bluuebunny/crossref_metadata_2025 huggingface dataset:
Ultrasensitive probing of nonequilibrium thermodynamics at liquid–vapor interfaces using dark hollow beams
Authors: Eshaghi M. , Acevedo C. H. , Dogariu A.
Published: February 2025

Quantifying the thermodynamic properties of liquid–vapor interfaces is crucial for understanding the fundamental physics governing phase transitions and interfacial phenomena. Traditional scattering methods, although effective for remote and noninvasive characterization, face challenges in truly detecting the weak inhomogeneities of thermal origin. We introduce a novel scattering approach that takes advantage of the unique properties of dark hollow beams to not only provide unprecedented sensitivity to temperature variations at liquid–vapor interface, but also simplify the detection of such subtle fluctuations. Our results expand the conventional understanding of interface dynamics by providing experimental means to examine the complex molecular interactions during nonequilibrium thermodynamic processes such as evaporation and condensation. This characterization technique could significantly impact applications in chemical analysis, remote sensing, environmental monitoring, and industrial processes, where simple and robust sensing systems providing accurate real-time data are vital.
Prediction of discharge coefficients for contaminated circular multi-hole orifice flow meters with deep learning algorithms
Authors: Almutairi Jaber Hmoud , Pašić Mirza , Džaferović Ejub , Hasečić Amra
Published: February 2025

This study investigates the use of deep learning algorithms to predict the discharge coefficient (Cd) of contaminated multi-hole orifice flow meters with circular opening. Datasets (MHO1 and MHO2) were obtained from computational fluid dynamic simulations for two circular multi-hole orifice flow meters of different geometries. To evaluate the performance and generalization capabilities of different models, three distinct scenarios, each involving different dataset configurations and normalization techniques were designed. For each scenario, three deep learning models (feedforward neural networks, convolutional neural network, and recurrent neural network) were implemented and evaluated based on their performance metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2). For all three scenarios eight models for each neural network model were developed (FFNN – four models, CNN – two models, RNN – two models). The same structure of models was used across all scenarios to ensure consistency in the evaluation process. Key input parameters include geometrical and flow variables such as β – parameter, contamination thickness, radial distance, Reynolds number, and orifice diameters. Results demonstrate the effectiveness of deep learning in accurately predicting discharge coefficient for different contamination conditions and different geometries. This study showed that deep learning models can be used for prediction of discharge coefficients for multi-hole orifice flow meters of similar geometry, based on data obtained from one orifice flow meter for different contamination parameters.
Machine learning drives fast and accurate calibration of carbon nanotube contact parameters
Authors: Gao Chenyu , Zhang Xijun , Chu Dianming , Bai Wenjuan , Liu Mingrui , Li Yan , He Yan
Published: February 2025

The contact parameters of carbon nanotubes (CNTs) particles play a crucial role in measuring mobility, predicting structure, optimizing materials, and many other aspects. However, the complexity and severe challenges posed by the micro- and nano-scale sizes, along with the inhomogeneity of the particles, significantly impede experimental investigations, making it difficult to acquire precise contact parameters. To date, CNTs have not been found as a contact model for agglomerated particles. In this paper, a new method of contact parameter refinement is proposed for determining the contact parameters of CNTs agglomerated particles during fluidization. Initially, the angle of repose (AOR) and density of CNTs agglomerated particles are determined based on physical tests, leading to the identification of recommended ranges for six contact parameters. Subsequently, the three contact parameters with the most significant effects are screened based on Plackett-Burman. The Response Surface Methodology, Sympy, and Fully Connected Neural Network (FCNN) are then employed as prediction models for comparison against numerical simulations for validation, resulting in a reduction of the FCNN model's relative error from 14.81% to 2.09%. Finally, the optimal multi-objective parameter combination coefficients (0.45/0.45/0.121/0.76/0.74/0.13) are determined by optimizing the inversion based on the selected best FCNN model under the experimentally measured AOR of 40.3°. Numerical simulations based on this parameter and comparative experiments with an error of only 0.07° fully demonstrate the effectiveness of the proposed strategy. This study provides the possibility to accurately simulate the fluidization pattern of CNTs and further investigate their growth mechanism.
Nonlinear optical spectroscopy of open quantum systems
Authors: Sun Haoran , Harbola Upendra , Mukamel Shaul , Galperin Michael
Published: February 2025

