bluuebunny/crossref_metadata_2025_split huggingface dataset:
Topological signatures of triply degenerate fermions in Heusler alloys: an ab initio study
Authors: Dhori Bhautik R , Mohanta Aritri , Jha Prafulla K 
Published: October 2024

Abstract
Triply degenerate nodal point (TP) fermions, lacking elementary particle counterparts, have been theoretically anticipated as quasiparticle excitations near specific band crossing points constrained by distinct space-group symmetries instead of Lorentz invariance. Here, based on first-principles calculations and symmetry analysis, we demonstrate the presence of TP fermions in Heusler alloys. Furthermore, we predict that these Heusler alloys are dynamically stable, exhibiting TP fermions along four distinct C
3 axes in the F-43m space group. We show that α-LiCaPdSb harbours peculiar Fermi arcs and surface states on the (111) and (001) crystal facets, owing to the coexistence of threefold rotational and time reversal symmetry. More interestingly, a modest tensile strain can increase the distance of fermions along the Γ-L high symmetric line by as much as 21.10%, which give rise to measurable Fermi arcs. Furthermore, we investigate non-trivial topological insulator phase in β-LiCaPdSb, by changing the chemical environment through placing transition metal atoms at various Wyckoff positions. The β-LiCaPdSb harbour a semi-metallic nature, and by breaking cubic symmetry, it undergoes a transition from semi-metal to a non-trivial topological insulator. In addition, for the first time, rare-earth LaPtBi half-Heusler alloy is examined under strain to uncover multiple band inversions associated with the TP fermionic phase. The observed multiple band inversion is entirely unaffected by spin–orbit coupling. We show that the LaPtBi compound hosts TP fermions, which are linked to a

Z

2

topological invariant. Remarkably, with clear band crossings and multiple band inversion, we point out the possibilities of the LaPtBi for displaying a rich topological phase diagram. Our work provides a prototype material platform for experimental detection through angle-resolved photoemission spectroscopy or scanning tunnelling spectroscopy and practical spintronic applications.
Recent advances in understanding and manipulating magnetic and electronic properties of EuM2X2 (M = Zn, Cd; X = P, As)
Authors: Chen Xiyu , Dong Shuai , Wang Zhi-Cheng 
Published: October 2024

Abstract
Over the past five years, significant progress has been made in understanding the magnetism and electronic properties of CaAl2Si2-type EuM
2
X
2 (M = Zn, Cd; X = P, As) compounds. Prior theoretical work and experimental studies suggested that EuCd2As2 had the potential to host rich topological phases, particularly an ideal magnetic Weyl semimetal state when the spins are polarized along the c axis. However, this perspective is challenged by recent experiments utilizing samples featuring ultra-low carrier densities, as well as meticulous calculations employing various approaches. Nonetheless, the EuM
2
X
2 family still exhibit numerous novel properties that remain to be satisfactorily explained, such as the giant nonlinear anomalous Hall effect and the colossal magnetoresistance effect. Moreover, EuM
2
X
2 compounds can be transformed from semiconducting antiferromagnets to metallic ferromagnets by introducing a small number of carriers or applying external pressure, and a further increase in the ferromagnetic transition temperature can be achieved by reducing the unit cell volume. These features make the EuM
2
X
2 family a fertile platform for studying the interplay between magnetism and charge transport, and an excellent candidate for applications in spintronics. This paper presents a comprehensive review of the magnetic and transport behaviors of EuM
2
X
2 compounds with varying carrier densities, as well as the current insights into these characteristics. An outlook for future research opportunities is also provided.
Strain induced phase transitions and hysteresis in aluminium nitride: a density functional theory study
Authors: Namir O , Chen J , Belabbas I 
Published: October 2024

