bluuebunny/crossref_metadata_2025_split huggingface dataset:
A computational study of the double hysteresis phenomenon during reactive sputtering
Authors: Van Bever J , Strijckmans K , Depla D 
Published: June 2022

Abstract
When process parameters such as the reactive gas partial pressure or the discharge voltage are studied as a function of the reactive gas flow during reactive magnetron sputtering, the obtained curve has an S-shape. A direct consequence of this behaviour is that process control based on the reactive gas flow exhibits hysteresis. Under specific conditions, it is possible to observe two S-shaped curves: one when the reactive gas pressure is increased, the other during the return to the initial state by decreasing the reactive gas pressure. This behaviour has been described as double hysteresis behaviour. The origin of the double hysteresis behaviour is computationally studied by high-throughput calculations using a previously developed model. The influence of different process and material parameters were evaluated based on newly developed measures to characterize the calculated process curves. This high-throughput analysis reveals that the double hysteresis behaviour is linked to the difference in the removal rate of non-reacted implanted ions during the increase and decrease of the reactive gas pressure. Within the parameter space a region can be defined for which the double hysteresis behaviour is strong. The latter can not only assist further experiments to study this behaviour but also defines conditions to limit its impact. For Al, a discharge current density of approximately 0.025 A cm−2 was found to maximize double hysteresis.
Effects analogous to the Kekulé distortion induced by pseudospin polarization in graphene nanoribbons: confinement and coupling by breakdown of chiral correlation
Authors: Mendoza Michel , López Luis I A 
Published: June 2022

Abstract
We show here that potential barriers, applied to armchair nanoribbons, induce a hexagonal effective lattice, polarized in pseudospin on the sides of the barriers system, which has an effective unit cell greater than that of infinite graphene (pseudospin superstructure). This superstructure is better defined with the increase of the barrier potential, until a transport gap is generated. The superstructure, as well as the induced gap, are fingerprints of Kekulé distortion in graphene, so here we report an analogous effect in nanoribbons. These effects are associated with a breakdown of the chiral correlation. As a consequence, an effective zigzag edge is induced, which controls the electronic transport instead of the original armchair edge. With this, confinement effects (quasi-bound states) and couplings (splittings), both of chiral origin (decorrelation between chiral counterparts), are observed in the conductance as a function of the characteristics of the applied barriers and the number of barriers used. In general, the Dirac-like states in the nanoribbon can form quasi-bound states within potential barriers, which explains the Klein tunneling in armchair nanoribbons. On the other hand, for certain conditions of the barriers (width L and potential V) and the energy (E) of the quasi-particle, quasi-bound states between the barriers can be generated. These two types of confinement would be generating tunneling peaks, which are mixed in conductance. In this work we make a systematic study of conductance as a function of E, L and V for quantum dots systems in graphene nanoribbons, to determine fingerprints of chirality: line shapes and behaviors, associated with each of these two contributions. With these fingerprints of chirality we can detect tunneling through states within the barriers and differentiate these from tunneling through states formed between the barriers or quantum dot. With all this we propose a technique, from conductance, to determine the spatial region that the state occupies, associated with each tunneling peak.
Thermodynamics and phase stability of Li8
XO6 octalithium ceramic breeder materials (X = Pb, Ce, Ge, Zr, Sn)
Authors: Davies Andrew W , Murphy Samuel T 
Published: June 2022

Abstract
Octalithium ceramics with their high stoichiometric concentration of lithium offer exceptionally high tritium breeding ratios in comparison with other candidate breeder materials for tokamak fusion reactors, this is especially true with incorporation of a neutron multiplier into the crystal structures. Although, there are concerns surrounding the stability of these materials at operational temperatures. Therefore in this paper, we explore the thermodynamic properties of a selection of candidate octalithium ceramics in low and high temperature regimes (0–1200 K) using density functional perturbation theory. Enthalpies as well as Gibbs formation energies were used to distinguish candidates which may or may not be susceptible to degradation.
Heterogeneous Pd-PdO mesoporous film for ammonia electrosynthesis
Authors: Zhang Mei , Cui Lin , Mao Qiqi , Wang Ziqiang , Yu Hongjie , Xu You , Li Xiaonian , Wang Liang , Wang Hongjing 
Published: June 2022

