bluuebunny/crossref_metadata_2025_split huggingface dataset:
Localization of Plastic Deformation in the Copper and Stainless Steels Samples, Irradiated with Neutrons
Authors: Merezhko Mikhail , Merezhko Diana 
Published: January 2022

Abstract
The reduction of ductility of austenitic stainless steels as a result of long-term operation in the nuclear reactor core is an important problem of modern radiation materials science. Understanding the mechanisms of the effect of neutron irradiation on the mechanical properties of austenitic steels is impossible without research of localization processes occurring during the deformation. In this paper, it was found that the value of the true local deformation corresponding to the onset of neck formation in face-centered cubic structured metals decreases with an increase in the radiation dose, while the true stress remains almost constant. Additional hardening of AISI 304 steel due to the intensive formation of the martensitic α’-phase increases not only the stress at which a neck is formed in this alloy, but also the true local deformation. As a result, the uniform elongation increases and remains high after neutron irradiation to 0.05 dpa. The forehanded formation of the martensitic α’-phase in sufficient quantity before the necking onset can be considered as an additional deformation mechanism that will increase the ability of the material to deform uniformly.
Tests of lithium CPS on basis of carboxylic fabric, reinforced with carbon nanotubes under high thermal and radiation loads
Authors: Tulubayev Ye Yu , Ponkratov Yu V , Baklanov V V , Bochkov V S , Karambayeva I S 
Published: January 2022

Abstract
This work is devoted to testing a lithium CPS based on carbon fabric reinforced with carbon nanotubes under conditions of thermal and radiation loads. The paper considers and analyzes: the properties of carbon nanotubes and methods of their synthesis, the nature of the interaction of carbon materials with liquid lithium at different temperatures. A description of all the main stages in the manufacture of lithium CPS based on carbon fabric reinforced with carbon nanotubes is given. Microstructural studies of a manufactured lithium CPS sample based on carbon fabric reinforced with carbon nanotubes are presented. Studies have shown that a carbon fabric with a fiber surface reinforced with carbon nanotubes is completely wetted by liquid lithium. The developed technology is fully suitable for the manufacture of lithium CPS samples for further research. The results of experiments on the interaction of lithium CPS based on graphite fabric reinforced with carbon nanotubes with hydrogen isotopes under thermal and radiation loads are presented.
Determination of the content of impurity elements in a sample of high purity India by the method of radiochemical neutron activation analysis
Authors: Sadikov I.I. , Kh. Yarmatov B. , Usmanov T.M. , Sadikova Z.O. 
Published: January 2022

Abstract
The present article is devoted to the development of a method for neutron activation analysis of a microimpurity composition of high-purity indium with the radiochemical separation of indium radionuclides from radionuclides of the determined elements by extraction chromatography in the system tributyl phosphate (TBP) - solutions of hydrobromic acid (HBr).
Neutron activation analysis (NAA) has a special place among the physical methods for determining the trace composition of high-purity substances. Low detection limits, the possibility of simultaneous determination of a large number of impurity elements from one sample, no correction for the control experiment, the possibility of using inactive carriers in separating traces of radionuclides of impurity elements from matrix elements ensured the widespread use of NAA in the analysis of highly pure substances.
The developed technique allows separating matrix radionuclides from radionuclides of impurity elements with high efficiency and determination of 28 impurity elements in high-purity indium with detection limits of 0.7 ppm to 0.05 ppb.
Classification of Cardiac Arrhythmia using improved Feature Selection methods and Ensemble Classifiers
Authors: Jain Rajat , Betrabet Pranam R , Ashwath Rao B , Subba Reddy N V 
Published: January 2022

Abstract
Arrhythmia is one of the life-threatening heart diseases which is diagnosed and analyzed using electrocardiogram (ECG) recordings and other symptoms namely rapid heartbeat or chest-pounding, shortness of breath, near fainting spells, insufficient pumping of blood from the heart, etc along with sudden cardiac arrest. Arrhythmia records a hasty and aberrant ECG. In this implementation, the arrhythmia dataset is collected from the UCI machine learning repository and then classified the records into sixteen stated classes using multiclass classification. The large feature set of the dataset is reduced using improved feature selection techniques such as t-Distributed Stochastic Neighbor Embedding (TSNE), Principal Component Analysis (PCA), Uniform Manifold Approximation, and Projection (UMAP) and then an Ensemble Classifier is built to analyse the classification accuracy on arrhythmia dataset to conclude when and which approach gives optimal results.
Study of distance metrics on k - nearest neighbor algorithm for star categorization
Authors: Nayak Swathi , Bhat Manisha , Subba Reddy N V , Ashwath Rao B 
Published: January 2022

