bluuebunny/crossref_metadata_2025_split huggingface dataset:
Stability analysis of alpha driven toroidal Alfvén eigenmodes observed in JET deuterium-tritium internal transport barrier plasmas
Authors: Fitzgerald M. , Dumont R. , Keeling D. , Mailloux J. , Sharapov S. , Dreval M. , Figueiredo A. , Coelho R. , Ferreira J. , Rodrigues P. , Nabais F. , Borba D. , Štancar Ž. , Szepesi G. , Tinguely R.A. , Puglia P.G. , Oliver H.J.C. , Kiptily V. , Baruzzo M. , Lennholm M. , Siren P. , Garcia J. , Maggi C.F. ,   JET Contributors
Published: October 2023

AbstractA toroidal Alfvén eigenmode (TAE) has been observed to be driven by alpha particles in a JET deuterium-tritium internal transport barrier plasma. The observation occurred 50 ms after the removal of neutral beam heating (NBI). The mode is observed on magnetics, soft-xray, interferometry and reflectometry measurements. We present detailed stability calculations using a similar tool set validated during deuterium only discharges. These calculations strongly support the conclusion that the observed mode is a TAE, and that this mode was destabilized by alpha particles. Non-ideal effects from the bulk plasma are interpreted as responsible for suppressing the majority of TAEs which were also driven by alpha particles, but the modes that match the observations are predicted to be particularly weak for these non-ideal effects. This mode located far from the core on the outboard midplane is found to be driven by both trapped and passing particles despite alpha particles originating in the core.
Effect of the isotope mass on pedestal structure, transport and stability in D, D/T and T plasmas at similar β
N and gas rate in JET-ILW type I ELMy H-modes
Authors: Frassinetti L. , Perez von Thun C. , Chapman-Oplopoiou B. , Nyström H. , Poradzinski M. , Hillesheim J.C. , Horvath L. , Maggi C.F. , Saarelma S. , Stagni A. , Szepesi G. , Bleasdale A. , Chomiczewska A. , Morales R.B. , Brix M. , Carvalho P. , Dunai D. , Field A.R. , Fontdecaba J.M. , Sun H.J. , King D.B. , Kos D. , Kowalska E. , Labit B. , Lennholm M. , Menmuir S. , Rachlew E. , Refy D.I. , Schneider P.A. , Solano E.R. , Vianello N. , Vécsei M. ,   JET Contributors
Published: October 2023

Abstract
The work describes the pedestal structure, transport and stability in an effective mass (A
eff) scan from pure deuterium to pure tritium plasmas using a type I ELMy H-mode dataset in which key parameters that affect the pedestal behaviour (normalized pressure, ratio of the separatrix density to the pedestal density, pedestal ion Larmor radius, pedestal collisionality and rotation) are kept as constant as possible. Experimental results show a significant increase of the density at the pedestal top with increasing A
eff, a modest reduction in the temperature and an increase in the pressure. The variations in the pedestal heights are mainly due to a change in the pedestal gradients while only small differences are observed in the pedestal width. A clear increase in the pedestal density and pressure gradients are observed from deuterium to tritium. The experimental results suggest a reduction of the pedestal inter-edge localized mode (inter-ELM) transport from deuterium to tritium. The reduction is likely in the pedestal inter-ELM particle transport, as suggested by the clear increase of the pedestal density gradients. The experimental results suggest also a possible reduction of the pedestal inter-ELM heat transport, however, the large experimental uncertainties do not allow conclusive claims on the heat diffusivity. The clear experimental reduction of η
e (the ratio between density and temperature gradient lengths) in the middle/top of the pedestal with increasing A
eff suggests that there may be a link between increasing A
eff and the reduction of electron scale turbulent transport. From the modelling point of view, an initial characterization of the behaviour of pedestal microinstabilities shows that the tritium plasma is characterized by growth rates lower than the deuterium plasmas. The pedestal stability of peeling-ballooning modes is assessed with both ideal and resistive magnetohydrodynamics (MHD). No significant effect of the isotope mass on the pedestal stability is observed using ideal MHD. Instead, resistive MHD shows a clear increase of the stability with increasing isotope mass. The resistive MHD results are in reasonable agreement with the experimental results of the normalized pedestal pressure gradient. The experimental and modelling results suggest that the main candidates to explain the change in the pedestal are a reduction in the inter-ELM transport and an improvement of the pedestal stability from deuterium to tritium.
Supervised learning techniques for detection of Lung Carcinoma
Authors: Jalall S K , Harsha K , Dutta K K , Sarita K , Banik S , Sakambari N P 
Published: October 2023

