emerging field.展开更多
This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Ir...This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Iraq,is a vital water resource area facing increasing water demands and environ-mental challenges.The three-dimensional nature of the groundwater flow system is crucial for accurately understanding and managing water resources in the basin.The modeling process involved data collection,geological and hydrogeological characterization,conceptual model development,and numerical simulation using GMS software MODFLOW 2000 package.Various parameters such as hydraulic conductivity,recharge rates,and boundary conditions were integrated into the model to represent the complex hydrogeo-logical conditions of the basin.Model calibration was performed by comparing simulated groundwater levels with observed data from monitoring wells across the basin,using the automatic calibration method of automated Parameter Estimation(PEST).Pilot points were applied to adjust the hydraulic conductivity in the model area spatially.Sensitivity analysis was conducted to assess the influence of key parameters on model predictions and to identify areas of uncertainty.The developed three-dimensional groundwater flow model provides valuable insights into the dynamics of groundwater flow,recharge-discharge mechanisms,and potential impacts of future scenarios such as climate change and water resource management strategies.It serves as a useful tool for decision-makers,water resource managers,and researchers to evaluate differ-ent management scenarios and formulate sustainable groundwater management policies for the Erbil Basin.In conclusion,this study demonstrates the effectiveness of using GMS for developing three-dimensional groundwater flow models in complex hydrogeological settings like the Erbil Basin,contributing to improved understanding and management of groundwater resources in the region.展开更多
Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are sim...Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.展开更多
The present study establishes a new estimation model using an artificial neural network(ANN) to predict the mechanical properties of the AISI 1035 alloy.The experiments were designed based on the L16 orthogonal array ...The present study establishes a new estimation model using an artificial neural network(ANN) to predict the mechanical properties of the AISI 1035 alloy.The experiments were designed based on the L16 orthogonal array of the Taguchi method.A proposed numerical model for predicting the correlation of mechanical properties was supplemented with experimental data.The quenching process was conducted using a cooling medium called “nanofluids”.Nanoparticles were dissolved in a liquid phase at various concentrations(0.5%,1%,2.5%,and 5% vf) to prepare the nanofluids.Experimental investigations were done to assess the impact of temperature,base fluid,volume fraction,and soaking time on the mechanical properties.The outcomes showed that all conditions led to a noticeable improvement in the alloy's hardness which reached 100%,the grain size was refined about 80%,and unwanted residual stresses were removed from 50 to 150 MPa.Adding 5% of CuO nanoparticles to oil led to the best grain size refinement,while adding 2.5% of Al_(2)O_(3) nanoparticles to engine oil resulted in the greatest compressive residual stress.The experimental variables were used as the input data for the established numerical ANN model,and the mechanical properties were the output.Upwards of 99% of the training network's correlations seemed to be positive.The estimated result,nevertheless,matched the experimental dataset exactly.Thus,the ANN model is an effective tool for reflecting the effects of quenching conditions on the mechanical properties of AISI 1035.展开更多
The influence of La_(2)O_(3) inclusion(0-3 wt%) on the micro structure,phase formation and mechanical properties of zirconia toughed alumina(ZTA) added with 5.0 wt% CeO_(2) was investigated.ZTA CeO_(2) composites were...The influence of La_(2)O_(3) inclusion(0-3 wt%) on the micro structure,phase formation and mechanical properties of zirconia toughed alumina(ZTA) added with 5.0 wt% CeO_(2) was investigated.ZTA CeO_(2) composites were sintered at 1600℃ for 4 h.The microstructure,phase formation,density,fracture toughness and hardness properties were characterised through FESEM,Microscopy Image Analysis Software and XRD diffractometer,Archimedes principle and Vickers indentation technique,respectively.The XRD,image processing and FESEM reveal the existence of LaAl_(11)O_(18).The addition of La_(2)O_(3) incites the sintering,microstructure refinement,densification of ZTA-CeO_(2) matrix and phase transformation.Hence,the hardness of ZTA-CeO_(2) ceramics is increased rapidly based on refinement of Al_(2)O_(3) grains,densification of ZTA-CeO_(2) composites and porosity reduction.It is observed that the fracture toughness is enhanced through in situ formation of elongated LaAl_(11)O_(18) grains.The addition of 0.7 wt% La_(2)O_(3) culminated in the achievement of the optimum findings for density(4.41 g/cm^(3)),porosity(0.46%),hardness(1792 HV) and fracture toughness(8.8 MPa·m^(1/2)).Nevertheless,excess La_(2)O_(3) is proven to be detrimental as it displays poor mechanical properties due to the poor compactness of numerous LaAl_(11)O_(18) grains,coarsening of Al_(2)O_(3) grains and decline in density.展开更多