Abstract
Classification of stars is essential to investigate the characteristics and behavior of stars. Performing classifications manually is error-prone and time-consuming. Machine learning provides a computerized solution to handle huge volumes of data with minimal human input. k-Nearest Neighbor (kNN) is one of the simplest supervised learning approaches in machine learning. This paper aims at studying and analyzing the performance of the kNN algorithm on the star dataset. In this paper, we have analyzed the accuracy of the kNN algorithm by considering various distance metrics and the range of k values. Minkowski, Euclidean, Manhattan, Chebyshev, Cosine, Jaccard, and Hamming distance were applied on kNN classifiers for different k values. It is observed that Cosine distance works better than the other distance metrics on star categorization.
Emerging Military Applications of Free Space Optical Communication Technology: A Detailed Review
Authors: Kumar Suresh , Sharma Nishant 
Published: January 2022

Abstract
Free Space Optical Communication (FSOC) technology, designed to operate through free space as medium is rapidly emerging as reliable, fast & secure alternative for broadband communication. It is being researched & investigated for applications and technical utilities in both civil & military domain systems owing to its immense benefits including high security, better data rates & fast installations, no requirement of licensed spectrum, best costs & simplicity of design as compared to contemporary Radio Frequency (RF) systems. FSOC networks once established are difficult to detect & intercept in view of small divergence of the laser beams. This paper reviews the FSOC technology and presents features based merits as well as unmatched advantages & associated major applications in various fields collating them into a single reference point for future research. Efforts have also been invested to present a review of FSOC’s limitations & innovative emerging mitigation techniques which can prove to be a one stop feeder & a launch pad for future research in FSOC domain. A literature survey has been undertaken of available FSOC related military applications to review & gather relevant inputs to throw light on emerging trends in military applications including recent experiments & researched areas pertaining to laser systems & weapons, Unmanned Aerial Vehicles (UAVs), under sea usages, terrestrial applications, aerial, naval ships/shore based applications & RF/hybrid systems. It has been endeavored to shed light on findings & developments in these classified military domains to generate inputs for future work in this domain. Finally, a future technical roadmap and a way ahead & suggestions have been coined up pointing towards required impetus & focus areas in FSOC research domain.
Cardiac Disease Prediction using Supervised Machine Learning Techniques.
Authors: Gupta Chiradeep , Saha Athina , Subba Reddy N V , Dinesh Acharya U 
Published: January 2022

Abstract
Diagnosis of cardiac disease requires being more accurate, precise, and reliable. The number of death cases due to cardiac attacks is increasing exponentially day by day. Thus, practical approaches for earlier diagnosis of cardiac or heart disease are done to achieve prompt management of the disease. Various supervised machine learning techniques like K-Nearest Neighbour, Decision Tree, Logistic Regression, Naïve Bayes, and Support Vector Machine (SVM) model are used for predicting cardiac disease using a dataset that was collected from the repository of the University of California, Irvine (UCI). The results depict that Logistic Regression was better than all other supervised classifiers in terms of the performance metrics. The model is also less risky since the number of false negatives is low as compared to other models as per the confusion matrix of all the models. In addition, ensemble techniques can be approached for the accuracy improvement of the classifier. Jupyter notebook is the best tool, for the implementation of Python Programming having many types of libraries, header files, for accurate and precise work.
Enhancement of Security of Diffie-Hellman Key Exchange Protocol using RSA Cryptography.
Authors: Gupta Chiradeep , Subba Reddy N V 
Published: January 2022

Abstract
Cryptography is related and referred to as the secured transmission of messages amongst the sender and the intended receiver by ensuring confidentiality, integrity, and authentication. Diffie – Hellman (DH) key exchange protocol is a well-known algorithm that would generate a shared secret key among the sender and the intended receiver, and the basis of cryptosystems for using public and private key for encryption and decryption process. But it is severely affected by the Man in the Middle (MITM) attack that would intercept and manipulate thus eavesdropping the shared secret key. This paper proposes a model of integrating the public-key RSA cryptography system with the DH key exchange to prevent the MITM attack. The performance of the proposed work has been compared to the DH Key Exchange algorithm as well as RSA Cryptosystem to conclude for effectiveness of the proposed model.
Accelerating Smith-Waterman Algorithm for Faster Sequence Alignment using Graphical Processing Unit
Authors: Kaur Karamjeet , Chakraborty Sudeshna , Kumar Gupta Manoj 
Published: January 2022

