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In the context of the description of how the vortical motion, produced in peripheral heavy-ion collisions, is transferred to the spin of hadrons, we compute the $\sigma$-meson propagator at finite temperature and baryon density. This propagator encodes the properties of a medium consisting mainly of nucleons{, and can be used to model the main interactions between hadrons} in the corona region of the reaction. We compute the one-loop $\sigma$ self-energy in an approximation that accounts for the large nucleon mass. From the real part of the self-energy, we find the dispersion relation and show that the $\sigma$-mass receives a non-negligible thermal and baryon chemical dependent {contribution}. From the imaginary part, we also compute the spectral density, which we show to contain a piece coming from the branch cut associated with Landau damping. We also present approximations for the dispersion relation and the residue at the pole in the small- and large-momentum regimes and complement the calculation, providing the sum rules satisfied by the propagator. This study aims to determine one of the elements needed to compute how the vortical motion in the corona region of the reaction is transferred to the spin of $\Lambda$ hyperons that can interact with nucleons by $\sigma$-meson exchange., Comment: 9 pages, 5 figues

The Bondi-Hoyle-Lyttleton (BHL) accretion scheme applied to binary systems has long struggled to produce reliable mass accretion efficiencies when the stellar wind velocity of the donor star is smaller than the orbital velocity of the accretor. This limitation is significant in symbiotic systems where such conditions exist. Recently, our group introduced a geometric correction to the standard implementation of the BHL model that demonstrates improved agreement with numerical simulations. The present work investigates the impact of this new implementation on the evolution of symbiotic systems. We model systems where 0.7 and 1 M$_\odot$ white dwarfs accrete material from Solar-like stars with initial masses of 1, 2, and 3 M$_\odot$. The primary star is evolved using the MESA stellar evolution code, while the orbital dynamics of the system are calculated using REBOUND. The analysis focuses on the red giant branch and the thermally-pulsating asymptotic giant branch phases. We compare three scenarios: no accretion, standard BHL accretion, and the improved wind accretion. Our results show that the choice of accretion prescription critically influences the evolution of symbiotic systems. Simulations using the modified model did not reach the Chandrasekhar limit, suggesting that type Ia supernova progenitors require accretors originating from ultra-massive WDs. In contrast, the standard BHL model predicts WD growth to this limit in compact systems. This discrepancy suggests that population synthesis studies adopting the traditional BHL approach may yield inaccurate results. The revised model successfully reproduces the accretion properties of observed symbiotic systems and predicts transitions between different accretion regimes driven by donor mass-loss variability. These results emphasize the need for updated wind accretion models to accurately describe the evolution of symbiotic binaries., Comment: 16 pages; 14 figures; 3 Tables; Submitted to MNRAS (comments are welcome)

In everyday life, we perform tasks (e.g., cooking or cleaning) that involve a large variety of objects and goals. When confronted with an unexpected or unwanted outcome, we take corrective actions and try again until achieving the desired result. The reasoning performed to identify a cause of the observed outcome and to select an appropriate corrective action is a crucial aspect of human reasoning for successful task execution. Central to this reasoning is the assumption that a factor is responsible for producing the observed outcome. In this paper, we investigate the use of probabilistic actual causation to determine whether a factor is the cause of an observed undesired outcome. Furthermore, we show how the actual causation probabilities can be used to find alternative actions to change the outcome. We apply the probabilistic actual causation analysis to a robot pouring task. When spillage occurs, the analysis indicates whether a task parameter is the cause and how it should be changed to avoid spillage. The analysis requires a causal graph of the task and the corresponding conditional probability distributions. To fulfill these requirements, we perform a complete causal modeling procedure (i.e., task analysis, definition of variables, determination of the causal graph structure, and estimation of conditional probability distributions) using data from a realistic simulation of the robot pouring task, covering a large combinatorial space of task parameters. Based on the results, we discuss the implications of the variables' representation and how the alternative actions suggested by the actual causation analysis would compare to the alternative solutions proposed by a human observer. The practical use of the analysis of probabilistic actual causation to select alternative action parameters is demonstrated., Comment: 20 pages, 13 figures

