bluuebunny/crossref_metadata_2025_split huggingface dataset:
Particles depositions onto non-smooth surfaces of ventilation ducts
Authors: Orabi M 
Published: December 2024

Abstract
In a previous paper, a model was established to account for particles’ deposition onto the inner surfaces of small spaces. In that paper, only smooth surfaces were considered. A modification of that model is proposed here in this article to provide a description for non-smooth (rough) surfaces. This is attained by altering the limits of the boundary-layer that are used to do the integrations required to calculate the deposition speeds. To apply the model, deposition of particles onto the inner rough surfaces of a ventilation-duct is considered, as an example. Deposition speeds onto the walls, floor and ceiling of the duct are estimated with comparing them to the experimental-data. By considering different values of the friction velocities, the alterations of the lower-limit in the boundary-layer are mathematically related to the corresponding friction velocities. The proposed model has the advantage of predicting the alteration in the limits of the boundary-layer, once the friction-velocity is provided. This is very important as the friction-velocity is a key parameter used in the descriptions of particles’ deposition and penetration.
Bent zigzag graphene nanoribbons for spintronic applications
Authors: Farghadan Rouhollah 
Published: December 2024

Abstract
We investigate a spintronic device utilizing bent zigzag graphene nanoribbons (ZGNRs) to explore their spin-dependent electronic and transport properties. Employing a mean-field Hubbard model and the Landauer-Büttiker formalism, we examine the effects of curvature on a large-scale bent ZGNR, revealing a spin-semiconducting phase in the antiferromagnetic ground state. The device comprises a circularly curved nanoribbon connected to two straight ZGNRs, forming a two-terminal, stretchable system. Our findings demonstrate that the spin energy gap and spin-splitting effects in bent ZGNRs are highly tunable via curvature parameters-total rotation, radius, and width. Even minimal curvature induces significant spin-dependent behavior and spin Seebeck coefficient (SSC), resulting in full spin polarization in both the density of states and the transmission coefficient. The degree of spin polarization increases with the bending parameter, leading to enhanced spin-polarized current and a substantial SSC. These results suggest that bent ZGNRs are promising for advancing spintronic applications, particularly in flexible device technologies.
Improved photocatalytic degradation of methylene blue and rhodamine G dye under UV-radiation by rGO-MoO3
Authors: Ushal Ruhinaz , Ahmed Bilal , Singh Arvind , Ojha Animesh Kumar 
Published: December 2024

Abstract
In this work, we report facile synthesis of rGO-MoO3 nanocomposites as photocatalysts via one-step hydrothermal method. Initially, the synthesized materials were characterized by different physicochemical methods to reveal the structural, morphological, optical, and other properties. The transmission electron microscopy (TEM) and Raman spectroscopy results validate the formation of rGO-MoO3 nanocomposites. The optical bandgap values of pristine MoO3 (3.47 eV) and rGO-MoO3 (3.36 eV), reveal that they can serve as ultra-violet light photocatalysts, for the decomposition of organic pollutants e.g. methylene blue (MB) and rhodamine G (RhG) dye. The rGO-MoO3 nanocomposites significantly enhanced the photodegradation efficiency as compared to MoO3, due to the interfacial interaction between the composite parts and promoted the transfer of charge carriers from MoO3 to rGO, causing a reduced rate of electron-hole recombination. The highest degradation efficiency was observed for rGO-MoO3 nanocomposite {for MB (∼50%) and RhG (∼96%)}, whereas for MoO3 {for MB (∼40%) and RhG (∼43%)}. This report highlights a noteworthy impact of rGO with MoO3 nanocomposites as a photocatalyst.
OAM mode propagation and supercontinuum generation in a nested photonic crystal fiber
Authors: Punia Sushma , Saharia Ankur , Ismail Yaseera , Petruccione Francesco , Bourdine Anton V. , Morozov Oleg G. , Meshkov Ivan K. , Singh Ghanshyam , Tiwari Manish 
Published: December 2024

