bluuebunny/crossref_metadata_2025_split huggingface dataset:
Radial dependence of ionization clustering around a gold nanoparticle irradiated by X-rays under charged particle equilibrium
Authors: Thomas Leo , Schwarze Miriam , Rabus Hans 
Published: August 2024

Abstract

Objective. This work explores the enhancement of ionization clustering and its radial dependence around a gold nanoparticle (NP), indicative of the induction of DNA lesions, a potential trigger for cell-death. Approach. Monte Carlo track structure simulations were performed to determine (a) the spectral fluence of incident photons and electrons in water around a gold NP under charged particle equilibrium conditions and (b) the density of ionization clusters produced on average as well as conditional on the occurrence of at least one interaction in the NP using Associated Volume Clustering. Absorbed dose was determined for comparison with a recent benchmark intercomparison. Reported quantities are normalized to primary fluence, allowing to establish a connection to macroscopic dosimetric quantities. Main results. The modification of the electron spectral fluence by the gold NP is minor and mainly occurs at low energies. The net fluence of electrons emitted from the NP is dominated by electrons resulting from photon interactions. Similar to the known dose enhancement, increased ionization clustering is limited to a distance from the NP surface of up to

200

nm

. The number of clusters per energy imparted is increased at distances of up to

150

nm

, and accordingly the enhancement in clustering notably surpasses that of dose enhancement. Smaller NPs cause noticeable peaks in the conditional frequency of clusters between

50

nm

–

100

nm

from the NP surface. Significance. This work shows that low energy electrons emitted by NPs lead to an increase of ionization clustering in their vicinity exceeding that of energy imparted. While the electron component of the radiation field plays an important role in determining the background contribution to ionization clustering and energy imparted, the dosimetric effects of NPs are governed by the interplay of secondary electron production by photon interaction and their ability to leave the NP.
Remote sensing of high energy charged particle current (HEC) for megavoltage therapeutic electron beams
Authors: Zygmanski Piotr , Rajapakse Arith , Brivio Davide 
Published: August 2024

Abstract

Objective. We demonstrate detection of high energy particle current (HEC) for MeV therapeutic electron beams. Detection of HEC comprises of remote sensing or acquiring information about HEC inside radiation transport medium from a distance outside of the medium. Approach. HEC is self-propelled motion of charged particles through a radiation transport medium. Remote sensing of HEC is embodied in an experimental setup, which includes homogeneous and heterogeneous phantoms irradiated with 4–15 MeV electron beams and two large area parallel-plane electrodes extraneous to the phantoms providing two-parameter detection. We also introduce a new type of scanning method (depth-scan) for probing object properties along the beamline axis. Main Results. Deterministic radiation transport simulations and measurements agree, considering differences in simulation vs experimental geometry and experimental uncertainties. Significance. This method may be suitable for range detection of charged particle beams, or for probing of radiation opaque objects in non-destructive testing.
A novel low-resistance solder-free copper bonding joint using a warm pressure welding method for REBCO coated conductors
Authors: Zhou Yuwei , Huang Zhen 
Published: August 2024

Abstract
Constrained by the fabrication of second-generation high-temperature superconducting (2G HTS) tapes, connecting multiple pieces of tapes through joints is often necessary in large-scale applications. In the application of HTS magnets, joint technology is key for achieving closed-loop operation and reducing thermal loads. However, most soldered joints still cannot achieve the expected results. Thus, there is an urgent need to find a method for easily fabricating low-resistance joints. In this study, a low-resistance solder-free copper bonding joint for 2G HTS copper-plated tapes is proposed. The formation mechanism of the joint is presented, and the effects of the bonding temperature and pressure on the electrical and mechanical properties of the copper bonding joint are investigated. The results show that the copper bonding joint can be manufactured by pretreating the tape for 5 min and bonding it in the air for 3 min at 333 MPa at temperatures higher than (or equal to) 150 °C or at pressures greater than (or equal to) 250 MPa and 180 °C. The characteristic resistance of this joint is approximately 16.8 nΩ cm2, which is approximately one-third lower than that of soldered joints, and it has mechanical properties similar to those of soldered joints under axial tension. We believe that the application of this type of copper bonding joint can significantly aid in the design and manufacturing of large HTS magnets.
Comprehensive analysis of structural, mechanical, optoelectronic, and thermodynamic properties of Ba2XBiO6 (X = Y, La) double perovskites using density functional theory
Authors: Rahman Nasir , Husain Mudasser , Ullah Wasi , Azzouz-Rached Ahmed , Albalawi Hind , Bayhan Zahra , A Alsalhi Sarah 
Published: August 2024