Foundation Models (FM) have increasingly drawn the attention of researchers due to their scalability and generalization across diverse tasks. Inspired by the success of FMs and the principles that have driven advancements in Large Language Models (LLMs), we introduce MoFM as a novel Motion Foundation Model. MoFM is designed for the semantic understanding of complex human motions in both time and space. To facilitate large-scale training, MotionBook, a comprehensive human motion dictionary of discretized motions is designed and employed. MotionBook utilizes Thermal Cubes to capture spatio-temporal motion heatmaps, applying principles from discrete variational models to encode human movements into discrete units for a more efficient and scalable representation. MoFM, trained on a large corpus of motion data, provides a foundational backbone adaptable to diverse downstream tasks, supporting paradigms such as one-shot, unsupervised, and supervised tasks. This versatility makes MoFM well-suited for a wide range of motion-based applications.

Paired inspiratory-expiratory CT scans enable the quantification of gas trapping due to small airway disease and emphysema by analyzing lung tissue motion in COPD patients. Deformable image registration of these scans assesses regional lung volumetric changes. However, variations in reconstruction kernels between paired scans introduce errors in quantitative analysis. This work proposes a two-stage pipeline to harmonize reconstruction kernels and perform deformable image registration using data acquired from the COPDGene study. We use a cycle generative adversarial network (GAN) to harmonize inspiratory scans reconstructed with a hard kernel (BONE) to match expiratory scans reconstructed with a soft kernel (STANDARD). We then deformably register the expiratory scans to inspiratory scans. We validate harmonization by measuring emphysema using a publicly available segmentation algorithm before and after harmonization. Results show harmonization significantly reduces emphysema measurement inconsistencies, decreasing median emphysema scores from 10.479% to 3.039%, with a reference median score of 1.305% from the STANDARD kernel as the target. Registration accuracy is evaluated via Dice overlap between emphysema regions on inspiratory, expiratory, and deformed images. The Dice coefficient between inspiratory emphysema masks and deformably registered emphysema masks increases significantly across registration stages (p<0.001). Additionally, we demonstrate that deformable registration is robust to kernel variations., Comment: Accepted at SPIE Medical Imaging 2025, Clinical and Biomedical Imaging

The number of known planets around intermediate-mass stars (1.5 M$_{\odot}$ < M$_{\star}$ < 3.5 M$_{\odot}$) is rather low. We aim to test whether the correlation between the metallicity of the star and the presence of gas-giant planets found for MS low-mass stars still holds for intermediate-mass stars. In particular, we aim to understand whether or not the planet-metallicity relation changes as stars evolve from the pre-MS to the red giant branch. Our results confirm that pre-MS stars with transitional discs with gaps show lower metallicities than pre-MS with flat discs. We show a tendency of intermediate-mass stars in the MS to follow the gas-giant planet-metallicity correlation, although the differences in metal content between planet and non-planet hosts are rather modest and the strength of the correlation is significantly lower than for the less massive FGK MS stars. For stars in the red giant branch, we find a strong planet-metallicity correlation, compatible with that found for FGK MS stars. We discuss how the evolution of the mass in the convective zone of the star's interior might affect the measured metallicity of the star. The lack of a well-established planet-metallicity correlation in pre-MS and MS intermediate-mass stars can be explained by a scenario in which planet formation leads to an accretion of metal-poor material on the surface of the star. As intermediate-mass stars are mainly radiative the metallicity of the star does not reflect its bulk composition but the composition of the accreted material. When the star leaves the MS and develops a sizeable convective envelope, a strong-planet metallicity correlation is recovered. Thus, our results are in line with core-accretion models of planet formation and the idea that the planet-metallicity correlation reflects a bulk property of the star., Comment: accepted by Astronomy and Astrophysics