Abstract
The proposed work presents a nested Photonic Crystal Fiber(PCF) with a hexagonal lattice of air holes, capable of supporting Orbital Angular Momentum modes(OAM) and generating a wide-ranging supercontinuum spectrum, further enhancing its utility in various optical applications. The Photonic Crystal Fiber consists of a dense flint SF6 and a very light flint LLF1, fabricated from Schott. Finite Element Method is used for the analysis of Nested Photonic Crystal Fiber characteristics using COMSOL Multiphysics. A total of 120 Orbital Angular Momentum modes modes have been obtained in the fiber, with 86 in the outer core and 34 in the inner core. The generalised Nonlinear Schrodinger Equation provides a numerical description of the pulse that is propagating through the PCF. In this article, the proposed nested PCF can be used to generate supercontinuum by pumping with a hyperbolic secant pulse, with red an average power of 71 mW. The resulting supercontinuum spans an impressive bandwidth, extending from approximately 1122 to 2072 nm.
Near-isotropic sub-Ångstrom 3d resolution phase contrast imaging achieved by end-to-end ptychographic electron tomography
Authors: You Shengbo , Romanov Andrey , Pelz Philipp M 
Published: December 2024

Abstract
Three-dimensional atomic resolution imaging using transmission electron microscopes is a unique capability that requires challenging experiments. Linear electron tomography methods are limited by the missing wedge effect, requiring a high tilt range. Multislice ptychography can achieve deep sub-Ångstrom resolution in the transverse direction, but depth resolution is limited to 2 to 3 nanometers. In this paper, we propose and demonstrate an end-to-end approach to reconstructing the electrostatic potential volume of the sample directly from the 4D-STEM datasets. End-to-end multislice ptychographic tomography recovers several slices at each tomography tilt angle and compensates for the missing wedge effect. The algorithm is initially tested in simulation with a Pt@Al2O3 core–shell nanoparticle, where both heavy and light atoms are recovered in 3D from an unaligned 4D-STEM tilt series with a restricted tilt range of 90 degrees. We also demonstrate the algorithm experimentally, recovering a Te nanoparticle with sub-Ångstrom resolution.
A 1T1M-based efficient probability storage and computing cell for the VAE-SBN hybrid model
Authors: Dai Yuehua , Tan Su , Feng Zhe , Zou Jianxun , Guo Wenbin , Yu Ruihan , Wang Zeqing , Hu Yang , Wang Haochen , Ruan Hao , Hao Yang , Lin Zhihao , Xu Zuyu , Zhu Yunlai , Wu Zuheng 
Published: December 2024

Abstract
Bayesian network are crucial components for uncertainty reasoning and knowledge representation in probabilistic graphical models. As model complexity increases, traditional digital methods face challenges in efficiency and resource utilization. In this work, a novel Bayesian network optimization method based on one-transistor one-memristor (1T1M) units for storing and accessing probability values was proposed. This method leverages the sigmoid-like characteristics of 1T1M for computational optimization, integrates probability storage into sigmoid belief network (SBN), and extends to the variational autoencoder (VAE) framework. By storing and manipulating probabilities at hardware level, this approach enhances storage efficiency and access speed while enhancing computational precision and model expressiveness through the sigmoid-like space of 1T1M. Validation experiments using the MNIST digit generation task demonstrate that this method exhibits unique advantages in SBN structures, particularly in handling complex conditional probabilities and inference tasks. Compared to traditional digital implementations, the proposed method exhibits potential advantages in inference speed and hardware utilization.
Periodic and chaotic behaviors of a compound pendulum driven by a horizontal periodic external force
Authors: Wu Zuo-Bing 
Published: December 2024

Abstract
Based on the oscillation experiments of a compound pendulum driven by a horizontal periodic external force, a nonlinear dynamical model is established and studied numerically. The periodic, quasiperiodic and chaotic orbits are found in the motions of the compound pendulum in response to several driving forces. The numerical results are in qualitative agreement with those in the experiments. It is found that the chaotic attractor in the three-dimensional phase space displays a two-dimensional torus structure. On a Poincare section, the chaotic attractor consists of the relative rotating core and its trailing tails. The fractal dimension of the chaotic attractor on the Poincare section is determined by using the box-counting method. The bifurcation diagram shows that the transition of the system from periodic motion to chaos is realized by the period-doubling bifurcation. The first Feigenbaum universal constant is approximately determined in the period-doubling bifurcation process. The competition between the inherent vibration and the external driving vibration of the system is thus thought as the physical mechanism for leading to the complex phenomena such as the period-doubling bifurcation and chaos. The numerical results combining with the experimental ones can provide an intuitive and in-depth understanding of chaotic phenomena, which is of great significance for the optimization design and the stability control in the engineering technology.
The studies of topological phases and energy braiding of non-Hermitian models using machine learning
Authors: Shi Shuwei , Chu Shibing , Xie Yuee , Chen Yuanping 
Published: December 2024

