bluuebunny/crossref_metadata_2025_split huggingface dataset:
Proactive perceptive road: a digital twin-driven intelligent road infrastructure towards proactive road state perception and deduction
Authors: Han Tao , Ma Tao , Xiao Jiangyin 
Published: January 2025

Abstract
The emergence of the digital twin (DT) concept provides a new development direction for the digital transformation and upgrading of road health monitoring and maintenance management. Current road digitization technologies primarily focus on the integration and management of easily perceived and measurable asset and maintenance data. However, they struggle to effectively connect the physical state of the road with various computational and analytical modules, making it difficult to sense and integrate complex, hard-to-measure implicit state information and resulting in an inability to support more comprehensive road condition monitoring and precise maintenance decision-making. This paper introduces the concept of proactive perceptive road (PPR), an intelligent road infrastructure driven by DT technology, capable of actively sensing and deducing explicit and implicit road state information. PPR comprises physical space (air, surface, and internal subspaces) entities and DT models and sensing engines in the virtual space. The paper outlines the application logic and core capabilities of PPR and, based on this, proposes an example architecture for its implementation. Two application case studies are presented—one for monitoring construction quality and another for sensing road structure responses—demonstrating the value and potential of PPR in real-time sensing of implicit road state information. The challenges that remain in practical application are also discussed including perceptual, data, and simulation techniques. The proposed concept of PPR represents the next phase of development, offering insights and methodologies for sensing and extracting complex, implicit structural performance data and road health conditions. The presented cases can serve as references for applying DT in the road sector and guide the development of customized solutions for specific scenarios.
An integrated vibration energy harvesting-storage-injection system based on piezoelectric bistable
Authors: Ye Zhenting , Li Xinxin , Tang Weili , Huang Kexue , Wei Yuming , Mo Fujun 
Published: January 2025

Abstract
Efficiently capturing energy from environmental vibrations remains a significant challenge in sustainable energy systems. This paper presents a novel integrated vibrational energy harvesting-storage-injection system (VEHSIS) to improve energy capture efficiency. The VEHSIS comprises four main components: an energy harvesting module, an energy storage module, an energy injection module, and a microcontroller control module. The VEHSIS employs a four-magnet piezoelectric bi-stable structure, which decreases the height of the potential barriers and thus enables efficient energy harvesting at lower vibration amplitudes. The proposed VEHSIS incorporates a dual-capacitor mode to improve the synchronous electric charge extraction (SECE) circuit and further presents a phase-locked loop circuit and a microcontroller control method to precisely control the synchronous switching of the SECE circuit to optimize energy storage and utilization. By improving the voltage pulse perturbation technique and using the microcontroller to monitor the system status in real-time, the system is enabled to maintain high-energy orbital oscillation output under different conditions. Experimental analysis reveals that the VEHSIS improves harvesting efficiency by 3.2 times after energy injection, providing 827.5 μW in dual piezoelectric cantilever beam mode. Compared to classical energy harvesting systems, the VEHSIS offers a spontaneous energy harvesting method and a significant improvement in energy conversion efficiency, providing a new solution for self-powered devices.
Design and modeling of a novel bi-directional piezoelectric damping unit
Authors: Xu Penghang , Chen Weifang , Zhu Rupeng 
Published: January 2025

Abstract
This article focuses on the study of a piezoelectric shunt damping unit used in the vibration damping device of the gear transmission system. The existing butterfly and diamond shaped damping units have a single mode of operation and are inefficient in terms of vibration energy consumption. Therefore, in order to improve the performance of the damping ring, a bi-directional compression piezoelectric shunt damping unit is proposed in this paper. Firstly, a high accuracy finite element model of the bi-directional piezoelectric damping unit was established on the basis of the piezoelectric stack equivalent model. Second, a test prototype was fabricated. The open-circuit voltage and displacement of this unit under bi-directional loading were verified on a high-precision test bench. The experimental results show that the model errors are all within 15%, indicating the high accuracy of the finite element model. It is also confirmed that the proposed bi-directional structure can realize the transfer of tensile and compressive loads. On this basis, the effects of the structural parameters on the open-circuit voltage, deformation displacement, and Mises stress are revealed by the finite element model. The results show that increasing the thickness of the connecting rod will reduce the open-circuit voltage output, but can effectively improve the deformation and stress of the structure. Increasing the connecting rod length increases the open-circuit voltage output to some extent, however, can be detrimental to the deformation and stress of the structure. Increasing the connecting rod spacing has a minimal effect on the open-circuit voltage and deformation displacement, but can effectively reduce the structural stress. This research work has beneficial effect on the design of vibration damping ring for gear transmission system.
Detecting memberships in multiplex networks via nonnegative matrix factorization and tensor decomposition
Authors: Tang Fengqin , Wang Xiaozong , Zhao Xuejing , Wang Chunning 
Published: January 2025