The development of experimental techniques at the nanoscale has enabled the performance of spectroscopic measurements on single-molecule current-carrying junctions. These experiments serve as a natural intersection for the research fields of optical spectroscopy and molecular electronics. We present a pedagogical comparison between the perturbation theory expansion of standard nonlinear optical spectroscopy and the (non-self-consistent) perturbative diagrammatic formulation of the nonequilibrium Green’s functions method (which is widely used in molecular electronics), highlighting their similarities and differences. By comparing the two approaches, we argue that the optical spectroscopy of open quantum systems must be analyzed within the more general Green’s function framework.
State-to-state integral cross sections for the Cl + CHD3 (vCH = 0,1) → HCl + CD3 reactions
Authors: Liu Shu , Zhang Dong H.
Published: February 2025

To precisely study the effect of CH stretch excitation, we calculate the first seven-dimensional state-to-state integral cross sections (ICSs) for the Cl + CHD3(vCH = 0,1) → HCl + CD3 reactions using the time-dependent wave-packet approach on a highly accurate fundamental invariant neural network potential energy surface. Compared to experiments that focus solely on CD3(v2 = 0), our theoretical calculations give a more global picture. It is found that the vibrational enhancement factors of the total ICS are less than 1 only in the very low collision energy region (<0.55 kcal/mol); moreover, the CH stretch-excited reaction also produces vibrationally cold HCl. These two findings indicate that Polanyi’s rules apply to this late-barrier polyatomic reaction, not only in terms of the energy requirement in the breaking bond but also in the energy disposal in the newly formed bond. In addition, although the experimental and theoretical product HCl(v1 = 1) branching fractions for CD3(v2 = 0) are in quantitative agreement, the new quantum vibrational enhancement factor for CD3(v2 = 0) is ∼2.7 times greater than the experimental results.
Cell-based glycoengineering for production of homogeneous and specific glycoform-enriched antibodies with improved effector functions
Authors: Huang Han-Wen , Zeng Yi-Fang , Shivatare Vidya S. , Tseng Tzu-Hao , Wong Chi-Huey
Published: February 2025

Glycosylation of humanized antibody at Fc-Asn297 significantly affects the Fc-mediated killing of target cells through effector functions, especially antibody-dependent cellular cytotoxicity (ADCC), antibody-dependent cell-mediated phagocytosis (ADCP), and antibody-dependent vaccinal effect (ADVE). Previous studies showed that therapeutic immunoglobulin G (IgG) antibodies with α2,6-sialyl complex type (SCT) glycan attached to Fc-Asn297 exhibited optimal binding to the Fc receptors on effector cells associated with ADCC, ADCP, and ADVE. However, the production of antibodies with homogeneous Fc-SCT glycan requires multiple in vitro enzymatic and purification steps. In this study, we report two cell-based methods to produce Fc-GlcNAc antibody and Fc-SCT-enriched antibodies with improved effector functions. First, we expressed endoglycosidase S2 in Expi293F GnT1- cells to trim all N-glycans to Fc-GlcNAc antibody for in vitro transglycosylation to generate homogeneous antibodies with well-defined Fc glycan. Second, we engineered the glycosylation pathway of HEK293T cells through knock-out of undesired glycosyltransferases and knock-in of desired glycosyltransferases to produce Fc-SCT-enriched antibodies and evaluated their binding to Fc receptors, and we found that the Fc-SCT-enriched antibody is like or better than the homogeneous Fc-SCT antibody in binding to the Fc receptors associated with ADCC, ADCP, and ADVE.
Pipeline leak diagnosis using multisource multiscale attention entropy and enhanced least square twin support vector machine
Authors: Zhou Hongbiao , Zhang Shilin , Li Longfeng , Wang Le , Ma Congguo
Published: February 2025

