bluuebunny/crossref_metadata_2025_split huggingface dataset:
High temperature superconducting closed-loop coils: constructures, key technologies, applications, and future suggestions
Authors: Zhong Zhuoyan , Wu Wei 
Published: February 2025

Abstract
The high-temperature superconducting (HTS) closed-loop coil, characterised by shorted coil terminals and the low resistance of HTS conductors, can sustain a persistent DC current with minimal decay. These coils enable the generation of a DC magnetic field without the need for current leads or a power supply during operations, offering several advantages: (i) the development of compact, lightweight and portable DC magnet systems; (ii) the elimination of heat leakage and ohmic losses associated with current leads; and (iii) the removal of magnetic field harmonics caused by current supply. Recent advancements have revealed promising applications for HTS closed-loop coils, including maglev trains, nuclear magnetic resonance, scientific instruments, and energy storage systems. This paper firstly reviews various HTS closed-loop coils constructions, focusing on their distinctive characteristics. Then, the key research aspects of HTS closed-loop coils are overviewed, highlighting the latest advancements in persistent-current joint technologies, excitation methods, current control methods, current decay mechanisms and suppression techniques, simulation models, and quench detection and protection methods. Next, the applications of HTS closed-loop coils are analysed, emphasising their current status and future challenges. A detailed account is provided of our group’s progress in developing an electrodynamic suspension train in Changchun, China, where all onboard magnets exclusively utilise HTS closed-loop coils. Finally, suggestions for future research directions are proposed.
Magnetized ICF implosions: non-axial magnetic field topologies
Authors: Walsh C.A. , Strozzi D.J. , Povilus A. , O’Neill S.T. , Leal L. , Pollock B. , Sio H. , Hammel B. , Djordjević B.Z. , Chittenden J.P. , Moody J.D. 
Published: February 2025

Abstract
This paper explores four different magnetic field topologies for application to spherical inertial confinement fusion implosions: axial, mirror, cusp and closed field lines. A mirror field is found to enhance the impact of magnetization over an axial field; this is because the mirror field more closely follows the hot-spot surface. A cusp field, while simple to generate, is not found to have any benefits over the tried-and-tested axial field. Closed field lines are found to be of the greatest benefit to hot-spot performance, with the simulated design undergoing a 2× increase in ion temperature before α-heating is considered. The plasma properties of the simulation with closed field lines are radically different from the unmagnetized counterpart, with electron temperatures in excess of 100 keV, suggesting that a fundamental redesign of the capsule implosion is possible if this method is pursued.
A Combined Segmentation and Classification Pipeline for Breast Tumors Analysis on Ultrasound Image
Authors: Nguyen Cong Thanh , Huynh Quang Linh 
Published: February 2025

Abstract
Breast cancer is a life-threatening disease characterized by the abnormal and uncontrollable growth of breast cells, leading to tumor development. Ultrasound is currently an essential non-invasive imaging technique for evaluating the features of breast tumors. During the diagnostic process, radiologists perform tumor segmentation and classification into benign or malignant categories. This manual process presents challenges due to the need for high accuracy to ensure effective diagnosis. Therefore, an automated approach is necessary to enhance precise tumor segmentation and classification as a technical diagnostic tool. This study developed a tool that integrates automated segmentation and classification of breast tumor ultrasound images using deep learning models. Firstly, the tumor segmentation process was implemented with a Deep Residual UNET model to identify the suspect region on breast ultrasound images. The original breast ultrasound image was then combined with the identified tumor area from the segmentation process to increase the information available during the classification process. The VGG16 model was ultimately employed to classify breast tumors as either benign or malignant. These two deep learning models were trained on a public breast ultrasound dataset comprising 437 benign and 210 malignant tumors. Model validation was conducted using 5-fold cross-validation. The segmentation-alone model achieved an accuracy of 98.93% ± 0.40% and a Dice coefficient of 89.57% ± 2.16%. The classification model and the combined model achieved mean accuracies of 98.3% and 78%, respectively, and weighted F1-scores of 98.30% and 78.27%, respectively. This work presents a combined breast tumor segmentation and classification tool with considerable performance. Nevertheless, additional efforts are required to enhance the performance of the combined model.
Positive and Negative Emotions Recognition using Multichannel EEG Analysis with Spectral Entropy and Machine Learning approaches
Authors: Gia Bao Tran Trong , Vu Hao 
Published: February 2025

