bluuebunny/crossref_metadata_2025_split huggingface dataset:
Electroosmotic modulated unsteady squeezing flow with temperature-dependent thermal conductivity, electric and magnetic field effects
Authors: Prakash J , Tripathi Dharmendra , Bég O Anwar , Sharma Ravi Kumar 
Published: January 2022

Abstract
Modern lubrication systems are increasingly deploying smart (functional) materials. These respond to various external stimuli including electrical and magnetic fields, acoustics, light etc. Motivated by such developments, in the present article unsteady electro-magnetohydrodynamics squeezing flow and heat transfer in a smart ionic viscous fluid intercalated between parallel plates with zeta potential effects is examined. The proposed mathematical model of problem is formulated as a system of partial differential equations (continuity, momenta and energy). Viscous dissipation and variable thermal conductivity effects are included. Axial electrical distribution is also addressed. The governing equations are converted into ordinary differential equations via similarity transformations and then solved numerically with MATLAB software. The transport phenomena are scrutinized for both when the plates move apart or when they approach each other. Also, the impact of different parameters such squeezing number, variable thermal conductivity parameter, Prandtl number, Hartmann number, Eckert number, zeta potential parameter, electric field parameter and electroosmosis parameter on the axial velocity and fluid temperature are analysed. For varied intensities of applied plate motion, the electro-viscous effects derived from electric double-capacity flow field distortions are thoroughly studied. It has been shown that the results from the current model differ significantly from those achieved by using a standard Poisson–Boltzmann equation model. Axial velocity acceleration is induced with negative squeeze number (plates approaching, S < 0) in comparison to that of positive squeeze number (plates separating, S > 0). Velocity enhances with increasing electroosmosis parameter and zeta potential parameter. With rising values of zeta potential and electroosmosis parameter, there is a decrease in temperatures for U
e > 0 for both approaching i.e. squeezing plates (S < 0) and separating (S > 0) cases. The simulations provide novel insights into smart squeezing lubrication with thermal effects and also a solid benchmark for further computational fluid dynamics investigations.
Rotation included 3-axis scanning free-space measurement and curvature compensation for electromagnetic evaluation of leading-edge and curved stealth structures
Authors: Din Salah Ud , Hyun Jong-Min , Son Dae-Sung , Lee Jung-Ryul 
Published: January 2022

Abstract
Since the last decade, stealth technology has been developing at a much higher speed with much research going on to evaluate the properties of radar absorbing structures (RASs) and scattering parameters of the developed stealth structures at different phases of practical use. A vector network analyzer plays an important role in RAS performance evaluation of stealth structures. Different scientists have used closed cell measurement techniques utilizing coaxial cables and waveguides to evaluate RAS materials at high accuracy, while others have used free-space measurement techniques utilizing radiating antennas to evaluate the RAS performance of larger stealth structures without damaging the specimen. All these conventional evaluation systems can scan a single point at a time. A scanning free-space measurement (SFM) system was an initial step toward automated scanning of large stealth structures. This system was capable of scanning 1000 mm × 1000 mm flat structures using raster scanning with two linear stages. However, the two-dimensional SFM was not applicable to leading-edge and cylindrical structures, and the signal-to-noise ratio (SNR) is highly affected by the specimen curvature. In this paper, a 3-axis scanning free-space measurement (3-axis SFM) system was developed. The system has two linear stages and a rotational stage for raster scanning of the specimen. The system performance was checked by scanning cylindrical RAS specimens with a radius of 100 mm. The results were then compared to the SFM system, which showed that the SNR of the 3-axis SFM is quite high compared to the SFM system. A coordinate-based scanning algorithm was also developed for 3-axis SFM to ensure a fixed interval scan of complex geometrically shaped specimens, such as wing leading edges, at lower incident angles. The algorithm achieved a zero-degree incident angle scan with ±1 degree of accuracy for the NACA-M3 symmetrical leading-edge specimen. Different scan parameters and results are visualized in the developed graphical user interface software. Finally, the inspected results were curvature-compensated to obtain the actual RAS performance of the specimen. The accuracy of the geometry-based perfect electric conductor (PEC) reflection-loss prediction algorithm was further improved by adding a stand-off distance effect and more specimen curvature data to the algorithm. The accuracy of the algorithm was verified by comparing the predicted PEC results to the measured PEC results of the corresponding specimen.
Laser ultrasonic scanning for detection of damage in copper pipelines using blind compressive sensing and the adjacent area difference coefficient
Authors: Hong Xiaobin , Huang Liuwei , Liufu Yonghong , Wang Zixin , Zhang Bin , Liu Yuan 
Published: January 2022