Abstract
Exploring cost-effective and highly active electrocatalysts is of great significance for sustainable electrochemical NH3 synthesis. Palladium (Pd)-based catalysts have been unanimously considered as one of the most efficient catalysts for the nitrogen reduction reaction (NRR). Herein, self-supported mesoporous Pd film with partial oxidation on Ni foam (mPd-PdO/NF) was synthesized through the micelle-assisted chemical replacement method coupled with air oxidation under 260 °C, and the mPd-PdO/NF electrocatalyst exhibited superior NRR performance with the maximum values of r
NH3 (24.8 mg h−1 mgcat.
−1) and FE (16.64%) were obtained at −0.1 V, relative to the single counterparts (mPd/NF and mPdO/NF). It is proposed that both metallic Pd and its oxide domains when co-existing with a phase boundary between them can facilitate nitrogen activation and hydrogenation, resulting in an enhanced NRR performance. This work provides an inspiring strategy for the rational design of highly active and durable metal–metal–oxide nanoarchitectonics for ammonia electrosynthesis.
Effect of cubic and hexagonal boron nitride additions on the microstructure and properties of bulk MgB2 superconductors
Authors: Gao Zilin , Santra Sangeeta , Grovenor Chris R M , Speller Susannah C 
Published: June 2022

Abstract
MgB2 is a promising material for intermediate temperature applications where conventional low temperature superconductors cannot be used, especially if the range of magnetic fields over which is has acceptable current carrying performance can be expanded. However, its applicability is limited by poor properties at elevated magnetic fields. Carbon-based dopants can be used to dramatically improve the high-field performance of MgB2, but at the cost of a reduction in the superconducting transition temperature (T
c) that limits the operation temperature to 20 K or below. Here we report an enhancement of superconducting performance of MgB2 with the addition of cubic and hexagonal boron nitride (BN), without any significant reduction in T
c. Ex-situ bulk samples of MgB2 with two forms of BN addition were manufactured by the field assisted sintering technique after high energy ball milling of powder mixtures. We find that hexagonal BN (hBN) nanoparticles mixed homogenously with MgB2 powder react much more easily to produce Mg–N–B impurities than larger cubic BN (cBN) particles (∼10 µm) under the same processing conditions. The addition of 1 wt% hBN or 5 wt% cBN combined with 6 h of milling has been demonstrated to improve the critical current density (J
c) of MgB2 over the entire magnetic field range. It is proposed that the nano-sized Mg–N–B impurities, that typically reside at MgB2 grain boundaries, increase pinning strength by introducing additional flux pinning centres. In addition, excess Mg may benefit the low-field performance by improving the connectivity. This work shows the significance of microstructural characterization on inhomogeneous superconducting materials to analyse their performance.
Imaginary spin-orbital coupling in parity-time symmetric systems with momentum-dependent gain and loss
Authors: Qin Jieli , Zhou Lu , Dong Guangjiong 
Published: June 2022