Body composition analysis provides valuable insights into aging, disease progression, and overall health conditions. Due to concerns of radiation exposure, two-dimensional (2D) single-slice computed tomography (CT) imaging has been used repeatedly for body composition analysis. However, this approach introduces significant spatial variability that can impact the accuracy and robustness of the analysis. To mitigate this issue and facilitate body composition analysis, this paper presents a novel method to generate 3D CT volumes from limited number of 2D slices using a latent diffusion model (LDM). Our approach first maps 2D slices into a latent representation space using a variational autoencoder. An LDM is then trained to capture the 3D context of a stack of these latent representations. To accurately interpolate intermediateslices and construct a full 3D volume, we utilize body part regression to determine the spatial location and distance between the acquired slices. Experiments on both in-house and public 3D abdominal CT datasets demonstrate that the proposed method significantly enhances body composition analysis compared to traditional 2D-based analysis, with a reduced error rate from 23.3% to 15.2%.

The interconnection between the human lungs and other organs, such as the liver and kidneys, is crucial for understanding the underlying risks and effects of lung diseases and improving patient care. However, most research chest CT imaging is focused solely on the lungs due to considerations of cost and radiation dose. This restricted field of view (FOV) in the acquired images poses challenges to comprehensive analysis and hinders the ability to gain insights into the impact of lung diseases on other organs. To address this, we propose SCOPE (Spatial Coverage Optimization with Prior Encoding), a novel approach to capture the inter-organ relationships from CT images and extend the FOV of chest CT images. Our approach first trains a variational autoencoder (VAE) to encode 2D axial CT slices individually, then stacks the latent representations of the VAE to form a 3D context for training a latent diffusion model. Once trained, our approach extends the FOV of CT images in the z-direction by generating new axial slices in a zero-shot manner. We evaluated our approach on the National Lung Screening Trial (NLST) dataset, and results suggest that it effectively extends the FOV to include the liver and kidneys, which are not completely covered in the original NLST data acquisition. Quantitative results on a held-out whole-body dataset demonstrate that the generated slices exhibit high fidelity with acquired data, achieving an SSIM of 0.81.

Healthcare simulations help learners develop teamwork and clinical skills in a risk-free setting, promoting reflection on real-world practices through structured debriefs. However, despite video's potential, it is hard to use, leaving a gap in providing concise, data-driven summaries for supporting effective debriefing. Addressing this, we present TeamVision, an AI-powered multimodal learning analytics (MMLA) system that captures voice presence, automated transcriptions, body rotation, and positioning data, offering educators a dashboard to guide debriefs immediately after simulations. We conducted an in-the-wild study with 56 teams (221 students) and recorded debriefs led by six teachers using TeamVision. Follow-up interviews with 15 students and five teachers explored perceptions of its usefulness, accuracy, and trustworthiness. This paper examines: i) how TeamVision was used in debriefing, ii) what educators found valuable and challenging, and iii) perceptions of its effectiveness. Results suggest TeamVision enables flexible debriefing and highlights the challenges and implications of using AI-powered systems in healthcare simulation., Comment: Accepted to CHI 2025

A new probability distribution to study lifetime data in reliability is introduced in this paper. This one is a first approach to a non-homogeneous phase-type distribution. It is built by considering one cut-point in the non-negative semi-line of a phase-type distribution. The density function is defined and the main measures associated, such as the reliability function, hazard rate, cumulative hazard rate and the characteristic function are also worked out. This new class of distributions enables to decrease the number of parameter in the estimate when inference is considered. Besides, the likelihood distribution is built to estimate the model parameters by maximum likelihood. Several applications by considering Resistive Random Access Memories compare the adjustment when phase type distributions and one cut-point phase-type distributions are considered. The developed methodology has been computationally implemented in R-cran.

