bluuebunny/crossref_metadata_2025_split huggingface dataset:
Relativistic coupled cluster calculations of the electron affinity and ionization potential of Nh(113)
Authors: Guo Yangyang , Borschevsky Anastasia , Eliav Ephraim , Pašteka Lukáš F 
Published: June 2022

Abstract
Theoretical calculations based on the Dirac–Coulomb–Breit relativistic coupled cluster method have been carried out for the electron affinities and ionization potentials (IPs) of the superheavy element nihonium (Nh) and its lighter homologues In and Tl. The In and Tl calculations are in agreement with measurement within uncertainties. For Nh, where experiment is yet unknown, we predict the IP of 7.569(48) eV and electron affinity of 0.776(30) eV.
Rolling bearing fault diagnosis method based on SSAE and softmax classifier with improved K-fold cross-validation
Authors: Wang Junxiang , Zhan Changshu , Yu Di , Zhao Qiancheng , Xie Zhijie 
Published: June 2022

Abstract
Since rolling bearings determine the stable operation of industrial equipment, it is necessary to diagnose thir faults. To improve fault diagnosis accuracy, this paper proposes a method based on a stacked sparse autoencoder (SSAE) combined with a softmax classifier. First, SSAE is used to extract the frequency-domain features of vibration signals. Then, an improved K-fold cross-validation is employed to obtain the features’ pre-train set, train set, and test set. Finally, the SSAE-model is constructed via the pre-train set, while the tuned model is built via the train set. The model performance is evaluated based on accuracy, macro-precision, macro-recall, and macro-F1 score. The proposed model is validated by the Case Western Reserve University and XJTU-SY data with 99.15% and 100% accuracy, respectively.
Quantum lower and upper speed limits using reference evolutions
Authors: Takahashi Kazutaka 
Published: June 2022

Abstract
We derive generalized quantum speed limit inequalities that represent limitations on the time evolution of quantum states. They are extensions of the original inequality and are applied to the overlap between the time-evolved state and an arbitrary state. We can discuss the lower limit of the Bures angle, in addition to the upper limit as in the original inequality, which allows us to evaluate the lower and upper bounds of processing time for the evolution toward a target state. The inequalities are written by using an arbitrary reference state and are flexibly used to obtain a tight bound. We demonstrate these properties by using the twisted Landau–Zener model, the Grover Hamiltonian, and a periodically-oscillating Hamiltonian.
Anomalous transport in driven periodic systems: distribution of the absolute negative mobility effect in the parameter space
Authors: Wiśniewski Mateusz , Spiechowicz Jakub 
Published: June 2022

Abstract
Absolute negative mobility is one of the most paradoxical forms of anomalous transport behaviour. At the first glance it contradicts the superposition principle and the second law of thermodynamics, however, its fascinating nature bridges nonlinearity and nonequilibrium in which these fundamental rules are no longer valid. We consider a paradigmatic model of the nonlinear Brownian motion in a driven periodic system which exhibits the absolute negative mobility. So far research on this anomalous transport feature has been limited mostly to the single case studies due to the fact that this model possesses the complex multidimensional parameter space. In contrast, here we harvest GPU supercomputers to analyze the distribution of negative mobility in the parameter space. We consider nearly 109 parameter regimes to discuss how the emergence of negative mobility depends on the system parameters as well as provide the optimal ones for which it occurs most frequently.
Topological and dynamical phase transitions in the Su–Schrieffer–Heeger model with quasiperiodic and long-range hoppings
Authors: Zhang Wei-Jie , Wu Yi-Piao , Tang Ling-Zhi , Zhang Guo-Qing 
Published: June 2022

Abstract
Disorders and long-range hoppings can induce exotic phenomena in condensed matter and artificial systems. We study the topological and dynamical properties of the quasiperiodic Su–Schrier–Heeger model with long-range hoppings. It is found that the interplay of quasiperiodic disorder and long-range hopping can induce topological Anderson insulator phases with non-zero winding numbers

ω
=
1
,
2
,

and the phase boundaries can be consistently revealed by the divergence of zero-energy mode localization length. We also investigate the nonequilibrium dynamics by ramping the long-range hopping along two different paths. The critical exponents extracted from the dynamical behavior agree with the Kibble–Zurek mechanic prediction for the path with

W
=
0.90
.