The rapid adoption of the Internet of Things(IoT)across industries has revolutionized daily life by providing essential services and leisure activities.However,the inadequate software protection in IoT devices exposes...The rapid adoption of the Internet of Things(IoT)across industries has revolutionized daily life by providing essential services and leisure activities.However,the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences.Intrusion Detection Systems(IDS)are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic.The security research community has shown particular interest in leveraging Machine Learning(ML)approaches to develop practical IDS applications for general cyber networks and IoT environments.However,most available datasets related to Industrial IoT suffer from imbalanced class distributions.This study proposes a methodology that involves dataset preprocessing,including data cleaning,encoding,and normalization.The class imbalance is addressed by employing the Synthetic Minority Oversampling Technique(SMOTE)and performing feature reduction using correlation analysis.Multiple ML classifiers,including Logistic Regression,multi-layer perceptron,Decision Trees,Random Forest,and XGBoost,are employed to model IoT attacks.The effectiveness and robustness of the proposed method evaluate using the IoTID20 dataset,which represents current imbalanced IoT scenarios.The results highlight that the XGBoost model,integrated with SMOTE,achieves outstanding attack detection accuracy of 0.99 in binary classification,0.99 in multi-class classification,and 0.81 in multiple sub-classifications.These findings demonstrate our approach’s significant improvements to attack detection in imbalanced IoT datasets,establishing its superiority over existing IDS frameworks.展开更多
Purpose: Intrapartum Doppler velocimetry is a non-invasive investigation method. This method is useful for evaluating the pathophysiological mechanisms underlying changes in fetal heart rate. This study aimed to deter...Purpose: Intrapartum Doppler velocimetry is a non-invasive investigation method. This method is useful for evaluating the pathophysiological mechanisms underlying changes in fetal heart rate. This study aimed to determine the usefulness of Doppler ultrasound findings during labor in high-risk women on determining the mode of delivery and outcome of the newborn. Methods: A cross-sectional study was conducted from January 1, 2015 to December 31, 2015 on 100 high-risk pregnant women during labor. The study was performed in the Maternity Teaching Hospital in Erbil City, Kurdistan Region, Iraq. Results: The majority of women (77%) had a normal end-diastolic blood flow pattern through the umbilical artery. A total of 76.9% of those with abnormal Doppler results had a Cesarean delivery compared with only 9.5% of those with normal Doppler results (p p p = 0.07) and fifth minutes (p = 0.01). Conclusions: Abnormal umbilical artery Doppler ultrasound findings in high-risk pregnancies for women in labor are associated with increased rates of emergency cesarean sections and a poor outcome of the newborn.展开更多
Aspergillus spp. has been widely found as useful microorganism in biotechnology. They have a high ability in the production of secondary metabolites. Therefore, isolates of Aspergillus were isolated from healthy rice ...Aspergillus spp. has been widely found as useful microorganism in biotechnology. They have a high ability in the production of secondary metabolites. Therefore, isolates of Aspergillus were isolated from healthy rice field located in Selangor State/Malaysia. The obtained strain (UPMZ01) was conducted against Pyricularia oryzea by applying dual culture and culture filtrate technique. The antagonism of strain UPMZ01 in the dual culture was 81.326% inhibition percentage against P. oryzae given the optimum inhibitory percentage 100% at all concentration of secondary metabolites aged 14 days. The isolate (UPMZ01) was identified as Aspergillus niger with accession number (KY698415). The environment factors such as pH and temperature influencing on production of secondary metabolites. The results were shown that pH at level 5.0 and temperature between 21℃?to 29℃?is the optimum condition for A. niger to produce efficient antifungal metabolites which given 100% PIGR against blast pathogen. The secondary metabolites compounds were identified by Gas Chromatography-Mass Spectrometry (GC-MS). Fifteen compounds were recognized as major compounds which may have the possibility of possessing antifungal characteristics. Most of identified compounds are Oleic Acid, n-Hexadecanoic acid, Hexose, Glycerol, Stearic acid, Tetradecanoic acid, Dodecanoic acid and 5-Hydrxoymethylfurfural.展开更多
The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours.The detection of tumors is performed with the help of automatic computing te...The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours.The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work.The non-active cells in brain region are known to be benign and they will never cause the death of the patient.These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels.The Magnetic Resonance(MR)image contrast is improved by the cost map construction technique.The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method.This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features.Using k-mean clustering algorithm the tumor regions in glioma are classified.The proposed algorithm provides high sensitivity,specificity and tumor segmentation accuracy.展开更多