Abstract
Complex-energy bands in non-Hermitian systems can exhibit diverse topological braiding, yet identifying these braids remains challenging and has garnered limited attention in previous studies. In this work, we explore energy braiding in one-dimensional non-Hermitian systems through both unsupervised and supervised learning techniques. For unsupervised learning, we apply diffusion maps to effectively identify non-Bloch energy braiding without requiring prior knowledge and use k-means clustering to categorize different topological features, such as Unlink and Hopf link configurations. In the supervised learning phase, we train a Convolutional Neural Network (CNN) on Bloch energy data to predict both Bloch and non-Bloch energy braiding with nearly 100% accuracy. Through an analysis of the CNN, we confirm that the model has successfully developed the capacity to recognize the braiding topology of the energy bands. Our findings reveal that unsupervised learning can rapidly detect phase transition points, while the CNN is capable of predicting braid degrees even for models not included in the training set.
Fractal configurations of zigzag hexagonal type coronoid molecules: Graph-theoretical modeling and its impact on physicochemical behavior
Authors: Ullah Asad , Nazir Muhammad , Zaman Shahid , Hamed Y S , Jabeen Safina 
Published: December 2024

Abstract
Coronene, a benzenoid compound, holds significant potential for applications in diverse fields, including organic chemistry, materials science, and pharmaceuticals. This study focuses on the structural analysis of Zigzag Hexagonal Coronene Fractal (ZHCF), a unique molecular configuration with significant implications for materials science and nanotechnology. Utilizing topological indices across two-dimensional chemical structure networks, we evaluate critical physicochemical properties of these molecules. Analytical expressions for a wide range of connection number-based topological descriptors are derived, enabling the prediction of properties such as entropy, enthalpy of vaporization, boiling point, and the acentric factor. The use of these mathematical tools provides a deeper understanding of the molecular connectivity and distribution patterns within the ZHCF framework, revealing insights into its stability and potential functionality. The results demonstrate how these indices can effectively capture the structural nuances of complex molecular graphs, aiding in the rational design of advanced nanomaterials with improved optical and electronic properties. This research not only showcases the predictive power of topological descriptors but also highlights the potential applications of coronoid-based structures in creating high-performance materials for various technological and scientific advancements. The findings pave the way for future exploration of coronoid structures in developing innovative solutions across diverse fields.
On the gravitational hysteresis in the kinetic theory
Authors: Moti Raihaneh , Shojai Ali 
Published: December 2024

Abstract
General theory of relativity is non-linear in nature and therefore can result in hysteresis-like effects and cause systems to remember the footprint of the gravitational field. Here we have investigated this effect using the Kinetic theory in curved spacetime. It is shown that the entropy rate experiences this hysteresis behavior. The effect is then considered for some special spacetimes, including Schwarzschild black hole, Friedmann-Lemaître-Robertson-Walker cosmological solution, and the flat Minkowski spacetime perturbed by a gravitational wave pulse. It is shown that there is some hysteresis effect for the entropy rate.
Whispers from the quantum core: the ringdown of semiclassical stars
Authors: Arrechea Julio , Liberati Stefano , Vellucci Vania 
Published: December 2024

