bluuebunny/crossref_metadata_2025_split huggingface dataset:
A first-principles investigation of the structural, mechanical, dynamic, electronic, magnetic, and optical properties of Ti2AC (A = Cd, S) MAX phase compounds
Authors: Rijal Om Shree , Neupane Hari Krishna , Oli Dipak , Neupane Ravi Kiran , Shrestha Pitamber , Sharma Shriram , Joshi Leela Pradhan , Parajuli Rajendra 
Published: January 2025

Abstract
MAX phase compounds have a unique blend of ceramic and metallic properties and have been established as promising materials for device applications. In the present work, we have investigated the structural, dynamical, mechanical, electronic, magnetic, and optical properties of Ti2CdC and Ti2SC MAX phase compounds using a density functional theory approach through the Vienna Ab initio Simulation Package. The structural, dynamical, and mechanical properties of both MAX phase compounds are studied and found to be stable. Additionally, the mechanical properties reveal that both compounds have hardness and a crystalline anisotropic, bond-stretching, and brittle nature. From the analysis of elastic anisotropy calculations, it is found that Ti2CdC has metallic-like bonding, while Ti2SC has covalent-like bonding. The electronic and magnetic properties of the Ti2CdC and Ti2SC compounds are determined, and it is found that they have metallic and non-magnetic properties for the generalized gradient approximation (GGA): Perdew–Burke–Ernzerh (PBE) functional, whereas they have metallic and magnetic properties for the GGA: PBE + U functional(U is Hubbard parameter). We have explored the optical properties of the considered materials and found that they have compelling interactions with electromagnetic radiation, notable values of absorption energy, that less transparency occurred at the lower energy region, and that they have high thermal properties. Based on the major findings of the above considered materials, Ti2CdC and Ti2SC can be used in the fields of optoelectronic devices, sensors, advanced photonic devices, infrared imaging, solar thermal collectors, thermal insulation, nuclear devices, and energy dissipation devices.
Raman signatures of inversion symmetry breaking structural transition in quasi-1D compound, (TaSe4)3I
Authors: Bera Arnab , Rana Partha Sarathi , Pradhan Suman Kalyan , Palit Mainak , Saha Surabhi , Kalimuddin Sk , Bera Satyabrata , Debnath Tuhin , Das Soham , Roy Deep Singha , Datta Subhadeep , Mondal Mintu 
Published: January 2025

Abstract
The breaking of inversion symmetry combined with spin-orbit coupling, can give rise to intrigu-&#xD;ing quantum phases and collective excitations. Here, we report systematic temperature dependent&#xD;Raman scattering and theoretical calculations of phonon modes across the inversion symmetry-&#xD;breaking structural transitions in a quasi-one-dimensional compound (TaSe4)3I. Our investigation&#xD;revealed the emergence of three additional Raman-active modes in Raman spectra of the low-&#xD;temperature (LT) non-centrosymmetric (NC) structure of the material. From polarization depen-&#xD;dent Raman spectra and phonon mode symmetry analysis, we have identified the origin of these&#xD;three newly appeared additional Raman-active modes. Notably, two of these modes become Raman&#xD;active due to the loss of inversion symmetry, while the third mode is identified as a soft phonon mode,&#xD;arising from the distinctive vibrational motion of tantalum (Ta) atoms along the -Ta-Ta- chains.&#xD;Furthermore, the temperature evolution of self-energy parameters indicates significant changes in&#xD;the characteristics of the Raman modes across the transition. Latent heat measurements near the&#xD;phase transition using Differential Scanning Calorimetry (DSC) confirm the first-order nature of&#xD;the transition. Theoretical analysis, including group theory and modeling, reaffirms the displacive&#xD;first-order nature of the structural transition. Our findings establish (TaSe4)3I as a model quasi-&#xD;one-dimensional system with broken inversion symmetry facilitated through a displacive first-order&#xD;structural transition.
A multi-dictionary fusion method based on improved dynamic time warping for moveable rail damage localization
Authors: Song Shuzhi , Zhang Xin , Chang Yongqi , Chen Yifei , Cui Jiazhong , Song Qinghua , Shen Yi 
Published: January 2025