Iraq is one of the most affected countries in the Middle East concerning the occurrences of sand and dust storms. The frequency of the occurrence has increased drastically in the last decade and it is increasing conti...Iraq is one of the most affected countries in the Middle East concerning the occurrences of sand and dust storms. The frequency of the occurrence has increased drastically in the last decade and it is increasing continuously. The events of sand and dust storms are either regional or local. The former, however, is more frequent than the latter. The regional event, generally extends outside the Iraqi territory, into different directions, but usually covers part of Syria, crossing the Iraqi territory towards Kuwait and Saudi Arabia, and/or towards the Arabian Gulf, and less frequently extends to Iran. The main causes in the development of sand and dust storms, in Iraq are discussed. The causes are also either regional or local. The former, however, causes more economic losses and harsh effect on the human’s health, as compared with the latter. One of the main reasons behind the development of sand and dust storms is the climatic changes within the region, especially the drastic decrease in the annual rate of rain fall, besides environmental changes, such as drying of the marshes, land degradation, and desertification. From the local causes, the most effective reason is the haphazard driving and military operations, especially in the Iraqi Southern Desert. Prudent management of water resources by using non-conventional resources and adapting suitable irrigation methods can greatly help to overcome this phenomenon and minimize the number of dust storm.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
BACKGROUND Myocardial infarction,particularly ST-segment elevation myocardial infarction(STEMI),is a key global mortality cause.Our study investigated predictors of mortality in 96 STEMI patients undergoing primary pe...BACKGROUND Myocardial infarction,particularly ST-segment elevation myocardial infarction(STEMI),is a key global mortality cause.Our study investigated predictors of mortality in 96 STEMI patients undergoing primary percutaneous coronary intervention at Erbil Cardiac Center.Multiple factors were identified influencing in-hospital mortality.Significantly,time from symptom onset to hospital arrival emerged as a decisive factor.Consequently,our study hypothesis is:"Reducing time from symptom onset to hospital arrival significantly improves STEMI prognosis."AIM To determine the key factors influencing mortality rates in STEMI patients.METHODS We studied 96 consecutive STEMI patients undergoing primary percutaneous coronary intervention(PPCI)at the Erbil Cardiac Center.Their clinical histories were compiled,and coronary evaluations were performed via angiography on admission.Data included comorbid conditions,onset of cardiogenic shock,complications during PPCI,and more.Post-discharge,one-month follow-up assessments were completed.Statistical significance was set at P<0.05.RESULTS Our results unearthed several significant findings.The in-hospital and 30-d mortality rates among the 96 STEMI patients were 11.2%and 2.3%respectively.On the investigation of independent predictors of in-hospital mortality,we identified atypical presentation,onset of cardiogenic shock,presence of chronic kidney disease,Thrombolysis In Myocardial Infarction grades 0/1/2,triple vessel disease,ventricular tachycardia/ventricular fibrillation,coronary dissection,and the no-reflow phenomenon.Specifically,the recorded average time from symptom onset to hospital arrival amongst patients who did not survive was significantly longer(6.92±3.86 h)compared to those who survived(3.61±1.67 h),P<0.001.These findings underscore the critical role of timely intervention in improving the survival outcomes of STEMI patients.CONCLUSION Our results affirm that early hospital arrival after symptom onset significantly improves survival rates in STEMI patients,highl展开更多
Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly d...Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels.展开更多
Clustering schemes in vehicular networks organize vehicles into logical groups.They are vital for improving network performance,accessing the medium,and enabling efficient data dissemination.Most schemes rely on perio...Clustering schemes in vehicular networks organize vehicles into logical groups.They are vital for improving network performance,accessing the medium,and enabling efficient data dissemination.Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles.However,the periodic exchange of messages overwhelms the system and reduces efficiency.This paper proposes the Flag-based Vehicular Clustering(FVC)scheme.The scheme leverages a combination of Fitness Score(FS),Link Expiration Time(LET),and clustering status flags to enable efficient cluster formation in a hybrid manner.The FVC relies on the periodic broadcast of the basic safety message in the Dedicated Short-Range Communications(DSRC)standard for exchanging the vehicle’s status,FS,and joining request.Piggybacking extra information onto the existing periodic beacon reduces the overhead of exchanging additional control messages,which is the main contribution of this work.The scheme is implemented in a hybrid manner by utilizing a Road Side Unit(RSU)to implement a clustering algorithm.This work considered the FastPAM algorithm,a fast version of the Partitioning Around Medoids(PAM)clustering algorithm,to generate a list of potential cluster heads.The FVC scheme uses the LET as the clustering metric with the FastPAM algorithm.Moreover,the Lightweight FastPAM Vehicular Clustering(LFPVC)algorithm is considered by selecting the initial cluster heads based on the FS instead of the greedy FastPAM’s build stage.In the absence of the RSU,the vehicles utilize the FS with proper back-off time to self-elect the cluster head.The hybrid FVC scheme increased the cluster lifetime by 32%and reduced the control-message overhead by 63%compared to the related work.Moreover,the LFPVC algorithm achieved similar results to the FastPAM algorithm.展开更多
Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locatio...Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locations.To ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in WSNs.Moreover,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of WSNs.Here,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and authentication.By examining state information,SLA projects a trust model to enhance efficacy of data delivery.Indeed,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational costs.Specifically,this model acts as a verification strategy to accelerate?attackers and to handle isolation.This strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic routing.Simulation is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other approaches.The anticipated model shows better trade off in comparison to the prevailing ones.展开更多
文摘This paper presents the rigorous study of mobile robot navigation techniques used so far.The step by step investigations of classical and reactive approaches are made here to understand the development of path planning strategies in various environmental conditions and to identify research gap.The classical approaches such as cell decomposition(CD),roadmap approach(RA),artificial potential field(APF);reactive approaches such as genetic algorithm(GA),fuzzy logic(FL),neural network(NN),firefly algorithm(FA),particle swarm optimization(PSO),ant colony optimization(ACO),bacterial foraging optimization(BFO),artificial bee colony(ABC),cuckoo search(CS),shuffled frog leaping algorithm(SFLA)and other miscellaneous algorithms(OMA)are considered for study.The navigation over static and dynamic condition is analyzed(for single and multiple robot systems)and it has been observed that the reactive approaches are more robust and perform well in all terrain when compared to classical approaches.It is also observed that the reactive approaches are used to improve the performance of the classical approaches as a hybrid algorithm.Hence,reactive approaches are more popular and widely used for path planning of mobile robot.The paper concludes with tabular data and charts comparing the frequency of individual navigational strategies which can be used for specific application in robotics.
基金supported by the National Natural Science Foundation of China(No.51578068)"One Thousand Talent Plan" Youth Program
文摘In recent years,volatile fatty acid(VFA)production through anaerobic fermentation of sewage sludge,instead of methane production,has been regarded as a high-value and promising roadmap for sludge stabilization and resource recovery.This review first presents the effects of some essential factors that influence VFA production and composition.In the second part,we present an extensive analysis of conventional pretreatment and co-fermentation strategies ultimately addressed to improving VFA production and composition.Also,the effectiveness of these approaches is summarized in terms of sludge degradation,hydrolysis rate,and VFA production and composition.According to published studies,it is concluded that some pretreatments such as alkaline and thermal pretreatment are the most effective ways to enhance VFA production from sewage sludge.The possible reasons for the improvement of VFA production by different methods are also discussed.Finally,this review also highlights several current technical challenges and opportunities in VFA production with spectrum control,and further related research is proposed.
文摘Wireless Sensor Networks(WSNs)comprises low power devices that are randomly distributed in a geographically isolated region.The energy consumption of nodes is an essential factor to be considered.Therefore,an improved energy management technique is designed in this investigation to reduce its consumption and to enhance the network’s lifetime.This can be attained by balancing energy clusters using a meta-heuristic Firefly algorithm model for network communication.This improved technique is based on the cluster head selection technique with measurement of the tour length of fireflies.Time Division Multiple Access(TDMA)scheduler is also improved with the characteristics/behavior of fireflies and also executed.At last,the development approach shows the progression of the network lifetime,the total number of selected Cluster Heads(CH),the energy consumed by nodes,and the number of packets transmitted.This approach is compared with Ad hoc On-Demand Distance Vector(AODV),Dynamic Source Routing(DSR)and Low Energy Adaptive Clustering Hierarchy(LEAH)protocols.Simulation is performed in MATLAB with the numerical outcomes showing the efficiency of the proposed approach.The energy consumption of sensor nodes is reduced by about 50%and increases the lifetime of nodes by 78%more than AODV,DSR and LEACH protocols.The parameters such as cluster formation,end to end delay,percentage of nodes alive and packet delivery ratio,are also evaluated...The anticipated method shows better trade-off in contrast to existing techniques.
文摘This study attempts to quantify the influence of spatial configuration on the functional efficiency of mosque layouts in the early Ottoman period. The literature review consists of two parts. The first part is a theoretical study of the relationship between spatial configuration and functional efficiency of mosque layouts. This part highlights the key syntactical characteristics and effect of spatial configuration on the level of functional efficiency using space syntax theory. The second part is an analytical comparative description of the changes and transfor- mations in the configurations of mosque layouts. The architectural styles are classified into six types based on layout designs. The main benchmarks and indicators involved in measuring the functional efficiency of mosque layouts are analyzed using the A-graph 2009 software program, which provides numerical results. This analysis compares entire samples of mosque layout designs for each category. The numerical results indicate the effect of spatial configurations and the functions of mosque layouts. This study shows that mosques with courtyard layouts are accessible, efficient, and flexible in terms of function because of their distinct syntactical and morphological spatial structures.
