bluuebunny/crossref_metadata_2025_split huggingface dataset:
A reconfigurable compliant robotic gripper based on flexible scissors mechanism
Authors: Xie Wenhao , Li Yunquan , Mo Liyan , Fu Shiling , Qu Jinting , Zhong Yong , Zhang Yuan-Fang , Hu Nan , Wu Changchun , Ren Tao 
Published: November 2024

Abstract
Traditional robotic grippers are constrained by their fixed structures and limited grasping range, which poses difficulties when handling objects of various shapes and sizes. In contrast, reconfigurable grippers can flexibly adjust their grasping mode and configuration based on object properties, enabling them to accommodate a wider range of geometries. However, existing reconfigurable grippers face challenges in achieving structural consistency, large-scale continuous deformation, and ease of manufacturing and operation. To overcome these limitations, this study proposes a reconfigurable compliant robotic gripper (RCRG) based on a flexible scissor mechanism finger (FSMF). The FSMF incorporates an anisotropic scissor unit design that decouples extension and bending motions, achieving up to 4.5 times continuous finger length adjustment. Multiple FSMF modules can be mounted on a rigid scissor mechanism base to form either a two-fingered or three-fingered RCRG, depending on task requirements. With this configuration, the RCRGs can transition from a compact, retracted form to a significantly expanded state by deforming both the base and the FSMFs, thereby reconfiguring the grasping space to accommodate objects of various sizes, shapes, and orientations for diverse tasks. Theoretical modeling and experimental validation were conducted to analyze the mechanical performance of the FSMF under different extension ratios. Various grasping tests results demonstrate that the proposed RCRGs design efficiently and stably handles various objects, with a grasping envelope variation of up to 64 times, showcasing high adaptability and flexibility.
Study on the dual-segment arctangent model of magnetorheological damper (MRD)
Authors: Zhang Yanjuan , Luo Tianzhou , Guo Jiaxuan , Li Xin 
Published: November 2024

Abstract
Magnetorheological fluid damper (MRD) is a damping device that utilizes the instantaneous variable characteristics of magnetorheological fluid in magnetic field to control damping force. At present, there are some problems with the existing mechanical parametric models of MRD, such as the complexity of parameters in the Bouc–Wen model, low model accuracy, and the inability of the Bingham model to accurately capture the hysteresis characteristics of damping force with velocity variation. In response to these issues, on the base of MRD performance testing, a dual-segment arctangent model based on arctangent function is proposed. This model uses the arctangent function to describe the nonlinearity of the damping force output curve. And using piecewise fitting method to accurately capture the hysteresis characteristics of the output curve. Compared with existing parametric models such as the Bingham model, Sigmoid model, Bouc–wen improved model, et al, the dual-segment arctangent model shows simpler parameters and higher model fitting accuracy. The comparative analysis of the fitting results based on experimental data confirms the effectiveness and accuracy of the proposed model, providing-valuable reference for the development of high-precision MRD mechanical models.
Local structure analysis of 300 keV He+ irradiated REBCO coated conductor using polarisation dependent Cu K edge EXAFS
Authors: Lewis Jarrod C , Adams Kirk , Iliffe William , Aramini Matteo , Grovenor Chris R M , Nicholls Rebecca J , Diaz-Moreno Sofia , Speller Susannah C 
Published: November 2024

Abstract
Understanding irradiation damage of REBCO is increasingly of interest for compact tokamak fusion reactor designs, as these materials are critical for the proposed magnetic plasma confinement systems. Here commercially sourced samples of REBCO coated conductor are irradiated with 300 keV He+ ions to a damage level of 169 × 10−3 displacements-per-atom, to the point where superconductivity is no longer detectable, meaning these samples correspond to a non-functional end-of-life component in a fusion reactor context. Subsequent analysis of the crystal structure through a combination of x-ray diffraction and x-ray absorption spectroscopy measurements reveals a complex variation away from the as-grown structure. The local structure probed by the spectroscopy measurements is further observed to change as a function of the relative polarisation of the incident x-ray beam, indicating that within this damage regime the structural anisotropy of the REBCO unit cell plays a determining role in where defects accumulate within the material. Here the local structure measurements probing the a-b plane of the system vary significantly less than those probing the c-axis direction following irradiation, mirroring the observed trend in the x-ray diffraction data that the a:b ratio is preserved upon irradiation whilst the absolute values increase, whereas the c-axis parameter expands. These observations highlight the role of oxygen defect formation in driving the degradation of superconductivity within irradiated REBCO. These changes are observed to preferentially accumulate along the c-axis of the material, indicating a possible mechanistic signature of the degradation of the superconducting properties within these systems that are evident using a local structure probe such as extended x-ray absorption fine structure.
Tensor quantum programming
Authors: Termanova A , Melnikov Ar , Mamenchikov E , Belokonev N , Dolgov S , Berezutskii A , Ellerbrock R , Mansell C , Perelshtein M R 
Published: November 2024