Abstract
The structural, mechanical, optoelectronic, and thermodynamic properties of Ba2XBiO6 (X = Y, La) double perovskites are critically analyzed using density functional theory. The Birch-Murnaghan equation of state confirms their structural stability, supported by tolerance factor analysis. For Ba2YBiO6 and Ba2LaBiO6, negative formation energies are −0.935 eV for Ba2YBiO6 and −0.836 eV for Ba2LaBiO6, confirming thermodynamic stability. Phonon dispersion curves indicate stable lattice vibrations, reducing the likelihood of spontaneous structural changes or phase transitions. Ba2YBiO6 possesses higher elastic constants C
11 = 222 GPa shows stiffness and exhibits brittle mechanical behavior, whereas the Pugh ratio and C
11 = 217 GPa for Ba2LaBiO6 shows ductility. The Poisson ratio classifies Ba2YBiO6 as a non-central force crystal and Ba2LaBiO6 as a central force crystal. Both materials are indirect band gap semiconductors, with band gap values of 0.75 eV for Ba2YBiO6 and 2.05 eV for Ba2LaBiO6. Optical properties suggest applications in UV and visible light-based optoelectronic devices. Thermodynamic properties, such as Debye temperature and heat capacity, support the idea that these materials are suitable for high mechanical applications. These findings provide insights for designing high-performance optoelectronic and mechanical devices.
Nano-eco catalysis: carbendazim degradation by engineered BiVO4 nanoparticles
Authors: Zeshan Muhammad , Bahader Ali , Muhammad Taj , Sandhu Zeshan Ali , Asam Raza Muhammad , Khan Noor Mohammad , Al-Sehemi Abdullah G 
Published: August 2024

Abstract
This study utilized the solvothermal approach for the effective synthesis of BiVO4 nanoparticles. Morphological examination of BiVO4 was conducted via scanning electron microscopy (SEM), while the elemental composition was determined using energy dispersive x-ray spectroscopy (EDX). The crystallinity and functional groups were assessed through x-ray diffraction (XRD) and Fourier transform infrared spectroscopy (FT-IR), respectively.The synthesized material’s efficacy in UV-induced breakdown of carbendazim was investigated. The study explored various factors affecting the photodegradation process, including radiation duration, initial concentration of carbendazim, catalyst dosage, and catalyst regeneration. Interesingly, 97% degradation of carbendazim was observed at optimized conditions. These results emphasise the potential of BiVO4 nanoparticles as catalysts for environmental remediation applications, especially in the degradation of harmful contaminants.
Investigation of thermal properties of Hulthén potential from statistical and superstatistical perspectives with various distributions
Authors: Khorram manesh Amir Hossein , Sadeghi J , Noori Gashti Saeed 
Published: August 2024

Abstract
The Hulthén potential is a short-range potential widely used in various fields of physics. In this paper, we investigate the distribution functions for the Hulthén potential by using statistical and superstatistical methods. We first review the ordinary statistics and superstatistics methods. We then consider distribution functions, such as uniform, 2-level, gamma, and log-normal and F distributions. Finally, we investigate the behavior of the Hulthén potential for statistical and superstatistical methods and compare the results with each other. We use the Tsallis statistics of the superstatistical system. We conclude that the Tsallis behavior of different distribution functions for the Hulthén potential exhibits better results than the statistical method. We examined the thermal properties of the Hulthén potential for five different distributions: Uniform, 2-level, Gamma, Log-normal, and F. We plotted the Helmholtz free energy and the entropy as functions of temperature for various values of q. It shows that the two uniform and 2-level distributions have the same results due to the universal relationship and that the F distribution does not become ordinary statistics at q = 1. It also reveals that the curves of the Helmholtz free energy and the entropy change their order and behavior as q increases and that some distributions disappear or coincide at certain values of q. One can discuss the physical implications of our results and their applications in nuclear and atomic physics in the future.
The renormalization group for large-scale structure: primordial non-Gaussianities
Authors: Nikolis Charalampos , Rubira Henrique , Schmidt Fabian 
Published: August 2024

