bluuebunny/crossref_metadata_2025_split huggingface dataset:
Phase space structure of symmetric teleparallel theory of gravity
Authors: Saha Dalia , Sanyal Abhik Kumar 
Published: December 2024

Abstract
The ‘generalized symmetric teleparallel gravity’ (GSTG) does not admit diffeomorphic invariance, since the auxiliary field as well as the shift vector act as non-propagating dynamical variables carrying 1/2 degrees of freedom each. We show that in a minisuperspace model, which is devoid of the shift vector, the problem is alleviated for locally Lorentz invariant GSTG theory, and diffeomorphic invariance is established at least for one connection. However, the eerie structure of the Hamiltonian constructed even in the background of spatially flat isotropic and homogeneous Robertson–Walker space-time, can not be maneuvered. In contrast, the other two spatially flat connections containing an arbitrary time dependent function, doesŉt admit non-linear extension to ‘symmetric teleparallel equivalent to general relativity’ (STEGR). We therefore construct the phase-space structure with three different spatially flat connections for the ‘Lorentz invariant’ linear-scalar–vector–tensor GSTG action. Diffeomorphic invariance is established and the associated Hamiltonians are found to be well behaved for all the three cases.
Some remarks on Wang–Yau quasi-local mass
Authors: Zhao Bowen , Andersson Lars , Yau Shing-Tung 
Published: December 2024

Abstract
We review the definition of Wang–Yau quasi-local mass from the point of view of the gravitational Hamiltonian. This makes clear the relation between Wang–Yau definition and Brown–York or even global ADM definition. We make a brief comment on admissibility condition in the definition of the Wang–Yau quasi-lcoal mass. We extend the positivity proof for Wang–Yau quasi-local energy to allow possible presence of strictly stable apparent horizons through establishing solvability of Dirac equation in certain 3-manifolds that possess cylindrical ends, as in the case of Jang graph blowing up at marginally outer trapped surfaces.
Data-driven discovery of sparse dimensionless numbers and nondimensional relations for low-temperature plasmas
Authors: Miller Victor V , Shao Ketong , Chan Kimberly J , Mesbah Ali 
Published: December 2024

Abstract
Low-temperature plasmas (LTP) are increasingly used in medicine and biotechnology, materials processing, and environmental remediation, but their complex physics often render design and scale-up of LTP processes challenging. Scaling and similarity relations can aid in understanding and designing LTP processes, as they can be used to construct reduced-order models, relate design levers to discharge behavior, and predict discharge properties in novel systems. For example, the reduced electric field highlights the fundamental role of electron energy gained between collisions, and has been instrumental in scaling up LTP processes. Historically, similarity parameters and scaling laws are expressed as dimensionless relations. This paper introduces a learning-based approach to discover sparse dimensionless numbers (i.e. dimensionless numbers with as few process variables as possible while having an adequate predictive capability) and their relations. The method uses

ℓ
1

-penalization in regression to find coefficients for null-space basis vectors of the dimension matrix, eliminating irrelevant process variables in discovery of dimensionless relationships. The method is demonstrated for learning nondimensional relations for electron number density and electron temperature in a nanosecond-pulsed packed and unpacked dielectric-barrier discharge in He. It is observed that process variables such as the electron density, the off-pulse electron temperature and the density of excited He can be related independently of reactor volume or operating conditions for the conditions studied. Further, it is shown that the proposed method can effectively discard input process variables that may not be relevant to describing a target dimensionless number. The resulting interpretable relations not only extrapolate, but also provide insights that are not readily apparent from conservation laws.
Machine learning applications to computational plasma physics and reduced-order plasma modeling: a perspective
Authors: Faraji Farbod , Reza Maryam 
Published: December 2024