To achieve rapid and precise identification of water supply pipeline leakage faults, this study introduces a diagnostic framework that integrates multisource multiscale attention entropy (MMATE), an enhanced least-squares twin support vector machine (ELSTSVM), and an adaptive boosting (AdaBoost) algorithm. The MMATE-ELSTSVM-AdaBoost architecture follows a three-stage workflow. First, an improved wavelet threshold denoising (IWTD) technique featuring a novel threshold-processing function is developed to suppress signal noise. Second, MATE values are extracted from the denoised signals, and a multidimensional feature vector is constructed by integrating MATE characteristics across multisource sensors. Third, the ELSTSVM’s objective function is enhanced through a reformulated distance metric, augmented with regularization and compactness terms to optimize model robustness and generalizability. The refined MMATE feature vectors are then fed into the ELSTSVM-AdaBoost ensemble for fault classification. Experimental evaluations demonstrate that the proposed framework significantly outperforms conventional single-feature approaches, standalone models, and state-of-the-art SVM variants, achieving a peak classification accuracy of 99.10% in leakage detection tasks.
High temperature superconducting closed-loop coils: constructures, key technologies, applications, and future suggestions
Authors: Zhong Zhuoyan , Wu Wei
Published: February 2025

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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.
The Odyssey, Otherwise
Authors: Shahar Galili
Published: February 2025

This chapter considers how authors such as Franz Kafka, Theodor Adorno, Max Horkheimer, Erich Auerbach, and Emmanuel Levinas recalled Homer’s Odyssey as a tale of ‘homecoming’, with which they juxtaposed Jewish conceptions of migration and exile, while referring to the biblical tradition and Midrash. The chapter focuses on the Sirens episode, the Homeric myth central to Odysseus's journey. It examines its modern Jewish receptions in literature and philosophy within the wider context of world literature. By exploring how these Jewish authors engaged with Homer, the chapter demonstrates how the Sirens episode became crucial for articulating the modern condition of being Jewish.
From Enemy to Ruins
Authors: Loi Giacomo
Published: February 2025

This chapter shows how archaeology occupies a central role in the relationship between the Israeli Jewish public and the classical past. Graeco-Roman ruins are physically present, scattered throughout the country; on an ideological level, Roman ruins represent the defeat of the Romans, the destroyers of the ancient Jewish state. The chapter illustrates the political nature of this relationship through the archaeologist Yigael Yadin’s presentation of his excavations at the Bar Kochba Caves (1960–61). It is against this broader ideological background that the chapter presents two Israeli Hebrew-language poems dealing with archaeological ruins: Dan Pagis’s ‘Decline of the Empire’ (1946) and Haim Gouri’s ‘Change’ (1979).
Toward an End
Authors: Hawking Michael K , Frush Benjamin W , Yoon John D
Published: February 2025

In the Aristotelian tradition, medicine is often presented as a prototypical example of a technê. The triple character of medicine—understood as epistêmê, technê, and phronesis—must be attended to in any inquiry into the virtues which allow for its excellent practice. Edmund Pellegrino, a physician, Aristotelian, and one of the founders of modern Bioethics, reinvigorated the understanding of medicine as a practice with particular excellences, contending that the cultivation of particular “medical virtues” were necessary in order to train excellent physicians. Medical training—with its innumerable exams, taxing intellectual and emotional character, often inhumane hours, and prolonged course—through which the eventual physician must persevere (or leave medicine’s practice) illustrates the essentiality of the virtue of perseverance in anyone who is to flourish as an excellent practitioner of medicine. The chapter draws on the broader Aristotelian tradition, medicine’s own wisdom literature, and more recent literature on vocational calling and resilience to share insights about perseverance and its character and cultivation, as a contribution to the wider discourse about this essential virtue.
Harmonizing Religion and Philosophy in Al-Fārābī’s Book of Letters
Authors: Galston Miriam
Published: February 2025