Abstract
Emotion recognition is a critical component of human-computer interaction, facilitating more natural and effective communication between users and machines. This study explores the recognition of positive and negative emotions using a multichannel Electroencephalogram (EEG) analysis approach, incorporating spectral entropy and machine learning techniques. Spectral entropy, which quantifies the complexity and irregularity of EEG signals, is employed to extract relevant features from the raw EEG data. These features are then fed into various machine learning models to classify emotions accurately. The research leverages a comprehensive dataset of EEG recordings from participants exposed to emotion-inducing stimuli, ensuring a robust analysis of both positive and negative emotional states. Preprocessing steps, including noise reduction and artifact removal, are meticulously applied to ensure the quality and reliability of the EEG signals. Spectral entropy features are extracted from multiple frequency bands, capturing the nuanced variations in brain activity associated with different emotional states. Several machine learning algorithms, including Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbor (kNN), Decision Tree (DT), and Neural Network (NN), are evaluated for their performance in classifying the emotions. The study also examines the impact of feature selection and dimensionality reduction techniques on the accuracy and efficiency of the classifiers. Cross-validation and other statistical methods are employed to validate the models and prevent overfitting. The preliminary results from the integration of spectral entropy features with advanced machine learning approaches demonstrate an accuracy of 73.7% and f1-scores of approximately 0.5974 to 0.7500 when differentiating between negative, positive and neutral emotional states. The findings suggest that multichannel EEG analysis, when combined with sophisticated feature extraction and classification techniques, holds great promise for developing more intuitive and responsive human-computer interfaces. Future research directions include the application of deep learning models to further improve the robustness and scalability of emotion recognition systems.
Effect of rib shapes on droplet generation in a T-junction microchannel
Authors: Duong Quoc-Bao , Nguyen Song-Thanh-Thao 
Published: February 2025

Abstract
Microfluidic technology is attracting the attention from researchers and is currently applied in many fields such as biology, pharmaceuticals and chemistry. This technology is considered to have many advantages over traditional methods, including time saving, sample volume reduction and cost effectiveness. The goal of this study was to investigate the effects of rib shapes on droplet generation process in a T-junction microchannel. By using a three-dimensional (3D) laminar model with the Volume Of Fluid (VOF) based on ANSYS FLUENT software, numerical models of T-junction with rib was proposed to predict fluid phenomena at the microscale. This study showed that the droplet size could be affected by the flow rate of continuous phase and flow rate of dispersed phase. In addition, the rib shapes in T-junction significantly impacted the droplet size, which helps to optimize the droplet generation. These small ribs of different shapes can be manufactured by using photolithography techniques. These results will therefore contribute significantly to the fabrication of high-precision microfluidic devices. The ability to control the droplet size in a T-junction through rib shapes not only improves the efficiency and reliability of microfluidic systems but also extends the potential for new significant applications in various scientific fields. In conclusion, this research not only highlights the advantages of this technology but also lays the foundation for further exploration of the optimization and performance enhancement of microfluidic devices. The insights gained from this study into the relationship between rib shapes and droplet generation were important contributions to the design and manufacturing of next-generation microfluidic devices.
First-principles studies on the work function changes of ZnO(

2

1
¯

1
¯

0

) surfaces induced by NO and NO2 adsorption for gas sensors
Authors: Yu Cheng-Lung , Chen Guan-Yu , Ching Congo Tak Shing , Liu Po-Liang 
Published: February 2025

Abstract
This study employs first-principles calculations based on density functional theory to investigate the work function changes induced by the adsorption of nitric oxide and nitrogen dioxide on clean ZnO(

2

1
¯

1
¯

0

) surfaces and Ag-decorated ZnO(

2

1
¯

1
¯

0

) surfaces. The results indicate that the maximum work function change for NO adsorption on clean ZnO(

2

1
¯

1
¯

0

) surfaces is −0.78 eV, while the maximum work function change for NO2 adsorption on Ag-decorated ZnO(

