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Unraveling the role of dual Ti/Mg metals on the ignition and combustion behavior of HTpB-boron-based fuel

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Defence Technology（防务技术） 2024年第2期32卷 134-143页

作者： Arijit Debnath Yash pal Sri nithya Mahottamananda Djalal Trache School of Aeronautical Science Hindustan Institute of Technology and ScienceChennai 603103India Department of Aerospace Engineering B S Abdur Rahman Crescent Institute of Science and TechnologyChennai 600048India Energetic Materials Laboratory(EMLab) Teaching and Research Unit of Energetic ProcessesEcole Militaire PolytechniqueBP 17Bordj El-Bahri16046AlgiersAlgeria

Metal additives play an essential role in explosive and propellant formulations. Boron(B) is widely used in propellant applications owing to its high energetic content. The addition of B to explosives and propellants increases their energy density, making them more efficient and powerful. Nevertheless, B forms oxide layers on its surface during combustion, slowing down the combustion rate and reducing rocket motor efficiency. To overcome this issue, other metal additives such as aluminum(Al), magnesium(Mg),and titanium(Ti) are revealed to be effective in boosting the combustion rate of propellants. These additives may improve the combustion rate and therefore enhance the rocket motor’s performance. The present study focused on preparing and investigating the ignition and combustion behavior of pure hydroxyl-terminated polybutadiene(HTpB)-B fuel supplemented with nano-titanium and nanomagnesium. The burn rates of HTpB-B fuel samples were evaluated on the opposed flow burner(OFB)under a gaseous oxygen oxidizer, for which the mass flux ranges from 22 kg/(m^(2)·s) to 86 kg/(m^(2)·s). The addition of Ti and Mg exhibited higher regression rates, which were attributed to the improved oxidation reaction of B due to the synergetic metal combustion effect. The possible combustion/oxidation reaction mechanism of B-Mg and B-Ti by heating the fuel samples at 900℃ and 1100℃ was also examined in a Nabertherm burnout furnace under an oxygen atmosphere. The post-combustion products were collected and further subjected to X-ray diffraction(XRD) and field emission scanning electron microscopy(FE-SEM) analyses to inspect the combustion behavior of B-Ti and B-Mg. It has been observed that the B oxide layer at the interface between B-Ti(B-Mg) is removed at lower temperatures, hence facilitating oxygen transfer from the surroundings to the core B. Additionally, Ti and Mg decreased the ignition delay time of B, which improved its combustion performance.

关键词： Boron B_(2)O_(3) Opposed flow burner Combustion Magnesium

Metric and Upper Dimension of Extended Annihilating-Ideal Graphs

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Algebra Colloquium 2024年第2期31卷 221-238页

作者： S.nithya G.Elavarasi Genghua Fan Department of Mathematics St.Xavier's College(Autonomous)Palayamkottai 627002Tamil NaduIndia Afiliated to Manonmaniam Sundaranar University Abishekapatti Tirunelveli 627012Tamil NaduIndia pG and Research Department of Mathematics St.Xavier's College(Autonomous)Palayamkottai 627002Tamil NaduIndia Affiliated to Manonmaniam Sundaranar University Abishekapatti Tirunelveli 627012Tamil NaduIndia 不详

The metric dimension problem is called navigation problem due to its application to robot navigation in *** this concept has wide applications in motion planning,sonar and loran station,and so *** this paper,we study certain results on the metric dimension,upper dimension and resolving number of extended annihilating-ideal graph EAG(R)associated to a commutative ring R,denoted by dim M(EAG(R)),dim+(EAG(R))and res(EAG(R)),*** we prove the finiteness conditions of dim M(EAG(R))and dim+(EAG(R)).In addition,we characterize dim M(EAG(R)),dim+(EAG(R))and res(EAG(R))for artinian rings and the direct product of rings.

关键词： extended annihilating-ideal graph metric dimension upper dimension resolving number

Evaluating Ignition and Combustion performance with Al-Metal- Organic Frameworks and Nano-Aluminum in HTpB Fuel

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火炸药学报 2024年第5期47卷 413-421,I0003页

作者： Sri nithya Mahottamananda Yash pal Yarravarapu Sai Sriram Subha S Djalal Trache Department of Aerospace Engineering B S Abdur Rahman CrescentInstitute of Science and TechnologyChennai-600048India School of Aeronautical Science Hindustan Institute of Technologyand ScienceChennai-603103India Energetic Materials Laboratory(EMLab) Teaching and ResearchUnit of Energetic ProcessesEcole Militaire PolytechniqueBP 17Bordj El-Bahri16046AlgiersAlgeria