AbstractThe renormalization group for large-scale structure (RG-LSS) describes the evolution of galaxy bias and stochastic parameters as a function of the cutoff Λ. In this work, we introduce interaction vertices that describe primordial non-Gaussianity into the Wilson-Polchinski framework, thereby extending the free theory to the interacting case. The presence of these interactions forces us to include new operators and bias coefficients to the bias expansion to ensure closure under renormalization. We recover the previously-derived “scale-dependent bias” contributions, as well as a new (subdominant) stochastic contribution. We derive the renormalization group equations governing the RG-LSS for a large class of interactions which account for vertices at linear order infNLthat parametrize interacting scalar and massive spinning fields during inflation. Solving the RG equations, we show the evolution of the non-Gaussian contributions to galaxy clustering as a function of scale.
Research on butterfly coil EMAT sound field characteristics
Authors: Wang Chaoqun , Ma Jian , Wang Luyao , Bai Xue , Chen Jianwei 
Published: August 2024

AbstractAs a main coil structure of an electromagnetic ultrasonic transducer (EMAT), the butterfly coil can excite transverse waves under the action of the vertical magnetic field, which is widely used in the thickness measurement of metal plates and the detection of internal buried defects. This paper discusses the directivity of the butterfly coil in the acoustic field to facilitate the design and application of the electromagnetic ultrasonic transducer and obtain the energy distribution state of the radiated acoustic field. The acoustic field directivity of butterfly coil EMAT is discussed in this paper. Firstly, the ultrasonic excitation principle of the butterfly coil EMAT is analyzed, and the radiated acoustic field of the butterfly coil is theoretically calculated by equating the main flap of the butterfly coil to a rectangular wave source. The experimental system of acoustic field directivity measurement was constructed to measure the directivity data of the radiated acoustic field of the ultrasonic shear wave. The acoustic field was analyzed for different working frequencies and different polarization directions of the relative semicircular specimen. The experimental data matched the theoretical calculations well, and this paper provides a reference basis for the engineering application of this transducer.
Study on bird strike analysis of a civil aircraft equipped with satcom antenna
Authors: Gao Jun , Zhu Yuanzhe 
Published: August 2024

Abstract
Civil aircraft have specific requirements for the anti-bird strike performance of the structure, so the installation of various types of antennas must carry out bird strike assessment to avoid catastrophic consequences because of bird strikes. In this paper, the anti-bird strike performance of a civil aircraft equipped with an antenna is studied by numerical calculation. It shows that the antenna adapter may be plastically deformed after a bird strike, but it will not be damaged or fall off from the body structure as a whole. Therefore, the satcom antenna will not cause catastrophic consequences after a bird strike and meet the airworthiness requirements.
Construction of digital twin workshop integrated with edge computing and deep learning
Authors: Liu Xinning , Xie Tianxia , Wu Bo , Li Mingjun , Li Haotian 
Published: August 2024

Abstract
This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments.
Research on diagnosis method of motor vibration signal based on MSCNN-LSTM
Authors: Guan Wenhui , Li Binbin 
Published: August 2024

Abstract
Vibration signal is often considered an important basis for diagnosing motor faults. However, the original vibration signal features a single time series that needs to be shorter. This paper introduces a fault diagnosis approach, MSCNN-LSTM, which integrates a multi-scale one-dimensional convolutional neural network with a long short-term memory network, reflecting the ongoing advancements in deep learning for fault diagnosis. Convolution kernels of varying sizes are accustomed to realizing information integration of various scales and broadening the dimensions of vibration signals. In addition, one-dimensional convolution networks effectively solve the problem of excessively long timing of original signals, and LSTM captures timing dependence for diagnosing motor faults. The experimental results indicate that MSCNN-LSTM has high accuracy in motor fault diagnosis based on vibration signals.
Wave refrigeration control via deep reinforcement learning
Authors: Cui Xuesong , Hu Xiaopeng , Wang Fan , Li Qi 
Published: August 2024

Abstract
Wave refrigerating technology is a new type of refrigerating technology that performs more efficiently and is more environmentally friendly. Nowadays, the commonly used control method is the PID control method. However, although the above control methods are simple in structure, they have the problems of difficult parameter tuning, large overshoot, and unstable and inaccurate output response. Therefore, to improve the above problems, we propose a framework of deep reinforcement learning-based wave refrigerating control system. An improved SAC algorithm is used to learn the control system. The evaluation method is established according to the working condition requirements as well as the safety requirements in the operation process. Experiments show that this method can effectively improve the control efficiency as well as the control quality, which can make the control system operate stably.
Fault diagnosis method for distribution network based on improved deep learning
Authors: Li Jian , Sun Tao 
Published: August 2024