Abstract
Lung diseases are the most common ailments seen among people with the history of smoking. Prompt and timely recognition and diagnosis may help in saving many lives. In order to detect cancer at early stages machine learning algorithms can be employed. Use of simple machine learning algorithms will help identify the carcinoma faster with high accuracy and lesser expense. This work shows the use three of simple machine learning (ML) algorithms like Logistic Regression, Support Vector Machine (SVM), and K-Nearest Neighbours (KNN). ML models were built using lung cancer patients’ dataset. The dataset was used to train the model as well as test the model. The three classifiers will detect the presence of lung cancer. For each classifier the Accuracy, Mean Square Error(MSE), precision, and Recall (R2) was calculated. A comparative study of the classifiers was done to identify which among the three was the best one. The main objective of the paper is to identify the best efficient machine-learning algorithm in terms of confusion matrices, accuracy, and precision for the prediction and diagnosis of lung cancer
Realtime Evaluation of Cardiac Activities Using Multi Features with KNN Classifier - A Pilot Study
Authors: Arun U , Sriraam N , Srinivasulu Avvaru 
Published: October 2023

Abstract
Monitoring of cardiac episodes is essential for patients with suspected cardiac arrhythmias. The quantitative evaluation of cardiac activities during continuous unattended cardiac monitoring will support clinician to understand the nature of the physical activities. This pilot study is proposed to assess the cardiac activities using multi features with KNN classifier. ECG was collected from healthy volunteers under rest and during exercise. The time and frequency domain features such as BPM, QRS duration, RR interval, SD, CV, RMSRR, NN50, SDNN, Shannon entropy, log entropy, and sample entropy were extracted. Further, multi-classification was employed using KNN classifier. The performance of KNN classifier is tested in terms of classification accuracy and elapsed time and it was found that Euclidean distance for k=2 was showing better performance. Statistical analysis was performed to confirm the suitability of the features using Pearson’s correlation coefficient and box plot. Among the features BPM, QRS, RR, sample and log entropies were found to be significant. Specifically, sample entropy provided to be the best candidate with p<0.01. The study reveals an overall classification accuracy of 90% was obtained.
Automated Recognition of Congenital Heart Defects (CHD) from Ultrasonic Fetal Heart Biometry
Authors: Sushma T.V. , Sriraam N , Suresh S 
Published: October 2023

Abstract
The growth and development of the fetus is monitored widely using Ultrasound imaging modality and has become synonymous with prenatal scanning. Analysing the images helps in the detection of congenital defects at an early stage which help to plan the delivery appropriately or for taking some corrective measures. The inherent noise of ultrasound imaging makes analysis of the images largely dependent on the expertise of the clinician and is prone to errors. Computer Aided Diagnostic (CAD) tools used in medical diagnosis assist the clinicians in analysing the images and reducing the chances of missing the defect. AI tools, machine learning and deep learning algorithms have shown improved classification performance of medical images in recent times have resulting in widespread implementation of these technologies in CAD tools. The proposed work discusses the use of machine learning for detection of Congenital heart disease (CHD). Frames resulting from the B mode Ultrasound cineloop sequence have been used for the study. A total of 870 frames with 550 normal frames and 320 abnormal frames are used for the study. Law’s texture feature have been extracted from the pre processed frames which in turn is used for classification. The performance of each of these features is compared using different kernels of SVM classifier. Tuning parameters regularization and gamma have been used to optimise the classification performance using grid search algorithm. It is observed that Laws texture feature results in better classification performance with an average accuracy and precision of 95% and specificity, sensitivity of 98%. Precision Recall curves and AUC-ROC plots have been used for comparing the performance of different kernels.
On metric dimensions of flower graphs
Authors: Girisha A , Rajendra P , Vijaya Chandra Kumar U , Pushpa S 
Published: October 2023