Abstract
Traditional structural health monitoring (SHM) of rails relies on a fixed single sensor, limited by detection range and noise interference. Therefore, a multi-dictionary fusion method for movable rail damage localization is proposed based on improved dynamic time warping (DTW). The approach combines onboard acoustic emission sensors with peak detection frames to measure the moving distance of the inspection wheels and monitor a wide range of rails. Aiming to enhance the damage information, an innovative DTW-based multi-dictionary sparse representation algorithm is presented for data fusion. The second-order difference of the Mahalanobis distance is employed to optimize the fusion weights from the global property. A two-feature adaptive threshold is designed to precisely detect and localize damage on rails. The effectiveness of this method is verified at laboratory testing speeds less than 0.75 m s−1. The results demonstrate that it can accurately detect 2 mm deep strip and square damage, providing new inspiration for rail SHM.
Looseness detection system of bolted joints using a VMD-based nonlinear transformation approach with deep residual network
Authors: Kim Dong-Yoon , Kim Min-Je , Kim Chun-Il , Yoon Gil Ho 
Published: January 2025

Abstract
Bolted structures are subject to various vibrations, external forces and environmental factors, all of which can reduce their structural stability and compromise the integrity of bolted connections. Detecting bolt loosening in advance is crucial, as these effects often cause bolts to become loose, potentially leading to structural failure or collapse. However, identifying looseness in complex or large structures poses significant challenges, particularly when there is insufficient prior information about the loose-fit condition. To address this issue, the present study proposes a novel detection system for bolted joint looseness, employing a variational mode decomposition (VMD)-based nonlinear transformation (NT) approach integrated with a deep residual neural network, under several underlying assumptions. The proposed method utilizes VMD to decompose transverse vibrational modes into intrinsic mode functions (IMFs), selectively extracting signals with desired modes. The NT method is then applied to scale and shift the extracted signals, transforming them into a form that facilitates approximate classification. Image-based spectrograms are generated from the differences between transformed and reference signals, which are subsequently analyzed by the deep residual network. To validate the proposed method, several plates with bolted joints are considered.
Technology for detecting small-aperture leaks in natural gas pipelines utilizing transfer learning methodologies
Authors: Tang Jie , Wang Xiufang , Bi Hongbo , Dai Rui , Yue Youchen , Yang Jinghao , Feng Chao 
Published: January 2025

Abstract
To address the challenge of limited training samples in natural gas pipeline leakage detection, a novel transfer learning framework is proposed, which requires only a small amount of leakage data from a specific leak aperture as the source domain training set. Additionally, a new residual structure, named Dual-Pieces Net, is designed. This structure combines the cross-layer fusion of residual networks with a convolutional fragmenting mechanism. By processing feature maps in various ways through fragmentation, it enhances the model’s ability to refine and diversify signal processing. Unlike traditional detection models that rely on large amounts of diverse leakage data, the proposed transfer learning (TL) framework deepens the model’s extraction of invariant leakage features from the signal through stacked Dual-Pieces Net modules. This improves the TL performance and provides a solid technical foundation for more diverse TL scenarios. All transfer experiments were conducted on an experimental transport pipeline with a length of 169 m. The experimental results demonstrate that the proposed method offers significant advantages compared to other methods, proving its effectiveness and practical relevance in real-world applications.
Modelling diffusive phase transformations in multiphase systems using the Voronoi implicit interface method
Authors: Jacobsson E , Hallberg H , Hektor J , Ristinmaa M 
Published: January 2025