Abstract
In bioinformatics, sequence alignment is very important task to compare and find similarity between biological sequences. Smith Waterman algorithm is most widely used for alignment process but it has quadratic time complexity. This algorithm is using sequential approach so if the no. of biological sequences is increasing then it takes too much time to align sequences. In this paper, parallel approach of Smith Waterman algorithm is proposed and implemented according to the architecture of graphic processing unit using CUDA in which features of GPU is combined with CPU in such a way that alignment process is three times faster than sequential implementation of Smith Waterman algorithm and helps in accelerating the performance of sequence alignment using GPU. This paper describes the parallel implementation of sequence alignment using GPU and this intra-task parallelization strategy reduces the execution time. The results show significant runtime savings on GPU.
Hamiltonian Laceability in Book Graphs
Authors: Sumitra Devi M.R. , Girisha A 
Published: January 2022

Abstract
A connected graph network G is a Hamilton-t-laceable (Hamilton- t*-laceable) if Ǝ in G a Hamilton path connecting each pair (at least one pair) of vertices at distance ‘t’ in G such that 1 ≤ t ≤ diameter (G). In this paper, we discuss the f-edge-Hamilton-t-laceability of Book and stacked Book graphs, where 1 ≤ t ≤ diameter (G).
Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)
Authors: Nigam Rohan , Rao Meghana , Rian Dias Nihal , Hariharan Arjun , Choraria Amit , Tendolkar Atharv , Manohara Pai M M 
Published: January 2022

Abstract
Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.
InRFNet: Involution Receptive Field Network for COVID-19 Diagnosis
Authors: Dhruv M , Sai Chandra Teja R , Sri Devi R , Nagesh Kumar S 
Published: January 2022

Abstract
COVID-19 is an emerging infectious disease that has been rampant worldwide since its onset causing Lung irregularity and severe respiratory failure due to pneumonia. The Community-Acquired Pneumonia (CAP), Normal, and COVID-19 Computed Tomography (CT) scan images are classified using Involution Receptive Field Network from Large COVID-19 CT scan slice dataset. The proposed lightweight Involution Receptive Field Network (InRFNet) is spatial specific and channel-agnostic with Receptive Field structure to enhance the feature map extraction. The InRFNet model evaluation results show high training (99%) and validation (96%) accuracy. The performance metrics of the InRFNet model are Sensitivity (94.48%), Specificity (97.87%), Recall (96.34%), F1-score (96.33%), kappa score (94.10%), ROC-AUC (99.41%), mean square error (0.04), and the total number of parameters (33100).
Algorithms for Extracting various Local Texture Features
Authors: Ashwath Rao B , Gopalakrishna Kini N 
Published: January 2022

Abstract
In the machine learning and computer vision domain, images are represented using their features. Color, shape, and texture are some of the prominent types of features. Over time, the local features of an image have gained importance over the global features due to their high discerning ability in localized regions. The texture features are widely used in image indexing and content-based image retrieval. In the last two decades, various local texture features have been formulated. For a complete description of images, effective and efficient features are necessary. In this paper, we provide algorithms for 10 local texture feature extraction. These texture descriptors have been formulated since the year 2015. We have designed algorithms so that they are time efficient and memory space-efficient. We have implemented these algorithms and verified their output correctness.
Detection of fraudulent credit card transactions: A comparative analysis of data sampling and classification techniques
Authors: Praveen Mahesh Konduri , Ashar Afrouz Shaik , Shaju Areeckal Anu 
Published: January 2022

Abstract
Every year there is an increasing loss of a huge amount of money due to fraudulent credit card transactions. Recently there is a focus on using machine learning algorithms to identify fraud transactions. The number of fraud cases to non-fraud transactions is very low. This creates a skewed or unbalanced data, which poses a challenge to training the machine learning models. The availability of a public dataset for this research problem is scarce. The dataset used for this work is obtained from Kaggle. In this paper, we explore different sampling techniques such as under-sampling, Synthetic Minority Oversampling Technique (SMOTE) and SMOTE-Tomek, to work on the unbalanced data. Classification models, such as k-Nearest Neighbour (KNN), logistic regression, random forest and Support Vector Machine (SVM), are trained on the sampled data to detect fraudulent credit card transactions. The performance of the various machine learning approaches are evaluated for its precision, recall and F1-score. The classification results obtained is promising and can be used for credit card fraud detection.
Design and Analysis of Reconfigurable Antenna for Wide Band Applications
Authors: MallikharjunaRao Sathuluri , SettyVennelaSrujana Thirumala , Bhuvana Bindu Gurivinadagunta , Garlapatikotinagapavani  
Published: January 2022

Abstract
The relevance of reconfiguration in a dynamic environment is to improve an antenna’s performance by allowing it to transition between multiple frequencies. In this paper, we designed a reconfigurable patch antenna and fed it by strip line feeding by placing 2 slots to obtain different resonant frequencies. The feature of reconfigurability is attained by using Pin Diodes. In our design, we take a 2 pin diode. The proposed Antenna can operate on different frequencies i.e. 2.88GHz, 5.5GHz, 10.8GHz and 11.1GHz with the efficiency of 90% and more at different conditions of the diodes. This analysis is done by using HFSS Software.
A Proposed Exploratory Study of Object Detectors to Learn the Influence of Datasets on Model Performance
Authors: Kamath Vidya , Renuka A. 
Published: January 2022

