bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of thermo-hydraulic properties of flow in an innovative plate heat exchanger using machine learning algorithms
Authors: Aboul Khail Ahmad , Bakır Rezan , Bakır Halit 
Published: August 2024

Abstract
Reducing fuel consumption and toxic gas emissions is a major concern in modern energy research. This paper investigates the performance and heat transfer enhancement of an innovative plate heat exchanger (IPHE) using machine learning techniques. By optimizing the geometric parameters of the plate, we predict thermohydraulic characteristics—represented by the Nusselt number (Nu), coefficient of friction (f), and performance (P) within the Reynolds number range of 500–5000 based on numerical modeling data. This study addresses the need for improved efficiency in plate heat exchangers (PHEs) amid rising energy demands and environmental concerns. Traditional methods like numerical simulations or costly experiments have limitations, prompting interest in artificial intelligence (AI) and machine learning (ML) for thermal analysis and property prediction in PHEs. Various ML models, including Decision Trees, XGBoost, Gradient Boosting, and ensemble methods, are evaluated in predicting f, Nu, and overall performance (P). Our comprehensive experimentation and analysis identify top-performing models with robust predictive capabilities. For f, the highest R2 score was 0.98, indicating excellent prediction accuracy, with mean squared error (MSE) values consistently below 0.0016. Similarly, for Nu and P, top models achieved R2 scores of 0.979 and 0.9628, respectively, with MSE values below 0.0347 and 0.05. These results highlight the effectiveness of machine learning techniques in accurately predicting thermohydraulic properties and optimizing PHE performance.
Ultra-broadband terahertz metamaterial absorber based on a hollow vanadium dioxide circular-truncated cone resonator
Authors: Hu Dan , Jia Nan , Jiang Rongping , Shang Hongwei , Li Yaqin , Feng Mingchun , Yang Gui 
Published: August 2024

Abstract
Terahertz (THz) broadband metamaterial absorbers with high absorptivity have been of great interest due to their potential applications. However, their limited bandwidth severely hinders their further development and wide applications. To address this issue and achieve ultra-broadband and strong absorption properties at THz frequencies, we present an ultra-broadband THz perfect metamaterial absorber using a hollow vanadium dioxide (VO2) circular-truncated cone array and a VO2 film supported by an Au ground plane. Simulated results show that the absorptivity of the absorber reaches more than 90% in the frequency range of 0.31–10 THz at normal incidence with a relative absorption bandwidth of 188%. At the same time, the average absorptivity in the frequency range reaches 99.2%. The physical origin of broadband perfect absorption has been elucidated by impedance matching theory and wave interference theory, respectively. The electric field distribution is further discussed to explore the physical mechanism of this absorber. Additionally, it also has the characteristics of polarization insensitivity and wide incidence angle stability. The proposed absorber can have many promising applications in the THz region, such as thermal imaging, thermal detection, and cloaking.
Inverse problem of dynamics, Galiullin and Szebehely methods and curl force trajectories
Authors: Guha Partha 
Published: August 2024

Abstract
At first we study Galiullin’s construction of Bertrand problem and compare it with Szebehely’s method, latter is based on a first order partial differential equation for the unknown potential that produces a prescribed monoparametric family of planar trajectories. In the second part of the paper we study the inverse problem of the trajectories such that the corresponding force is a nonconservative position dependent one, satisfying the non-vanishing curl condition and not the gradient of a potential function. Recently this force has been introduced and popularized by Berry and Shukla (J. Phys. A 45 (2012) 305 201). We connect the inverse problem dynamics of these curl force trajectories with the generalized potentials obtained by Sarlet-Mestdag-Prince (Rep. Math. Phys. 72(2013) 65-84) from the inverse problem of ϕ(x, y) = xy

m
for m ≠ 0, m ≠ –1. Finally we show that the analog of these curly trajectories in momentum space can be manifested as kinetic energies of the pair of Calogero-Leyvraz Hamiltonians (J. Nonlinear Math. Phys. 26 (2019) 147-154) describing the motion of a particle in a magnetic field with friction.
Understanding energy-level structure using a quantum rubik’s cube
Authors: Wang Yu , Bo Maolin 
Published: August 2024