Abstract
Computer atomistic simulations based on density functional theory were carried out to investigate strain induced phase transitions in aluminium nitride (AlN). The wurtzite to graphitic and graphitic to wurtzite transformations were investigated at the atomic level and their physical origins were identified. Both phase transitions were found to be of the first order. The wurtzite to graphitic phase transition takes place in the tensile regime at a strain value of +7%. The driving force for this transformation was identified to be an elastic instability induced by tensile strain. A hysteresis was demonstrated where the graphitic structure is separated from the wurtzite by a kinetic energy barrier. The origin of the observed hysteresis is due to the asymmetry of bond formation and bond breaking associated with the wurtzite to graphitic and graphitic to wurtzite transitions, respectively. A dynamic instability taking place at +3%, along the graphitic path, prevents the hysteresis to fully occur. The possible occurrence of the hysteresis has then to be taken into account when growing the graphitic phase by heteroepitaxy. Otherwise, maintaining the graphitic structure at low strain, through the hysteresis, offers new possibilities in the development of novel future applications.
A multi-level prediction model of concrete dam displacement considering time hysteresis and residual correction
Authors: Xu Bo , Zhang Hu , Xia Hui , Song Dalai , Zhu Zhenhao , Chen Zeyuan , Lu Junyi 
Published: October 2024

Abstract
Displacement directly reflects the operational status of dams and exhibits time hysteresis. Moreover, data-driven predictive models fail to account for all physical mechanisms, leaving valuable information uncaptured in residuals. Hence, this study establishes a multi-level displacement prediction model for concrete dams considering the time hysteresis of monitoring data and residual correction. Initially, employing Bayesian optimization gated recurrent units (BO-GRU) and considering multivariate time series (MTS) prediction, a single-level displacement prediction model, MTS-BO-GRU, is developed to capture displacement time hysteresis. Subsequently, acknowledging the chaotic characteristics in residual sequences, a random forest (RF) model is utilized in conjunction with univariate time series (UTS) prediction to correct residual sequences, resulting in the UTS-RF model. The corrected values are then combined with the predicted values of the MTS-BO-GRU model to establish the MTS-BO-GRU+ multi-level displacement prediction model. Finally, employing an in-service concrete dam as a case study, the performance of the proposed multi-level model is compared to validate and evaluate its superiority. Results demonstrate that the MTS-BO-GRU+ model, considering displacement time hysteresis and residual correction, exhibits the best predictive performance. Additionally, MTS prediction effectively captures displacement time hysteresis, while the UTS-RF model efficiently identifies valuable information in residual sequences. This research provides scientific basis and technical support for dam safety monitoring, health service diagnosis, and operational management, offering new insights for structural health monitoring.
Hydraulic valve fault diagnosis based on EEWT and POA-KELM
Authors: Chen Rongchun , Chen Chao , Kelimu Muhetaer 
Published: October 2024

Abstract
With the wide application of hydraulic systems in the industrial and mechanical fields, the analysis and study of hydraulic valve failures have become increasingly important. This study aims to identify and analyze possible faults by analyzing the operating status of hydraulic valves under different loads. In this study, we propose using a combination of two phases for the failure analysis of hydraulic valves. The first stage is the signal processing stage, in which the pressure signals of hydraulic valves under different loads are collected, the pressure signals are decomposed using the extended empirical wavelet transform (EEWT) to obtain a series of sub-signal components, and the 17 eigenvectors of these components are computed, and the feature selection is carried out using the method of sequence backward selection. Next is the classification stage, where the features selected in the first stage are input into a kernel extreme learning machine (KELM) classifier improved by the Pelican optimization algorithm (POA) to classify and identify the anomalous signals. The experimental results show that the fault diagnosis method based on EEWT and POA-KELM can effectively detect and recognize hydraulic valve faults.
Denoising diffusion probabilistic model enhanced tool condition monitoring method under imbalanced conditions
Authors: Fu Yuandie , Zhong Meipeng , Huang Junfeng , Jiang Youhui , Sun Weifang , Lou Yongjian , Wang Gonghai , Zhou Yuqing 
Published: October 2024