Abstract
Running quantum algorithms often involves implementing complex quantum circuits with such a large number of multi-qubit gates that the challenge of tackling practical applications appears daunting. In this article, we propose a novel approach called Tensor Quantum Programming, which leverages tensor networks (TNs) for hybrid quantum computing. Our key insight is that the primary challenge of algorithms based on TNs lies in their high ranks (bond dimensions). Quantum computing offers a potential solution to this challenge, as an ideal quantum computer can represent tensors with arbitrarily high ranks in contrast to classical counterparts, which indicates the way towards quantum advantage. While tensor-based vector-encoding and state-readout are known procedures, the matrix-encoding required for performing matrix-vector multiplications directly on quantum devices is much less studied. We introduce an algorithm that encodes matrix product operators into quantum circuits with a depth that scales linearly with the number of qubits. We demonstrate the algorithm’s performance for matrices commonly encountered in differential equations, optimization problems, and quantum chemistry, for systems involving up to 50 qubits.
Characterization and effectiveness analysis of ternary organic eutectic mixture/hybrid-nanoparticles based phase change material for cold storage applications
Authors: Jain Nilesh Kumar , Paliwal Deepak , Jain Pankaj 
Published: November 2024

Abstract
A novel organic ternary mixture composed of Lauric acid, Myristic acid, and Dodecanol (referred to as LMD) has been synthesized as a Phase Change Material (PCM), specifically designed for cold storage applications. Although organic PCMs are highly effective, their low thermal conductivity frequently restricts their heat transfer performance. To address this issue, a hybrid Nano-enhanced PCM (HNe-PCMs) has been developed in this study. This involved dispersing two types of nanoparticles, Graphene nanoparticles (GNP)-Al2O3 and GNP-CuO, each at a 1% weight fraction, within the LMD matrix. The thermal and chemical characteristics of developed pure LMD and HNe-PCMs were studied using Electron microscope scanning (SEM), Fourier transform infrared spectrophotometer (FTIR), x-ray diffractometer (XRD), Thermal conductivity analyser (TC), Differential scanning calorimeter (DSC), and Thermo gravimetric analyser (TGA) and explored their potential in cold storage application. Chemical and thermal characterization revealed that the freezing and melting temperatures of LMD are 8.4 ± 0.1 °C and 15.6 ± 0.1 °C, respectively, with corresponding latent heats (LH) of freezing/melting of 125.4 ± 1.8 J g−1 and 131.5 ± 1.8 J g−1. Upon incorporation of the nanoparticles, the thermal conductivities of LMD/GNP-Al2O3 and LMD/GNP-CuO were notably enhanced by 57.4% and 49.8%, respectively, compared to pure LMD. However, a slight deviation in the melting/freezing LH of 2.9% and 1.9%, and in phase change temperature of 12.1% and 7.1%, respectively, was observed for the LMD/GNP-Al2O3 HNe-PCMs compared to the pure LMD. Through effectiveness analysis, it was depicted that the LMD/GNP combined with Al2O3 exhibits a reduction of 13.94% and 12.4% in charging-discharge time compared to pure LMD. In contrast, the LMD/GNP with CuO showed a 12.73% and 10.87% reduction in these times respectively. Overall, LMD/GNP-Al2O3 emerges as a promising material for passive cold storage applications, improving thermal conductivity while preserving similar phase change characteristics to pure LMD performance.
Accurate density measurement of a small solid sample using a combination of hydrostatic weighing and pressure-of-flotation methods
Authors: Nishihashi Kanako , Waseda Atsushi , Kondo Yohan , Bitou Youichi , Kuramoto Naoki 
Published: November 2024