Studying the typical entanglement entropy of a bipartite system when averaging over different ensembles of pure quantum states has been instrumental in different areas of physics, ranging from many-body quantum chaos to black hole evaporation. We extend such analysis to open quantum systems and mixed states, where we compute the typical mutual information in a bipartite system averaged over the ensemble of mixed Gaussian states with a fixed spectrum. Tools from random matrix theory and determinantal point processes allow us to compute arbitrary k-point correlation functions of the singular values of the corresponding complex structure in a subsystem for a given spectrum in the full system. In particular, we evaluate the average von Neumann entropy in a subsystem based on the level density and the average mutual information. Those results are given for finite system size as well as in the thermodynamic limit., Comment: 39 pages, 7 figures

MADMAX, an axion dark matter search experiment, is currently in the prototype testing phase. Its working principle is based on the conversion of axions in a magnetic field into photons. This signal is then enhanced by a booster made of dielectric disks placed in front of a mirror. In order to test MADMAX prototypes at cryogenic temperatures in a magnetic field parallel to the disks, a new G-10 glass-fiber cryostat of 0.25 m$^3$ inner volume was designed, tested and used in a CERN magnet. The design allows to minimise cost as well as cooling down and warming up times. Using continuous circulation flow of gaseous helium, the MADMAX prototype was cooled down reproducibly to temperatures below 10 K for more than 24 hours. This procedure allowed, for the first time, to perform a calibration of the booster response and to run a dark matter axion search in a magnetic field at low temperatures. This novel type of cryostat, with a low manufacturing cost, fast and easy to operate, could be used for other purposes beyond MADMAX.

Context. The characterization of exoplanets requires a good description of the host star. Stellar activity acts as a source of noise which can alter planet radii as derived from the transit depth or atmospheric characterization. Aims. Here, we propose PAStar, a model to describe photospheric activity in the form of spots and faculae which could be applied to a wide range of stellar observations, from photometric to spectroscopic time series, to be able to correctly extract planetary and stellar properties. Methods. The adopted stellar atmosphere is a combination of three components, the quiet photosphere, spots and faculae. The model takes into account the effects of star inclination, doppler shifts due to stellar rotation as well as for limb darkening, independent for each component. Several synthetic products have been presented to show the capabilities of the model. Results. The model is able to retrieve the input surface inhomogeneities configuration through photometric or spectroscopic observations. The model has been validated against optical solar data and compared to alternative stellar surface activity models; e.g. SOAP code. The Sun is a unique laboratory to test stellar models because of the possibility to relate unambiguously flux variations to surface inhomogeneities configuration. This validation has been done by analyzing a photometric time series from the VIRGO photometer on board of SOHO mission. Results have been compared to real solar images from the HMI instrument on board of SDO to confirm the goodness of the results in terms of surface inhomogeneities position and dimensions. Conclusions. The description of stellar activity is a fundamental step in several astrophysical contexts and it is covered by the method we have presented. Our model offers a flexible and valuable tool to describe the activity of stars when it is dominated by spots and faculae., Comment: 17 pages, 16 figures; accepted for publication in Sect. 8. Stellar atmospheres of A&A; official acceptance date is 12/12/2024

When finetuning multiple tasks altogether, it is important to carefully weigh them to get a good performance, but searching for good weights can be difficult and costly. Here, we propose to aid the search with fast previews to quickly get a rough idea of different reweighting options. We use model merging to create previews by simply reusing and averaging parameters of models trained on each task separately (no retraining required). To improve the quality of previews, we propose a Bayesian approach to design new merging strategies by using more flexible posteriors. We validate our findings on vision and natural-language transformers. Our work shows the benefits of model merging via Bayes to improve multitask finetuning.

The central star of the Helix Nebula, WD 2226$-$210 presents enigmatic hard X-ray emission and mid-IR excess. The latter has been attributed to a dusty disk or a cloud-like structure around WD 2226$-$210 formed from material of Kuiper Belt-like or comet-like objects in highly eccentric orbits. We present here a detailed analysis of multi-epoch Chandra and XMM-Newton X-ray observations of WD 2226$-$210, comparing these to previous Einstein and ROSAT data. The luminosity of the hard X-ray component of WD 2226$-$210 has remained basically constant in the decade from 1992 to 2002, with very subtle evidence for variability in timescales of hours. Under the assumption that the X-ray emission from WD 2226$-$210 is due to accretion of material, an accretion rate of $\dot{M}\approx10^{-10}$ M$_\odot$ yr$^{-1}$ is estimated. The origin of the material accreted by WD 2226$-$210 is uncertain, and can be attributed to the disk-like structure around it or to a sub-stellar donor companion. The accretion rate proposed for the continuous replenishment by bombardment of the mid-IR-emitting structure around WD 2226$-$210 cannot match that required by the X-ray emission., Comment: 8 pages, 3 figures, 3 tables; accepted to MNRAS