Abstract
Machine learning (ML) offers a diverse array of tools and architectures for transforming data from simulations and experiments into explainable science, thereby augmenting domain knowledge. ML-enhanced numerical modeling has the potential to revolutionize scientific computing for complex engineering systems, enabling detailed analyses of technologies’ operation and facilitating automation in optimization and control. While ML applications have grown significantly in various scientific fields—particularly in fluid mechanics, where ML has demonstrated immense potential in computational fluid dynamics (CFD)—numerical plasma physics research has yet to see similar advancements. However, the strong parallels between fluid mechanics and plasma physics provide an opportunity to transfer ML advances in fluid flow modeling to computational plasma physics. This Perspective outlines a roadmap for such progress. It begins by introducing fundamental aspects of ML, including the main categories of algorithms and the types of problems they can address. For each problem type, we highlight specific examples of ML applications in CFD, reviewing several notable efforts. We also review recent ML applications in plasma physics and discuss promising future directions and pathways for development in plasma modeling across different problem types. Finally, we elaborate on key challenges and requirements that must be addressed to unlock ML’s full potential in computational plasma physics, including the development of cost-effective, high-fidelity simulation tools for extensive data generation.
Differential code biases estimation from Sentinel-6A GPS and Galileo combined observations
Authors: Peng Hui , Jin Shuanggen , Chen Zilong 
Published: December 2024

Abstract
Differential code bias (DCB) is the hardware delay in the ranging code of the global navigation satellite system (GNSS), which critically impacts ionospheric total electron content estimation, precise positioning and accurate timing. The GNSS receiver aboard the Sentinel-6A low Earth orbit satellite is a crucial payload for precise orbit determination and topside ionospheric studies, while the GNSS receiver DCB is one of the errors. In this paper, the DCB variations of both GNSS satellites and receivers are estimated and analyzed using dual-frequency observations from the Sentinel-6A GNSS receiver (GPS and Galileo) during different solar activity periods. The results show that DCB estimates from GPS and Galileo combined observations are significantly more accurate than those from single-system estimates. The combined estimate results show that the overall monthly mean differences between the DCB estimates of GPS and Galileo satellites and the reference values from the German Aerospace Center and the Chinese Academy of Sciences are stable at 0.31, 0.34, 0.15 and 0.19 ns, respectively. During the high solar activity, the mean difference between the combined estimation result and the reference value is 0.34 ns and 0.16 ns. The difference for the GNSS receiver DCB estimates between single-system and GPS + Galileo combined estimations is minimal, with mean differences within 0.11 ns. Using GPS + Galileo combined observations enhances the stability of the GNSS receiver DCB estimates by 7% and 32%, respectively when compared to single-system estimates. The GPS + Galileo combined observations provide a good DCB estimation for Sentinel-6A precise orbit determination and topside ionosphere modeling.
Hierarchical elastic and conductive multiwall carbon nanotube carboxymethylcellulose silica hybrid aerogel for strain sensors
Authors: Wang Chaoming , Xie Shuaiao , Hu Hangyu , Zhang Lei , Liu Junjie , Li Xiangyu 
Published: December 2024

Abstract
Flexible and wearable piezoelectric pressure sensors have attracted extensive interest for their potential applications in human health monitoring. However, it remains a challenge to fabricate exceedingly elastic, conductive and steady strain-sensitive aerogels. Herein, an elastic and conductive carboxymethylcellulose sodium/methyltrimethoxysilane/multiwall carbon nanotube (CMC/MTMS/MWCNT) aerogel with interconnected porous microstructure was fabricated by a solution mixing and freeze-drying technique. Owing to covalent, ionic and hydrogen-bonding interactions between flexible CMC chains, MTMS and MWCNTs, the as-prepared CMC/MTMS/MWCNT hydrophobic aerogel with 50 wt% MWCNT loading exhibits good elasticity (1500 steady compression cycles at 30% strain), a broad pressure detection range (0–150 kPa) and reasonable compression sensitivity with gauge factor of 1.02 under 20%–30% strain. Additionally, the CMC/MTMS/MWCNT aerogel was used as a strain sensor and successfully demonstrated human motion detection—not only large-scale actions (finger bending) but also small-scale muscle movements (swallowing). With these results, our CMC/MTMS-50 aerogel is a promising candidate for utilization in flexible and wearable strain sensors.
Optimal Analysis of MHD Peristaltic flow of Viscoplastic nanofluid with chemical reaction and Electro-Osmotic effect via artificial neural networks and response surface methodology
Authors: Alqahtani Ali S. , rehman amad ur , Asghar Zaheer , Zeeshan Ahmad , Malik M. Y 
Published: December 2024