Abstract
This study combines the quantum Rubik’s Cube matrix with the Benalcazar–Bernevig–Hughes model, defines a matrix algorithm based on the reverse convolution process, and constructs an expression for the quantum Rubik’s Cube matrix and Hamiltonian. Furthermore, to make the operation of the quantum Rubik’s Cube matrix clearer, we use a Josephus ring to draw a topological graph of the Rubik’s Cube expansion. This study uses a quantum Rubik’s Cube to calculate energy-level transitions of electrons and shows that its operation corresponds to path integration. The band dispersion is obtained. This study provides new insights and methods for calculating Hamiltonians and studying energy-level structure.
On attempts to avoid the contradiction between the first law of thermodynamics and energy-momentum tensor conservation in cosmology
Authors: Yu Hao , Zhong Yi , Luo Zhi , Li Jin 
Published: August 2024

Abstract
For a general ideal fluid, the first law of thermodynamics (energy conservation in thermodynamics) and energy-momentum tensor conservation may be contradictory in the context of cosmology. In this work, it is found that the contradiction can be avoided by modifying the energy conservation equation of the ideal fluid in thermodynamics or redefining the equation of state of the ideal fluid in cosmology. We first study a special ideal fluid, i.e., ideal gas-like dust, whose thermodynamic properties are the same as the ones of the ideal gas. Employing these two schemes to the ideal gas-like dust respectively, we mathematically implement the consistency between energy conservation in thermodynamics and energy-momentum tensor conservation in cosmology. The entropy evolution of the ideal gas-like dust manifests that both schemes can be thermodynamically compliant under certain conditions. Finally, we extend the research to a general ideal fluid and obtain conditions for the ideal fluid to satisfy the second law of thermodynamics.
Enhanced the performance of lead-free material-based magnetoresistance random access memory
Authors: Kumari Seema , Yadav Rekha 
Published: August 2024

Abstract
The field of Spintronics uses a spin-dependent current to control the electron spin. Magnetic tunnel junctions (MTJs) are essential in spintronics because of their wide range of applications, especially their great scaling ability, fast write speeds, and high relative magnetoresistances. The performance of MTJ devices based on a single MgO barrier is poor compared to a double barrier layer. The lead free double perovskite materials can be used to enhance the performance and stability of MTJ. In this paper, perovskite materials (Cs2CuBiCl6, Cs2CuBiF6, and Cs2CuBiI6) are added to the MgO dielectric layer to form a Penta layer MTJ. The MTJ device performance parameters, such as tunneling magnetoresistance (TMR), differential resistance, antiparallel and parallel resistance, and differential TMR, with these materials as composite dielectrics, have been investigated using the non-equilibrium Green’s function (NEGF) simulator. The TMR (%) of Fe-MgO- Cs2CuBiF6 - Fe-MgO-based MTJ devices at room temperature is 1307%. The implementation of the magnetoresistance random access memory based on the suggested MTJ model has been demonstrated using the LTspice. The power dissipation of the Fe-MgO- Cs2CuBiF6- MgO-Fe-based MTJ model is low as compared to the other two double perovskite-based MTJ models. Hence, MTJ memory devices with composite dielectrics based on Cs2CuBiF6 perform better.
Phenomenology of Horndeski gravity under positivity bounds
Authors: de Boe Dani , Ye Gen , Renzi Fabrizio , Albuquerque Inês S. , Frusciante Noemi , Silvestri Alessandra 
Published: August 2024