Abstract
The metric dimension of a connected graph G is the smallest number of nodes (resolving set) required to identify all other nodes based on shortest path distances uniquely. The notion of resolving set is vital in robotic navigation and to create various plan of action for the mastermind game. If certain vertices of a set S ⊂ V(G) resolve each pair of nodes u and v of G, then that set is a resolving set. A metric basis represents the lowest number of nodes in the set S. In this study, we describe the distance matrix and metric dimension of sunflower graphs and flower snarks.
Survey of the use of AI models and techniques in the analysis and prediction of neuro-degenerative diseases
Authors: Prabhu Srikanth , Rathinam Suryaansh , Rao Chirag , Chowdhury Anurag 
Published: October 2023

Abstract
Neurodegenerative diseases are caused by the gradual breakdown of the nerve connections. The detection and prediction of these diseases has been attempted with a variety of different artificial intelligence techniques, using the wide range of modalities available resulting in variations in accuracy and other metrics. This paper aims to offer a methodical as well as quantitative comparison among the various predictive methodologies that have been conducted for three neurodegenerative diseases, namely Alzheimer’s, Schizophrenia and Dementia. Lastly this paper focuses on the variation in occurrences of these diseases with changing demographics such as age, sex and location.
3T-SAPON technique to reduce Power Consumption
Authors: Sreeja Garlapati , Shama Kumara 
Published: October 2023

Abstract
Scaling down CMOS technology has rapidly improved cost, area effectiveness, switching speed of the circuits & high device reliability. However, scaling has an immense impact on static power consumption in VLSI circuits. For battery operating applications higher power dissipation is not desirable because it reduces the efficiency and cooling effects of the battery. Thus, designing a low-power consuming circuit has become the major criteria in circuit designs. A new circuit model called 3T-SAPON is designed to reduce static power consumption. 3T-SAPON technique power consumption has been reduced around 45%. Using this technique inverter, NAND are implemented and compared with some of the existing standard techniques like LCNT, LECTOR, ONOFIC and SAPON. All these circuits are simulated in Cadence 90nm technology.
An Optimized Reinforcement Learning-Based Routing Protocol Using Spider Monkey Optimization Algorithm
Authors: Radhika G , Radhika N 
Published: October 2023

Abstract
In Wireless Sensor Networks, numerous machine-learning algorithms are used in the packet routing strategy. The recommended routing approach is implemented using adaptive reinforcement learning and a spider monkey optimization algorithm. Data packets are sent over the network via optimum route establishment—the above attempts to save energy by changing the Q-value at each instance. Technology enables adaptive mechanisms to demonstrate intelligent behavior in complex and dynamic situations such as Wireless Sensor Networks (WSN). Recently, new routing methods for WSNs have been devised in response to such a notion. Due to user interference and overall network performance, the numerous routing pathways cause frequent channel-switching occurrences. The spider monkey optimization method obtains routing metrics such as bandwidth, end-to-end latency, throughput, overhead, and packet delivery ratio. Compared to other machine learning algorithms, such as RLBR, the performance of WSN is improved and obtained better performance results.
A Comparative Study of an Energy Efficient Algorithm for Wireless Sensor Networks
Authors: Roy Manish Kumar , Kumar Pramod 
Published: October 2023