Abstract
A new high-precision density measurement method for small non-silicon samples was developed. Hydrostatic weighing (HW) and the pressure-of-flotation method (PFM) are high-precision techniques for determination solid sample densities. However, in HW, the uncertainty of the density measurement is quite large when the volume of the sample is much smaller than that of a 1 kg silicon sample owing to the uncertainty of the auxiliary weights during the measurement. Additionally, the PFM is not applicable for measuring the density of non-silicon materials. We developed a new method that combines HW and the PFM. We prepared a small 45 g Si3N4 sphere sample with a diameter of 30 mm. By employing two silicon discs with these masses that were nearly equivalent to the apparent masses of the sample, the same auxiliary weights were used for both the standards and the sample. The new method was validated by measuring the density of the 45 g Si3N4 sphere with a relative standard uncertainty of 4.6 × 10−6, which is significantly lower than that obtained by traditional HW methods. This approach demonstrates substantial improvements in the precision of small-sample density measurements, thus offering a robust alternative to conventional techniques.
Errors in single pixel laser imaging emerging from spatial size limits in the bucket detector
Authors: Scheidt Dennis , Quinto-Su Pedro A 
Published: November 2024

Abstract
In single pixel imaging (SPI) an image is sampled with an orthogonal basis projected by a programmable optical element like a digital micromirror array or a spatial light modulator. The reflected or diffracted light is collected by a lens and measured with a photodiode or bucket detector. In this work we demonstrate that SPI of laser light fields can exhibit cross talk with its phase when the bucket detector is not sufficiently large (i.e. fast photodiodes) and does not integrate the full spectrum of each basis element. In that regime, structured phases can emerge from errors in the optical or light collection system (i.e. misaligned optical elements or aberrations) and can appear in the reconstructed amplitude image. We show this behavior in simulations and experiments where the reconstructed amplitude image will exhibit phase cross-talk if the detector size is smaller than between 50% and 75% of the maximum area spanned by the projected spectrum of the measurement basis. This is important because photodiodes with a fast response time have a small effective area.
Flat bands without twists through the use of a one harmonic Moiré systems: topological nature of modes and electron–electron pairing in periodic uniaxial strained or crenellated graphene nanoribbons
Authors: Andrade Elias , López-Urías Florentino , Naumis Gerardo G 
Published: November 2024

Abstract
Flat bands are known to produce highly correlated phases, leading to superconductivity and charge fractionalization. In two-dimensional systems, they have been extensively studied in magic-angle twisted bilayer graphene (MATBG). However, from both theoretical and experimental perspectives, MATBG remains a challenging system. Here, we present an alternative untwisted Moiré system to avoid such difficult twists. This system reproduces many of the interesting physical effects observed in Moiré systems, particularly flat bands and electron–electron pairing via a repulsive mechanism. The system consists of a graphene nanoribbon with periodic strain or corrugation, induced by a suitable substrate, such as a crenellated h-BN substrate. The strain is periodic with a single harmonic, but the key is to apply a spatial frequency slightly detuned from the condition that changes sign between neighboring sites. This produces a deformation that changes sign between neighbors in graphene while introducing a long-wavelength deformation visible only on each of the graphene’s bipartite lattices. This induces a spatially dependent effective mass, obtained using one harmonic. The system maps onto a Jackiw–Rebbi model, with flat-band modes identified as topological soliton modes. Electron–electron interactions are included in the system using the Hubbard Hamiltonian. The main result is the emergence of an effective attraction between electrons, accompanied by spin polarization coupled to the electron pseudospin. These observations align with the Kohn–Luttinger theory of superconductivity in other bipartite lattices, such as high-Tc cuprates. Since the Jackiw–Rebbi model exhibits charge fractionalization, similar phases are also expected to appear, as observed in MATBG.
Exploring the fresh and rheology properties of 3D printed concrete with fiber reinforced composites (3DP-FRC): a novel approach using machine learning techniques
Authors: Rasel Risul Islam , Hossain Md Minaz , Zubayer Md Hasib , Zhang Chaoqun 
Published: November 2024