文摘To research solar energy's efficiency and environmental benefits,the thermal efficiency,exergy,and entropy of solar collectors were calculated.The experiment involved two glass-topped collectors,fluid transfer tubes,and aluminum heat-absorbing plates.Glass wool insulation minimized heat loss.A 0.5% TiO_(2)/Water nanofluid was created using a mechanical and ultrasonic stirrer.Results showed that solar radiation increased thermal efficiency until midday,reaching 48.48% for water and 51.23% for the nanofluid.With increasing mass flow rates from 0.0045 kg/s to 0.02 kg/s,thermal efficiency improved from 16.26% to 47.37% for water and from 20.65% to 48.76% for the nanofluid.Filtered water provided 380 W and 395 W of energy in March and April,while the nanofluid increased it to 395 W and 415 W during these months.Mass flow generated energy,and the Reynolds number raised entropy.The noon exergy efficiency for nanofluids was 50%-55%,compared to 30% for water.At noon,the broken exergy measured 877.53 W for the nanofluid and 880.12 W for water.In Kirkuk,Iraq,the 0.5% TiO_(2)/Water nanofluid outperformed water in solar collectors.
文摘Objective Pharmacopoeias regulate the manufacture of potentised pharmaceutical preparations used in different branches of complementary and integrative medicine.The physicochemical properties and biological activity of these preparations are often investigated in preclinical research,yet no guidelines for experimental research currently exist in this area.The present PrePoP guidelines aim to provide recommendations to promote high-quality,statistically sound,and reproducible preclinical research on potentised preparations.Methods Input was gathered from researchers nominated by the relevant scientific societies using a simplified Delphi consensus approach covering the most relevant aspects of basic research methodology in the field including appropriate controls,sample preparation and handling,and statistics.After three rounds of feedback,a consensus was finally reached on the most important aspects and considerations for conducting high-quality research on potentised preparations.Results We present a series of recommendations on a range of topics including experimental controls,system stability,blinding and randomisation,environmental influences,and procedures for the preparation of potentised samples and controls,and we address some specific challenges of this research field.Conclusion This expert consensus process resulted in a robust set of methodological guidelines for research on potentised preparations and provides a valuable framework that will inform and improve the quality of subsequent research in this emerging field.
文摘This study presents the development of a comprehensive three-dimensional groundwater flow model for the Erbil Basin utilizing the Groundwater Modeling System(GMS).The Erbil Basin,situated in the Kurdistan Region of Iraq,is a vital water resource area facing increasing water demands and environ-mental challenges.The three-dimensional nature of the groundwater flow system is crucial for accurately understanding and managing water resources in the basin.The modeling process involved data collection,geological and hydrogeological characterization,conceptual model development,and numerical simulation using GMS software MODFLOW 2000 package.Various parameters such as hydraulic conductivity,recharge rates,and boundary conditions were integrated into the model to represent the complex hydrogeo-logical conditions of the basin.Model calibration was performed by comparing simulated groundwater levels with observed data from monitoring wells across the basin,using the automatic calibration method of automated Parameter Estimation(PEST).Pilot points were applied to adjust the hydraulic conductivity in the model area spatially.Sensitivity analysis was conducted to assess the influence of key parameters on model predictions and to identify areas of uncertainty.The developed three-dimensional groundwater flow model provides valuable insights into the dynamics of groundwater flow,recharge-discharge mechanisms,and potential impacts of future scenarios such as climate change and water resource management strategies.It serves as a useful tool for decision-makers,water resource managers,and researchers to evaluate differ-ent management scenarios and formulate sustainable groundwater management policies for the Erbil Basin.In conclusion,this study demonstrates the effectiveness of using GMS for developing three-dimensional groundwater flow models in complex hydrogeological settings like the Erbil Basin,contributing to improved understanding and management of groundwater resources in the region.
文摘Implementing machine learning algorithms in the non-conducive environment of the vehicular network requires some adaptations due to the high computational complexity of these algorithms.K-clustering algorithms are simplistic,with fast performance and relative accuracy.However,their implementation depends on the initial selection of clusters number(K),the initial clusters’centers,and the clustering metric.This paper investigated using Scott’s histogram formula to estimate the K number and the Link Expiration Time(LET)as a clustering metric.Realistic traffic flows were considered for three maps,namely Highway,Traffic Light junction,and Roundabout junction,to study the effect of road layout on estimating the K number.A fast version of the PAM algorithm was used for clustering with a modification to reduce time complexity.The Affinity propagation algorithm sets the baseline for the estimated K number,and the Medoid Silhouette method is used to quantify the clustering.OMNET++,Veins,and SUMO were used to simulate the traffic,while the related algorithms were implemented in Python.The Scott’s formula estimation of the K number only matched the baseline when the road layout was simple.Moreover,the clustering algorithm required one iteration on average to converge when used with LET.