Abstract
This paper presents a general level set framework for modelling diffusive solid-state phase transformation processes in binary systems comprising several grains and phases. Notably, it is demonstrated how the Voronoi implicit interface method (VIIM) can be used to simulate microstructure evolution in polycrystals driven by diffusion. The key advantage of VIIM is that a single level set function can be utilized to describe the evolution of the microstructure. Thus, the computational cost is significantly reduced compared to more traditional approaches, in which each grain in the microstructure is assigned a separate level set function. Moreover, VIIM facilitates the reconstruction of a fully compatible grain boundary network without the presence of any void regions or grain overlaps. The reconstruction procedure does not require any special handling of junctions and can be easily extended to higher dimensions. Moreover, the reconstruction step enables an adaptive remeshing technique to be employed such that the mesh nodes conform with the location of the grain boundaries. Hence, the diffusion problem can be solved in distinct phase domains, demarcated by sharp interfaces. The kinetics of the phase interfaces are described by the amount of flux towards the interface in accordance with the theory of diffusion-controlled phase transformations. To examine the capabilities of the proposed model, both single-phase and multiphase problems are examined. For the single-phase problem, it is shown that the model behaves in accordance with analytical solutions and maintains mass conservation. For the multiphase problems, two model cases of polycrystals that share the same initial geometry but exhibit different phase distributions are investigated, as well as a representative microstructure example. The evolution of the microstructures towards an equilibrium state is in agreement with the expected phase fractions and demonstrates that the model produces reliable results. The error in mass conservation of the considered multiphase examples is within a margin of ±5%.
Modeling of β-Ga2O3 based double gate drain extended junction less FET and its parameter extraction methodology
Authors: Manukrishna Vanajakumari Raju , Nikhil KrishnanNadar Savithry 
Published: January 2025

Abstract
A static current model of β—Gallium Oxide (β-Ga2O3) based double gate drain extended junction less field effect transistor (DG-DeJLFET) is proposed. The model consists of two voltage-controlled current sources connected in series. One of the current sources accounts for the operation of the junction less field effect transistor whereas, the other takes care of the drift region current. The model is formulated by considering the mobility degradation caused by moderately elevated electric fields. A parameter extraction methodology for this model is also proposed by considering the dominance of certain parameters in the specific regions of operation. In general, the parameter extraction technique is based on the variation in the device current behavior at moderate electric fields. The proposed parameter extraction methodology is validated by comparing the results with the data obtained from the TCAD simulation.
Self-critical strategy adjustment based artificial intelligence method in generating diagnostic reports of respiratory diseases
Authors: Chen Binyue , Liu Guohua , Zhang Quan 
Published: January 2025

Abstract
Objective. Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnosis approaches can enhance the efficiency of diagnosis but still face challenges. For example, single-modal data suffer from information redundancy or loss, difficulty in learning relationships between features, and revealing the obscure characteristics of complex diseases. Therefore, it is critical to explore a method that can assist clinicians in detecting lesions early and in pre-diagnosing corresponding diseases. Approach. This paper introduces a novel network structure, SCSCS-Net, which can effectively extract image features from chest X-ray images and generate medical image descriptions, assist clinicians in analyzing patients' medical imaging information, deeply explore potential disease characteristics, and assist in making pre-diagnostic decisions. The SCSCS-Net consists of a reinforced cross-modal feature representation model (RCMFR) and a self-critical cross-modal alignment model (SCCMA), which are responsible for learning the features interdependence between images and reports by using a multi-subspace self-attention structure and guiding the model in learning report generation strategies to improve the professionalism and consistency of medical terms in generated reports, respectively. Main results. We further compare our model with some advanced models on the same dataset, and the results demonstrate that our method achieves better performance. Finally, the CE and NLG metrics further confirm that the proposed method acquires the ability to generate high-quality medical reports with higher clinical consistency in generating medical reports. Significance. Our novel method has the potential to improve the early detection and pre-diagnosis of respiratory diseases. The model proposed in this paper allows to narrow the gap between Artificial intelligence technology and clinical medical diagnosis and provides the possibility for in-depth integration.
Deep learning generalization for diabetic retinopathy staging from fundus images
Authors: Men Yevgeniy , Fhima Jonathan , Celi Leo Anthony , Ribeiro Lucas Zago , Nakayama Luis Filipe , Behar Joachim A 
Published: January 2025