2

1
¯

1
¯

0

) surfaces is – 1.74 eV. On the Ag atom initially adsorbed on the Zn site of the ZnO(

2

1
¯

1
¯

0

) surface denoted as the Ag-ZnO-Zn surface, the work function change induced by NO2 is approximately 0.89 eV more negative than that induced by NO. On the Ag atom initially adsorbed on the O site of the ZnO(

2

1
¯

1
¯

0

) surface denoted as the Ag-ZnO-O surface, the work function change induced by NO2 is approximately 0.87 eV more negative than that induced by NO. In contrast, NO adsorption on Ag-decorated ZnO(

2

1
¯

1
¯

0

) surfaces leads to notable work function changes (−0.74 eV and −0.87 eV), especially on Ag-ZnO-O surfaces, showing higher sensitivity. The largest work function changes are observed for NO2 adsorption on Ag-decorated ZnO surfaces (−1.63 eV and −1.74 eV), particularly on Ag-ZnO-O surfaces, indicating extremely high sensitivity. These findings suggest that Ag-decorated ZnO surfaces exhibit the highest sensitivity to NO2, especially on Ag-ZnO-O surfaces, making them the optimal choice for designing high-efficiency NO2 gas sensors. Furthermore, Ag-decorated ZnO materials also show high potential in sensing NO. These results provide important references for designing high-performance gas sensors, particularly in the context of environmental monitoring and health protection. Further research and experiments can validate these theoretical results and lead to the development of practical sensor devices.
Coupled Finite Element Analysis and Multivariate Adaptive Regression Splines for predicting the bearing capacity of conical footings on slopes
Authors: Pham Gia Huy , Duong Nhat Tan , Lai Van Qui 
Published: February 2025

Abstract
Exploring the behavior of conical footings is a significant aspect of geotechnical engineering, particularly in supporting wind turbine towers on mountain slopes. This study employs Finite Element Analysis (FEA) within Plaxis 3D to investigate the behavior of conical footings. The Mohr-Coulomb material is assumed, and the research focuses on two pivotal parameters of soil shear strength: c (cohesion) and φ (friction angle). The study aims to assess the impacts of geometry parameters, consisting of the conical angle (α), the setback ratio (b/B), and the slope angle (β), on the failure mechanisms of conical footing. Furthermore, Multivariate Adaptive Regression Splines (MARS) models are implemented with the FEA dataset to propose a predictive formula of the UBC (ultimate bearing capacity) factor, indicating the relationship between these five input parameters and the outcome UBC. A sensitivity analysis was also examined, revealing the contribution of each variable to the UBC of conical footings on slopes.
A Study on the Kalman Filter Based PID Controller for Mecanum-wheeled Mobile Robot
Authors: Le Thanh-Long , Khang Nghiem Gia , Thien Vuong Dinh 
Published: February 2025

Abstract
Recently, the mecanum-wheeled mobile robot (MWMR) has been widely adopted in many industries because of its enhanced mobility and the ability of the mecanum wheel (sometimes called the Swedish wheel). However, this implementation will increase the complexity of the robot, resulting in the phenomena of slip or vibration. On the other hand, the Kalman filter has remained a cornerstone in system state estimation and is an effective tool in the noise-reducing process. Therefore, this study aims to design a PID controller, which is used popularly in the industrial landscape, with the integration of the Kalman filter to reduce the noise within the operation of the robot system. This approach has rarely been explored in academic literature, to address this gap, this research will build a mechanical model and implement it into the Matlab/Simulink environment for simulation. At its core, the study aims to harness the predictive capabilities of the Kalman filter, integrating it with the PID controller to minimize MWMR errors during operation. Through this integrated methodology, the paper has proved the value of the Kalman filter when integrating with PID by comparing the results with the non-Kalman filter cases.
Size-dependent thermomechanical vibration of FGP microbeams using a higher-order shear deformable beam element
Authors: Dang Ngoc Duyen , Le Ngoc Anh T. , Ich Le Cong 
Published: February 2025