Abstract
A set of conditions that any effective field theory needs to satisfy in order to allow for the existence of a viable UV completion, has recently gained attention in the cosmological context under the name of positivity bounds. In this paper we revisit the derivation of such bounds for Horndeski gravity, highlighting the limitations that come from applying the traditional methodology to a theory of gravity on a cosmological background. We then translate these bounds into a complete set of viability conditions in the language of effective field theory of dark energy. We implement the latter into EFTCAMB and explore the large scale structure phenomenology of Horndeski gravity under positivity bounds. We build a statistically significant sample of viable Horndeski models, and derive the corresponding predictions for the background evolution, in terms of w
DE, and the dynamics of linear perturbations, in terms of the phenomenological functions μ and Σ, associated to clustering and weak lensing, respectively. We find that the addition of positivity bounds to the traditional no-ghost and no-gradient conditions considerably tightens the theoretical constraints on all these functions. The most significant feature is a strengthening of the correlation μ ≃ Σ, and a related tight constraint on the luminal speed of gravitational waves c
2
T ≃ 1. In this work we demonstrate the strong potential of positivity bounds in shaping the viable parameter space of scalar-tensor theories. This is certainly promising, but it also highlights the importance of overcoming all issues that still plague a rigorous formulation of the positivity bounds in the cosmological context.
Kerr-MOG-(A)dS black hole and its shadow in scalar-tensor-vector gravity theory
Authors: Liu Wentao , Wu Di , Fang Xiongjun , Jing Jiliang , Wang Jieci 
Published: August 2024

AbstractThe scalar-tensor-vector gravity (STVG) theory has attracted significant interest due to its ability to effectively address the issue of galaxy rotation curves and clusters of galaxies without considering the influence of dark matter. In this paper, we construct rotating black hole solutions with a cosmological constant in the STVG theory (i.e., Kerr-MOG-(A)dS black hole solutions), where the import of a gravitational charge as a source modifies the gravitational constant, determined byGG =GN(1+α). For Kerr-MOG-dS spacetime, the observer is situated at a specific location within the domain of outer communication, rather than being located infinitely far away. Since black hole shadows are shaped by light propagation in spacetime, the interaction between the MOG parameter and the cosmological constant is expected to produce novel effects on these shadows. As the cosmological constant Λ increases, the apparent size of the black hole shadow decreases. Additionally, the shadow expands with an increase in the MOG parameterα, reaching a maximum at a certain value, and its shape becomes more rounded under an arbitrary rotation parameter, which leads to degeneracy between different black hole parameters. However, by employing numerical ray-tracing techniques, we have found that gravitational lensing and the frame-dragging effect effectively distinguish this degeneracy. Our work contributes to a deeper understanding of black holes in modified gravity, their observational signatures, and constraints.
A new perspective on the diffuse gamma-ray emission excess*
Authors: Chen 陈 En-Sheng 恩生 , Fang 方 Kun 堃 , Bi 毕 Xiao-Jun 效军 
Published: August 2024

Abstract
The Large High-Altitude Air Shower Observatory (LHAASO) recently published measurements of diffuse Galactic gamma-ray emission (DGE) in the 10−1000 TeV energy range. The measured DGE flux is significantly higher than the expectation from hadronic interactions between cosmic rays (CRs) and the interstellar medium. This excess has been proposed to originate from unknown extended sources produced by electron radiation, such as pulsar wind nebulae or pulsar halos (PWNe/halos). In this paper, we propose a new perspective to explain the DGE excess observed by LHAASO. The masking regions used in the LHAASO DGE measurement may not fully encompass the extended signals of

PWNe/halos. By employing a two-zone diffusion model for electrons around pulsars, we find that the DGE excess in most regions of the Galactic plane can be well explained by the signal leakage model under certain parameters. Our results indicate that this signal leakage from known sources and contributions from unresolved sources should be considered as complementary in explaining the DGE excess.
Advancing ambient water quality monitoring and management through citizen science in low- and middle-income countries
Authors: Castro Jose , Mateo-Sagasta Javier , Nowicki Saskia 
Published: August 2024