Abstract
Due to its good thermal conductivity and corrosion resistance, copper has become a common material for transmission pipelines. It is necessary to detect the early signs of damage in copper pipelines effectively and quickly. Laser ultrasound scanning is a non-contact and non-destructive damage identification method, which can realize high-precision, non-contact detection. At the same time, with the progress of internet technology, traditional damage testing has begun to use advanced technologies such as the internet of things and cloud computing to promote an upgrade of the testing industry from an offline industry to an online industry. However, obtaining a large amount of wavefield vibration data is time consuming. In this paper, we present a laser ultrasonic scanning cloud platform damage detection method for copper pipelines based on alternating learning blind compressive sensing (BCS) and the adjacent area difference coefficient (AADC); this approach can improve real-time performance and detection accuracy. First, the damage detection method is introduced in detail. BCS is used to compress the laser scanning signal at the data acquisition terminal, and then transmitted to a data processing cloud platform for reconstruction. Copper pipeline damage imaging is realized by taking the AADC value of each detection point as the pixel value. The simulated detection data of the copper pipeline are then obtained through a finite element model, and the weighted vectors of the AADC are determined by a genetic algorithm. Finally, experimental data are used to verify the effectiveness of this method, and the experimental results are analyzed and discussed. The AADC and other distance damage imaging methods are compared. The results demonstrate that this method can compress the wavefield data to 13% of the original data, and the detection of crack damage is realized.
Operando XRD studies on Bi2MoO6 as anode material for Na-ion batteries
Authors: Brennhagen Anders , Cavallo Carmen , Wragg David S. , Vajeeston Ponniah , Sjåstad Anja O. , Koposov Alexey Y. , Fjellvåg Helmer 
Published: January 2022

Abstract
Based on the same rocking-chair principle as rechargeable Li-ion batteries, Na-ion batteries are promising solutions for energy storage benefiting from low-cost materials comprised of abundant elements. However, despite the mechanistic similarities, Na-ion batteries require a different set of active materials than Li-ion batteries. Bismuth molybdate (Bi2MoO6) is a promising NIB anode material operating through a combined conversion/alloying mechanism. We report an operando x-ray diffraction (XRD) investigation of Bi2MoO6-based anodes over 34 (de)sodiation cycles revealing both basic operating mechanisms and potential pathways for capacity degradation. Irreversible conversion of Bi2MoO6 to Bi nanoparticles occurs through the first sodiation, allowing Bi to reversibly alloy with Na forming the cubic Na3Bi phase. Preliminary electrochemical evaluation in half-cells versus Na metal demonstrated specific capacities for Bi2MoO6 to be close to 300 mAh g−1 during the initial 10 cycles, followed by a rapid capacity decay. Operando XRD characterisation revealed that the increased irreversibility of the sodiation reactions and the formation of hexagonal Na3Bi are the main causes of the capacity loss. This is initiated by an increase in crystallite sizes of the Bi particles accompanied by structural changes in the electronically insulating Na–Mo–O matrix leading to poor conductivity in the electrode. The poor electronic conductivity of the matrix deactivates the Na
x
Bi particles and prevents the formation of the solid electrolyte interface layer as shown by post-mortem scanning electron microscopy studies.
X-ray CT adaptation based on a 2D–3D deformable image registration framework using simulated in-room proton radiographies
Authors: Palaniappan Prasannakumar , Meyer Sebastian , Rädler Martin , Kamp Florian , Belka Claus , Riboldi Marco , Parodi Katia , Gianoli Chiara 
Published: January 2022

Abstract
The aim of this work is to investigate in-room proton radiographies to compensate realistic rigid and non-rigid transformations in clinical-like scenarios based on 2D–3D deformable image registration (DIR) framework towards future clinical implementation of adaptive radiation therapy (ART). Monte Carlo simulations of proton radiographies (pRads) based on clinical x-ray CT of a head and neck, and a brain tumor patients are simulated for two different detector configurations (i.e. integration-mode and list-mode detectors) including high and low proton statistics. A realistic deformation, derived from cone beam CT of the patient, is applied to the treatment planning CT. Rigid inaccuracies in patient positioning are also applied and the effect of small, medium and large fields of view (FOVs) is investigated. A stopping criterion, as desirable in realistic scenarios devoid of ground truth proton CT (pCT), is proposed and investigated. Results show that rigid and non-rigid transformations can be compensated based on a limited number of low dose pRads. The root mean square error with respect to the pCT shows that the 2D–3D DIR of the treatment planning CT based on 10 pRads from integration-mode data and 2 pRads from list-mode data is capable of achieving comparable accuracy (∼90% and >90%, respectively) to conventional 3D–3D DIR. The dice similarity coefficient over the segmented regions of interest also verifies the improvement in accuracy prior to and after 2D–3D DIR. No relevant changes in accuracy are found between high and low proton statistics except for 2 pRads from integration-mode data. The impact of FOV size is negligible. The convergence of the metric adopted for the stopping criterion indicates the optimal convergence of the 2D–3D DIR. This work represents a further step towards the potential implementation of ART in proton therapy. Further computational optimization is however required to enable extensive clinical validation.
Cesium seeding for effective electron transpiration cooling in hypersonic flows
Authors: Sahu Rupali , Tropina Albina , Andrienko Daniil , Miles Richard 
Published: January 2022