In particular, the dynamical exponent of the path crossing the multicritical point is numerical obtained as

1
/
6
∼
0.167
,

which agrees with the unconventional finding in the previously studied XY spin model. Besides, we discuss the anomalous and non-universal scaling of the defect density dynamics of topological edge states in this disordered system under open boundary condictions.
Confinement of passing and trapped runaway electrons in the simulation of an ITER current quench
Authors: Särkimäki Konsta , Artola Javier , Hoelzl Matthias , the JOREK Team  
Published: June 2022

Abstract
Runaway electrons (REs) present a high-priority R&D issue for ITER but little is known about the extent to which RE generation is affected by the stochastic field intrinsic to disrupting plasmas. RE generation can be modelled with reduced kinetic models and there has been recent progress in involving losses due to field stochasticity, either via a loss-time parameter or radial transport coefficients which can be estimated by tracing test electrons in 3D fields. We evaluate these terms in ITER using a recent JOREK 3D MHD simulation of plasma disruption to provide the stochastic magnetic fields where RE markers are traced with the built-in particle tracing module. While the MHD simulation modelled only the current quench phase, the case is MHD unstable and exhibits similar relaxation as would be expected during the thermal quench. Therefore, the RE simulations can be considered beginning right after the thermal quench but before the MHD relaxation is complete. The plasma is found to become fully stochastic for 8 ms and the resulting transport is sufficient to overcome RE avalanche before flux surfaces are reformed. We also study transport mechanisms for trapped REs and find those to be deconfined as well during this phase. While the results presented here are not sufficient to assess the magnitude of the formed RE beam, we show that significant RE losses could be expected to arise due to field stochasticity.
Development of irradiation tolerant tungsten alloys for high temperature nuclear applications
Authors: Terentyev Dmitry , Jenus Petra , Sal Elisa , Zinovev Aleksandr , Chang Chih-Cheng , Garcia-Rosales Carmen , Kocen Matej , Novak Saša , Van Renterghem W. 
Published: June 2022

Abstract
Development of refractory metals for application as plasma-facing armour material remains among priorities of fusion research programmes in Europe, China and Japan. Improving the resistance to high temperature recrystallization, enhancing material strength to sustain thermal fatigue cracking and tolerance to neutron irradiation are the key indicators used for the down selection of materials and manufacturing processes to be applied to deliver engineering materials. In this work we investigate the effect of neutron irradiation on mechanical properties and microstructure of several tungsten grades recently developed. Neutron irradiation campaign is arranged for screening purposes and therefore is limited to the fluence relevant for the ITER plasma facing components. At the same time, the neutron exposure covers a large span of irradiation temperatures from 600 up to 1000 °C. Four different grades are included in the study, namely: fine-grain tungsten strengthened by W-carbide (W–4wt.% W2C), fine-grain tungsten strengthened by Zr carbides (W–0.5% ZrC), W alloyed with 10 at.% chromium and 0.5 at.% yttrium (W–10Cr–0.5Y) and technologically pure W plate manufactured according to the ITER specification by Plansee (Austria). The strengthening by W2C and ZrC particles leads to an enhanced strength, moreover, the W–0.5ZrC material exhibits reduced DBTT (compared to ITER specification grade) and is available in the form of thick plate (i.e. high up-scaling potential). The W–10Cr–0.5Y grade is included as the material offering the self-passivation protection against the high temperature oxidation.
QoS-Aware Traffic Offloading for Energy Efficient D2D Communication in 5G Network for IoT application
Authors: Chandra Saurabh , Prateek  , Arya Rajeev 
Published: June 2022