Abstract
This study focuses on the prediction models for four parameters related to the fresh and rheological properties of 3DP-FRC: spreading diameters (SPD), dynamic yield stress (DYs), static yield stress (SYs) and plastic viscosity (PV), respectively. Five machine learning (ML) algorithms were employed, namely artificial neural network (ANN), random forest (RF), decision tree (DT), categorical boosting (CatBoost), and extreme gradient boosting (XGBoost). An extensive dataset was compiled including 373 (SPD) and 219 (SYs, DYs, PV) from various literature comprising experimental results. Fifteen input parameters were identified as the most influential factors affecting the fresh and rheological properties. These parameters include OPC, W/B, W/S, FA, LP, SF, SP, VMA, W, h
f
, R
i
, AR, t
sf
, F
t
, and S
time
/R
time
. This study found strong correlations between the developed ML models and the experimental outcomes from both the training and testing datasets. The models demonstrated exceptional accuracy and provided precise predictions for SPD, SYs, DYs, and PV. The correlation coefficients (R2) ranged from 0.94 to 0.99 for SPD, 0.93 to 0.99 for SYs, 0.98 to 0.99 for DYs, and 0.98 to 1.00 for PV, with consistent results observed across both the training and testing datasets. Moreover, the model’s precision was assessed using different error metrics, including root mean square error (RMSE), mean square error (MSE), coefficient of variation in root-mean-square error (CVRMSE), and mean absolute error (MAE). Sensitivity analysis was performed to identify their impact. Additionally, fiber dependent analysis was conducted to assess the effectiveness of different fiber types on the fresh and rheological properties (SPD, SYs, DYs, and PV). In conclusion, the ML models were effectively trained and optimized, resulting in accurate and highly predictive capabilities for the parameters of interest.
Assessing the validity of a wearable shoulder motion tracking system through comparison with dartfish in patients undergoing shoulder joint replacement surgery
Authors: MilaniZadeh Sohrob , MacDermid Joy C , Langohr G Daniel , Johnson James 
Published: November 2024

Abstract
Objective assessments of shoulder motion are paramount for effective rehabilitation and evaluation of surgical outcomes. Inertial Measurement Units (IMU) have demonstrated promise in providing unbiased movement data. This study is dedicated to evaluating the concurrent construct validity and accuracy of a wearable IMU-based sensor system, called ‘Motion Shirt’, for the assessment of humero-thoracic motion arcs in patients awaiting shoulder replacement surgery. This evaluation was conducted by comparing Motion Shirt data with the Dartfish Motion Analyzer software during the Functional Impairment Test-Hand and Neck/Shoulder/Arm (FIT-HaNSA) test. Thirteen patients (age > 50), who were awaiting shoulder replacement surgery, were recruited. The Motion Shirt was employed to measure angular humero-thoracic movements in two planes during the FIT-HaNSA test. Simultaneously, two cameras recorded the participants’ movements to provide reference data. Bland-Altman plots were generated to visualize agreement between the Motion Shirt and the reference data obtained from the Dartfish Motion Analyzer software. The data analysis on Bland-Altman plots revealed a substantial level of agreement between the Motion Shirt and Dartfish analysis in measuring humero-thoracic motion. In Task-1, no significant systematic errors were exhibited, with only 3.27% and 2.18% of points exceeding the limits of agreement (LOA) in both elevation and the Plane of Elevation (POE), signifying a high level of concordance. In Task-2, a high level of agreement was also observed in Elevation, with only 3.8% of points exceeding the LOA. However, 5.98% of points exceeded LOA in POE for Task-2. In Task-3, focused on sustained overhead activity, the Motion Shirt showed strong agreement with Dartfish in Elevation (2.44% points exceeded LOA), but in POE, 7.32% points exceeded LOA. The Motion Shirt demonstrated a robust concordance with Dartfish Motion Analyzer system in assessing humerothoracic motion during the FIT-HaNSA test. These results affirm the Motion Shirt’s suitability for objective motion analysis in patients awaiting shoulder replacement surgery.
Quantum phase transition detection via quantum support vector machine
Authors: Wang Youle , Cao Linyun 
Published: November 2024