AbstractSpin-orbital coupling (SOC) and parity-time(PT)symmetry both have attracted paramount research interest in condensed matter physics, cold atom physics, optics and acoustics to develop spintronics, quantum computation, precise sensors and novel functionalities. Natural SOC is an intrinsic relativistic effect. However, there is an increasing interest in synthesized SOC nowadays. Here, we show that in aPT-symmetric pseudo spin-1/2 system (in other words, two-state system), the momentum-dependent balanced gain and loss can synthesize a new type of SOC, which we call imaginary SOC. The imaginary SOC can substantially change the energy spectrum of the system. Firstly, we show that it can generate a pure real energy spectrum with a double-valleys structure. Therefore, it has the ability to generate supersolid stripe states. Especially, the imaginary SOC stripe state can have a high contrast of one. Moreover, the imaginary SOC can also generate a spectrum with tunable complex energy band, in which the waves are either amplifying or decaying. Thus, the imaginary SOC would also find applications in the engineering ofPT-symmetry-based coherent wave amplifiers/absorbers. Potential experimental realizations of imaginary SOC are proposed in cold atomic gases and systems of coupled waveguides constituted of nonlocal gain and loss.
Constraining non-minimally coupled β-exponential inflation with CMB data
Authors: dos Santos F.B.M. , Santos da Costa S. , Silva R. , Benetti M. , Alcaniz J.S. 
Published: June 2022

Abstract
The β-exponential inflation is driven by a class of primordial potentials, derived in the framework of braneworld scenarios, that generalizes the well-known power law inflation. In this paper we update previous constraints on the minimal coupled β-exponential model [1] and extend the results also deriving the equations for the non-minimal coupled scenario. The predictions of both models are tested in light of the latest temperature and polarization maps of the Cosmic Microwave Background and clustering data. We also compare the predictions of these models with the standard ΛCDM cosmology using the Deviance Information Criterion (DIC), and find that the observational data show a moderate preference for the non-minimally coupled β-exponential inflationary model.
Carrier-envelope phase effect on light bullet dynamics
Authors: Zaloznaya Elizaveta , Kompanets Victor , Savvin Alexander , Dormidonov Alexander , Chekalin Sergey , Kandidov Valery 
Published: June 2022

Abstract
A light bullet (LB) is a wave packet of a few optical cycles that is extremely compressed in space and time, which is formed in the bulk transparent dielectric during femtosecond filamentation under anomalous group velocity dispersion. We demonstrate for the first time that the carrier-envelope phase shift during propagation of a near single-cycle LB causes synchronous oscillations of the spatial, temporal and energy parameters of its core with the period decreasing with increasing carrier wavelength. When analyzing the structure of color centers and induced plasma channels in fluorides, it was experimentally found that LB parameter oscillations lead to a periodic change in the nonlinear optical interaction with the dielectric.
Geodesic equation in non-commutative gauge theory of gravity*
Authors: Touati Abdellah , Zaim Slimane 
Published: June 2022

Abstract
In this study, we construct a non-commutative gauge theory of the modified structure of the gravitational field using the Seiberg-Witten map and the general tetrad fields of Schwarzschild space-time to show that the non-commutative geometry removes the singularity at the origin of the black hole, thus obtaining a non-singular Schwarzschild black hole. The geodetic structure of this black hole presents new types of motion next to the event horizon within stable orbits that are not allowed by the ordinary Schwarzschild spacetime. The noncommutative periastron advance of the Mercury orbit is obtained, and with the available experimental data, we find a parameter of non-commutativity on the order of

. This result shows that the new fundamental length,

, is on the order of

.
Human-in-the-loop optimization of visual prosthetic stimulation
Authors: Fauvel Tristan , Chalk Matthew 
Published: June 2022

Abstract

Objective. Retinal prostheses are a promising strategy to restore sight to patients with retinal degenerative diseases. These devices compensate for the loss of photoreceptors by electrically stimulating neurons in the retina. Currently, the visual function that can be recovered with such devices is very limited. This is due, in part, to current spread, unintended axonal activation, and the limited resolution of existing devices. Here we show, using a recent model of prosthetic vision, that optimizing how visual stimuli are encoded by the device can help overcome some of these limitations, leading to dramatic improvements in visual perception. Approach. We propose a strategy to do this in practice, using patients’ feedback in a visual task. The main challenge of our approach comes from the fact that, typically, one only has access to a limited number of noisy responses from patients. We propose two ways to deal with this: first, we use a model of prosthetic vision to constrain and simplify the optimization. We show that, if one knew the parameters of this model for a given patient, it would be possible to greatly improve their perceptual performance. Second we propose a preferential Bayesian optimization to efficiently learn these model parameters for each patient, using minimal trials. Main results. To test our approach, we presented healthy subjects with visual stimuli generated by a recent model of prosthetic vision, to replicate the perceptual experience of patients fitted with an implant. Our optimization procedure led to significant and robust improvements in perceived image quality, that transferred to increased performance in other tasks. Significance. Importantly, our strategy is agnostic to the type of prosthesis and thus could readily be implemented in existing implants.
Dislocation structure of tungsten irradiated by medium to high-mass ions
Authors: Wielunska B. , Płociński T. , Schwarz-Selinger T. , Mayer M. , Jacob W. , Ciupiński L. 
Published: June 2022