Abstract

Objective. Diabetic retinopathy (DR) is a serious diabetes complication that can lead to vision loss, making timely identification crucial. Existing data-driven algorithms for DR staging from digital fundus images (DFIs) often struggle with generalization due to distribution shifts between training and target domains. Approach. To address this, DRStageNet, a deep learning model, was developed using six public and independent datasets with 91 984 DFIs from diverse demographics. Five pretrained self-supervised vision transformers (ViTs) were benchmarked, with the best further trained using a multi-source domain (MSD) fine-tuning strategy. Main results. DINOv2 showed a 27.4% improvement in L-Kappa versus other pretrained ViT. MSD fine-tuning improved performance in four of five target domains. The error analysis revealing 60% of errors due to incorrect labels, 77.5% of which were correctly classified by DRStageNet. Significance. We developed DRStageNet, a DL model for DR, designed to accurately stage the condition while addressing the challenge of generalizing performance across target domains. The model and explainability heatmaps are available at www.aimlab-technion.com/lirot-ai.
Mechanical behavior of HTS tapes in highly flexible REBCO cable under torsional and tensile loads
Authors: Tang Shengyi , Peng Enze , Li Yihao , Wei Liangyu , Shi Yunfan , Ge Zhendong , Yong Huadong , Zhou Youhe 
Published: January 2025

Abstract
Different cables composed of high-temperature superconducting (HTS) tapes (REBCO) have been developed for the generation of high magnetic fields and the transmission of large currents. Among the different HTS cables, the conductor on round core (CORC®) cable has become a promising candidate structure. In order to improve the flexibility of CORC® cable, a new kind of cable called HFRC (highly flexible REBCO cable) has been proposed, which has a similar configuration to CORC® cable. During the production of HFRC, REBCO tapes are wound on the surface of a spiral tube layer by layer. Compared to the solid core of traditional CORC® cable, the spiral tube is more prone to complex deformation under external loads, which results in special characteristics of REBCO tapes. In this paper, we analyze the deformation processes of spiral tubes under tension or torsion. Based on the geometrical relationship between the tapes and the cable core, the strain in REBCO tapes is predicted. Finite element models of HFRC/traditional CORC-like cable are built and verified by experiments. Through simulations, the variation of the strain and local creases of the tapes with the design parameters of the core is investigated. Relevant findings provide a reference for the structural optimization of HFRC.
Energy shift with coupling (ESC): a new quench protection method
Authors: Ravaioli E , Verweij A , Wozniak M 
Published: January 2025

Abstract
Quench protection of full-size, high-field accelerator magnets poses significant challenges. Maintaining the hot-spot temperature and peak voltage-to-ground within acceptable limits requires a protection system that quickly transitions most of the coil turns to the normal state. Existing magnet protection technologies, such as quench protection heaters or the coupling loss induced quench system (CLIQ), have been successfully applied. However, they both present shortcomings since they require either thin insulation between the heaters and the magnet conductor—in the case of heaters, or direct electrical connections to the magnet coil—in the case of CLIQ. A novel quench protection method, energy shift with coupling (ESC), is presented, which can achieve excellent quench protection performance without requiring any electrical connection or close physical contact with the coils to protect. ESC relies on normal-conducting auxiliary coils strongly magnetically coupled with the magnet coils to protect. Upon quench detection, capacitive units connected across the auxiliary coils introduce a high current change in the auxiliary coils, causing a rapid shift of magnet stored energy from the magnet coils to the auxiliary coils. This has three beneficial effects: sudden reduction of ohmic loss in the normal zone of the magnet conductor, introduction of high transient losses in the magnet conductor, thus causing a quick transition to the normal state, and extraction of a part of the magnet’s stored energy to the auxiliary coils. The applicability of the ESC concept on an existing magnet design is analyzed with electro-magnetic and thermal transient simulations performed with the STEAM-LEDET program. The advantages and disadvantages of ESC are discussed and compared to other conventional quench protection methods. Simulation results show that ESC can be applied to protect full-scale magnets with reasonable requirements in terms of size and location of the auxiliary coils, and of capacitive unit parameters.
Analysis of the the $K^- p \to \gamma \Lambda$ reaction
Authors: Pan Yi , Li Rong , Liu Bo-Chao 
Published: January 2025

