bluuebunny/crossref_metadata_2025_split huggingface dataset:
Charge-ordering breakdown dynamics and ferromagnetic resonance studies of B-site Cu diluted Pr1‒x
Sr
x
MnO3
Authors: Bordoloi A , Jena S K , Tiwari P , Datta A , Weise B , Medwal R , Rawat R S , Thota S 
Published: April 2024

Abstract
Here, we report the influence of Jahn–Teller active Cu substitution on the charge-ordering (CO) characteristics of one of the well-known manganite Pr0.45Sr0.55MnO3 (S55) with a distorted tetragonal structure. Magnetization studies unveil a complex magnetic phase diagram for S55, showing distinct temperature ranges corresponding to various magnetic phases: a ferromagnetic phase dominated by the Double Exchange interaction with T
C ∼ 220.5 K, an antiferromagnetic phase below T
N ∼ 207.6 K induced by CO with a transition temperature of T
CO ∼ 210 K consistent with the specific heat C
P(T) data, and a mixed phase in the range T
N < T < T
CO due to the competitive interplay of these two interactions. Dilute substitution of Cu at the Mn B-sites disrupts the robust charge-ordered state, leading to enhanced ferrimagnetic order with T
FN ∼273 K and significant magnetocrystalline anisotropy as confirmed by ferromagnetic resonance (FMR) studies. The Cu-substituted system displays a distinct cationic distribution compared to the pristine S55, contributing to its diverse magnetic structure. Our findings also reveal the irreversible metamagnetic transition (H
T-Max ∼ 8.85 kOe at 180 K) associated with the CO phenomena in S55 and the first-order nature of the phase transition across T
CO. The magnetic heat capacity critical analysis (C
Mag
=
A (T − T
N)−α
) yields the exponent, α
= 0.097 (0.154) in the region T > T
N (T < T
N) consistent with the magnetic structure. The temperature dependence of FMR resonance field ΔH
Res(T), peak-to-peak width H
PP(T), and Gilbert damping factor α
G(T) show clear anomalies across the magnetic transitions signifying the important role of admixtured (3+/4+) electronic state of Mn. Additionally, a strong correlation between the FMR α
G(T) and switchable magnetic entropy change (ΔS
Max
∼ −8/+ 3 J kg−1 K−1 for ΔH
= 90 kOe) has also been established in S55.
Interlayer coupling modulated for thermoelectric transport characterization of black arsenic nanoscale devices
Authors: Shi Yuechao , Xu Jintao , Qiu Yifeng , Peng Xiaoge , Liu Ye , Liu Shuaixian , Zhang Bei , Long Mengqiu 
Published: April 2024

Abstract
Modulating interlayer coupling modes can effectively enhance the thermoelectric properties of nanomaterials or nanoscale devices. By using density functional theory combined with non-equilibrium Green’s function method, we investigate the thermoelectric properties of zigzag-type black arsenic nanoscale devices with varying interlayer coupling modes. Our results show that altering the interlayer coupling mode significantly modulates the thermoelectric properties of the system. Specifically, we consider four coupling modes with different strengths, by modulating different interlayer overlap patterns. Notably, in the weaker interlayer coupling mode, the system exhibits enhanced thermoelectric properties due to increased interface phonon scattering, for example, the M4 reaching a peak value of 2.23 at μ = −0.73 eV. Furthermore, we explore the temperature-dependent behavior of each coupling model. The results suggest that the thermoelectric characteristics are more sensitive to temperature variations in the weaker coupling modes. These insights provide valuable guidance for enhancing the thermoelectric performance of nanoscale devices through precise interlayer coupling modulation.
Investigation of membrane stress continuously adjustable fiber optic acoustic sensor based on inverse piezoelectric effect
Authors: Luo Xiang , Ding Keqin , Sun An 
Published: April 2024