The neutral sodium resonance doublet (Na i D) has been detected in the upper atmosphere of several close-in gas giants, through high-resolution transmission spectroscopy. We aim to investigate whether its variability is linked to the planets' properties, the data quality, or the accuracy of the system parameters used. Using the public code SLOPpy, we extracted the transmission spectrum in the Na i D region of ten gas giants for which a large number of HARPS-N observations are available. We modelled the absorption signals found, performing an MCMC analysis, and converted the measured absorption depth to the corresponding atmospheric height over which most sodium absorption occurs. While two targets (GJ 436 b and KELT-7 b) show no Na i D feature, we found variability in the transmission spectrum of the other targets. Three of them (HD 209458 b, WASP-80 b, and WASP-127 b) present absorption on only some nights, while in the other five targets (HD 189733 b, KELT-9 b, KELT-20 b, WASP-69 b, and WASP-76 b), a significant absorption signal is present on most of the nights analysed. Except for WASP-69 b, the measured absorption depths lead to a ratio of the two Na I D depths that is compatible with or slightly larger than one. As was expected from literature, the relative atmospheric height follows an empirical exponential trend as a function of a scaled product of the planet's equilibrium temperature and surface gravity. We confirm the sodium detection on HD 189733 b, KELT-9 b, KELT-20 b, WASP-69 b, and WASP-76 b. The signal detected in WASP-127 b requires further observations for definitive confirmation. We exclude a planetary origin for the signals found on HD 209458 b and WASP-80 b. The sodium absorption variability does not appear to be related to planetary properties, but rather to data quality, sub-optimal data treatment, or stellar activity., Comment: Accepted for publication in Astronomy & Astrophysics on 26 November 2024. Additional material (Figures B.1-B.3 and Tables B.1-B.9) are available on Zenodo

Transforming mathematical expressions into LaTeX poses a significant challenge. In this paper, we examine the application of advanced transformer-based architectures to address the task of converting handwritten or digital mathematical expression images into corresponding LaTeX code. As a baseline, we utilize the current state-of-the-art CNN encoder and LSTM decoder. Additionally, we explore enhancements to the CNN-RNN architecture by replacing the CNN encoder with the pretrained ResNet50 model with modification to suite the grey scale input. Further, we experiment with vision transformer model and compare with Baseline and CNN-LSTM model. Our findings reveal that the vision transformer architectures outperform the baseline CNN-RNN framework, delivering higher overall accuracy and BLEU scores while achieving lower Levenshtein distances. Moreover, these results highlight the potential for further improvement through fine-tuning of model parameters. To encourage open research, we also provide the model implementation, enabling reproduction of our results and facilitating further research in this domain., Comment: 7 pages; 3 figures

The stochastic resonance phenomenon has been studied experimentally and theoretically for a state-of-art metal-oxide memristive device based on yttria-stabilized zirconium dioxide and tantalum pentoxide, which exhibits bipolar filamentary resistive switching of anionic type. The effect of white Gaussian noise superimposed on the sub-threshold sinusoidal driving signal is analyzed through the time series statistics of the resistive switching parameters, the spectral response to a periodic perturbation and the signal-to-noise ratio at the output of the nonlinear system. The stabilized resistive switching and the increased memristance response are revealed in the observed regularities at an optimal noise intensity corresponding to the stochastic resonance phenomenon and interpreted using a stochastic memristor model taking into account an external noise source added to the control voltage. The obtained results clearly show that noise and fluctuations can play a constructive role in nonlinear memristive systems far from equilibrium.