Abstract
Multiplex networks provide a powerful data structure for capturing diverse relationships among nodes, and the challenge of community detection within these networks has recently attracted considerable attention. We propose a general and flexible generative model-the mixed membership multilayer stochastic block model, in which layers with meaningful similarities are grouped together. Within each layer group, the layers share the same mixed membership assignments of nodes to communities, but with distinct community link probability matrices. To address this, we developed non-negative matrix factorization and tensor decomposition (NMFTD), a joint clustering approach, to identify cohesive layer groups and determine the mixed memberships of nodes within them. Our method first clusters the layers using matrix factorization with graph regularization, followed by a tensor decomposition strategy enhanced by a corner-finding algorithm to uncover the nodes’ mixed memberships in each group. The proposed method is asymptotically consistent, and its effectiveness is validated through experiments on synthetic and real-world multilayer networks. The results show that NMFTD exhibits robustness across various parameter settings, outperforming or competing closely with other methods.
Electron thermalization and ion acceleration in XUV-produced plasma from nanoparticles in He gas environment
Authors: Klimešová Eva , Kulyk Olena , Martin Lucas J , Schütte Bernd , Frühling Ulrike , Drescher Markus , Pan Rui , Stojanovic Nikola , Bermudez Macias Ivette J , Düsterer Stefan , Andreasson Jakob , Wieland Marek , Krikunova Maria 
Published: January 2025

Abstract
We use intense femtosecond extreme ultraviolet (XUV) pulses with a photon energy of 92 eV from the FLASH free electron laser to irradiate substrate-free CsCl nanoparticles surrounded by a He gas with a number density of around 1015  cm−3. By simultaneously detecting electrons and energetic ions from the laser-irradiated micron-size target we study the acceleration mechanism of light ions at the microplasma-vacuum boundary as well as at the layer close to the nanoparticle surface. When the XUV pulse interacts with the gas alone, helium ions are accelerated to energies exceeding 100 eV. In the presence of the nanoparticle, light ions gain additional energy in the electric field around the ionized nanoparticle and their energy spectrum changes considerably. We present an electrostatic model to explain the ion acceleration mechanisms both with and without the nanoparticle and discuss the role of the gas environment in experiments.
VELM: a voting based ensemble learning model for breast cancer prediction
Authors: Singh Archana , Kaswan Kuldeep Singh , Rajani  
Published: January 2025

Abstract
Today breast cancer (BC) is the most common and dangerous type of cancer in women. The increasing numbers of cancer cases have been realized in the recent past all over the world. Based on the World Cancer Survey Report, in 2022; the global incidence of cancer is 2.3 million new cases of breast cancer and 670,000 deaths all over the world. Breast cancer screening and timely diagnosis greatly affect the life expectancy since current interventions cannot adequately treat individuals in the later stages. Nonetheless, the development of various expert systems has been initiated for the early diagnosis of breast cancer, wherein a major concern is that many of these systems often fail to localize potential causes such as class imbalance, appropriate methods for data pre-processing, and systematic feature selection appropriately. Therefore, this work develops a model named ‘Voting-based Ensemble Learning Model for Breast Cancer Prediction’ (VELM) to improve BC prediction based on machine learning. In the present work, the class imbalance problem is solved using the ‘SMOTE’ method, while ‘SelectKBest’ is used to determine the most appropriate features of the breast cancer set. In order to compare the proposed VELM and analyse its performance, the results of the various models including all individual classifiers, ensemble models and models proposed in literature have been computed based on accuracy, precision, recall, F1-score and AUC ROC. The outcome shows that our proposed model VELM has the highest, accuracy of 0. 9912 and other metrices in comparison with other models discussed in the literature.
Quantum neural network-inspired variational quantum circuit for simulating diamond 14NV center Hamiltonian with a proximal 13C isotope
Authors: Daka Chisomo , Bhattacharyya Somnath 
Published: January 2025