Abstract
The thermoelastic vibration of porous microbeams is explored via a higher-order shear deformable element. The porosities are smoothly graded in the beam thickness, while the elastic moduli are temperature-dependent. The theory of third-order shear deformation is employed to formulate a size-dependent beam element, in which the theory of couple stress theory is used to model the microstructural size effect. The frequencies are predicted for microbeams with different and conditions. The performance of the formulated element is shown through a comparison study. The impacts of the size scale parameter, coefficient of porosities as well as the boundary conditions on the frequencies are highlighted. The impacts of temperature change on the vibration of the porous microbeams are also studied and discussed.
Effects of Rotor Eccentricity on Interior Permanent Magnet Motor Performance in Electric Vehicles
Authors: Hoang Ho Truc , Chayopitak Nattapon , Pupadubsin Ruchao , Depaiwa Nattawoot , Saito Takushi 
Published: February 2025

Abstract
Interior Permanent Magnet (IPM) motors are a popular choice in electric vehicle (EV) powertrains because of their high efficiency, compact size, and controllability. Rotor eccentricity remains a significant challenge because it introduces unbalanced magnetic forces, which increase vibrations, noise, and the risk of premature motor failure. This study investigated the effects of static, dynamic, and mixed eccentricity on the performance of IPM motors and explored how numerical analysis can aid in fault detection during the design and diagnostic phases. This study aims to explore eccentricity fault conditions and their impacts on the motor torque, cogging torque, and magnetic flux density using Finite Element Analysis (FEA) with frequency spectrum analysis. The results show that three types of eccentricity being considered: static, dynamic, and mixed type, produce distinct harmonic signatures that significantly affect the motor’s performance characteristics. Specific harmonic ranges are particularly sensitive to different fault types that provide key indicators for early fault detection.
Static and free vibration analyses for Timoshenko micro-beam problems using finite element method
Authors: Nguyen Duy Thien Hoang , Nguyen Nha Thanh 
Published: February 2025

Abstract
The objective of the work is to examine the static and dynamic behavior of micro beams using a beam finite element, with an emphasis on size-dependent effects. The model employs a Timoshenko beam formulation and is based on the Modified Couple Stress Theory (MCST). The main characteristic of the new element is that it incorporates a length scale parameter into the element matrices to account for size effects. The element is simplified to the typical Timoshenko beam element when the parameter is set to zero. Static and free vibration studies are conducted under different boundary conditions to assess the accuracy of the model. The comparison confirms the reliability of the proposed model by demonstrating that the newly created element beam in this study agrees well with the reference material. Additionally, the MATLAB algorithm programmed in this paper simplifies the ability to cyclically compute the matrices for four types of beam element: Classical Euler-Bernoulli, micro Euler-Bernoulli, classical Timoshenko, and micro Timoshenko beam, by incorporating theoretical conversion parameter. As a result, the relationships between these beam types are clarified and applied flexibly in beam analysis.
HKROC: an integrated readout chip designed to facilitate the readout of a large number of photomultiplier tubes for the next generation of neutrino experiments
Authors: Conforti Di Lorenzo S. , Beauchêne A. , Bolognesi S. , Bouyjou F. , Carabadjac D. , Dulucq F. , El Berni M. , Firlej M. , Fiutowski T. , Gastaldi F. , Guilloux F. , Idzik M. , de La Taille C. , Moron J. , Nanni J. , Quilain B. , Rogly R. , Swientek K. , Thienpont D. 
Published: February 2025

Abstract
The HKROC ASIC was designed to read out Photo-Multiplier Tubes (PMTs) for next-generation neutrino experiments. These experiments are increasing in scale, with a larger number of PMTs and, consequently, a greater number of read-out channels. A highly integrated read-out system is required to facilitate construction while also meeting stringent requirements for noise, speed, and dynamic range. The HKROC ASIC was designed with 36 channels to measure the Time of Arrival (ToA) and the charge of the input signal. Working as a waveform digitizer, it measures the ToA with a resolution of 200 ps (at 1 photon-electron i.e. 1 p.e.) and the charge with a linearity of around 1% across a dynamic range of up to 1250 p.e. In this paper, we will describe the HKROC architecture, the motivation behind two successive prototypes, and the latest measurements.
The ineffectiveness of current environmental and fire policies in the world’s largest wetland
Authors: Barros-Rosa Lucas , Moura Peluso Luiza , Lemes Priscila , Johnson Mark Stephen , Dalmagro Higo José , Nunes da Cunha Cátia , Zanella de Arruda Paulo Henrique , Mateus Lúcia , Penha Jerry 
Published: February 2025