Abstract
Single crystalline tungsten was irradiated by the medium-mass ion Si with 7.5 MeV and high mass-ion W with 20.3 MeV up to a calculated peak damage level of 0.04 dpa and 0.5 dpa. The obtained dislocation structure of the damage zone was investigated by transmission electron microscopy and systematically compared with each other. Bright-field kinematical images were taken under four different two-beam diffraction conditions g = −200, 020, −110, 110 close to the [100] zone axis. The observed damage depth and damage peak position is in good agreement with the SRIM calculated damage depth profiles. The dislocation structures were investigated at the region of the damage peak because there the damage levels are comparable. In both irradiations (Si and W), the dislocation structures were similar. At the low damage level of 0.04 dpa dislocation loops and dislocation-loop clusters were found. The size of the dislocation loops in the W-irradiated tungsten sample was up to 20% higher than for the Si-irradiated sample. At the high damage level of 0.5 dpa a dislocation network consisting of dislocation-loop chains and dislocation lines was found for both irradiations. The dislocation line density was about 12% higher for the W-irradiated sample. Through comparison of the damage zone to SRIM damage depth profiles it was found that the transition from dislocation loops and dislocation-loop clusters to an ordered dislocation network takes place at about 0.08–0.1 dpa. Despite the large differences in ion mass and irradiation energy the dislocation structures were very similar.
5G and Beyond Networks for 3D MIMO Using Artificial Intelligence in 5G Network
Authors: Yadav Ranjeet , Tripathi Ashutosh , Pathak Sunil , Singh Gill Harjot 
Published: June 2022

Abstract
Today’s wireless systems would be incomplete without MIMO systems, which have become increasingly common in recent years due to their ability to increase both spectrum efficiency and energy efficiency at a significant rate. Prior MIMO, the most often utilised system was a single-input, single-output system, which had limited capability and could not reliably support a huge number of users. Throughout this study, an artificial intelligence machine training MIMO channel is developed to handle the huge customer demand.. However, the ever-increasing needs are not met by this new technology. AI techniques becoming more prominent in determining the angle of elevation and angle of arrival for 3D MIMO in 5G system. These days, more and more people are using wireless devices, and these devices generate an enormous volume of information that must be processed quickly and reliably. AI based machine learning solve the issue and provide more throughput and energy efficient 5G system.
Development of an optimal operating policy of multi-reservoir systems in Mahanadi Reservoir Project Complex, Chhattisgarh
Authors: Shashikant Verma , Sahu Ramgopal T. , Prasad A.D. , Verma Mani Kant 
Published: June 2022