Abstract
Due to the complexity of the grid structure, limited fault samples, and difficulty in obtaining them, there is a need to improve the accuracy of fault diagnosis in distribution networks. To address the issue of insufficient training samples in the target domain leading to poor training results, a fault diagnosis method for distribution networks based on improved deep learning is proposed. Firstly, finite element simulation is employed to generate fault data under different operating conditions, obtaining a sufficient number of samples for feature extraction and enhancement. Secondly, a deep convolutional neural network (DCNN) model is constructed, and the model is trained using the sample data. Finally, the model is used for fault diagnosis in distribution networks under different operating conditions. Experimental results demonstrate that the fault diagnosis model achieves high testing accuracy and is capable of detecting fault locations and types with high precision.
Quantitative characterization and application of deep learning carbonate reservoir
Authors: Li Tingli , Yuan Zhiqian , Meng Peng , Zhu Yuguo 
Published: August 2024

Abstract
Quantitative reservoir characterization is an important research task in carbonate reservoir development. At present, the comprehensive evaluation of drilling, logging, seismic, and productivity testing data is usually used, which requires a large number of test data and tedious manual data collation. Deep learning technology can independently learn complex reservoir characteristics, has a strong ability for high-dimensional data structure mining, and has a strong advantage in solving complex nonlinear reservoir description problems closely related to various geological characteristics. Therefore, this paper applies deep learning technology to reservoir characterization. First, a deep neural network model is constructed, and then seven characteristic parameters such as fracture density, fracture opening, fracture porosity, matrix porosity, total porosity, effective thickness, and specific oil production index are taken as input variables. After model training and verification, the effective permeability distribution map of the reservoir in the target area can be automatically generated. The results show that the deep learning method has strong adaptability and practicability for the quantitative characterization of complex reservoirs, and provides a new way of thinking for the quantitative characterization of reservoirs.
Coverage-guided fuzz testing method based on reinforcement learning seed scheduling
Authors: Tao Jiaye , Hong Chao , Fu Yun , Yang Yiwei , Wei Lipeng , Liang Zhihong , Liu Junrong 
Published: August 2024

Abstract
The existing fuzz testing methods for industrial control protocols suffer from insufficient coverage, false positives, and an inability to handle protocol semantics. This paper proposes a reinforcement learning-based seed scheduling coverage-guided fuzz testing method. Building upon coverage-guided fuzz testing techniques, we integrate reinforcement learning with seed scheduling to optimize the seed selection strategy, thereby enhancing the efficiency of protocol vulnerability detection. Experimental results demonstrate the feasibility and effectiveness of this approach. Through reinforcement learning guidance, seed scheduling is optimized, thereby strengthening the performance of fuzz testing in exploring vulnerabilities in industrial control protocols.
Long-read sequencing analysis of the chloroplast genome in sandalwood (Santalum album L.)
Authors: Aryana AMM , Majiidu M , Pratama R , Dwiyanti FG , Siregar IZ 
Published: August 2024

AbstractSantalum albumL. (sandalwood) is a tree species renowned for producing essential oil with high economic value. However, the potential ofS. albumfaces significant challenges due to unsustainable management practices within its natural population. Genetic techniques present a viable solution to mitigatingS. albumsustainability challenges, offering insights crucial for its conservation and long-term management. Hence, there is a recognised necessity to furnish chloroplast genome information forS. album, thereby augmenting the existing genetic database and facilitating the formulation of effective conservation strategies for the sustainability of this species. This research aims to obtain the long-read sequence data using MinION from Oxford Nanopore Technologies for generating theS. albumchloroplast genome, and to analyse genetic relationships ofS. albumwith other species through a phylogenetic tree. The study successfully generated a completed chloroplast genome ofS. album, consisting of 143.261 base pairs, which has the potential to be used in various applications of genetic analysis. These applications hold promise for the future ofS. albumconservation, offering new avenues for sustainable management and preservation.
Biodiversity and ecosystem functioning in palm oil landscapes: a systematic literature review
Authors: Andriyanningsih S , Siregar I Z , Tarigan S D 
Published: August 2024

Abstract
Palm oil plantations contribute to global trade for affordable vegetable oil supply. To ensure sustainability, there’s a growing global emphasis on understanding and preserving plantation biodiversity and its diverse ecosystem functions. Based on biodiversity indicators, this study aims to identify and examine research trends related to palm oil plantations’ ecological roles or ecosystem functions. We obtained a total of 959 articles from the Web of Science. The utilization of systematic literature review methodology and bibliometric analysis allows for addressing the research inquiries’ focal points. Research trends on palm oil biodiversity and ecosystem function from 2010 to 2023 correlate with palm production. Quadrant 3 in bibliometric maps draws attention to rarely discussed yet impactful topics, such as the effects of deforestation and ecosystem services, which in turn prompts further research. Boxplot analysis reveals a greater focus on insect research, including ants, butterflies, and spiders, compared to avian studies.