基金Kut Technical Institute for their funding supports。
文摘The present study establishes a new estimation model using an artificial neural network(ANN) to predict the mechanical properties of the AISI 1035 alloy.The experiments were designed based on the L16 orthogonal array of the Taguchi method.A proposed numerical model for predicting the correlation of mechanical properties was supplemented with experimental data.The quenching process was conducted using a cooling medium called “nanofluids”.Nanoparticles were dissolved in a liquid phase at various concentrations(0.5%,1%,2.5%,and 5% vf) to prepare the nanofluids.Experimental investigations were done to assess the impact of temperature,base fluid,volume fraction,and soaking time on the mechanical properties.The outcomes showed that all conditions led to a noticeable improvement in the alloy's hardness which reached 100%,the grain size was refined about 80%,and unwanted residual stresses were removed from 50 to 150 MPa.Adding 5% of CuO nanoparticles to oil led to the best grain size refinement,while adding 2.5% of Al_(2)O_(3) nanoparticles to engine oil resulted in the greatest compressive residual stress.The experimental variables were used as the input data for the established numerical ANN model,and the mechanical properties were the output.Upwards of 99% of the training network's correlations seemed to be positive.The estimated result,nevertheless,matched the experimental dataset exactly.Thus,the ANN model is an effective tool for reflecting the effects of quenching conditions on the mechanical properties of AISI 1035.
基金This work is financially supported by research university grantUniversiti Sains Malaysia RUI 1001/PBAHAN/811212teaching fellow scheme USM.9/25 Jld.ⅩⅢ。
文摘The influence of La_(2)O_(3) inclusion(0-3 wt%) on the micro structure,phase formation and mechanical properties of zirconia toughed alumina(ZTA) added with 5.0 wt% CeO_(2) was investigated.ZTA CeO_(2) composites were sintered at 1600℃ for 4 h.The microstructure,phase formation,density,fracture toughness and hardness properties were characterised through FESEM,Microscopy Image Analysis Software and XRD diffractometer,Archimedes principle and Vickers indentation technique,respectively.The XRD,image processing and FESEM reveal the existence of LaAl_(11)O_(18).The addition of La_(2)O_(3) incites the sintering,microstructure refinement,densification of ZTA-CeO_(2) matrix and phase transformation.Hence,the hardness of ZTA-CeO_(2) ceramics is increased rapidly based on refinement of Al_(2)O_(3) grains,densification of ZTA-CeO_(2) composites and porosity reduction.It is observed that the fracture toughness is enhanced through in situ formation of elongated LaAl_(11)O_(18) grains.The addition of 0.7 wt% La_(2)O_(3) culminated in the achievement of the optimum findings for density(4.41 g/cm^(3)),porosity(0.46%),hardness(1792 HV) and fracture toughness(8.8 MPa·m^(1/2)).Nevertheless,excess La_(2)O_(3) is proven to be detrimental as it displays poor mechanical properties due to the poor compactness of numerous LaAl_(11)O_(18) grains,coarsening of Al_(2)O_(3) grains and decline in density.
文摘The rapid adoption of the Internet of Things(IoT)across industries has revolutionized daily life by providing essential services and leisure activities.However,the inadequate software protection in IoT devices exposes them to cyberattacks with severe consequences.Intrusion Detection Systems(IDS)are vital in mitigating these risks by detecting abnormal network behavior and monitoring safe network traffic.The security research community has shown particular interest in leveraging Machine Learning(ML)approaches to develop practical IDS applications for general cyber networks and IoT environments.However,most available datasets related to Industrial IoT suffer from imbalanced class distributions.This study proposes a methodology that involves dataset preprocessing,including data cleaning,encoding,and normalization.The class imbalance is addressed by employing the Synthetic Minority Oversampling Technique(SMOTE)and performing feature reduction using correlation analysis.Multiple ML classifiers,including Logistic Regression,multi-layer perceptron,Decision Trees,Random Forest,and XGBoost,are employed to model IoT attacks.The effectiveness and robustness of the proposed method evaluate using the IoTID20 dataset,which represents current imbalanced IoT scenarios.The results highlight that the XGBoost model,integrated with SMOTE,achieves outstanding attack detection accuracy of 0.99 in binary classification,0.99 in multi-class classification,and 0.81 in multiple sub-classifications.These findings demonstrate our approach’s significant improvements to attack detection in imbalanced IoT datasets,establishing its superiority over existing IDS frameworks.