Incorporating aluminum metal-organic frameworks(Al-MOFs)as energetic additives for solid fuels presents a promising avenue for enhancing combustion *** study explores the potential benefits of Al-MOF(MIL-53(Al))energetic additive on the combustion performance of hydroxyl-terminated polybutadiene(HTpB)*** HTpB-MOF fuel samples were manufactured using the vacuum-casting technique,followed by a comprehensive evaluation of their ignition and combustion properties using an opposed flow burner(OFB)setup utilizing gaseous oxygen as an *** gauge the effectiveness of Al-MOFs as fuel additives,their impact is compared with that of nano-aluminum(nAl),another traditional additive in HTpB *** results indicate that the addition of 15%(mass fraction)nAl into HTpB resulted in the shortest ignition delay time(136 ms),demonstrating improved ignition performance compared to pure HTpB(273 ms).The incorporation of Al-MOF in HTpB also reduced ignition delay times to 227 ms and 189 ms,***,under high oxidizer mass flux conditions(79—81 kg/(m^(2)s)),HTpB fuel with 15%nAl exhibited a substantial 83.2%increase in regression rate compared to the baseline HTpB fuel,highlighting the positive influence of nAl on combustion *** contrast,HTpB-MOF with a 15%Al-MOF additive showed a 32.7%increase in regression rate compared to pure *** results suggest that HTpB-nAl outperforms HTpB-MOF in terms of regression rates,indicating a more vigorous and rapid burning behavior.

关键词： ignition combustion enhancement MOF HTpB regression rate

Smart Healthcare Activity Recognition Using Statistical Regression and Intelligent Learning

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Computers, Materials & Continua 2024年第1期78卷 1189-1205页

作者： K.Akilandeswari nithya Rekha Sivakumar Hend Khalid Alkahtani Shakila Basheer Sara Abdelwahab Ghorashi Department of Computer Sciences Government Arts College(Autonomous)Salem636007India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and *** exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and *** many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during ***,this paper proposes a Statistical partial Regression and Support Vector Intelligent Agent Learning(SpR-SVIAL)for Smart Healthcare *** first,the Statistical partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction ***,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable *** attain highly accurate Smart Healthcare Monitoring with less time,partial Least Square helps extract the dimensionality-reduced *** that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and *** evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several *** quantitatively analyzed results indicate the better performance of our proposed SpR-SVIAL method when compared with two state-of-the-art methods.

关键词： Internet of Things smart health care monitoring human activity recognition intelligent agent learning statistical partial regression support vector

An Adaptive Classifier Based Approach for Crowd Anomaly Detection

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Computers, Materials & Continua 2022年第7期72卷 349-364页

作者： Sofia Nishath p.S.nithya Darisini School of Computer Science and Engineering Vellore Institute of TechnologyChennai600127India

Crowd Anomaly Detection has become a challenge in intelligent video surveillance system and *** video surveillance systems make extensive use of data mining,machine learning and deep learning *** this paper a novel approach is proposed to identify abnormal occurrences in crowded situations using deep *** this approach,Adaptive GoogleNet Neural Network Classifier with Multi-Objective Whale Optimization Algorithm are applied to predict the abnormal video frames in the crowded *** use multiple instance learning(MIL)to dynamically develop a deep anomalous ranking *** technique predicts higher anomalous values for abnormal video frames by treating regular and irregular video bags and video *** use the multi-objective whale optimization algorithm to optimize the entire process and get the best *** performance parameters such as accuracy,precision,recall,and F-score are considered to evaluate the proposed technique using the python simulation *** simulation results show that the proposed method performs better than the conventional methods on the public live video dataset.

关键词： Abnormal event detection adaptive GoogleNet neural network classifier multiple instance learning multi-objective whale optimization algorithm

Metaheuristic Secure Clustering Scheme for Energy Harvesting Wireless Sensor Networks

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Computer Systems Science & Engineering 2023年第4期45卷 497-512页

作者： S.nithya Roopa p.Anandababu Sibi Amaran Rajesh Verma Department of Computer Science and Engineering Kumaraguru College of TechnologyCoimbatore641049India Department of Computer and Information Science Faculty of ScienceAnnamalai UniversityChidambaram608002India Department of Computing Technologies SRM Institute of Science and TechnologyChennai603203India Department of Electrical Engineering King Khalid UniversityAbha61411Kingdom of Saudi Arabia

Recently,energy harvesting wireless sensor networks(EHWSN)have increased significant attention among research *** harvesting energy from the neighboring environment,the sensors in EHWSN resolve the energy constraint problem and offers lengthened network *** is one of the proficient ways for accomplishing even improved lifetime in *** clustering process intends to appropriately elect the cluster heads(CHs)and construct *** several models are available in the literature,it is still needed to accomplish energy efficiency and security in *** this view,this study develops a novel Chaotic Rider Optimization Based Clustering protocol for Secure Energy Harvesting Wireless Sensor Networks(CROC-SEHWSN)*** presented CROC-SEHWSN model aims to accomplish energy efficiency by clustering the node in *** CROC-SEHWSN model is based on the integration of chaotic concepts with traditional rider optimization(RO)***,the CROC-SEHWSN model derives a fitness function(FF)involving seven distinct parameters connected to *** accomplish security,trust factor and link quality metrics are considered in the *** design of RO algorithm for secure clustering process shows the novelty of the *** order to demonstrate the enhanced performance of the CROC-SEHWSN approach,a wide range of simulations are carried out and the outcomes are inspected in distinct *** experimental outcome demonstrated the superior performance of the CROC-SEHWSN technique on the recent approaches with maximum network lifetime of 387.40 and 393.30 s under two scenarios.