Abstract
In this paper, a fiber optic acoustic sensor with continuously adjustable membrane stress technology is proposed, in which the stress exerted on the membrane can be flexibly adjusted by employing the inverse piezoelectric effect of lead zirconate titanate (PZT) tubular pedestal. First, the response mechanism of stress on the membrane is investigated theoretically. Based on this principle, the effect of adjustable stress on the flat response region and sensitivity of a single mode-multimode-single mode (SMS) fiber optic acoustic sensor are studied experimentally. The experimental results reveal that the flat response region of the sensor extends from 250 Hz to 450 Hz following the continuous increase in the membrane stress by changing the positive direct current voltage applied on PZT from 0 V to 100 V; meanwhile, the sensitivity decreases gradually from 336.5 mV Pa−1 to 162.7 mV Pa−1. The results also verify that the average minimum detectable pressure of the sensor is maintained at 0.54 mPa Hz−1/2 with a fluctuation of 0.08 mPa Hz−1/2 during the tuning process of membrane stress, which provides a flexible method for performance and applicability improvement of membrane-based fiber optic acoustic sensors.
Intelligent fault diagnosis of photovoltaic systems based on deep digital twin
Authors: Liu Sizhe , Qi Yongsheng , Ma Ran , Liu Liqiang , Li Yongting 
Published: April 2024

Abstract
The energy loss and substantial costs associated with faults in photovoltaic (PV) systems impose significant limitations on their efficiency and reliability. Addressing current issues in PV fault diagnosis such as the lack of typical fault data, imbalanced data distribution, and poor diagnostic performance, this paper proposes an intelligent fault diagnosis method for PV systems, deep digital twins (DDT) with information gain stacking sparse autoencoders (IGSSAEs). Initially, the method designs a novel DDT modeling framework tailored to actual PV system specifications. This framework utilizes a mechanism simulation model to generate typical data under various states. Simultaneously, a deep data model is constructed to learn the distribution characteristics of the mechanism model and complete data diversification, achieving the fusion and complementation of data from both models. Subsequently, a diagnostic network using IGSSAE is introduced. This network utilizes information gain ratio to assess feature classification contributions, enabling automatic feature selection. Based on the input features, a stacked sparse autoencoder fault classification network is designed, incorporating multi-level feature compression to enhance the model’s stability and diagnostic accuracy. Finally, a case study is conducted using a 250 kW grid-connected PV system, thoroughly validating the method’s effectiveness with a diagnostic accuracy of 98.4%.
A validation study of citizen science-based rainfall observation
Authors: Wang Xing , Zhao Jiuwei , He Yufeng 
Published: April 2024

Abstract
Citizen science-based data collection approaches offer new opportunities to produce high-quality rainfall products. One of the most promising options, personal rain gauges (PRGs), allows for high spatial and temporal resolution rainfall observation and has received much attention in recent years. Doubts about the accuracy and stability of PRGs, however, have led many researchers to remain hesitant about using PRG-based rainfall datasets. It is, therefore, of great importance to investigate the effectiveness of the PRG rainfall observation network. In this paper, 294 daily (24 hr) rainfall data between June 2022 and June 2023 was collected in the Netherlands using the PRG network and compared with ground rain gauge and radar estimations. The experimental results indicate that: (1) during the large-scale rainfall events, PRG achieved a Pearson correlation performance of 0.498, 0.488, and 0.324 compared to the corrected/uncorrected rain gauge network and radar observations, respectively, which decreased to 0.11, 0.101, and 0.108 for small-scale rainfall. It can be concluded that the PRG network shows a certain degree of agreement with ground rainfall gauges and radar measurements and is more suitable for large-scale rainfall observation tasks; (2) the relative errors between the PRGs and compared rainfall products indicate that the accuracy of the PRG network still needs to be improved. This study could offer a useful complement to the existing rainfall observation system while providing a helpful supplement to the development of citizen science.
Multiscale and multiperception feature learning for pancreatic lesion detection based on noncontrast CT
Authors: Yan Tian , Tang Geye , Zhang Haojie , Liang Lidu , Ma Jianhua , Gao Yi , Zhou Chenjie , Li Shulong 
Published: April 2024