Abstract
In contexts where conventional environmental monitoring has historically been limited, citizen science (CS) for monitoring efforts can be an effective approach for decentralized data generation that also raises scientific literacy and environmental awareness. To that end, the United Nations Environmental Program is considering CS as a mechanism for producing ambient water quality data to track progress on sustainable development goal (SDG) indicator 6.3.2: ‘proportion of bodies of water with good ambient water quality’. However, the alignment of SDG 6.3.2 monitoring requirements with CS capacity and results in low- or middle-income countries has not been assessed. Through a systematic literature review of 49 journal publications, complemented by 15 key informant interviews, this article examines the methods and outputs of CS programs in resource-constrained settings. We explore the potential of these programs to contribute to tracking SDG 6.3.2. Using the CS impact assessment framework, we evaluate broader outcomes of CS programs across 5 domains: society, economy, environment, governance, and science and technology. Despite large variability in scope, CS programs were consistently found to generate useful data for national-level reporting on physicochemical and ecological parameters; however, data quality is a concern for CS measurement of microbiological parameters. The focus in literature to-date is predominantly on scientific data production which falls only within the ‘science and technology’ outcome domain. Societal, governance, economic, and environmental outcomes are infrequently evaluated. Of the studies reviewed in this article, 75% identified some form of pollution but only 22% of them reported follow-up actions such as reporting to authorities. While CS has important potential, work is still needed towards the ‘formalization’ of CS, particularly if intended for more vulnerable contexts.
Mitigating the effects of extreme weather on crop yields: insights from farm management strategies in the Netherlands
Authors: van der Veer S , Hamed R , Karabiyik H , Roskam J L 
Published: August 2024

Abstract
Weather extremes can drive substantial crop losses. Farm-level management strategies play a critical role in mitigating the impacts of and consequences for farmer livelihoods and food security. While the impacts of extreme weather on crop yields are well documented in recent studies, these predominantly focused on expansive geographical scales and commonly overlooked the critical role of management practices in modulating the dynamics of weather-crop sensitivities. We fill this gap in the literature by using a unique dataset that explores the timely relationship between extreme weather and crop yields at farm level in the Netherlands. We cover 10 types of crops and elucidate the role of soil types, irrigation and nutrient application in modulating the relationship between extreme weather and crops, by estimating fixed-effects regression models. We show substantial impacts from drought during the growing- and harvesting period and excessive precipitation during the planting- and growing period. Severe droughts show significant (

p
⩽
0.05

) reductions in yield for all crops, and lead to yield reductions up to 24 percent relative to average yields during the growing period. Meanwhile, eight crops show significant reductions in yield due to severe water excess during the planting period, with yield reductions up to 18 percent. Soils such as sand or loess amplify the negative impact of drought on crop yield, while softening the impact of excessive precipitation. Irrigation and to a lesser extent nutrient application are shown to moderately decrease the impact of extreme weather on crop yield. Our findings contribute valuable insights to guide local adaptation priorities which are critical given the projected increase in the intensity and frequency of extreme weather under climate change.
Behavioral plasticity of raccoon dogs (Nyctereutes procyonoides) provides new insights for urban wildlife management in metropolis Shanghai, China
Authors: Wang Yihan , Zhao Qianqian , Tang Lishan , Lin Weiming , Zhang Zhuojin , Diao Yixin , Weng Yue , Gu Bojian , Feng Yidi , Zhao Qing , Wang Fang 
Published: August 2024

Abstract
As urbanization exposes wildlife to new challenging conditions and environmental pressures, species that exhibit a high degree of behavioral plasticity are considered potentially capable of colonizing and adapting to urban environments. However, differences in the behavior of populations that inhabit urban and suburban landscapes pose unprecedented challenges to traditional methods in wildlife management which often fail to consider a species’ needs or mitigate human–wildlife conflict due to changes in species behavior in response to intensive human interference. Here, we investigate differences in the home range, diel activity, movement, and diet of raccoon dogs (Nyctereutes procyonoides) between residential districts and forest park habitats in Shanghai, China. Using GPS tracking data from 22 individuals, we find that the home ranges of raccoon dogs in residential districts (10.4 ± 8.8 ha) was 91.26% smaller than those in forest parks (119.6 ± 135.4 ha). We also find that raccoon dogs in residential districts exhibited significantly lower nocturnal movement speeds (134.55 ± 50.68 m h−1) compared to their forest park counterparts (263.22 ± 84.972 m h−1). An analysis of 528 fecal samples showed a significantly higher intake of ingredients from human food in residential districts (χ
2 = 4.691, P = 0.026), which indicates that urban raccoon dog foraging strategies differ from the forest park population due to the presence of discarded human food, cat food, and wet garbage in residential districts. Based on our findings, we propose a community-based wildlife management strategy and suggest modifying the current design of residential districts. Our results underscore the importance of mammal behavior studies in urban biodiversity management and provide a scientific basis for mitigating human–wildlife conflicts in urban environments in and beyond our study area.
Encoding optimization for quantum machine learning demonstrated on a superconducting transmon qutrit
Authors: Cao Shuxiang , Zhang Weixi , Tilly Jules , Agarwal Abhishek , Bakr Mustafa , Campanaro Giulio , D Fasciati Simone , Wills James , Shteynas Boris , Chidambaram Vivek , Leek Peter , Rungger Ivan 
Published: August 2024