Abstract
In this study the authors aim to investigate sensitivity analysis of pressure rise per wavelength and frictional forces in magnetohydrodynamics peristaltic flow of viscoplastic nanofluid with the effect of electro-osmotic and chemical reaction in tapered channel. To achieve this aim, we have adopted an empirical modeling methodology that establishing pressure rise per wavelength and frictional forces on upper and lower wall relationships as functions that vary according to the governing parameters essential to the problem. To analyze the sensitivity of transport parameters, including pressure rise per wavelengthand frictional forces on upper and lower wall, we initially develop an empirical model for key responses, (pressure rise per wavelengthand frictional forces on upper and lower wall). To my best knowledge, no empirical correlations for pressure rise per wavelength and frictional forces associated with viscoplastic nanofluid peristaltic flow in tapered channels have addressed in the literature. To develop this empirical model, we used the approach of Response Surface Methodology and Artificial Neural Networks. The appropriateness of empirical model is evaluated by determining the coefficient of determination and conducting an analysis of variance to ensure accuracy and reliability. The empirical model display good fit, with the coefficient of determination of 97.65% for all resulting responses, (pressure rise per wavelengthand frictional forces on upper and lower wall). Residuals are plotted to verify accuracy and reliability of empirical correlation. A sensitivity analysis was performed to identify the most influential input parameters. The results, presented graphically, show that the values of resulting responses (pressure rise per wavelengthand frictional forces on upper and lower wall) are highly sensitive to variation in Brownian motion parameter in most situations. This innovative approach contributes valuable insights for optimizing flow performance in biomedical and engineering applications, filling a critical gap in the existing body of knowledge.
Physical synthesis in distributed quantum architectures
Authors: Mousavi Seyed Mohammad , Mohammadzadeh Naser 
Published: December 2024

Abstract
The inherent power of quantum computing in parallelizing computations makes it possible to solve some specific problems in a much shorter time than classical computing. Considering the physical issues governing this type of computing, one of the approaches to increase the scalability of such systems is using distributed quantum architectures. A quantum algorithm requires a quantum circuit for a successful implementation. The quantum circuit design flow comprises two main parts: logical synthesis and physical design. Physical synthesis is one of the main steps of the physical design. It improves circuit metrics such as latency and area by changing the netlist or the layout using the information obtained from the initial layout and scheduling. In this paper, we have presented a new physical synthesis method to reduce the quantum circuit delay in distributed architectures. It moves some gates from one partition to another to reduce the number of teleportation operations on the critical path using the scheduled layout information. The experimental results show that this method improves the delay of benchmark circuits on average by 33.40%.
Enhancing permanent magnet stepper motor accuracy and precision in biomedical application via fuzzy logic positioning approach
Authors: Mahmoud Imed , Khedher Adel 
Published: December 2024

Abstract
The limitations of Permanent Magnet Stepper Motor (PMSM), such as their low angular resolution and rotor oscillations, pose significant challenges for their use in precision industrial applications, particularly in biomedical systems. Enhancing the performance of motor drives proves to be difficult due to uncertain system parameters and varying load conditions. To address these issues and overcome the drawbacks of traditional control methods, a fuzzy logic-based control concept is introduced. In this study, we propose a fuzzy logic concept (FLC) aimed at improving the angular resolution, rotor oscillations and compensation of the position error under load of PMSM specifically in an Electrical Syringe Pump application. The FLC incorporates membership functions and rules designed with value normalization, enabling the controller to offer flexibility across a wide range of applications. Through numerical validation, the effectiveness of the proposed FLC is confirmed.
Design optimization and analysis of steering knuckle for FSAE EV racing cars
Authors: Bin Amir Mohamad Akif Haiqal , Xuan Ku Pei 
Published: December 2024