Abstract

Background. Pancreatic cancer is one of the most malignant tumours, demonstrating a poor prognosis and nearly identically high mortality and morbidity, mainly because of the difficulty of early diagnosis and timely treatment for localized stages. Objective. To develop a noncontrast CT (NCCT)-based pancreatic lesion detection model that could serve as an intelligent tool for diagnosing pancreatic cancer early, overcoming the challenges associated with low contrast intensities and complex anatomical structures present in NCCT images. Approach. We design a multiscale and multiperception (MSMP) feature learning network with ResNet50 coupled with a feature pyramid network as the backbone for strengthening feature expressions. We added multiscale atrous convolutions to expand different receptive fields, contextual attention to perceive contextual information, and channel and spatial attention to focus on important channels and spatial regions, respectively. The MSMP network then acts as a feature extractor for proposing an NCCT-based pancreatic lesion detection model with image patches covering the pancreas as its input; Faster R-CNN is employed as the detection method for accurately detecting pancreatic lesions.
Main results. By using the new MSMP network as a feature extractor, our model outperforms the conventional object detection algorithms in terms of the recall (75.40% and 90.95%), precision (40.84% and 68.21%), F1 score (52.98% and 77.96%), F2 score (64.48% and 85.26%) and Ap50 metrics (53.53% and 70.14%) at the image and patient levels, respectively. Significance.The good performance of our new model implies that MSMP can mine NCCT imaging features for detecting pancreatic lesions from complex backgrounds well. The proposed detection model is expected to be further developed as an intelligent method for the early detection of pancreatic cancer.
Radial drift of plasma blobs in a toroidal magnetic field with fully kinetic and reduced fluid models
Authors: Mackey Nathan , Blinov Sergey , Stanier Adam , Le Ari 
Published: April 2024

Abstract
In curved magnetic geometries, field-aligned regions of enhanced plasma pressure and density, termed ‘blobs,’ move as coherent filaments across the magnetic field lines. Coherent blobs account for a significant fraction of transport at the edges of magnetic fusion experiments and arise in naturally-occurring space plasmas. This work examines the dynamics of blobs with a fully kinetic electromagnetic particle-in-cell code and with a drift-reduced fluid code. In low-beta regimes with moderate blob speeds, good agreement is found in the maximum blob velocity between the two simulation schemes and simple analytical estimates. The fully kinetic code demonstrates that blob speeds saturate near the initial sound speed, which is a regime outside the validity of the reduced fluid model.
Effects of cavity resonance and antenna resonance on mode transitions in helicon plasma
Authors: Zhang Tianliang , Cui Ying , Xia Zhangyu , Zheng Bocong , He Feng , Ouyang Jiting 
Published: April 2024

Abstract
Mechanisms of cavity resonance and antenna resonance and their coupling effects on mode transitions in argon helicon plasma excited by a half-helical antenna (14 cm in length) were investigated in this paper. Cavity length was changed to distinguish the effects of cavity and antenna resonances in experiments. Plasma parameters under various discharge conditions, such as input power (0–2500 W), magnetic field (0–1000 G) and cavity length (10–42 cm) were measured. Characteristics of helicon discharges and mode transitions in cases of fixed and continuously changing cavity lengths were compared. The results show that multiple axial eigenmodes (at least five in the present work) were observed in both cases. In fixed-length cavities, the helicon discharge changes abruptly during mode transitions, while in changeable-length cavities, discharge features can change continuously (e.g. in a large range of density from 1.7 × 1012 to 1.3 × 1013 cm−3) without mode transition. Mode transitions also occur as the cavity length increases at fixed input power and magnetic field with periodical variations of plasma parameters. Cavity resonance plays a dominant role in the formation of standing helicon waves of eigenmodes and mode transitions, while antenna resonance significantly affects the transition from inductively coupled modes to helicon wave modes. Enhanced inter-coupling of cavity resonance and antenna resonance appears at specific axial wavelengths of eigenmodes. The threshold conditions for mode transitions were deduced and the overall transition path of wave modes and the corresponding density were predicted quantitatively, which shows that cavity resonance determines the transition path of wave modes, while antenna resonance gives the lower limit of the transition path. Characteristics of helicon discharge and mode transition are closely related to the axial wavenumber. Cavity and antenna resonances influence the helicon discharge and mode transition by determining the axial wavenumber of eigenmodes.
Hybrid triboelectric-piezoelectric nanogenerator for long-term load monitoring in total knee replacements
Authors: Chahari Mahmood , Salman Emre , Stanacevic Milutin , Willing Ryan , Towfighian Shahrzad 
Published: April 2024