Learning analytics dashboards (LADs) simplify complex learner data into accessible visualisations, providing actionable insights for educators and students. However, their educational effectiveness has not always matched the sophistication of the technology behind them. Explanatory and interactive LADs, enhanced by generative AI (GenAI) chatbots, hold promise by enabling dynamic, dialogue-based interactions with data visualisations and offering personalised feedback through text. Yet, the effectiveness of these tools may be limited by learners' varying levels of GenAI literacy, a factor that remains underexplored in current research. This study investigates the role of GenAI literacy in learner interactions with conventional (reactive) versus scaffolding (proactive) chatbot-assisted LADs. Through a comparative analysis of 81 participants, we examine how GenAI literacy is associated with learners' ability to interpret complex visualisations and their cognitive processes during interactions with chatbot-assisted LADs. Results show that while both chatbots significantly improved learner comprehension, those with higher GenAI literacy benefited the most, particularly with conventional chatbots, demonstrating diverse prompting strategies. Findings highlight the importance of considering learners' GenAI literacy when integrating GenAI chatbots in LADs and educational technologies. Incorporating scaffolding techniques within GenAI chatbots can be an effective strategy, offering a more guided experience that reduces reliance on learners' GenAI literacy.

Multimodal Learning Analytics (MMLA) leverages advanced sensing technologies and artificial intelligence to capture complex learning processes, but integrating diverse data sources into cohesive insights remains challenging. This study introduces a novel methodology for integrating latent class analysis (LCA) within MMLA to map monomodal behavioural indicators into parsimonious multimodal ones. Using a high-fidelity healthcare simulation context, we collected positional, audio, and physiological data, deriving 17 monomodal indicators. LCA identified four distinct latent classes: Collaborative Communication, Embodied Collaboration, Distant Interaction, and Solitary Engagement, each capturing unique monomodal patterns. Epistemic network analysis compared these multimodal indicators with the original monomodal indicators and found that the multimodal approach was more parsimonious while offering higher explanatory power regarding students' task and collaboration performances. The findings highlight the potential of LCA in simplifying the analysis of complex multimodal data while capturing nuanced, cross-modality behaviours, offering actionable insights for educators and enhancing the design of collaborative learning interventions. This study proposes a pathway for advancing MMLA, making it more parsimonious and manageable, and aligning with the principles of learner-centred education.

Variability in memristive devices based on h-BN dielectrics is studied in depth. Different numerical techniques to extract the reset voltage are described and the corresponding cycle-to-cycle variability is characterized by means of the coefficient of variance. The charge-flux domain was employed to develop one of the extraction techniques, the calculation of the integrals of current and voltage to obtain the charge and flux allows to minimize the effects of electric noise and the inherent stochasticity of resistive switching on the measurement data. A model to reproduce charge versus flux curves has been successfully employed. The device variability is also described by means of the time series analysis to assess the memory effect along a resistive switching series. Finally, we analyzed I-V curves under ramped voltage stress utilizing a simulator based on circuit breakers, the formation and rupture of the percolation paths that constitute the conductive nanofilaments is studied to describe the set and reset processes behind the resistive switching operation.

In this work, we present a case study in implementing a variational quantum algorithm for solving the Poisson equation, which is a commonly encountered partial differential equation in science and engineering. We highlight the practical challenges encountered in mapping the algorithm to physical hardware, and the software engineering considerations needed to achieve realistic results on today's non-fault-tolerant systems., Comment: 8 pages, 6 figures, submitted on 2024-10-10 to IEEE Quantum Software and its Engineering

Resistive memories are outstanding electron devices that have displayed a large potential in a plethora of applications such as nonvolatile data storage, neuromorphic computing, hardware cryptography, etc. Their fabrication control and performance have been notably improved in the last few years to cope with the requirements of massive industrial production. However, the most important hurdle to progress in their development is the so-called cycle-to-cycle variability, which is inherently rooted in the resistive switching mechanism behind the operational principle of these devices. In order to achieve the whole picture, variability must be assessed from different viewpoints going from the experimental characterization to the adequation of modeling and simulation techniques. Herein, special emphasis is put on the modeling part because the accurate representation of the phenomenon is critical for circuit designers. In this respect, a number of approaches are used to the date: stochastic, behavioral, mesoscopic..., each of them covering particular aspects of the electron and ion transport mechanisms occurring within the switching material. These subjects are dealt with in this review, with the aim of presenting the most recent advancements in the treatment of variability in resistive memories.