文摘Purpose: Intrapartum Doppler velocimetry is a non-invasive investigation method. This method is useful for evaluating the pathophysiological mechanisms underlying changes in fetal heart rate. This study aimed to determine the usefulness of Doppler ultrasound findings during labor in high-risk women on determining the mode of delivery and outcome of the newborn. Methods: A cross-sectional study was conducted from January 1, 2015 to December 31, 2015 on 100 high-risk pregnant women during labor. The study was performed in the Maternity Teaching Hospital in Erbil City, Kurdistan Region, Iraq. Results: The majority of women (77%) had a normal end-diastolic blood flow pattern through the umbilical artery. A total of 76.9% of those with abnormal Doppler results had a Cesarean delivery compared with only 9.5% of those with normal Doppler results (p p p = 0.07) and fifth minutes (p = 0.01). Conclusions: Abnormal umbilical artery Doppler ultrasound findings in high-risk pregnancies for women in labor are associated with increased rates of emergency cesarean sections and a poor outcome of the newborn.
文摘Aspergillus spp. has been widely found as useful microorganism in biotechnology. They have a high ability in the production of secondary metabolites. Therefore, isolates of Aspergillus were isolated from healthy rice field located in Selangor State/Malaysia. The obtained strain (UPMZ01) was conducted against Pyricularia oryzea by applying dual culture and culture filtrate technique. The antagonism of strain UPMZ01 in the dual culture was 81.326% inhibition percentage against P. oryzae given the optimum inhibitory percentage 100% at all concentration of secondary metabolites aged 14 days. The isolate (UPMZ01) was identified as Aspergillus niger with accession number (KY698415). The environment factors such as pH and temperature influencing on production of secondary metabolites. The results were shown that pH at level 5.0 and temperature between 21℃?to 29℃?is the optimum condition for A. niger to produce efficient antifungal metabolites which given 100% PIGR against blast pathogen. The secondary metabolites compounds were identified by Gas Chromatography-Mass Spectrometry (GC-MS). Fifteen compounds were recognized as major compounds which may have the possibility of possessing antifungal characteristics. Most of identified compounds are Oleic Acid, n-Hexadecanoic acid, Hexose, Glycerol, Stearic acid, Tetradecanoic acid, Dodecanoic acid and 5-Hydrxoymethylfurfural.
文摘The identification of brain tumors is multifarious work for the separation of the similar intensity pixels from their surrounding neighbours.The detection of tumors is performed with the help of automatic computing technique as presented in the proposed work.The non-active cells in brain region are known to be benign and they will never cause the death of the patient.These non-active cells follow a uniform pattern in brain and have lower density than the surrounding pixels.The Magnetic Resonance(MR)image contrast is improved by the cost map construction technique.The deep learning algorithm for differentiating the normal brain MRI images from glioma cases is implemented in the proposed method.This technique permits to extract the linear features from the brain MR image and glioma tumors are detected based on these extracted features.Using k-mean clustering algorithm the tumor regions in glioma are classified.The proposed algorithm provides high sensitivity,specificity and tumor segmentation accuracy.
文摘Iraq is one of the most affected countries in the Middle East concerning the occurrences of sand and dust storms. The frequency of the occurrence has increased drastically in the last decade and it is increasing continuously. The events of sand and dust storms are either regional or local. The former, however, is more frequent than the latter. The regional event, generally extends outside the Iraqi territory, into different directions, but usually covers part of Syria, crossing the Iraqi territory towards Kuwait and Saudi Arabia, and/or towards the Arabian Gulf, and less frequently extends to Iran. The main causes in the development of sand and dust storms, in Iraq are discussed. The causes are also either regional or local. The former, however, causes more economic losses and harsh effect on the human’s health, as compared with the latter. One of the main reasons behind the development of sand and dust storms is the climatic changes within the region, especially the drastic decrease in the annual rate of rain fall, besides environmental changes, such as drying of the marshes, land degradation, and desertification. From the local causes, the most effective reason is the haphazard driving and military operations, especially in the Iraqi Southern Desert. Prudent management of water resources by using non-conventional resources and adapting suitable irrigation methods can greatly help to overcome this phenomenon and minimize the number of dust storm.