Abstract
The concept of electron transpiration cooling (ETC) uses thermionic emission of electrons from a low work function material to cool surfaces in hypersonic flight. A theoretical estimate of the emission current is given by the Richardson–Dushman equation. In hypersonic flights, the emission current can deviate from this estimate as the ambient air is partially ionized and a plasma sheath forms near the surface. Depending on the sheath structure, the emission current can be enhanced by the Schottky effect, or could be reduced by the space charge effects. In this study, we present a theoretical analysis of ETC of the leading-edge surface of a hypersonic vehicle, considering the transpiration of liquid cesium through a porous tungsten material. A part of the transpired cesium is adsorbed on the surface, which lowers the emitter work function, while the rest is evaporated due to high surface temperatures. Both the effects provide substantial cooling. The evaporated cesium is ionized in the ambient air, which alters the plasma conductivity and reduces space charge effects. The effect of individual fields of ionized species near the surface is found to be negligible. Cesium transpiration is found to eliminate the requirement for an applied surface potential and enable stable operation at surface temperatures below 2000 K.
Electrical devices based on hybrid membranes with mechanically and magnetically controllable, resistive, capacitive and piezoelectric properties
Authors: Bica Ioan , Anitas Eugen Mircea 
Published: January 2022

Abstract
Cotton fabric based membranes containing carbonyl iron microparticles with and without barium titanate nanoparticles (nBaTiO3) are fabricated. The hybrid membranes (hMs) are inserted between two copper electrodes, reinforced with glass fiber and epoxy resin. The resulted assembly is introduced in a silicone rubber sheath, and plane electrical devices (EDs) are obtained. Here, it is shown that using nBaTiO3, the EDs are characterized by resistive, capacitive and piezoelectric functions which have the property of being controllable in a field of mechanical forces, in a magnetic field or a combinations of the two. This is revealed by measuring the electrical resistance, capacitance and voltage at the output terminals of the devices. The electric voltage generator property of the devices is conferred by the presence of nBaTiO3. These effects allow us to conclude that the hMs offer the possibility of manufacturing low-cost and ecological EDs for various applications such as vibration, magnetic field and mechanical deformations sensors, electric generators etc.
Engineering the radiative dynamics of thermalized excitons with metal interfaces
Authors: Chen Grace H , Li David Z , Butcher Amy , High Alexander A , Chang Darrick E 
Published: January 2022

Abstract
As a platform for optoelectronic devices based on exciton dynamics, monolayer transition metal dichalcogenides (TMDCs) are often placed near metal interfaces or inside planar cavities. While the radiative properties of point dipoles at metal interfaces has been studied extensively, those of excitons, which are delocalized and exhibit a temperature-dependent momentum distribution, lack a thorough treatment. Here, we analyze the emission properties of excitons in TMDCs near planar metal interfaces and explore their dependence on exciton center-of-mass momentum, transition dipole orientation, and temperature. Defining a characteristic energy scale k
B
T
c = (ℏk)2/2m (k being the radiative wavevector and m the exciton mass), we find that at temperatures T ≫ T
c and low densities where the momentum distribution can be characterized by Maxwell–Boltzmann statistics, the modified emission rates (normalized to free space) behave similarly to point dipoles. This similarity in behavior arises due to the broad nature of wavevector components making up the exciton and point dipole emission. On the other hand, the narrow momentum distribution of excitons for T < T
c can result in significantly different emission behavior as compared to point dipoles. These differences can be further amplified by considering excitons with a Bose Einstein distribution at high phase space densities, such as in a condensate phase. We find suppression or enhancement of emission relative to the point dipole case by several orders of magnitude. These insights can help optimize the performance of optoelectronic devices that incorporate 2D semiconductors near metal electrodes and can inform future studies of exciton radiative dynamics at low temperatures. Additionally, these studies show that nanoscale optical cavities are a viable pathway to generating long-lifetime exciton states in TMDCs.
Physical Agents Quality Index (PAQI): application in a University Campus
Authors: Graziuso G , Francavilla A B , Mancini S , Guida M , Guarnaccia C 
Published: January 2022