Abstract
Steering knuckle is an important and complex component in both normal combustion cars and Formula SAE Electric Vehicle (FSAE EV) racing cars. This component is considered a crucial part that has a vital function in the automotive industry as it links the suspension to the wheel assemblies and is responsible for the precise steering, manoeuvrability, stability during turning, and general handling of the car. Its strategic location is evident as it might influence several aspects of a car’s steering system and thus, influencing the driving experience. Furthermore, this paper aimed to design steering knuckles that are specifically catered for FSAE EV racing cars and compare the outcomes with the current design of the steering knuckle. In this research, the ideation process of the steering knuckle designs for the FSAE EV racing cars involved brainstorming technique, taking into account the requirements and demands provided by the Taylor’s Racing Team (TRT). Through the utilization of SOLIDWORKS software, the 3D modelling process added detail to all the conceptual designs produced ensuring that they were clearly visualized. The ANSYS Simulation Software was used to conduct a Grid Independence Test (GIT) to determine the most suitable meshing size and shape, and the application of boundary conditions over the various parts of the steering knuckles at first. Then, ANSYS was also used to perform Static Structural Analysis to determine the stress distribution, total deformation and safety factor of steering knuckles designed for the FSAE EV racing car and the currently existing steering knuckle. All of the FSAE EV racing car steering knuckles were compared to the existing steering knuckle that was designed and provided by TRT to determine the enhancement in terms of stress distribution, total deformation, and safety factor. Therefore, the results have shown the importance of producing steering knuckles that are specifically catered for the FSAE EV racing car where the performance improvements were evident and ensured that the car could perform well on the track.
Experimental Study Modeling of The Effect of ESDD and NSDD on 150 kV Insulator
Authors: Warmi Yusreni , Amalia Sitti , Zulkarnaini  , Antonov  , Syofian Andi , Anthony Zuriman , Azhar Hamdi 
Published: December 2024

Abstract
This study investigates the impact of equivalent salt deposit density (ESDD) and non-soluble deposit density (NSDD) on the flashover voltage of 150 kV transmission line insulators. The high flashover rates on the Koto Panjang-Payakumbuh transmission line in Indonesia, particularly in hilly regions, indicate that contaminants significantly affect the insulator’s performance. The research utilizes the Anderson method and experimental data to analyze flashover voltage variations due to different contamination levels. Results show that the highest flashover voltage was observed at NSDD of 0.8401 mg/cm2 with values of 395.44 kV (Anderson) and 346 kV (experiment), and the lowest at NSDD of 1.0151 mg/cm2 with values of 394.40 kV (Anderson) and 341 kV (experiment). For ESDD, the highest voltage was at 160.8 mg/cm2 with 395.44 kV (Anderson) and 341 kV (experiment), and the lowest at 258.6 mg/cm2 with 394.40 kV (Anderson) and 341 kV (experiment). The strong correlation between flashover voltage and contaminant levels emphasizes the need for effective monitoring and control of contamination on insulators to improve reliability in power transmission systems.
CFD Simulation On Solder Joints Wetting Properties For A Low And High Temperature Solder
Authors: Sheng Wee Tze , Singh Amares 
Published: December 2024

Abstract
The soldering process involves the ability of a melted solder to spread and adhere on a substrate to form a metallic bond driven by the physical and chemical properties of the system. Thus, studying solder joint wettability due to the effect of solder’s wetting and thermal properties is crucial. The primary objective of this project is to develop multiple CFD simulation models to simulate solder joint wetting properties for different solders and compare the numerical results with theoretical results. Afterwards, the most suitable simulation model will be determined based on the accuracy, functionality, advantages, and disadvantages of the model. Initially, the thermal properties and theoretical wetting properties of different solders will be obtained to design the simulation geometry and determine the simulation boundary conditions. Then, each of the developed simulation models will be implemented in ANSYS Fluent, and the simulation will be carried out to simulate the solder joint wetting properties by applying the VOF and melting & solidification CFD models and their corresponding boundary conditions. The results show that all the developed simulation models were able to accurately simulate the wetting properties of the solder joint. However, only the reflow soldering process model has the functionality to perform temperature dependence simulation and the potential to perform time dependence simulation. Hence, the most suitable simulation model is the reflow soldering process model.
Impact of Surface Modification Techniques for Replaceable Cutting Inserts on Cutting Forces and Surface Finish in Machining Operations
Authors: Bílek O , Zlámal J , Knedlová J , Vrbová H 
Published: December 2024