We emulate the Tolman-Oppenheimer-Volkoff (TOV) equations, including tidal deformability, for neutron stars using a novel hybrid method based upon the Dynamic Mode Decomposition (DMD) for the first time. This new method, which we call Star Log-extended eMulation (SLM), utilizes the underlying logarithmic behavior of the differential equations to enable accurate emulation of the nonlinear system. We show predictions for well-known phenomenological equations of state (EOS) with fixed parameters and with a freely-varying parametric Quarkyonic EOS. Our results produce a significant computational speed-up of $\approx 3 \times 10^4$ compared to high fidelity (HF) calculations using standard Runge-Kutta methods, providing an efficient emulator for the numerous TOV evaluations required by multi-messenger astrophysical frameworks that infer constraints on the EOS from neutron star mergers. The ability of the SLM algorithm to learn a mapping between parameters of the EOS and subsequent neutron star properties also opens up potential extensions for assisting in computationally prohibitive uncertainty quantification (UQ) for any type of EOS. The source code for the methods developed in this work will be openly available in a public GitHub repository for community modification and use., Comment: 6 pages, 3 figures, 1 table

There are several methods to improve cancer patient survival rates by inducing hyperthermia in tumor tissues, which involves raising their temperature above 41{\deg}C. These methods utilize different energy sources to deliver heat to the target region, including light, microwaves or radiofrequency electromagnetic fields. We have developed a new, magnetically responsive nanocarrier, consisting of liposomes loaded with magnetic nanoparticles and cis-diamminedichloroplatinum (II) (CDDP), commonly known as Cisplatin. The resulting magnetoliposome (ML) is rapidly internalized by lung and pancreas tumor cell lines, stored in intracellular vesicles, and capable of inducing hyperthermia under magnetic fields. The ML has no significant toxicity both in vitro and in vivo and, most importantly, enhances cell death by apoptosis after magnetic hyperthermia. Remarkably, mice bearing induced lung tumors, treated with CDDP-loaded nanocarriers and subjected to an applied electromagnetic field, showed an improved survival rate over those treated with either soluble CDDP or hyperthermia alone. Therefore, our approach of magnetic hyperthermia plus CDDP-ML significantly enhances in vitro cell death and in vivo survival of treated animals., Comment: 35 pages, 7 figures

Light-induced phase transitions offer a method to dynamically modulate topological states in bulk complex materials. Yet, next-generation devices demand nanoscale architectures with contact resistances near the quantum limit and precise control over local electronic properties. The layered material WTe$_2$ has gained attention as a likely Weyl semimetal, with topologically protected linear electronic band crossings hosting massless chiral fermions. Here, we demonstrate a topological phase transition facilitated by light-induced shear motion of a single atomic layer at the surface of bulk WTe$_2$, thereby opening the door to nanoscale device concepts. Ultrafast terahertz fields enhanced at the apex of an atomically sharp tip resonantly couple to the key interlayer shear mode of WTe$_2$ via a ferroelectric dipole at the interface, inducing a structural phase transition at the surface to a metastable state. Subatomically resolved differential imaging, combined with hybrid-level density functional theory, reveals a shift of 7 $\pm$ 3 picometres in the top atomic plane. Tunnelling spectroscopy links electronic changes across the phase transition with the electron and hole pockets in the band structure, suggesting a reversible, light-induced annihilation of the topologically-protected Fermi arc surface states in the top atomic layer.