关键词： Clustering wireless sensor networks network lifetime energy efficiency metaheuristics energy harvesting rider optimization

Ensemble Deep Learning for IoT Based COVID 19 Health Care pollution Monitor

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Intelligent Automation & Soft Computing 2023年第2期35卷 2383-2398页

作者： nithya Rekha Sivakumar Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia

Internet of things(IoT)has brought a greater transformation in health-care sector thereby improving patient care,minimizing treatment *** pre-sent method employs classical mechanisms for extracting features and a regression model for *** methods have failed to consider the pollu-tion aspects involved during COVID 19 *** Ensemble Deep Learning and Framingham Feature Extraction(FFE)techniques,a smart health-care system is introduced for COVID-19 pandemic disease *** Col-lected feature or data via predictive mechanisms to form pollution *** maps are used to implement real-time countermeasures,such as storing the extracted data or feature in a Cloud server to minimize concentrations of air *** integrated with patient management systems,this solution would minimize pollution emitted via patient’s sensors by offering spaces in the cloud server when pollution thresholds are ***,the Gini Index factor infor-mation gain technique eliminates unimportant and redundant attributes while selecting the most relevant,reducing computing overhead and optimizing system ***,the COVID-19 disease prognosis ensemble deep learning-based classiﬁer is *** analysis is planned to measure the prediction accuracy,error,precision and recall for different numbers of *** results show that prediction accuracy is improved by 8%,error rate was reduced by 47%and prediction time is minimized by 36%compared to exist-ing methods.

关键词： Internet of Things Covid-19 ensemble deep learning framingham feature extraction

Earlier Detection of Alzheimer’s Disease Using 3D-Convolutional Neural Networks

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Computer Systems Science & Engineering 2023年第8期46卷 2601-2618页

作者： V.p.nithya N.Mohanasundaram R.Santhosh Department of Computer Science and Engineering Karpagam Academy of Higher EducationCoimbatoreIndia

The prediction of mild cognitive impairment or Alzheimer’s disease(AD)has gained the attention of huge researchers as the disease occurrence is increasing,and there is a need for earlier ***,due to the highdimensionality nature of neural data and the least available samples,modelling an efficient computer diagnostic system is highly *** approaches,specifically deep learning approaches,are essential in disease *** Learning(DL)approaches are successfully demonstrated for their higher-level performance in various fields like medical imaging.A novel 3D-Convolutional Neural Network(3D-CNN)architecture is proposed to predict AD with Magnetic resonance imaging(MRI)*** proposed model predicts the AD occurrence while the existing approaches lack prediction accuracy and perform binary *** proposed prediction model is validated using the Alzheimer’s disease Neuro-Imaging Initiative(ADNI)*** outcomes demonstrate that the anticipated model attains superior prediction accuracy and works better than the brain-image dataset’s general *** predicted model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is ***’experimentation is carried out,and the model’s superiority is compared with various advanced approaches for multi-level *** proposed model gives better prediction accuracy,precision,recall,and F-measure than other systems like Long Short Term Memory-Recurrent Neural Networks(LSTM-RNN),Stacked Autoencoder with Deep Neural Networks(SAE-DNN),Deep Convolutional Neural Networks(D-CNN),Two Dimensional Convolutional Neural Networks(2D-CNN),Inception-V4,ResNet,and Two Dimensional Convolutional Neural Networks(3D-CNN).

关键词： Alzheimer’s disease 3D CNN ADNI prediction accuracy highdimensionality data

Triplet Label Based Image Retrieval Using Deep Learning in Large Database

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Computer Systems Science & Engineering 2023年第3期44卷 2655-2666页

作者： K.nithya V.Rajamani Research Scholar Department of Information and Communication EngineeringAnna UniversityChennai600025India Department of Electronics and Communication Engineering Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering CollegeChennai600062India

Recent days,Image retrieval has become a tedious process as the image database has grown very *** introduction of Machine Learning(ML)and Deep Learning(DL)made this process more *** these,the pair-wise label similarity is used tofind the matching images from the *** this method lacks of limited propose code and weak execution of misclassified *** order to get-rid of the above problem,a novel triplet based label that incorporates context-spatial similarity measure is proposed.A point Attention Based Triplet Network(pABTN)is introduced to study propose code that gives maximum discriminative *** improve the performance of ranking,a corre-lating resolutions for the classification,triplet labels based onfindings,a spatial-attention mechanism and Region Of Interest(ROI)and small trial information loss containing a new triplet cross-entropy loss are *** the experimental results,it is shown that the proposed technique exhibits better results in terms of mean Reciprocal Rank(mRR)and mean Average precision(mAp)in the CIFAR-10 and NUS-WIpE datasets.

关键词： Image retrieval deep learning point attention based triplet network correlating resolutions classification region of interest