Abstract
This study investigates the impact of surface modification techniques, specifically microblasting and Magnetorheological Finishing (MRF), on the performance of uncoated sintered carbide replaceable cutting inserts (RCIs) during machining operations. The primary focus is on the relationship between surface roughness modifications and two key performance metrics: the quality of the workpiece surface finish and the cutting forces generated during turning operations. The study involved controlled experiments using RCIs that were untreated, sandblasted, or MRF-treated. Microblasting was found to increase surface roughness, leading to higher cutting forces and poorer workpiece surface quality. Conversely, MRF treatment reduced surface roughness, resulting in lower cutting forces and improved workpiece surface finishes.
Nanosecond photoluminescence decay in zinc molybdate oxide nanocrystals observed by TCSPC and phase shift methods
Authors: Remeš Z , Neyková N , Remeš Š , Novák R , Kučerková R , Jarý V , Mičová J 
Published: December 2024

Abstract
Hydrothermally grown zinc molybdate oxide (ZMO) nanocrystals were prepared for their interesting scintillation properties at room temperature. Here we report on the comparison of two time resolved photoluminescence (PL) decay measurements of ZMO: one using time correlated single photon counting (TCSPC) based on short pulse excitation and the second based on the frequency resolved phase delay between sinusoidal excitation and emission. Both methods allow PL decay to be measured with a time resolution about 1 ns. While the time resolution of the TCSPC was limited by the excitation pulse width (slightly below 1 ns), the time resolution of the phase shift method was limited by the maximum operating frequency of the conventional UV LED (3 MHz). The phase method offers a low-cost alternative to TCSPC measurements of ZnO nanorods.
Site-Controlled Growth of InGaAs Quantum Dots Based on Buried Stressors for the Development of Microlasers and Quantum Light Sources
Authors: Podhorský M , Klonz M , Limame I , Tripathi S , Gaur K , Palekar Ch C , Mudi P , Klenovský P , Rodt S , Reitzenstein S 
Published: December 2024

Abstract
Many applications of InGaAs quantum dots in various fields of photonics, including optoelectronics, quantum technologies, and telecommunications have emerged in recent years. The objective of this work is to evaluate and optimize the growth and fabrication parameters of site-controlled InGaAs quantum dots, grown with the buried stressor method. The aim is to enhance the precision, uniformity and reproducibility of the quantum dot placement and local density for advanced optoelectronic applications, such as low-treshold microlasers and quantum light sources. The influence of the growth and the fabrication processes on the structural and optical properties of site-controlled quantum dots is investigated in depth using cathodoluminescence mapping, scanning electron microscopy, confocal laser scanning microscopy, and photoluminescence spectroscopy.
Sorption Kinetics of Copper Ions on Biochar and Zeolite
Authors: Kucmanová A , Ščasná M , Sirotiak M , Šido J 
Published: December 2024

Abstract
This study evaluates the sorption kinetics and adsorption isotherms of copper ions (Cu2+) onto soil amended with biochar and natural zeolite. Biochar was produced from beech wood at pyrolysis temperatures of 400 °C and 500 °C, while clinoptilolite zeolite was used as a natural mineral sorbent. Sorption experiments assessed the efficiency and behaviour of Cu2+ uptake, with data fitted to linear, Freundlich, and Langmuir isotherm models. The results showed that biochar produced at 400 °C had the highest adsorption capacity, with a maximum value Smax of 172.58 mg/g, followed by zeolite at 162.52 mg/g and biochar at 500 °C with 158.86 mg/g. All sorbents exhibited strong linear adsorption behaviour, with correlation coefficients near or above 0.999, suggesting a uniform adsorption affinity. These findings demonstrate the effectiveness of biochar and zeolite as sustainable options for Cu2+ removal from contaminated soils, providing insights into optimising sorbent selection and pyrolysis conditions for environmental remediation.