Abstract
This investigation delves into the ringdown signals produced by semiclassical stars, which are ultra-compact, regular solutions of the Einstein equations incorporating stress-energy contributions from quantum vacuum polarization. These stars exhibit an approximately Schwarzschild exterior and an interior composed of a constant-density classical fluid and a cloud of vacuum polarization. By adjusting their compactness and density, we can alter the internal structure of these stars without modifying the exterior. This adaptability enables us to examine the sensitivity of the ringdown signal to the innermost regions of the emitting object and to compare it with similar geometries that differ substantially only at the core. Our results indicate that echo signals are intrinsically linked to the presence of stable light rings and can be very sensitive to the internal structure of the emitting object. This point was previously overlooked, either due to the imposition of reflective boundary conditions at the stellar surface or due to the assumption of low curvature interior geometries. Specifically, for stellar-sized semiclassical stars, we find that the interior travel time is sufficiently prolonged to render the echoes effectively unobservable. These findings underscore the potential efficacy of ultra-compact objects as black hole mimickers and emphasize that any phenomenological constraints on such objects necessitate a detailed understanding of their specific properties and core structure.
Open EFT treatment of inflation with thermal initial conditions
Authors: Tinwala Abbas , Narang Ashish , Mohanty Subhendra , Panda Sukanta 
Published: December 2024

Abstract
Investigating the thermal inflationary model, we introduce stochastic effects, incorporating a cutoff parameter σ which distinguishes between quantum and classical modes. Testing the model against Planck 2018 data, we observe a preference for a non-zero σ at least at 68% C.L., suggesting the classicalization of most modes and providing a theoretical foundation for the quantum to classical transition. As a result of introducing the stochastic effects, we find that the solution to the large-scale power deficit requires a lower comoving temperature of inflaton.
Research on miniaturized frequency steering system based on atomic clock
Authors: Guo Yonggang , Zhu Lili , Wang Kuan , Wang Shiwei , Shi Fucheng , Liu Zhidong 
Published: December 2024

Abstract
In order to generate a high-performance RF signal, by
analyzing the traditional frequency steering system, and aiming at
the goal of miniaturization and engineering, this paper designs a
miniaturized frequency steering system based on dual-atomic
clock. In addition, we have carried out research on key technologies
such as high-precision frequency deviation measurement and precision
frequency regulation. On this basis, a frequency deviation
measurement and correction system is designed. And according to the
theoretical analysis, the measuring accuracy of the frequency
deviation measurement approach can reach 1.0 × 10-14,
besides, the step frequency tuning can also reach
1.125 × 10-14. What's more, some related experiments, the
RF signal synthesized from a Rb clock and a master has an accuracy
of 2.26 × 10-13, and also the frequency stability of
104 s in terms of the Allan deviation reaches
2.36 × 10-14, suggesting that the overall performance of
the rubidium atomic clock has been greatly improved.
Incentivizing water conservation in agriculture: a case study of the Great Salt Lake watersheds
Authors: Li Man , Khan Asif Ahmed 
Published: December 2024

Abstract
Conserving agricultural water resources is crucial for sustainable development, yet, developing effective policies is challenging due to limited site-specific information. We present a framework combining economic models and remote-sensing data to spatially explicitly assess willingness-to-accept payments to irrigators and unit water-saving costs. Applied to three major tributary watersheds of the Great Salt Lake, this framework identifies areas with the highest conservation potential and cost-effectiveness. We find that an annual water conservation goal of 581 million m3, necessary to restore the lake within 30 years, can be met by fallowing irrigated alfalfa fields. With 95% certainty, this goal would be fully achieved with annual payments of US$325 million under site-specific payments or US$376 million under county-level payments, or at least 84% achieved with US$341 million under watershed-level payments. This framework can be applied to explore policy priorities and the economic viability of land-based natural resource protection, informing funding decisions and achieving conservation goals in various contexts.
Antisymmetric tensor fields in 4D: actions, symmetries and first order Duffin–Kemmer–Petiau formulations
Authors: Jarvis Peter D , Thierry-Mieg Jean 
Published: December 2024

Abstract
Analyzing the representations of the Lorentz group, we give a systematic count and construction of all the possible Lagrangians describing an antisymmetric rank two tensor field. The count yields two scalars, which provide the ingredients of the gauge invariant Kalb–Ramond model, equivalent to the sigma model and familiar from super gravity and string theory, and of the conformally invariant Avdeev–Chizhov model, which describes self-dual tensors. The count also includes a pseudoscalar with symmetric coupling, and an additional pseudoscalar, an antisymmetrized form of the Avdeev–Chizhov Lagrangian. This quantity was first derived in an