Abstract
Among the most common techniques and methodologies for the analysis of the exposure to polluting agents, the definition of environmental indexes represents a very useful tool able to describe the status of the environment quantitatively and qualitatively. In this perspective a general Physical Agents Quality Index (PAQI) describing the status of an environment in terms of presence of different physical pollutants was developed by authors in a previous work. In this paper the PAQI concept is extended from a punctual idea to a spatial analysis and a practical application in order to evaluate the potentiality of the index on a visual map is discussed. In particular, the PAQI visual map of the Campus of the University of Salerno (Italy) was redacted. The results are presented and lay down the basis for further improvements in the visual mapping and also in the calibration of the calculation of the index.
Open-source software tools for strategic noise mapping: a case study
Authors: Graziuso G , Francavilla A B , Mancini S , Guarnaccia C 
Published: January 2022

Abstract
Currently, the European Union has adopted a series of measures to combat noise problems, focusing on some key indicators, such as the daily and night level of noise. Among the possible actions to manage environmental noise, there are the creation of noise maps and the adoption of action plans. Noise maps are, usually, produced by modelling and simulating data relating to traffic and various activities of an area. However, in some cases, these input data are not available or it is not possible to include all noise sources in the models used, causing difficulties in the prediction and analysis of the environmental impacts of noise. In this paper, starting from the analysis of open-source software tools for data collection, noise modelling and mapping, i.e. Openstreet Map, Noise Modelling and QGIS, based on free and public licenses, a methodology for the creation of noise maps will be presented. Finally, the outputs of the application of the methodology to the city centre of the Municipality of Nocera Inferiore, in south Italy, will be the creation of noise maps characterized by the spatial representation of the main standard noise indicators.
Visualization of the Plasma Frequency by means of a Particle Simulation using a Normalized Periodic Model
Authors: Grunwald Joschka , Mercorelli Paolo 
Published: January 2022

Abstract
In plasmas the atoms are dissociated into ions and free electrons. Due to the high mobility of the electrons, plasmas are a vibrating medium. As a result, a plasma frequency can be determined, which is an important parameter for characterizing a material. In this study, the plasma frequency is to be visualized. For this purpose, a mathematical model will be set up to describe the electron kinetics. The electrons are briefly deflected with a force against the static ion background in order to examine the interaction between the particles in the form of electrostatic fields. The discretized equations are implemented in a numerical particle simulation to visualize the movement of the electrons. The Particle-In-Cell (PIC) method is used for this. A spatial mathematical grid is created on which the simulated particles are distributed in the cells. The equations of motion and field are then solved on this grid at different times.
Vision-Based Deep Learning Algorithm for Detecting Potholes
Authors: Gajjar K , Niekerk T van , Wilm Thomas , Mercorelli P 
Published: January 2022

Abstract
Potholes on roads pose a major threat to motorists. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836 s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.
Traffic noise prediction using machine learning and monte carlo data augmentation: a case study on the Patiala city in India
Authors: Singh Daljeet , Kaler Priyal , Lyall Ishita , Singh Aekamjot , Pannu H S 
Published: January 2022

Abstract
Traffic noise pollution is a serious problem in the modern urban areas especially to design new architecture of smart cities, highways, hospitals, schools for an efficient and healthy environment. To analyse this aspect, we have proposed a machine learning based prediction of sound pressure level on an original dataset collected in Patiala city in India. Vehicular traffic and sound pressure level data was collected on different sites in the city. A total of 502 data samples on the identified sites were obtained for the study. Further this data is augmented using Monte Carlo simulation to 10 times of its initial size and the Artificial Neural Networks (ANN) have been trained and compared with other Machine learning methods for the vehicular traffic noise prediction. The input parameters in the model are traffic volume Q, percentage of heavy vehicles P and the average speed of vehicles V and the output parameter is the equivalent continuous sound pressure level, Leq dB(A). The experimental results show ANN which is trained on the augmented data using Monte Carlo simulations outperforms other advanced methods making it an effective measure for vehicular traffic noise prediction to develop a healthy environment which is free of noise pollution.
Biometric Identification based on the Multidimensional Transient Functions of the Human Oculo-Motor System
Authors: Pavlenko Vitaliy , Shamanina Tetiana , Chori Vladysla 
Published: January 2022

Abstract
Recently, identification systems that use human biometric characteristics in solving the problem of access to information systems are becoming more common. The paper proposes a new method of biometric identification of users of computer systems based on the definition of integral Volterra models of the human oculomotor system (OMS) according to experimental research “input-output” using innovative technology of eye tracking. Developed in the Python IDLE programming environment software to identify OMS. Experimental studies of the OMS of two individuals were performed. Based on the data obtained using the TOBII PRO TX300 eye tracker, the transient functions of the first, second and third orders of the OMS are determined. There is a significant difference in the diagonal intersections of the transition functions of the second and third orders of the two individuals.