Abstract
In Device-to-Device (D2D) communication, where base station (BS) suffers from the issues of unbalanced loads of traffic due to interference which requires low power consumption. The energy efficiency of D2D communication in 5G Network for IoT application is a critical issue due to power consumption at BS is considerably high. In this paper, we propose D2D-based traffic offloading scheme that smartly allocate the traffic between D2D and base station while satisfying Quality of Service (QoS) requirements, which helps to minimize the load from the base station based on the information of users’ locations with low power consumption and high spectral efficiency which fulfils the QoS requirements. We make our proposed algorithm more efficient with less complexity which improves spectral efficiency, lowers the latency with minimum QoS performance of the network. With the help of simulation parameter, we obtained some results which determines that the proposed approach significantly outperforms the other existing approaches in terms of delay or latency, average energy consumption and average computing time up to 72%, 76% and 86% when the number of tasks/loads is 100 while satisfying the users’ demand. The work proposed in this article may be useful in energy efficient 5G-D2D Network for IoT application.
Analysis and Enhancement of Q-Factor in Thin-Film Bulk Acoustic Wave Resonator (FBAR)
Authors: Joshi P K , Latwe K R , Hasamnis M A 
Published: June 2022

Abstract
In MEMS technology, MEMS Resonators has shown high potential applications in wireless communications such as Bluetooth and WIFI range. The Bulk Acoustic Wave (BAW) Resonator is a device which has more potential to meet the needs of today’s wireless technology. The Acoustic wave technology has attracted more attention and it’s an effective solution for achieving high-performance RF filters applications due to its low cost and small size of the device. The performance analysis of thin film bulk acoustic resonator (FBAR) by varying different piezoelectric material, piezoelectric material’s thickness and electrode materials is demonstrated in this paper. It is understood that the Quality factor is considerable higher for AlN as compared to ZnO and LiNbO3. Among all the materials, Aluminium electrode achieved maximum quality factor. As the thickness of piezo material decreases, it is found that the resonant frequency and Quality factor increases. Along with the Quality Factor, the top and bottom electrode thickness has a significant impact on the resonant frequency.
Live Streaming of Face Mask and Body Temperature Detection System using Transfer Learning and IoT
Authors: Singh Nutan , Tripathi Priyanka 
Published: June 2022

Abstract
Since the outbreak of COVID-19 pandemic, enormous development has been done in computer vision systems in face masks and temperature detection. To enforce the mandate for wearing the mask in public places, the authors have suggested a solution using transfer learning (MobileNetV2) architecture as a foundation for image classification. The proposed solution is embedded devices with Raspberry Pi4 and python packages viz TensorFlow, OpenCV, and Keras. In this Work, a combination of transfer learning and IoT has been worked out using live streaming to detect an appropriate use of face masks along with body temperature. This technique includes the Single Shot Single box and Multi-box identification of the face mask. Further, a person can check the Temperature at the entrance. This temperature sensor is in noncontact mode, which senses a temperature from 2 cm to 5 cm and is displayed through LCD. It sends the data to the cloud for visualization and analysis, and an alert message is sent in a real-time environment based on that analysis.
Low Cost Hand Held Device For Leakage Detection From Microwave Oven
Authors: Mishra R , Chaurasiya U , Srivastava R , Shukla N K , Misal Ashish Kumar 
Published: June 2022

Abstract
Microwave heating systems for industrial uses, notably for food and farm goods have been extensively developed. Microwave energy is now being used in many ways, with greater focus on consumption. If utilized for a consumer product as a power source, microwaves are typically employed in a totally shielded enclosure that does not allow access while the microwave source is running. The radio frequency (RF) leakage above 5mW (+ 10 dBm) is dangerous to humans, according to the World Health Organization (WHO) guideline. This manuscript focuses on the low cost hand held device for detection of microwave energy radiation because it is very hazardous for human body. This kind of device will be very helpful for clean and green society.
A Survey on IBM Watson and Its Services
Authors: Kumar Avinash , Tejaswini Pallapothala , Nayak Omprakash , Kujur Anurag Deep , Gupta Rajkiran , Rajanand Ashish , Sahu Mridu 
Published: June 2022