Al-Fārābī’s depiction of religion as an image or imitation of philosophy suggests a fundamental harmony between the two domains. Part 2 of al-Fārābī’s Book of Letters reveals ways this harmony is more image than reality. Specifically, although the opening passages of Part 2 depict the relationship as fundamentally harmonious, subsequent passages suggest a discontinuity, or even potential antagonism, between them. Analyzing the reasons for this discontinuity suggests that the problems stem from both the religion’s primary function and the inherent limits of its mode of expression. This contribution also suggests that the potential harmony between religion and philosophy may be further undermined by al-Fārābī’s allusions to the incompleteness of metaphysics in Aristotle’s time and possibly in his own.
The Only Known Defense Against Ibn Rushd’s Critique of Ibn Sīnā’s Necessary Existent
Authors: Meyrav Yoav
Published: February 2025

Moses Halevi, an Andalusian Jewish philosopher who probably lived in the late twelfth century, wrote a detailed Metaphysical Treatise, which contains a lengthy discussion of Averroes/Ibn Rushd’s criticisms of Avicenna/Ibn Sīnā in his now lost treatise On the First Mover. Ibn Rushd’s criticisms are focused on Ibn Sīnā’s proofs for the existence of the Necessary Existent and for differentiating it from Aristotle’s first unmoved mover. This contribution presents and analyzes Moses’ responses on behalf of Ibn Sīnā to Ibn Rushd’s criticisms. This is the only known case of a philosopher defending Ibn Sīnā against Ibn Rushd’s attacks. Analysis of Moses’ discussion sheds new light on the reception of Ibn Sīnā in Andalusia and on what seems to be an early resistance to Ibn Rushd’s turn against Ibn Sīnā.
Heavenly Stuff
Authors: Toth Zita V
Published: February 2025

Take three entities of three very different kinds: Sophie the cat, the planet Venus, and the archangel Gabriel. One might be tempted to picture Sophie and Venus as material things, and Gabriel as something immaterial. Peter Auriol, an early fourteenth-century Franciscan theologian, disagrees. He thinks that while Sophie and Gabriel possess matter, Venus does not. This contribution traces what led Auriol to endorse these seemingly implausible claims: attributing matter to certain spiritual beings and denying it of certain physical ones. The contribution also examines the view that emerges from the combination of these two positions and shows that, far from being irreconcilable, they point to a particular conception of matter according to which matter is not to be treated as the physical stuff that makes something a body, nor as a mere substrate of natural change, but as a subject in pure potency that can take on multiple forms.
Beyond a fragmented account of conflicts in communication: a framework of informational conflicts
Authors: Zhao Wenqing , Jin Yan , van der Meer Toni , Hameleers Michael , Lu Xuerong
Published: February 2025

Numerous global trends related to communicative conflicts—like widespread public dissent, the increasing fragmentation of the digital media landscape, the fast-paced dissemination of mis- and disinformation, polarization of public debates, and delegitimizing populist rhetoric—form a perfect storm that significantly disrupts today’s society on multiple levels. Against this backdrop, research on content, causes, consequences, and counter-strategies of conflicts has been central to the discipline of communication science and beyond over the past decades. Although many of these societal challenges are rooted in communication problems similar in nature, current research lines appear to move forward in isolation, creating largely disconnected streams of research that could benefit from more integration. With this article, we aim to bring together disconnected strands of literature in communication that revolve around conflicts in mass and digital communication under the umbrella term “informational conflict.” This framework synthesizes existing knowledge, enabling us to better understand the root causes of increasing cleavages in society and to forward potential solutions leading to conflict resolution.
FORAlign: accelerating gap-affine DNA pairwise sequence alignment using FOR-blocks based on Four Russians approach with linear space complexity
Authors: Wei Yanming , Zhou Tong , Zhai Yixiao , Yu Liang , Zou Quan
Published: February 2025