Abstract
The Upper Paraguay Basin (UPRB) covers 361 338 km2 in Brazil and can be divided into the Planalto (plateau) and the Pantanal, the largest wetland in the world. This area faces pressures from climate change, land use and cover change (LULC), and infrastructure. Therefore, this study evaluates the effectiveness of current environmental policies on native vegetation conservation, water availability, and fire dynamics in the UPRB under current legislation in the state of Mato Grosso (MT). We used LULC data from 1985–2022 to estimate changes in water, native vegetation, exotic grasslands, and areas that could be converted to exotic grasslands for cattle ranching under state Decree N°.774/2024. We analyzed water coverage trends, including riparian preservation areas (RPAs) protected by federal law N°.12651/2012. Evapotranspiration comparisons were made between regions with and without artificial drainage. We also investigated trends in burned areas and the effectiveness of fire ban decrees. Our results indicate a significant increase in exotic grasslands across the entire UPRB portion of MT. We also found a significant decrease in water cover in the Pantanal, accompanied by an increase in native grass cover. Exotic grasslands can cover an area similar to native vegetation due to the new decree (774/2024), which includes 21 000 km2 of legal reserves (sustainable use areas) now available for grazing. The Pantanal has lost more than 8000 km2 of water over the past 37 yrs, with similar decreasing trend in water cover found within RPAs. Areas with artificial drainage showed lower evapotranspiration. Fire ban decrees were ineffective in reducing burned areas to expected levels. Current policies are insufficient to protect the interconnected ecosystems across the UPRB. Immediate measures, including adaptive management, strengthened legislation, and sustainable land and fire management practices, are essential to mitigate environmental impacts in the UPRB and safeguard its ecological and economic integrity.
Advancing regenerative medicine: the Aceman system’s pioneering automation and machine learning in mesenchymal stem cell biofabrication
Authors: Zhu Kai , Ding Yi , Chen Yuqiang , Su Kechuan , Zheng Jintu , Zhang Yu , Hu Ying , Wei Jun , Wang Zenan 
Published: February 2025

Abstract
Mesenchymal stem cells (MSCs) are pivotal in advancing regenerative medicine; however, the large-scale production of MSCs for clinical applications faces significant challenges related to efficiency, cost, and quality assurance. We introduce the Automated Cell Manufacturing System (Aceman), a revolutionary solution that leverages machine learning and robotics integration to optimize MSC production. This innovative system enhances both efficiency and quality in the field of regenerative medicine. With a modular design that adheres to good manufacturing practice standards, Aceman allows for scalable adherent cell cultures. A sophisticated machine learning algorithm has been developed to streamline cell counting and confluence assessment, while the accompanying control software features customization options, robust data management, and real-time monitoring capabilities. Comparative studies reveal that Aceman achieves superior efficiency in analytical and repeatable tasks compared to traditional manual methods. The system’s continuous operation minimizes human error, offering substantial long-term benefits. Comprehensive cell biology assays, including Bulk RNA-Seq analysis and flow cytometry, support that the cells produced by Aceman function comparably to those cultivated through conventional techniques. Importantly, Aceman maintains the characteristic immunophenotype of MSCs during automated subcultures, representing a significant advancement in cell production technology. This system lays a solid foundation for future innovations in healthcare biomanufacturing, ultimately enhancing the potential of MSCs in therapeutic applications.
Resisting Reception
Authors: Goldhill Simon 
Published: February 2025

Abstract
This chapter considers the general cultural and intellectual matrix within which modern Jewish scholars have responded to classical antiquity. The social history of the discipline of Classics reveals a repeated rejection of Jewish participants in its institutions, based on an anti-Semitism that culminated in the violence of the Shoah in the mid-twentieth century. The chapter considers how Jewish academics dealt with their relation to the past of classical antiquity and offers two test cases: Pierre Vidal-Naquet (1930–2006) and Moses Finley (1912–86). The analysis reveals two competing and contrasting trajectories that demonstrate the intricate positionality of being a Jewish scholar of ancient Greek and Roman texts.