Abstract
In recent years, tool condition monitoring (TCM) based on deep learning has been widely considered and achieved remarkable success. However, these methods typically require relatively large training samples to produce significant results, which are both imbalanced and rather troublesome to obtain in the practical application of TCM. To address this issue, a novel TCM method combined with multiscale permutation entropy (MPE), denoising diffusion probabilistic model (DDPM) and a residual network (ResNet) is proposed under conditions of sample imbalance. First, the one-dimensional sensing signal data is converted to a grayscale recurrence plot (RP) by minimizing the MPE of the signals in each channel. Second, combine and splice these grayscale RPs from different channels in each sample into color RPs. After that, the generated RP images using DDPM are added to the imbalanced dataset to augment the data to achieve a balanced state of the dataset. Finally, the balanced mixed data set of real and fake samples is input into a ResNet for recognition and monitoring tool conditions. TCM experiments are conducted to verify the performance of the proposed method with imbalanced dataset, and the results of experimental investigation demonstrate that the accuracy of the proposed method improved by 2%–18.8% compared to that of the other four sample augmentation methods using ResNet18 when the imbalance rate is 1:200.
Detection of delamination using laser-generated Lamb waves with improved wavenumber analysis
Authors: Yang Shixi , Guo Zixuan , He Jun , Cheng Qichao , Xiong Xin 
Published: October 2024

Abstract
Delamination is a typical failure mode in multilayered materials, which may potentially threaten the safety and reliability of the materials if not detected promptly. This paper proposes a novel quantitative detection method for delamination in multilayered materials based on laser-generated Lamb waves with improved wavenumber analysis. First, a three-dimensional (3D) finite element model is established to simulate the interaction between delamination and laser-generated Lamb waves. The propagation characteristics of Lamb waves in multilayered materials without defects and with delamination of three different shapes, i.e. rectangular, circular and triangular are analyzed. And the mechanism that delamination can lead to the appearance of scattered wave and new wavenumber components is investigated, facilitating the quantitative detection of delamination. Then, an improved wavenumber analysis method for quantitative detection of delamination is proposed. The new wavenumber components caused by delamination are extracted by broadband adaptive wavenumber filtering to reduce imaging interference and artifacts caused by irrelevant components, and the spatial wavenumber spectrum is obtained by fast broadband local wavenumber estimation algorithm to enhance the delamination imaging accuracy and spatial resolution. The experimental results indicate that, across various experimental scenarios, the proposed method achieves superior imaging precision compared to traditional wavenumber filtering or local wavenumber estimation methods, and can quantitatively identify delamination defects of various shapes and sizes.
Nonzero-sum game-based decentralized approximate optimal control of modular robot manipulators with coordinate operation tasks using value iteration
Authors: Dong Bo , Gao Yuhang , An Tianjiao , Jiang Hucheng , Ma Bing 
Published: October 2024

Abstract
Accurate trajectory tracking and appropriate contact force are crucial for the coordinated operation-oriented control of modular robot manipulators (MRMs). Considering the practical need for precision in system control, resource optimization, and disturbance compensation within the context of the coordinated operation tasks (COTs) of MRMs, this paper employs a value iteration (VI) technique to devise a decentralized approximate optimal control strategy grounded in nonzero-sum game (NZSG) theory. To obtain more accurate, reliable, and safe control, a dynamic model of the MRM is established using joint torque feedback technology; then, the problem of optimal control for MRM systems focused on coordinated operation-oriented control is reformulated as an NZSG involving multiple subsystems. The present study, grounded in the theoretical framework of the adaptive dynamic programming (ADP) algorithm, employs an event-triggered NZSG strategy, utilizing VI to resolve the coupled Hamilton–Jacobian equations, culminating in the derivation of the Nash equilibrium solutions. Through stringent stability analysis, it is established that the trajectory tracking error for the closed-loop MRM system engaged in COTs is uniformly ultimately bounded. The proposed method’s efficacy is subsequently corroborated through experimental validation.
Improved DV-Hop algorithm based on geometric Brownian motion model under communication interference
Authors: Zhang Lieping , Huang Zichen , Yin Yameng , Tan Mingyang , Zhang Cui 
Published: October 2024