Abstract
A qutrit represents a three-level quantum system, so that one qutrit can encode more information than a qubit, which corresponds to a two-level quantum system. This work investigates the potential of qutrit circuits in machine learning classification applications. We propose and evaluate different data-encoding schemes for qutrits, and find that the classification accuracy varies significantly depending on the used encoding. We therefore propose a training method for encoding optimization that allows to consistently achieve high classification accuracy, and show that it can also improve the performance within a data re-uploading approach. Our theoretical analysis and numerical simulations indicate that the qutrit classifier can achieve high classification accuracy using fewer components than a comparable qubit system. We showcase the qutrit classification using the encoding optimization method on a superconducting transmon qutrit, demonstrating the practicality of the proposed method on noisy hardware. Our work demonstrates high-precision ternary classification using fewer circuit elements, establishing qutrit quantum circuits as a viable and efficient tool for quantum machine learning applications.
Experimental and modeling approaches for electric vehicle battery safety: a technical review
Authors: Long Teng , Wang Leyu , Kan Cing-Dao 
Published: August 2024

Abstract
Driven by the rising number of fire incidents involving Battery Electric Vehicles (BEVs), this work reviews the current state of knowledge in electric vehicle battery safety, focusing on simulation and experiment methodologies. The critical importance of battery safety is emphasized by the potential for thermal runaway and fires due to various factors. These factors include design and manufacturing flaws, excessive current loads, mechanical damage, improper charging practices (overcharging/overdischarging), extreme temperature exposure, and even as-yet unidentified causes. This study provides a comprehensive review of methodologies employed in lithium-ion battery safety modeling and experiment for BEVs. The review includes various aspects. It includes the high voltage battery system in BEVs, battery safety considerations in BEVs, geometry modeling of battery cells, material modeling of battery cells, simulation framework for batteries, cell-level experiment, testing of materials for cell components, and the application of machine learning. Physics-based simulations that accurately predict battery thermal runaway are crucial for guaranteeing the safety and optimizing the performance of BEVs. While Finite Element Analysis (FEA) is a well-established technique for evaluating the crashworthiness of conventional vehicles, its application to BEVs presents several significant challenges. However, limited literature exists on cell-level experiments involving spray and dropping scenarios. Furthermore, additional data on melting points, thermal properties, and porosity is necessary for component-level testing. This work also highlights the need for robust friction and fatigue models, which remain a critical knowledge gap in this field. Finally, the integration of machine learning approaches for constitutive laws and the development of more complex frameworks are essential advancements for future research. This review is expected to provide a guide in simulation and experiment in EV battery safety engineering.
A Modern Lyric View of Catullus
Authors: Oade Stephanie 
Published: August 2024

Abstract
Chapter 3 considers ideas surrounding the lyric genre and the categorization of lyric texts from the Romantic period onwards and relates these ideas to a contemporary understanding of the Catullan lyric. It identifies important lyric traits that characterize Catullus’s particular lyric voice, including addressivity, generic proximity, the lyric setting, subjectivity, and ‘universality’. One idea that has not been formally theorized but has often appeared as a subtext in discussions of Catullan style is developed further: that is the continuous yet almost imperceptible movement through Catullus’s poems, as the component parts shift to create an unstable atmosphere (a concept identified here as das Gleitende).