Abstract
A self-powered and durable pressure sensor for large-scale pressure detection on the knee implant would be highly advantageous for designing long-lasting and reliable knee implants as well as obtaining information about knee function after the operation. The purpose of this study is to develop a robust energy harvester that can convert wide ranges of pressure to electricity to power a load sensor inside the knee implant. To efficiently convert loads to electricity, we design a cuboid-array-structured tribo-pizoelectric nanogenerator (TPENG) in vertical contact mode inside a knee implant package. The proposed TPENG is fabricated with aluminum and cuboid-patterned silicone rubber layers. Using the cuboid-patterned silicone rubber as a dielectric and aluminum as electrodes improves performance compared with previously reported self-powered sensors. The combination of 10

w
t
%

dopamine-modified BaTiO3 piezoelectric nanoparticles in the silicone rubber enhanced electrical stability and mechanical durability of the silicone rubber. To examine the output, the package-harvester assemblies are loaded into an MTS machine under different periodic loading. Under different cyclic loading, frequencies, and resistance loads, the harvester’s output performance is also theoretically studied and experimentally verified. The proposed cuboid-array-structured TPENG integrated into the knee implant package can generate approximately 15

μ

W of apparent power under dynamic compressive loading of 2200 N magnitude. In addition, as a result of the TPENG’s materials being effectively optimized, it possesses remarkable mechanical durability and signal stability, functioning after more than 30 000 cycles under 2200 N load and producing about 300 V peak to peak. We have also presented a mathematical model and numerical results that closely capture experimental results. We have reported how the TPENG charge density varies with force. This study represents a significant advancement in a better understanding of harvesting mechanical energy for instrumented knee implants to detect a load imbalance or abnormal gait patterns.
Effect of vertical electric field components on the local arc development along contaminated surfaces
Authors: Wen Lu , Cao Bin , Wang Liming 
Published: April 2024

Abstract
During operation, a vertical-component electric field emerges on the surface of high-voltage AC bushings. This unique electric field structure renders the bushing more susceptible to discharge and flashover. However, limited attention has been paid to studying the flashover characteristics resulting from surface pollution in such electric fields. This investigation, involving four differently structured bushings as well as post insulators, delves into the impact of pollution, creepage distance, and electric field structure on bushing flashover characteristics. The study reveals that increasing the creepage length of heavily polluted bushings does not significantly enhance their flashover voltage. Moreover, the role of the vertical electric field in the local arc development was analysed. This research could provide valuable references for future bushing design in polluted areas.
Medical image encryption system based on a simultaneous permutation and diffusion framework utilizing a new chaotic map
Authors: Le Zhen , Li Quanjun , Chen Huang , Cai Shuting , Xiong Xiaoming , Huang Linqing 
Published: April 2024

Abstract
In telemedicine, diverse medical images transmitted between doctors and patients contain sensitive personal information. Thus, there is an urgent need for reliable and efficient medical image encryption to protect these medical images during transmission. In this paper, a simultaneous permutation and diffusion framework (SPDF) is introduced for medical image encryption based on a new chaotic map. Firstly, combining the Chebyshev map and the iterative chaotic map with infinite collapse (ICMIC), we propose a one-dimensional chaotic system (1D-CICMIC) which exhibits higher ergodicity and unpredictability compared to other 1D chaotic maps through comprehensive analyses. Secondly, in order to enhance permutation effect, we modify traditional Josephus traversing with a dynamic scrambling method where the scrambling scheme of the current pixel depends on the value of the previous diffused pixel. Thirdly, we develop a simultaneous permutation and diffusion framework, wherein the diffusion is embedded into the modified Josephus traversing to prevent attackers from targeting the scrambling and diffusion phases separately. Finally, based on 1D-CICMIC and SPDF, an encryption system is proposed. It adopts plaintext correlation in the diffusion operation, which strikes a balance between ciphertext sensitivity and plaintext sensitivity, offering resistance against chosen-plaintext attack (CPA), noise attack and data loss. Simulation results show that the proposed algorithm has high encryption efficiency and can withstand various common attacks.
Optimal finite-differences discretization for the diffusion equation from the perspective of large-deviation theory
Authors: Smith Naftali R 
Published: April 2024