Abstract
Real-world optimization problems are usually non-linear and solving nonlinear problems requires time. Hence heuristic techniques are frequently used. Simulated annealing (SA) is one of the most commonly used heuristic algorithms. SA is useful in the narrow search area. In the present study, simulated annealing has been developed to derive the optimal for multi-reservoir systems. Problem statement: In respect of water release, multi-reservoir system in Mahanadi Reservoir Project is an example of such a complex system, therefore the optimum operation of such a reservoir is one of the complicated and uncertainxs issues water resource management. Approach: Traditional models to optimize water resource systems have proven to be ineffective due to increased decision variables and time constraints. In recent decades, the adaptive random research SA model has shown acceptable performance in solving major problems. Results: The model applies to derive the optimal operating policies of existing multi-reservoirs system in Chhattisgarh, India. The formulated objective function of the present study is to minimize the sum of squared deviation on downstream release and demand and compare the performance of the SA model to the existing reservoir systems operation policy. The overall performance of existing multi-reservoir system has increased on average by, 28.09%, 10.12%, 36.49% in case of reliability, resilience, sustainability respectively, and vulnerability reduced up to 11.83%. Conclusions/Recommendations: The results of these algorithms show that SA can deal with complex problems. SA is a novel promising reservoir optimization search technique. It merits further research for other applications in this field.
Crowd behavior anomaly detection using correlation of optical flow magnitude
Authors: Chakole Pallavi D , Satpute Vishal R , Cheggoju Naveen 
Published: June 2022

Abstract
Nowadays, crowd monitoring is a contentious issue. Because of the increasing population and diversity of human activities, crowd scenarios in the real world are becoming more common, demanding the need for an automotive anomaly detection system. Crowd behavior is influenced by the thoughts and attitudes of others around them. An unexpected event can turn a peaceful crowd into a riot. A mechanism based on optical flow must be implemented to compensate for all of these factors. The amount of motion present in two successive frames is estimated using optical flow. It includes information on velocity in the x & y plane, along with magnitude and line of action. By means of “anomalous event” in this paper is quick and sudden dispersal of the crowd. For detecting an event the magnitude of two successive frames should be taken into account followed by estimating a correlation. We expect a high correlation, slight motion, and low rate of change in velocities at non-anomalous events, but as soon as an anomalous event occurs, the correlation begins to decrease with a significant change in velocity and large motion vectors. The methodology was tested on a dataset from the University of Minnesota that included 11 movies from three different circumstances. Almost all anomalous occurrences in videos were successfully detected using this method.
A TinyML Approach to Human Activity Recognition
Authors: Gupta Shubham , Jain Dr. Sweta , Roy Bholanath , Deb Abhishek 
Published: June 2022

Abstract
Human Activity Recognition has been a favorite topic for the scholars not only because of its wide scale acceptance in the industry but areas which may help in medical and in our normal household works as well. Since to make this technology available to the last person standing in the queue it is important that models compiled and trained in this field are not just high performing but optimized as such with incurs the least overhead. And thus bringing TinyML into the picture which has specialty in the field of optimizing the model w.r.t. the size of the model, energy consumption, network bandwidth usage etc. Thus this work includes using optimizing techniques such as pruning and quantization on the pre-proposed models and analyze the changes it causes in such models w.r.t accuracy and size. Our work is able infer that by using both Pruning and Quantization techniques on a human activity recognition model we can compress a model up to 10 time without hampering severe diversion to the accuracy of the model. We have taken three models and UCI-HAR dataset and compare the outcomes of the experiment.
Deep Learning (CNN) and Transfer Learning: A Review
Authors: Gupta Jaya , Pathak Sunil , Kumar Gireesh 
Published: June 2022

Abstract
Deep Learning is a machine learning area that has recently been used in a variety of industries. Unsupervised, semi-supervised, and supervised-learning are only a few of the strategies that have been developed to accommodate different types of learning. A number of experiments showed that deep learning systems fared better than traditional ones when it came to image processing, computer vision, and pattern recognition. Several real-world applications and hierarchical systems have utilised transfer learning and deep learning algorithms for pattern recognition and classification tasks. Real-world machine learning settings, on the other hand, often do not support this assumption since training data can be difficult or expensive to get, and there is a constant need to generate high-performance beginners who can work with data from a variety of sources. The objective of this paper is using deep learning to uncover higher-level representational features, to clearly explain transfer learning, to provide current solutions and evaluate applications in diverse areas of transfer learning as well as deep learning.