Abstract
Wireless Sensor Networks are defined as a group of sensors that are spread over an area to gather data in a coordinated manner and send it to the sink where it is further processed and used for the application. The application could be as simple as gathering information from different parts of a confined accessible area or as extreme as tracking of wild animals of concern around a widely spread rainforest. The wide range of applicability of wireless sensor networks suggests that often, the sensors once deployed might not be accessible ever again and hence it becomes important to ensure that the sensors once deployed are functional for as long as possible. One of the hurdles in enhancing the functional lifetime of sensors is the energy consumption at the sensor level which can be broadly attributed to mainly two reasons: sensing and communicating; wherein the latter takes the bigger chunk. So, the goal would be to try and push the boundary viz-a-viz energy efficiency at the individual sensor nodes and enhance the lifetime of a WSN. In this paper, simulations have been carried out by varying the position of the base node to determine the number of rounds till which the network remains fully functional. It is found out that positioning the base node at the network centre significantly increases the rounds till which the network sustains as compared to positioning the node at a distance.
Implementation of EBG Structure to Reduce Surface Wave Excitations for IoT Range Applications
Authors: Gowri CH , Kumari B Leela 
Published: October 2023

Abstract
This paper presents the design of antenna for Internet of Things (IoT) applications. The performance of conventional antenna is limited by surface waves due to which it produces a low gain. An attempt is made to reduce the surface wave excitations in conventional antennas with the use of integration of Electromagnetic Band Gap (EBG) cells. The proposed work involves the design of Edge via EBG unit cell at 5.2 GHz and it is simulated with High Frequency Structure Simulator (HFSS) software. The simulation results show that the gain is enhanced by 10% and S11 is reduced by 72%, approximately in the rectangular microstrip patch antenna with Edge via EBGs.
Electronic, structural and optical properties of rare-earth based TbNiSb half Heusler compound
Authors: Mehta Shreya , Saini Sapan Mohan 
Published: October 2023

Abstract
Half-Heusler (HH) compounds have potential applications in many fields including spintronics and future energy applications. Rare earth based Heusler materials are best suitable for thermoelectric materials. To calculate some physical properties (the electronic, optical-properties) of rare earth (RE) based ternary HH TbNiSb compound, we applied full potential linearized augmented plane wave (FPLAPW) method which is based on Density Functional Theory (DFT). We use the Coulomb corrected generalized gradient approximation (GGA+U) for this study. The calculation of total density of states (DOS) of TbNiSb shows that Ni-3d states dominate in valence band (VB) around -2 eV below fermi energy level (EF) in both spin-up and spin-down configurations, while spin up Tb-5d & spin down Tb- 4f states constitute the conduction band (CB) around 4 eV above the EF. We found a narrow energy gap 0.115 eV is opened showing the semiconducting nature of the TbNiSb compound. Tb –4f states lie around -8 eV below EF and not participate to the DOS near EF. We have also calculated the optical properties like dielectric constant, extinction coefficient, refractive index and reflectivity of TbNiSb compound.
Photometric Study of the nuclear region of the galaxy NGC 5689
Authors: Verma Mahendra , Singh Avinash , Chaware Laxmikant 
Published: October 2023

Abstract
The nuclear regions of the galaxies are fascinating. It contains a vital clue about galaxy formation and evolution. This letter presents a detailed photometric study of the galaxy NGC 5689. The Hubble space telescope WFC2 PC and 3.6 micron Spitzer image are adopted for the analysis. A one-dimensional photometric study is used to analyze the nuclear galaxy profile. The two-dimensional analysis(GALFIT) is performed to study bulge and disc counterparts. Our study shows that the nuclear region of the NGC 5689 is incarnated with a colossal nuclear dust lane, which causes drastic changes in the position angle and ellipticity profile of the galaxy.
Design and development of rover with pick and place Mechanism
Authors: Jadhav Dhairyashil , Kurane Niraj , Mali Om , Pawar Rohan , Jadhav Anurag , Korwar Ganesh 
Published: October 2023