Abstract
Artificial Intelligence (AI) is changing the modern way of lifestyle by helping the person do their jobs in an efficient manner. The AI is currently in its starting phase and from now on it is of great use. IBM Watson is an AI which is used globally by different organizations, institutes and corporations. In this paper we have created a chatbot using IBM Watson Assistance which is helpful in querying about the disease and hospitals related query. This paper also discusses IBM Watson in detail, its applications, its working and case studies on the use of IBM Watson in the field of healthcare, visual recognition and a software company named BOX.
RETRACTED: Design and Development of Person Re-Identification system based spatial features
Authors: Bethu Srikanth , Babu B Sankara , Suneetha Ch. , Indupriya B , Ramya Manaswi V 
Published: June 2022

Abstract
Primary intention of this research is to design and develop an effectual approach for re-identifying the persons. The proposed approach involves four stages named human identification, object tracking, feature extraction, and matching. The four stages will be implemented by matching the probe videos and gallery videos. Initially, the videos will be subjected to the human identification phase. First stage will be the identification of human depending on the spatial features. It is important to consider that person re-identification will be carried out using the video surveillance data which can be probe data or gallery data. For human identification, the spatial features will be extracted and subjected to gait based Bayesian network for identifying the humans. Then, the object tracking phase will be carried out for tracking the objects from the videos. Here, the Neighborhood Search Algorithm will be used for tracking the objects. Then, the tracked objects will be given to the feature extraction process in which the features like mean, variance, speed, and motion deviations will be extracted. Finally, in matching phase, the matching will be performed between the probe videos and gallery videos and the person re-identification will be done using the extracted features based on the Bayesian probability-based matching. Finally, the matched outputs will be utilized for re-identifying the persons. The implementation of the proposed approach will be done using the Grid and Caviar Data sets. The performance of the proposed technique will be evaluated using three metrics, such as accuracy, sensitivity and specificity and will be compared with that of existing methods.
Deep Learning Techniques on Brain Images
Authors: Sahu Mridu , Upadhyay Yogita , Khoriya Namrata , Biswas Abhilash , Chandrawanshi Manas , Patel Omprakash 
Published: June 2022

Abstract
Our brain is the most composite organ of the human body with an aggregation of 100 million nerves which communicate in a nexus of synapses. All the activities from thinking, memorizing, storing information, the functioning of other organs of the body are all managed by the brain. Any disease which affects the brain affects the whole of the body. Severe brain diseases paralyze the body. Some of the common categories of brain diseases are seizures, trauma, tumor, and infections. Alzheimer’s, Epilepsy, Brain Cancer, and brain disorders. Research to use Image Processing Techniques in the field of brain diseases still has a long way to go. This paper is one such small step in the process of understanding Deep Learning in brain imaging. It is a detailed study on brain diseases and how algorithms can help in the current treatment. CNN is discussed in detail with its architecture and the reason of its popularity is discussed. This particular paper also comprises a case study of one such disease i.e brain tumor and the effect of various parameters in improving the accuracy of Convolutional neural networks on this particular data-set. The case study involves augmenting the data and applying CNN on it. The effect of CNN is then studied on the basis of three parameters which are Optimizers, Activation Function, and Loss Function. A comparative analysis is then drawn out among all the possible combinations and the best combination of these parameters are found. The models were evaluated in terms of accuracy and time required to train the algorithm. Using the comparison table important findings and conclusions were drawn out.
Predicting Heart Diseases through Feature Selection and Ensemble Classifiers
Authors: Diwan Shivangi , Thakur Gajendra Singh , Sahu Sunil K. , Sahu Mridu , Swamy N. K. 
Published: June 2022