Abstract
In a mobile sensor network, a traditional positioning algorithm is unable to locate unknown nodes when losing anchor positions caused by communication interference. To solve this problem, an improved DV-Hop algorithm based on a geometric Brownian motion (GBM) model was proposed including two main stages: location of sink node (LSN) and location of blind node (LBN). In the LSN stage, if the signal transmission of anchors is normal, the GBM model records the moving positions of the anchors. If not, the GBM model predicts the estimated average positions of the anchors using recorded data. Then, the trial count of the GBM model is optimized to further improve the prediction accuracy and computational overhead. In the LBN stage, the unknown nodes’ positions are obtained by the DV-Hop algorithm. In a traditional DV-Hop algorithm, the approximate minimum hop number and average hop distance may lead to huge deviation between true position and estimated position. To improve the positioning accuracy in the LBN stage, the strategies of multi-communication radius and hop distance weighting were adopted. The simulation results demonstrated that the proposed algorithm has the capability to resist communication interference and adaptability at different node speeds , maintaining a relatively high accuracy in locating unknown nodes.
Formation of the hard-magnetic epsilon iron oxide phase from akaganéite nanoparticles
Authors: Joseph Navya , Gopi Surya , Ullrich Aladin , Albrecht Manfred , Thomas Senoy 
Published: October 2024

Abstract
Elongated akaganéite (β-FeOOH) nanoparticles were prepared by a forced hydrolysis route using FeCl3
·6H2O employing various urea concentrations. β-FeOOH nanoparticles stabilized within the SiO2 matrix were annealed at different temperatures, ranging from 500 °C to 1300 °C. It was observed that β-FeOOH underwent a temperature-induced conversion to γ-Fe2O3 and subsequently to ϵ-Fe2O3. Due to the ϵ-Fe2O3 phase formation, the coercivity rapidly increased to 16 kOe for samples annealed at 900 °C and reached values up to 21.5 kOe when annealed at 1200 °C. At a higher temperature of 1300 °C, the ϵ-Fe2O3 phase transforms mainly into the α-Fe2O3 phase, which causes the coercivity to rapidly drop to negligible values.
NiO/MnO2 coated on carbon felt as an electrode material for supercapacitor applications
Authors: Azizi Sadegh , Askari Mohammad Bagher , Rozati Seyed Mohammad , Masoumnezhad Mojtaba 
Published: October 2024

Abstract
Transition metal oxides have demonstrated excellent capability for charge storage when used in supercapacitor electrodes. This study undertook the hydrothermal synthesis of bimetallic nickel and manganese oxide (NiO/MnO2) on a carbon-felt (CF) substrate. NiO/MnO2/CF electrode was characterized and examined in a three-electrode system in a potassium hydroxide electrolyte. Cyclic voltammetry, electrochemical impedance spectroscopy, and galvanostatic charge-discharge analyses revealed Faradaic behavior during charge storage, a specific capacity of 1627 F g−1, and a stability of 96.8% after 5000 consecutive charge-discharge cycles. Subsequent investigations were conducted in a two-electrode system for constructing a symmetrical supercapacitor using the NiO/MnO2/CF electrode. The energy and power densities were determined as 43Wh kg−1 and 559 W kg−1. Additionally, the stability of the constructed supercapacitor device was examined over 5000 consecutive cycles, verifying a 92% stability through charge-discharge cycles. Finally, the fabricated supercapacitor was utilized to power an LED lamp, successfully maintaining the illumination for 53 s.
Piezoelectric dispenser printing and intense pulsed light sintering of AgNW/PEDOT:PSS hybrid transparent conductive films
Authors: Noh Youngwook , Cho Kwan Hyun 
Published: October 2024