Abstract
Unveiling quantum phase transitions (QPTs) is important for characterising physical systems at low temperatures. However, the detection of these transitions is encumbered by significant challenges, especially in the face of the exponential growth in ground state complexity with system scale. The emergence of quantum machine learning has lately gained traction as a promising method for elucidating the properties of many-body systems, providing a different avenue to study QPT. In this paper, we propose a novel and efficient quantum algorithm for identifying QPT synthesising quantum feature with quantum machine learning. Our approach is anchored in the utilisation of quantum computers to directly encode the kernel matrix into Hilbert spaces, realised by the parallel implementation of the quantum feature map. Specifically, we generate a quantum state encoding the information of ground states of the given quantum systems by employing the parallel quantum feature map. The resultant state preparation circuit is then used to implement a block-encoding of the kernel matrix. Equipped with the associated labels and this encoding, we devise a new quantum support vector machine (QSVM) algorithm, forming the main ingredient of the classifier. The presented method refines the efficiency of the prevailing QSVM algorithm for processing quantum and classical data. We demonstrate the effectiveness of our quantum classifier in predicting QPT within the transverse-field Ising model. The findings affirm the efficacy of quantum machine learning in recognising QPT in many-body systems and offer insights into the design of quantum machine learning algorithms.
A review of phononic-crystal-based energy harvesters
Authors: Bahrami Ali , Motaei Farzaneh 
Published: November 2024

Abstract
Energy harvesting is a technique in which ambient energy is converted to useful energy to prevent wastefulness. Piezoelectric energy harvesting is described as conversion of mechanical energy into electrical energy. Vibrations, sounds, ocean motions, human activities, etc. are various nature sources of mechanical waves. Energy harvesting is used to directly power electronics or recharge a battery. Efficiency is the important parameter of an energy harvester. To achieve high electrical energy harvesting from mechanical waves, phononic crystals have been utilized. Phononic crystals are a type of metamaterial that can manipulate the propagation of mechanical waves. Researchers have proposed different techniques to concentrate acoustic waves in phononic crystals. In this paper, a comprehensive review of phononic-crystal-based energy-harvesting techniques has been conducted. An energy-harvesting capability comparison between proposed phononic-crystal-based energy harvesters is also included.
Joining of thin aluminum sheets by micro-friction stir welding (μFSW) using a pinless tool
Authors: Sen Meghnath , Puri Asit Baran 
Published: November 2024

Abstract
In the present research, butt welding of 0.8 mm thick aluminum 6061–T6 sheets has been executed using a pinless tool with variable tool rotational speed (1000-2500 rpm), welding speed (100–250 mm min−1), and a constant plunging depth (0.1 mm). Tensile and microhardness tests have been conducted to evaluate the joint’s mechanical strength. The joint developed at 2500 rpm, and 100 mm min−1 shows a maximum strength of 277.65 MPa (88.4% of original strength), indicating that higher tool rotational speed and slower welding speed enhance joint formation through adequate heat generation and material intermixing. Small amount of defects like flash, surface galling, and irregular shoulder marks have been found in the weld region.
Forecasting high-dimensional spatio-temporal systems from sparse measurements
Authors: Song Jialin , Song Zezheng , Ren Pu , Benjamin Erichson N , Mahoney Michael W , Li Xiaoye S 
Published: November 2024

Abstract
This paper introduces a new neural network architecture designed to forecast high-dimensional spatio-temporal data using only sparse measurements. The architecture uses a two-stage end-to-end framework that combines neural ordinary differential equations (NODEs) with vision transformers. Initially, our approach models the underlying dynamics of complex systems within a low-dimensional space; and then it reconstructs the corresponding high-dimensional spatial fields. Many traditional methods involve decoding high-dimensional spatial fields before modeling the dynamics, while some other methods use an encoder to transition from high-dimensional observations to a latent space for dynamic modeling. In contrast, our approach directly uses sparse measurements to model the dynamics, bypassing the need for an encoder. This direct approach simplifies the modeling process, reduces computational complexity, and enhances the efficiency and scalability of the method for large datasets. We demonstrate the effectiveness of our framework through applications to various spatio-temporal systems, including fluid flows and global weather patterns. Although sparse measurements have limitations, our experiments reveal that they are sufficient to forecast system dynamics accurately over long time horizons. Our results also indicate that the performance of our proposed method remains robust across different sensor placement strategies, with further improvements as the number of sensors increases. This robustness underscores the flexibility of our architecture, particularly in real-world scenarios where sensor data is often sparse and unevenly distributed.