Abstract
In this work, we study the $K^- p \to \gamma \Lambda$ reaction in an effective Lagrangian approach and isobar model. Compared to previous studies using an Regge-plus-resonance model, here we consider the contributions of the $t$-channel($K$ and $K^*$) and $u$-channel(proton) exchanges explicitly as the background contribution. To restore the gauge invariance of the amplitude violated by introducing the phenomenological form factors, we employ two different methods that adopted in literatures. Then we discuss the roles of possible resonance contributions in this reaction and present the predictions of $\Lambda$ polarization based on various models. We find that the contribution of the background terms play an important role in the present reaction. On the other hand, the $\Lambda(1520)$, $\Sigma(1660)$ and $\Sigma(1670)$ resonances may also contribute in this reaction. Due to the uncertainties of the present data and the relatively small contributions of the hyperon resonances, we can not identify the roles of various hyperon resonances in the present work. While, we show that the measurement of the spin polarization of the final $\Lambda$ will be helpful to verify various models.
Gold nanocages co-assembled with Spinacia oleracea extract combined photothermal/photodynamic therapy in 4T1 breast cancer cell line
Authors: Dehariya Dheeraj , Tarafdar Anindita , Pebam Monika , Sankaranarayanan Sri Amruthaa , Khatun Sajmina , Rengan Aravind Kumar 
Published: January 2025

Abstract
Photothermal therapy (PTT) and photodynamic therapy (PDT) have been emerging as potential alternatives to conventional cancer treatment modalities. Gold nanoparticles, owing to their surface plasmon resonance properties, have been promising in cancer phototherapies, and extracts from potent medicinal plants are commonly employed for the green synthesis of various nanoparticles. Some researchers have used photosensitizers like chlorophyll to promote reactive oxygen species generation. In this research, the photothermal ability of gold and the photon-absorbing capability of chlorophyll derived from Spinacia oleracea (S. oleracea) are combined to achieve the optimum results. Herein, we have synthesized the gold nanocages(AuNCs) co-assembled with S. oleracea extract (SPAuNCs; 70 ± 10 nm) to be employed as a PTT and PDT agent to treat triple-negative breast cancer. This study found that SPAuNCs are promising PTT and PDT agents against breast cancer cell line.
Transcytosis: an effective mechanism to enhance nanoparticle extravasation and infiltration through biological barriers
Authors: Zhang Qianyi , Wang Jiamian , Chen Zhiyang , Qin Hao , Zhang Qichen , Tian Bo , Li Xilei 
Published: January 2025

Abstract
Nanoparticles (NPs)1 have been explored as drugs carriers for treating tumors and central nervous system (CNS)2 diseases and for oral administration. However, they lack satisfactory clinical efficacy due to poor extravasation and infiltration through biological barriers to target tissues. Most clinical antitumor NPs have been designed based on enhanced permeability and retention effects which are insufficient and heterogeneous in human tumors. The tight junctions3

3
TJs: tight junctions
of the blood–brain barrier4

4
BBB: blood–brain barrier
and the small intestinal epithelium severely impede NPs from being transported into the CNS and blood circulation, respectively. By contrast, transcytosis enables NPs to bypass these physiological barriers and enhances their infiltration into target tissues by active transport. Here, we systematically review the mechanisms and putative application of NP transcytosis for targeting tumor and CNS tissues, explore oral NP administration, and propose future research directions in the field of NP transcytosis.