Abstract
Heart diseases or Cardiovascular Diseases are the leading cause of death globally. Amid the Covid-19 pandemic, the toll has further increased and is prevalent among all age groups. The reasons are associated with various side effects of lockdown or socio-economic affairs. It becomes extremely important to strengthen our research on diagnosis systems to timely and accurately identify the disease. This paper is an attempt to predict a healthy or heart patient using ensemble machine learning methods depending on selected features. The proposed model shows that after performing feature selection the ensemble models give optimum accuracy with significantly lesser features.
The effect of immersion time on singlemode-tapered multimode-singlemode (STMS) fabrication using a chemical etching method
Authors: Lesa A. D. , Puspita I. , Hatta A.M. , Kurniawan F. , Koentjoro S. 
Published: June 2022

Abstract
Singlemode-Multimode-Singlemode (SMS) is one of the fiber optic sensor structures that is widely applied for many sensing applications. High sensitivity is the preferred characteristic of SMS fiber structure. In this paper, a tapered structure is introduced to the SMS fiber structure to increase its performance. The tapered structure is made by the etching method by immersing the SMS multimode fiber in hydrofluoric acid (HF) solution. The immersion time was varied 5, 10, 15 and 20 minutes. The diameter of the multimode fiber decreases with the longer immersion time, while the length does not change. The STMS transmission spectrum was measured to investigate its performance. The chemical etching method using HF solution for tapering the SMS fibers has great potential as a simple method to achieve a smaller radius without lengthening multimode fibers.
Aero-acoustics Investigation of a Bionic Airfoil Horizontal Axis Wind Turbine Using LES-DCS Approach
Authors: Wu Weimin , Dong Wenling , Li Shiwei , Zhang Suocheng 
Published: June 2022

Abstract
The wind turbine noise has become an important issue in the current green energy field, therefore, it is necessary to use new and advantageous methods to study the problem. This research employed the LBM-DCS method to investigate the wake sound radiation characteristics for a bionic airfoil horizontal axis wind turbine, combining the large eddy simulation (LES) model and the wall-adapting local eddy-viscity (WALE) model. The obtained results show that the aeroacoustic model used here is reasonable and effective, which are consistent with the relevant experimental measurement result. The appearance and changing characteristics of the acoustic radiation ring have been discussed and analyzed to a certain extent. Based on the accumulation of this research, we will combine more experimental data to carry out a further systematic investigation on the acoustic radiation characteristics to the wind turbine.
The comparison of adjoint-based grid adaptation and feature-based grid adaptation method
Authors: Cui Pengcheng , Zheng Yongqian , Xu Peimin , Li Bin , Ma Mingsheng , Zhou Guiyu 
Published: June 2022

Abstract
Grid adaption is a popular method to enhance the resolution of flow field and the precision of numerical simulation, which automatically optimizes the grid distribution instead of manual complicated work. There exist usually two grid adaptation methods, the feature based grid adaption and adjoint based grid adaption, the former focuses on shocks, vortexes and other features of flow field, and the latter focuses on lift, drag and other aerodynamic characteristics. The comparison of adjoint based grid adaption and feature based grid adaption method is investigated in this paper. Numerical simulations show that both feature adaption and adjoint adaption could improve the resolution of flow field and the precision of numerical simulation such as lift and drag. As for the flow features, the feature adaptation could capture the obvious shock waves and vortexes in the flow field, the adjoint adaptation, however, only captures the flow features that are contributory to the accuracy of aerodynamic characteristics. As for the aerodynamic characteristics, some shock waves and vortexes have little influences to the forces, so the feature adaptation is not efficient as adjoint adaptation, which could greatly improve the accuracy of aerodynamic characteristics.
The influence of wind turbine blade rotation on Wind direction meter
Authors: Tian Lizhu , Zhou Yaqiang , Jiang Zhiwen , Li Yapeng , Wu Zhaohe , Qi Chenglong , Gou Yanxu , Xu Yonghe , Du Dayu , Wang Bao , Wu Yuan , Feng Wei , Li Peng 
Published: June 2022

Abstract
In this paper, a numerical analysis of the influence of the wind turbine impeller on the wind direction indicator is carried out. The layout position of the wind direction indicator and the wind speed change are analysed respectively, and the current equipment position is analysed whether it is suitable for the installation of wind measurement equipment, and the best layout is given.