Abstract
We present a variational quantum circuit (VQC) that utilizes the architecture of a quantum neural network (QNN) to simulate hyperfine interactions in diamond nitrogen vacancy (NV) centers, which is typically a challenging task for traditional computation methods. Isotopes of 13C, though naturally rare, possess a nuclear spin (

l
=

1

2

) which anisotropically interacts with the NV center’s electron spin, resulting in distinct hyperfine structure and coherence properties. The VQC’s ansatz unitary was implemented stepwise (through optimization Trotter steps) to mimic a multi-layered QNN configuration. The dynamical evolution of the 14NV-13C Hamiltonian was simulated while maintaining flexibility for optimization on noisy intermediate-scale quantum (NISQ) platforms. Qiskit SDK was used to design and execute the circuit on 127-qubit IBM quantum hardware. Ansatz parameters were optimized using six classical optimization algorithms initialized with pseudo-random parameter vectors. We successfully determined optimal ansatz parameters for estimating the minimum energy eigenvalues of the 14NV-13C observable using the VQC’s cost function, enabling us to model the system’s ground state dynamics. Our findings show that gradient-free optimizers are efficient for the designed Hardware Efficient SU(2) 2-local circuit (ESU2) ansatz, characterized by a barren plateau landscape. We further established that a maximally entangled VQC initialization (i.e., GHZ3) retains its entanglement scheme irrespective of the optimization pathway. Hence, this approach highlights the effectiveness of QNN-inspired VQC circuits for accurately simulating NV centers coupled with strongly interacting nearest-neighbor nuclear spins. Our work lays down a foundational step for realizing precise control on near-term quantum hardware that utilizes NV centers, leading to high fidelities.
Exploiting multi-scroll hyperchaotic system and heap-queue cross-block permutation for visually meaningful medical image encryption
Authors: Zhang Yiming , Chai Xiuli , Lu Yang , Xie Xiaodong , Sun Junwei , Wang Binjie 
Published: January 2025

Abstract
Medical images contain patients’ private health information, which is crucial to protect their security. Chaotic systems are frequently used in medical image encryption owing to their advantages, including unpredictability and sensitivity to initial conditions. However, existing low-dimensional, small-scroll chaotic systems exhibit limitations, leading to poor security of relevant medical image encryption schemes due to insufficient complexity and randomness. To tackle these issues, a novel 6-scroll Jerk hyperchaotic system (6-SJHS) is constructed and we investigate its application in securing medical images. The 6-SJHS is developed by extending the classical Jerk chaotic system (JCS), and its performance is evaluated comprehensively using the Lyapunov exponent (LE), phase diagram (PD), and the NIST randomness test, demonstrating excellent chaotic robustness and randomness. To reduce the adjacent pixels’ strong correlation in medical images, a cross-block permutation method based on the max heap and queue (CPMHQ) is introduced. Compared with the traditional method, the CPMHQ introduces a dynamic sorting mechanism and efficient block-level disorder, which can better reduce the strong correlation between pixels in medical images. Additionally, to improve the diffusion process, an extended RNA coding scheme is proposed, enabling a cross-plane diffusion method with extended RNA coding (CDERC) that propagates minor alterations across the entire image. The diffused ciphertext image is then subjected to secret image sharing (SIS), generating shadow images (SDIs) that are embedded into carrier images based on their features, resulting in visually meaningful ciphertext images. The analysis of simulation results and experiments confirms that our proposed encryption system offers significant improvements in security and robustness, can achieve more than 49 dB visual quality of steganographic images, and is applicable to encrypt images of different types and sizes, providing an effective solution for protecting medical image confidentiality.
A thermodynamically complete multiphase equation of state for dense and porous metals at wide ranges of temperature and pressure
Authors: Zhao Yanhong , Wang Li-Fang , Zhang Qili , Zhang Le , Song Hongzhou , Gao Xingyu , Sun Bo , Liu Haifeng , Song Haifeng 
Published: January 2025

Abstract
In this work, a thermodynamically complete multi-phase equation of state (EOS) applicable to both dense and porous metals at wide ranges of temperature and pressure is constructed. A standard three-term decomposition of the Helmholtz free energy as a function of specific volume and temperature is presented, where the cold component models both compression and expansion states, the thermal ion component introduces Debye approximation and melting entropy, and the thermal electron component employes the TFK model. The porosity of materials is considered by introducing the dynamic porosity coefficient α and the constitutive P-α relation, connecting the thermodynamic properties between dense and porous systems, allowing for an accurate description of the volume decrease caused by void collapse while maintaining the quasi-static thermodynamic properties of porous systems identical to dense ones. These models enable the EOS applicable and robust at wide ranges of temperature, pressure and porosity. A systematic evaluation of the new EOS is conducted with Aluminum (Al) as an example. 300 K isotherm, shock Hugoniot, as well as melting curves of both dense and porous Al are calculated, which shows great agreements with experimental data and validates the effectiveness of models and the accuracy of parameterizations. Notably, it is for the first time Hugoniot P-σ curves up to 106 GPa and shock melting behaviors of porous Al are derived from analytical EOS models, which predict much lower compression limit and shock melting temperatures than those of dense Al.
Incorporating climate data with machine learning can improve rice phenology estimation
Authors: Liu Yiqing , Liu Weihang , Ye Tao , Chen Shuo , Chen Xuehong , Li Zitong , Zhan Ning , Sun Ran 
Published: January 2025