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BACKGROUND Myocardial infarction,particularly ST-segment elevation myocardial infarction(STEMI),is a key global mortality cause.Our study investigated predictors of mortality in 96 STEMI patients undergoing primary percutaneous coronary intervention at Erbil Cardiac Center.Multiple factors were identified influencing in-hospital mortality.Significantly,time from symptom onset to hospital arrival emerged as a decisive factor.Consequently,our study hypothesis is:"Reducing time from symptom onset to hospital arrival significantly improves STEMI prognosis."AIM To determine the key factors influencing mortality rates in STEMI patients.METHODS We studied 96 consecutive STEMI patients undergoing primary percutaneous coronary intervention(PPCI)at the Erbil Cardiac Center.Their clinical histories were compiled,and coronary evaluations were performed via angiography on admission.Data included comorbid conditions,onset of cardiogenic shock,complications during PPCI,and more.Post-discharge,one-month follow-up assessments were completed.Statistical significance was set at P<0.05.RESULTS Our results unearthed several significant findings.The in-hospital and 30-d mortality rates among the 96 STEMI patients were 11.2%and 2.3%respectively.On the investigation of independent predictors of in-hospital mortality,we identified atypical presentation,onset of cardiogenic shock,presence of chronic kidney disease,Thrombolysis In Myocardial Infarction grades 0/1/2,triple vessel disease,ventricular tachycardia/ventricular fibrillation,coronary dissection,and the no-reflow phenomenon.Specifically,the recorded average time from symptom onset to hospital arrival amongst patients who did not survive was significantly longer(6.92±3.86 h)compared to those who survived(3.61±1.67 h),P<0.001.These findings underscore the critical role of timely intervention in improving the survival outcomes of STEMI patients.CONCLUSION Our results affirm that early hospital arrival after symptom onset significantly improves survival rates in STEMI patients,highl
文摘Automated Speech Emotion Recognition (SER) becomes more popular and has increased applicability.SER concentrates on the automatic identification of the emotional state of a humanbeing using speech signals. It mainly depends upon the in-depth analysis of the speech signal,extracts features containing emotional details from the speech signal, and utilises patternrecognition techniques for emotional state identification. The major problem in automatic SERis to extract discriminate, powerful, and emotional salient features from the acoustical content ofspeech signals. The proposed model aims to detect and classify three emotional states of speechsuch as happy, neutral, and sad. The presented model makes use of Convolution neural network– Gated Recurrent unit (CNN-GRU) based feature extraction technique which derives a set offeature vectors. A comprehensive simulation takes place using the Berlin German Database andSJTU Chinese Database which comprises numerous audio files under a collection of differentemotion labels.
文摘Clustering schemes in vehicular networks organize vehicles into logical groups.They are vital for improving network performance,accessing the medium,and enabling efficient data dissemination.Most schemes rely on periodically broadcast hello messages to provide up-to-date information about the vehicles.However,the periodic exchange of messages overwhelms the system and reduces efficiency.This paper proposes the Flag-based Vehicular Clustering(FVC)scheme.The scheme leverages a combination of Fitness Score(FS),Link Expiration Time(LET),and clustering status flags to enable efficient cluster formation in a hybrid manner.The FVC relies on the periodic broadcast of the basic safety message in the Dedicated Short-Range Communications(DSRC)standard for exchanging the vehicle’s status,FS,and joining request.Piggybacking extra information onto the existing periodic beacon reduces the overhead of exchanging additional control messages,which is the main contribution of this work.The scheme is implemented in a hybrid manner by utilizing a Road Side Unit(RSU)to implement a clustering algorithm.This work considered the FastPAM algorithm,a fast version of the Partitioning Around Medoids(PAM)clustering algorithm,to generate a list of potential cluster heads.The FVC scheme uses the LET as the clustering metric with the FastPAM algorithm.Moreover,the Lightweight FastPAM Vehicular Clustering(LFPVC)algorithm is considered by selecting the initial cluster heads based on the FS instead of the greedy FastPAM’s build stage.In the absence of the RSU,the vehicles utilize the FS with proper back-off time to self-elect the cluster head.The hybrid FVC scheme increased the cluster lifetime by 32%and reduced the control-message overhead by 63%compared to the related work.Moreover,the LFPVC algorithm achieved similar results to the FastPAM algorithm.
文摘Wireless Sensor Networks(WSN)has been extensively utilized as a communication model in Internet of Things(IoT).As well,to offer service,numerous IoT based applications need effective transmission over unstable locations.To ensure reliability,prevailing investigations exploit multiple candidate forwarders over geographic opportunistic routing in WSNs.Moreover,these models are affected by crucial denial of service(DoS)attacks,where huge amount of invalid data are delivered intentionally to the receivers to disturb the functionality of WSNs.Here,secure localization based authentication(SLA)is presented to fight against DoS attack,and to fulfil the need of reliability and authentication.By examining state information,SLA projects a trust model to enhance efficacy of data delivery.Indeed,of the prevailing opportunistic protocols,SLA guarantees data integrity by modelling a trust based authentication,providing protection against DoS attackers and diminishing computational costs.Specifically,this model acts as a verification strategy to accelerate?attackers and to handle isolation.This strategy helps SLA in eliminating duplicate transmission and by continuous verification that results from conventional opportunistic routing.Simulation is performed in a MATLAB environment that offers authentic and reliable delivery by consuming approximately 50%of the cost in contrast to other approaches.The anticipated model shows better trade off in comparison to the prevailing ones.