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superalgebraic model of the weak interactions. It is also conformally invariant, and naturally implements the Landau CP symmetry. Then, by extending the DKP ten component formalism, we recover the model Lagrangians as first order systems. To complete the analysis, we classify all local Lorentz invariant potentials (mass terms and quartic couplings) for charged antisymmetric tensor fields coupled to a Yang-Mills field.
Miniaturized distillation device for detection of hydrocarbons using distillation curve principle
Authors: Daei Mohammadreza , Rahbar Shahrouzi Javad , Valizadeh Ziba 
Published: December 2024

Abstract
The objective of this study is to miniaturize the distillation apparatus to detect and analyze the composition of hydrocarbon mixtures. To achieve this, a micrometric chip measuring 150 × 100 × 6 mm3 was fabricated using a CO2 laser engraving machine. The chip, made of poly-methyl methacrylate, consists of a mini heater, condenser, and column. Its purpose is to separate hydrocarbons and determine the distillation curve of the mixtures. To assess its performance, three hydrocarbons (iso-pentane, 1-pentene, and n-hexane) were injected into the chip in different percentages ranging from 0 to 100%. These hydrocarbons were selected to represent pure, binary, and ternary compounds. The chip then analyzed the samples, and the distillation curves were obtained by plotting the boiling temperature against the percentage of condensed volume. The results of the experiments revealed that each distillation test lasted about 15 minutes. Despite the close boiling temperatures of iso-pentane and 1-pentene (27.8 and 30 °C, respectively), the chip could accurately identify the composition of different mixtures in the analyzed materials. The repeatability test demonstrated that the average standard deviation for the examined binary mixtures ranged from 1–2%, indicating the reliable reproducibility of the results. In conclusion, the miniaturized distillation chip that was fabricated can effectively analyze hydrocarbon mixtures and determine their purity.
Fuzzy-UNet: semantic-fuzzy integration for fabric defect identification
Authors: Ren Jia , Chen Zhonghao , Jin Jiamin , Zhang Yibo 
Published: December 2024

Abstract
The detection of fabric surface defects is a critical component for quality assurance and operational efficiency within the textile industry. A significant challenge in this field is the effective incorporation of domain expertise to enhance the precision of defect recognition. This paper introduces Fuzzy-UNet, an innovative fabric defect detection system that integrates a semantic segmentation network with a fuzzy decision model, addressing the challenge of domain knowledge integration. By combining a cascaded data model with a knowledge-based model, Fuzzy-UNet harnesses the power of deep learning and artificial experience to refine the identification process. The data-driven model of our system is advanced UNet architecture, specifically tailored for detecting subtle defects in fabrics with non-standard aspect ratios. The system’s novel fuzzy decision model utilizes spatiotemporal data from multiple cameras, which is essential for enhancing the accuracy of the detection process. Our comprehensive experiments demonstrate the robustness of Fuzzy-UNet, with a significant increase in accuracy to 96.78% and a marked reduction in the False Positive Rate (FPR). The superior performance of Fuzzy-UNet over existing methods makes it a leading solution for industrial fabric defect detection.
Network science can improve the sustainable development of solar energy
Authors: Bacon Taylor , Ohlert Timothy , Toy Christopher , Siggers J Alexander , Sturchio Matthew A 
Published: December 2024

Abstract
The recent emergence of agrivoltaic and ecovoltaic approaches to ground-mounted photovoltaic (PV) energy provides a much-needed alternative to the environmentally disruptive practices employed in utility-scale solar development. Research on such land-sharing approaches has grown rapidly, with an emphasis on characterizing how PV arrays impact ecosystem processes and agricultural productivity. Although these studies have done well to quantify a variety of dual-use solar practices by employing site-specific sampling designs, this approach has limited our ability to synthesize results across sites, regions, and globally. We call for a network science approach for improved cross-site synthesis of dual-use solar research. We contend that a common approach for data collection and synthesis will facilitate a more rigorous investigation of the agricultural and ecological impacts of PV development across space and over time. The products of this scientifically informed approach can be directly applied to improve sustainable land management.