AbstractWhen applying the finite-differences method to numerically solve the one-dimensional diffusion equation, one must choose discretization steps Δx, Δtin space and time, respectively. By applying large-deviation theory on the discretized dynamics, we analyze the numerical errors due to the discretization, and find that the (relative) errors are especially large in regions of space where the concentration of particles is very small. We find that the choiceΔt=Δx2/(6D), whereDis the diffusion coefficient, gives optimal accuracy compared to any other choice (including, in particular, the limitΔt→0), thus reproducing the known result that may be obtained using truncation error analysis. In addition, we give quantitative estimates for the dynamical lengthscale that describes the size of the spatial region in which the numerical solution is accurate, and study its dependence on the discretization parameters. We then turn to study the advection–diffusion equation, and obtain explicit expressions for the optimal Δtand other parameters of the finite-differences scheme, in terms of Δx,Dand the advection velocity. We apply these results to study large deviations of the area swept by a diffusing particle in one dimension, trapped by an external potential∼|x|. We extend our analysis to higher dimensions by combining our results from the one dimensional case with the locally one-dimension method.
Military Unmanned Equipment Image Target Recognition Method based on Improved Deep Learning
Authors: Gao Liming , Li Chao , Wei Zhuo , Han Xiangdong , Dang Feng , Wei Xuemei 
Published: April 2024

Abstract
Military unmanned equipment target recognition is currently a research hotspot and trend in the field of military intelligence. The small sample size and complex recognition scenarios of military unmanned equipment image datasets result in low recognition accuracy. It is proposed a military unmanned equipment image target recognition method based on improved deep learning, which set Faster R-CNN as the network framework for target recognition, used the Kmeans++ algorithm to label boxes on customized datasets, and then added OHEM to the framework to improve the network’s recognition accuracy for difficult to recognize samples. The accuracy of the algorithm proposed in this article reaches 93.8%, which is 2.8% higher than the YOLOv5 algorithm, providing an improved deep learning method for military unmanned equipment image target recognition.
Effect of Door Openings on Train Fire Scenarios within a Subway Depot
Authors: Yang Xin , Li Yanfeng , Liu Huiqiang , Su Zhihe 
Published: April 2024

Abstract
It is challenging to control smoke in the case of a train fire occurred in the subway depot with a complex internal structure. In this paper, the effect of door opening state on smoke behavior characteristics induced by subway depot fire scenario was investigated. A series of numerical simulation were conducted by Fire Dynamics Simulator (FDS) software. Three key parameters were analyzed corresponding to the temperature, visibility and CO concentration for evaluating smoke propagation respectively. Results show that temperature distribution and CO concentration at 2 m height inside the train with train door opening state are lower than those with the train door closed. However, visibility at 2 m height inside the train shows the opposite trend. The results can provide reference in the ventilation system design and emergency evacuation scheme for the subway depot.
Evaluating the Feasibility of Low-Carbon Technology in the Sustainable Economic Development of Electricity Using Generative Adversarial Networks
Authors: Wang Guangming 
Published: April 2024

Abstract
Electricity occupies a central position in energy strategy, and the low-carbon development of the power industry is an important guarantee for achieving sustainable development and carbon dioxide reduction goals. Orderly carrying out low-carbon technology transformation and innovation in electricity, ultimately achieving the goal of sustainable development of the power industry, has strong practical significance. Therefore, this article conducts a feasibility analysis of using generative adversarial networks to evaluate low-carbon technologies in the sustainable economic development of electricity. Firstly, the background of the theory of technological and economic evaluation was analysed and used as a theoretical basis. Secondly, an adversarial network was generated for the study of low-carbon emissions in the power industry, and an evaluation system for low-carbon technology in the sustainable economic development of electricity was constructed. The study showed that the preliminary economic evaluation index system and comprehensive benefit evaluation model for low-carbon technology transformation projects in electricity established in this article have feasibility and reliability for systematic analysis and evaluation of actual projects.