Abstract
The quality of the images used to train the models in the field of object detection using deep learning models is critical in determining the model’s quality. However, there are very few methods for exploring these images in datasets to see what aspects in these images have a significant impact on the model’s performance. This could be one of the reasons why the models don’t match human perceptions. There is a need for more study that can suggest unique methodologies to address the topic at hand because the existing literature overlooks this line of thought. As a result, this paper provides a methodology based on exploratory sequential design, which may be used to identify several aspects of images in the dataset that influence model performance.
Late monsoon threatens coral refugia in the Andaman Sea
Authors: Liu Yanliang , Putchim Lalita , Li Kuiping , Gao Hongyan , Sun Lina , Khokiattiwong Somkiat , Yu Weidong 
Published: January 2022

Abstract
Internal waves (IWs) mitigate thermal stress and provide refugia for corals against increasingly frequent mass bleaching. However, climate events may bring uncertainty regarding the resistance of such refugia. Here, using in situ observation data in the Andaman Sea (AS), we conduct a case study in which a monsoon anomaly associated with an El Niño event threatens IW coral refugia. IW cooling in the AS coral reefs is modulated by the thermocline depth variation, which is driven, to a significant extent, by Kelvin wave signals from the equator. In the pre-monsoon period, distinct variations in IW cooling and surface heating form a time window of quickly-growing cumulative heat exposure. The El Niño induces a typical two-week delay of summer monsoon onset, which prolongs the duration of thermal stress growth and brings severe bleaching risk to corals. As global warming increases the frequency of extreme El Niño events, IW coral refugia will face great challenges in the future.
Climate change penalty and benefit on surface ozone: a global perspective based on CMIP6 earth system models
Authors: Zanis Prodromos , Akritidis Dimitris , Turnock Steven , Naik Vaishali , Szopa Sophie , Georgoulias Aristeidis K , Bauer Susanne E , Deushi Makoto , Horowitz Larry W , Keeble James , Le Sager Philippe , O’Connor Fiona M , Oshima Naga , Tsigaridis Konstantinos , van Noije Twan 
Published: January 2022

Abstract
This work presents an analysis of the effect of climate change on surface ozone discussing the related penalties and benefits around the globe from the global modelling perspective based on simulations with five CMIP6 (Coupled Model Intercomparison Project Phase 6) Earth System Models. As part of AerChemMIP (Aerosol Chemistry Model Intercomparison Project) all models conducted simulation experiments considering future climate (ssp370SST) and present-day climate (ssp370pdSST) under the same future emissions trajectory (SSP3-7.0). A multi-model global average climate change benefit on surface ozone of −0.96 ± 0.07 ppbv °C−1 is calculated which is mainly linked to the dominating role of enhanced ozone destruction with higher water vapour abundances under a warmer climate. Over regions remote from pollution sources, there is a robust decline in mean surface ozone concentration on an annual basis as well as for boreal winter and summer varying spatially from −0.2 to −2 ppbv °C−1, with strongest decline over tropical oceanic regions. The implication is that over regions remote from pollution sources (except over the Arctic) there is a consistent climate change benefit for baseline ozone due to global warming. However, ozone increases over regions close to anthropogenic pollution sources or close to enhanced natural biogenic volatile organic compounds emission sources with a rate ranging regionally from 0.2 to 2 ppbv C−1, implying a regional surface ozone penalty due to global warming. Overall, the future climate change enhances the efficiency of precursor emissions to generate surface ozone in polluted regions and thus the magnitude of this effect depends on the regional emission changes considered in this study within the SSP3_7.0 scenario. The comparison of the climate change impact effect on surface ozone versus the combined effect of climate and emission changes indicates the dominant role of precursor emission changes in projecting surface ozone concentrations under future climate change scenarios.
Integrable deformations of sigma models
Authors: Hoare Ben 
Published: January 2022

Abstract
In this pedagogical review we introduce systematic approaches to deforming integrable two-dimensional sigma models. We use the integrable principal chiral model and the conformal Wess–Zumino–Witten model as our starting points and explore their Yang–Baxter and current–current deformations. There is an intricate web of relations between these models based on underlying algebraic structures and worldsheet dualities, which is highlighted throughout. We finish with a discussion of the generalisation to other symmetric integrable models, including some original results related to

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cosets and their deformations, and the application to string theory. This review is based on notes written for lectures delivered at the school ‘Integrability, Dualities and Deformations’, which ran from 23 to 27 August 2021 in Santiago de Compostela and virtually.