Abstract
Crop phenology provides essential information for crop management and production. Satellite-based methods are commonly used for phenology estimation but still struggle to capture interannual variations of phenological events. The importance of climate variation in crop phenology has been well acknowledged, but the potential of incorporating climate data to improve phenology estimation remains unclear. Here, we developed a hybrid model by incorporating the growth-specific climate predictors and satellite-derived phenology using random forest approach. Results showed that our hybrid model successfully reduced errors by over 60% compared to traditional satellite-based methods. The inclusion of climate data provided additional contributions beyond what was offered by satellite data, resulting in a 13% average improvement in R
2. Among climate predictors, temperature-related indicators contributed the most to accuracy enhancement. Additionally, CSIF outperformed LAI in the hybrid model in terms of absolute error, due to its finer temporal resolution. Our hybrid model highlights the importance of considering the diverse climatic information to further improve crop phenology estimation, rather than relying solely on satellite data. We expect our proposed model can offer new insights into improving crop phenology estimation and understanding the effects of climate variations on crop phenology.
One-pot synthesis and characterization of a BSA-Cu2S/CuS quantum dots drug delivery system based on biuret’s reaction
Authors: Estrada-Rojas Porfirio , Hernández-Paz Juan Francisco , Rodríguez-González Claudia Alejandra , Mendoza-Duarte Mónica Elvira , Martel-Estrada Santos Adriana , Valencia-Gómez Laura Elizabeth , Camacho-Montes Héctor , Olivas-Armendáriz Imelda 
Published: January 2025

Abstract
Research on chalcogenide-based quantum dots (QDs) has advanced significantly over recent decades, largely due to their excellent optical properties, particularly photoluminescence, and relatively low toxicity. Among these, Cu2S and CuS quantum dots have demonstrated notable photothermal effects and antibacterial activity, making them promising candidates for various biomedical applications, including cancer treatment. This study focuses on the synthesis of CuS and Cu2S quantum dots functionalized with bovine serum albumin (BSA), marking an important step toward their potential biomedical use. The functionalization was achieved through a one-pot synthesis method, characterized by its ability to offer higher chemical yield, elimination of toxic solvents, and provide a method that is simple, economical, and well-controlled. This synthesis produced four samples with varying BSA concentrations. Due to the lower protein concentration, which provides fewer nucleation sites, sample A (BSA concentration 5 mg ml−1) showed a particle size distribution over 100 nm and low stability over time. Sample B (25 mg ml−1 BSA) and C (50 mg ml−1 BSA) showed excellent stability and particle sizes within the expected range for Cu2S and CuS quantum dots (<2.4 nm). Sample D was synthesized with lower BSA concentration (2.5 mg ml−1) and was used only for comparison in few analyses. Fourier-transform infrared (FTIR) spectroscopy confirmed the structural integrity of BSA after functionalization, while x-ray diffraction (XRD) analysis verified the formation of CuS and Cu2S crystals. Additional tests, including fluorescence and UV–vis spectroscopy, validated the optical properties of the quantum dots. Transmission electron microscopy (TEM) confirmed crystalline structure of the CuS and Cu2S quantum dots, with particle size measurements aligning with expectations. Finally, MTT assays confirmed that the synthesized quantum dots are non-toxic, further supporting their potential for biomedical applications.
DFT + U study of structural, electronic, optical and magnetic properties of LiFePO4 Cathode materials for Lithium-Ion batteries
Authors: Wabeto A K , Nigussa K N , Deja L D 
Published: January 2025

Abstract
In this study, we have investigated the structural, electronic, optical, and magnetic properties of LiFePO4 (LFP), which is a prominent cathode material for lithium-ion batteries (LIBs). In order to analyse the properties of LFP, we have carried out the calculations using Density Functional Theory (DFT) combined with the Hubbard (U) correction (i.e., DFT + U), as implemented in the quantum-espresso (QE) code. We performed the structural optimization and found that LFP has a stable olivine structure with equilibrium parameters of a = 4.64 Å, b = 5.98 Å, and c = 10.34 Å. It is shown that LFP has a direct band gap value of 3.82 eV, which is very close to experiment values of 3.6-4.0 eV. The projected density of states (PDOS) of the LFP closely matches with experimental reports of the electronic structure of the material, where the Fe 3d states form tight bands above the O 2p band. The analysis of optical properties shows a significant absorption property in the visible spectrum, reflecting its additional potential for photoactive application. We have also examined the magnetic properties, which reveal that LFP exhibits antiferromagnetic ordering, which plays a critical role in the stability of the material during lithium cycling. The investigated properties of LFP in this work, thus, highlight its viability as an effective cathode material for the next-generation battery technologies with a multifaced applications.