Pairwise sequence alignment (PSA) serves as the cornerstone in computational bioinformatics, facilitating multiple sequence alignment and phylogenetic analysis. This paper introduces the FORAlign algorithm, leveraging the Four Russians algorithm with identical upper-bound time and space complexity as the Hirschberg divide-and-conquer PSA algorithm, aimed at accelerating Hirschberg PSA algorithm in parallel. Particularly notable is its capability to achieve up to 16.79 times speedup when aligning sequences with low sequence similarity, compared to the conventional Needleman-Wunsch PSA method using non-heuristic methods. Empirical evaluations underscore FORAlign’s superiority over existing wavefront alignment (WFA) series software, especially in scenarios characterized by low sequence similarity during PSA tasks. Our method is capable of directly aligning monkeypox sequences with other sequences using non-heuristic methods. The algorithm was implemented within the FORAlign library, providing functionality for PSA and foundational support for multiple sequence alignment and phylogenetic trees. The FORAlign library is freely available at https://github.com/malabz/FORAlign.
CD163 detection in immune check-point inhibitors-related acute interstitial nephritis
Authors: Perier Thomas , Renaudineau Yves , Pellegrini Juliette , Colombat Magali , Ramirez Angie Arango , Guy Pierre , Jamme Thibaut , Van Acker Nathalie , Koundé Clément , Ribes David , Huart Antoine , Casemayou Audrey , Belliere Julie
Published: February 2025

Background

Acute interstitial nephritis (AIN) is the most common renal immune-related adverse event after immune check-point inhibitors (ICI). We hypothesised that alternatively activated macrophages (CD163-M) could be involved in ICI-AIN and wished to evaluate the use of their soluble urinary form (us)CD163 as a non-invasive diagnostic marker.

Methods

CD163-M infiltrates were evaluated by both immune-histochemistry and multiplex immunofluorescence and imaging. usCD163 was detected with ELLA technology and evaluated together with urinary creatinine to be expressed as a ratio to creatinuria in ng/mmol. Clinical data were collected to perform correlations with renal function assessed by estimated glomerular filtration rate (eGFR).

Results

A retrospective cohort of 63 ICI-exposed patients with tubular AKI profile requiring a biopsy were selected. AIN patients (n = 44) were compared to acute tubular necrosis (ATN) patients (n = 19). CD163-M staining was detectable in all ICI-AIN patients, which was significantly higher than in ATN patients (18.4 vs 3.6% of area, p = 0.005). CD163-M staining was restricted to the interstitial compartment. CD163-M infiltrate inversely correlated with initial eGFR (r=-0.6, p = 0.003), and was positively correlated with delta eGFR, reflecting a renal improvement outcome (r = 0.48; p = 0.02). usCD163 was well detected in urines of patients, but did not allow us to distinguish ATN from AIN patients at diagnosis. No correlation was observed, neither between usCD163 and CD163-M staining nor with renal response after three months of glucocorticoid tapering.

Conclusion

CD163-M are detected in ICI-AIN and correlate both with severity at diagnosis and better prognosis at three months. CD163-M may help us to distinguish AIN from ATN but, it does not allow us to assess ICI imputability. Although detected in urine, usCD163 is clearly not a surrogate biomarker for AIN diagnosis.
The role of antiarrhythmic drugs and stellate ganglion block in the acute management of electrical storm
Authors: Dusi Veronica , Angelini Filippo , Gravinese Carol , Frea Simone , De Ferrari Gaetano Maria
Published: February 2025

Electrical storm (ES) is a life-threatening condition characterized by at least three separate episodes of ventricular arrhythmia (VAs) over 24 h, each one requiring intervention. Early recognition and prompt treatment are crucial to improving outcomes. In addition to identifying and correcting potential reversible causes, performing acute cardiac life support if required, and interrogating/reprogramming the implantable cardioverter defibrillator in present, the acute management of ES (within 12–24 h upon presentation) nowadays mostly relies on antiarrhythmic drugs and percutaneous left ganglion sympathetic block (PLSGB), that will be the focus of the present review. The choice of the drug should consider several factors, including the aetiology and mechanism of VAs, the underlying cardiac function, and the potential risk of adverse events. Intravenous amiodarone, the most used and recommended drug in the setting of high burden VAs and structural heart disorders, mostly exerts dose and rate infusion dependent antiadrenergic effects in the first hours, and may lead to severe hypotension. PLSGB has an excellent safety-efficacy profile and can be easily performed by trained cardiologists at bedside.