Abstract
This work presents a unique rover design that can traverse over a rough terrain and incorporate a pick-and-place mechanism over there. In the fields of robotics and space technology, rovers are crucial. Moreover, the rocker bogie design has established itself as a reliable mobility solution recognized for its improved vehicle stability and capability to traverse over obstacles. ‘Mars Exploration Rover’ has played a vital role in space exploration because it was an apparent choice because of its design of rocker-bogie suspension. The rover’s design has been modelled with CAD software and validated through simulation testing. Thus, the various improvements ensures mechanical sustainability, structural soundness, stability, and lightweight design.
Design and Development of Multi-axis Manipulator for Data Acquisition for Neurosurgery Applications.
Authors: Sanap Madhuri U. , Tiwari Pratham B. , Anasane S. S. 
Published: October 2023

Abstract
Neurosurgery is the specialty of medical operations that deals with the nervous system such as the brain, spinal cord, etc. Stereotactic neurosurgery is an important technique in neurosurgery as it enables precise targeting of specific areas in the brain with minimal damage to surrounding tissues. Neuro-navigation is one of the most important techniques used in stereotactic neurosurgery to help accurately target specific brain locations during the operation. It involves a computer-based system that assists to create 3D images of the patient’s face using imaging processes such as CT/MRI. In the existing process, considerable time is required to reach a specific target location of the tumor. The study presented in this manuscript consists of some mechanical assembly surrounding the patient’s head and a preoperative imaging system. There are several problems associated with the technique. But the main challenge is an inoperative brain shift. There is an error in deviations from the target anatomy from the operative plan brain shifts up to 20 - 50 mm. It may affect the accuracy of targeting and treatment effectiveness. The paper represents the development of a multi-axis manipulator for the neurosurgery application. The main goal of the machine is to provide good accuracy at a low cost. This multi-axis manipulator can assist the neuro registration and perform physical movement of the surgical instrument. In this, the movement of the manipulator is controlled by using a microcontroller with different motors and drivers.
Automated smart waste segregation system using IoT technology
Authors: Chitale M R , Chitpur S J , Chivate A B , Chopade P D , Deshmukh S M , Marathe A A 
Published: October 2023

Abstract
One of the major challenges faced not just by India but globally as well is waste management. India is the second most populous country and naturally generates tonnes of garbage daily. It is common to find dustbins overflowing with junk scattered everywhere, not to mention that segregation of waste is conveniently neglected. Many-a-times it is observed that despite providing separate bins for dry and wet trash, the convention is carelessly ignored. With the traditional methods of periodic garbage collection and manual segregation of wet, metallic, non-recyclable, and recyclable waste proving to be inefficient, there is a need for a better solution. With the help of IoT, a more effective solution is proposed to improve the current system and take a step forward toward the vision of smart cities. The proposed system uses smart garbage tracking, segregation, and collection systems, interfaced with ultrasonic sensors, rainfall/moisture sensors, inductive proximity sensors, and servo motors, along with the necessary hardware, with Arduino as the main controlling unit. The Arduino platform is used as a software platform for the working of the model and obtaining the expected results.
Plant disease prediction system using advance computational Technique
Authors: Gulame Mayuresh B. , Thite Trupti. G. , Patil Kranti D. 
Published: October 2023

Abstract
A vital sector of India’s economy is agriculture. Identification of plant infections is crucial to preventing crop damage and further disease. The majority of plants, such as apple, tomato, cherry, and grapes, have leaves that appear to have disease signs. The plant health can be monitored through images to precisely predict the disease and to take early preventative action. The traditional method is to manually inspect the plant leaf to identify the kind of disease, as done by farmers or plant pathologists. In this research, we presented a deep CNN model termed as Decompose, Transfer, and Compose (DTComp) for the classification of plant disease. The deep learning model makes predictions more quickly and precisely than manual plant leaf observation. Out of all the pretrained deep models, the ResNet50 model achieves the highest accuracy for classification. DTComp can handle any anomalies in the images using class decomposition approach to examine the class boundaries. The experimental findings demonstrated DTComp capacity for detecting plant disease instances on dataset gathered from multiple villages using the Kaggel Open Source platform. DTComp can successfully identify plant disease with a high accuracy of 98.30% from images. Additionally, this model can be deployable on real-time systems equipped with a Raspberry Pi and a camera module.