Abstract
A hybrid transparent conductive films (TCFs) combining silver nanowires (AgNWs) and poly(3,4-ethylenedioxythiophene) polystyrene sulfonate (PEDOT:PSS) was fabricated using a piezoelectric dispenser printing method. The innovation lies in optimizing the ink composition and employing intense pulsed light sintering to enhance the TCF’s performance. The optimized AgNW/PEDOT:PSS mixture, with an 8:2 ratio, achieved a figure of merit (FOM) of 28.05 × 10−3 Ω−1, corresponding to a sheet resistance of 9.93 Ω sq−1 and a transmittance of 88.0%. This represents a significant improvement over the pre-sintering FOM of 24.09 × 10−3 Ω−1. Additionally, the hybrid TCFs exhibited outstanding structural stability, maintaining functionality after 7000 mechanical bending cycles. The results enable applications in flexible optoelectronic devices, and highlight the potential of this method to produce high-performance, flexible, and durable transparent electrodes, advancing the development of next-generation optoelectronic devices.
Generalized invariant measures for non-autonomous conformal iterated function systems
Authors: Nakajima Yuto , Takahashi Yuki 
Published: October 2024

Abstract
We consider Non-autonomous Conformal Iterated Function Systems (NCIFS) in the overlapping case. We focus on the parameterized families of NCIFS that satisfy the transversality condition, and give a dimension formula for the generalized invariant measure for a.e. parameter. We also give a sufficient condition that the generalized invariant measure is absolutely continuous for a.e. parameter.
Sparse-Laplace hybrid graph manifold method for fluorescence molecular tomography
Authors: Wang Beilei , Li Shuangchen , Zhang Heng , Zhang Lizhi , Li Jintao , Yu Jingjing , He Xiaowei , Guo Hongbo 
Published: October 2024

Abstract

Objective. Fluorescence molecular tomography (FMT) holds promise for early tumor detection by mapping fluorescent agents in three dimensions non-invasively with low cost. However, since ill-posedness and ill-condition due to strong scattering effects in biotissues and limited measurable data, current FMT reconstruction is still up against unsatisfactory accuracy, including location prediction and morphological preservation. Approach. To strike the above challenges, we propose a novel Sparse-Laplace hybrid graph manifold (SLHGM) model. This model integrates a hybrid Laplace norm-based graph manifold learning term, facilitating a trade-off between sparsity and preservation of morphological features. To address the non-convexity of the hybrid objective function, a fixed-point equation is designed, which employs two successive resolvent operators and a forward operator to find a converged solution. Main results. Through numerical simulations and in vivo experiments, we demonstrate that the SLHGM model achieves an improved performance in providing accurate spatial localization while preserving morphological details. Significance. Our findings suggest that the SLHGM model has the potential to advance the application of FMT in biological research, not only in simulation but also in in vivo studies.
Study on the impact of bowtie filter on photon-counting CT imaging
Authors: Zhang Xin , Xie Jixiong , Su Ting , Zhu Jiongtao , Xia Dongmei , Zheng Hairong , Liang Dong , Ge Yongshuai 
Published: October 2024

Abstract

Objective. The aim of this study was to investigate the impact of the bowtie filter on the image quality of the photon-counting detector (PCD) based CT imaging. Approach. Numerical simulations were conducted to investigate the impact of bowtie filters on image uniformity using two water phantoms, with tube potentials ranging from 60 to 140 kVp with a step of 5 kVp. Subsequently, benchtop PCD-CT imaging experiments were performed to verify the observations from the numerical simulations. Additionally, various correction methods were validated through these experiments. Main results. It was found that the use of a bowtie filter significantly alters the uniformity of PCD-CT images, depending on the size of the object and the x-ray spectrum. Two notable effects were observed: the capping effect and the flattening effect. Furthermore, it was demonstrated that the conventional beam hardening correction method could effectively mitigate such non-uniformity in PCD-CT images, provided that dedicated calibration parameters were used. Significance. It was demonstrated that the incorporation of a bowtie filter results in varied image artifacts in PCD-CT imaging under different conditions. Certain image correction methods can effectively mitigate and reduce these artifacts, thereby enhancing the overall quality of PCD-CT images.