We derive a mesoscopic theory of the Josephson junction from non-relativistic scalar electrodynamics. Our theory reproduces the Josephson relations with the canonical current phase relation acquiring a weak second harmonic term, and it improves the standard lumped-element descriptions employed in circuit quantum electrodynamics by providing spatial resolution of the superconducting order parameter and electromagnetic field. By providing an ab initio derivation of the charge qubit Hamiltonian that relates traditionally free qubit parameters to geometric and material properties, we progress toward the quantum engineering of superconducting circuits at the subnanometer scale.

In this paper, we revisit the extension of the classical non-standard cosmological model in which dissipative processes are considered through a bulk viscous term in the new field $\phi$, which interacts with the radiation component during the early universe. Specifically, we consider an interaction term of the form $\Gamma_{\phi} \rho_{\phi}$, where $\Gamma_{\phi}$ represents the decay rate of the field and $\rho_{\phi}$ denotes its energy density and a bulk viscosity described by $\xi=\xi_{0}\rho_{\phi}^{1/2}$, within the framework of Eckart's theory. This extended non-standard cosmology is employed to explore the parameter space for the production of Feebly Interacting Massive Particles (FIMPs) as Dark Matter candidates, assuming a constant thermal averaged Dark Matter production cross-section ($\langle\sigma v\rangle$), as well as a preliminary analysis of the non-constant case. In particular, for certain combinations of the model and Dark Matter parameters, namely ($T_\text{end}$,$\kappa$) and $(m_\chi,\langle\sigma v\rangle)$, where $T_\text{end}$ corresponds to the temperature at which $\phi$ decays, $\kappa$ is the ratio between the initial energy density of $\phi$ and radiation, and $m_\chi$ is the Dark Matter mass, we identify extensive new parameter regions where Dark Matter can be successfully established while reproducing the currently observed relic density, in contrast to the predictions of $\Lambda$CDM and classical non-standard cosmological scenarios., Comment: 17 pages and 23 figures. Sections I, II, and III have been revised, and a new Section III C has been added, specifically addressing the non-constant total thermally averaged DM production cross-section for FIMPs. The rest of the paper remains unchanged

The rapid integration of generative artificial intelligence (GenAI) technology into education necessitates precise measurement of GenAI literacy to ensure that learners and educators possess the skills to engage with and critically evaluate this transformative technology effectively. Existing instruments often rely on self-reports, which may be biased. In this study, we present the GenAI Literacy Assessment Test (GLAT), a 20-item multiple-choice instrument developed following established procedures in psychological and educational measurement. Structural validity and reliability were confirmed with responses from 355 higher education students using classical test theory and item response theory, resulting in a reliable 2-parameter logistic (2PL) model (Cronbach's alpha = 0.80; omega total = 0.81) with a robust factor structure (RMSEA = 0.03; CFI = 0.97). Critically, GLAT scores were found to be significant predictors of learners' performance in GenAI-supported tasks, outperforming self-reported measures such as perceived ChatGPT proficiency and demonstrating external validity. These results suggest that GLAT offers a reliable and valid method for assessing GenAI literacy, with the potential to inform educational practices and policy decisions that aim to enhance learners' and educators' GenAI literacy, ultimately equipping them to navigate an AI-enhanced future.

Ho$_3$ScO$_6$ harbours a frustrated Maple Leaf Lattice (MLL). It crystalizes in the Mg$_3$TeO$_6$-type structure, and has a centrosymmetric trigonal space group (R$\bar{3}$). This system contains stacked layers of magnetic rings along the c-axis consisting of six magnetic Ho$^{3+}$ ions forming Ho hexagons, which are connected into a 2-dimensional network by equilateral and isosceles triangles to form a rare example of a MLL. Long range magnetic order is reached below $T_N=4.1$ K with a 120$^\circ$ spin arrangement on the equilateral triangles resulting in a positive vector chirality ground state configuration.

Early identification of patients at risk of cardiovascular diseases (CVD) is crucial for effective preventive care, reducing healthcare burden, and improving patients' quality of life. This study demonstrates the potential of retinal optical coherence tomography (OCT) imaging combined with fundus photographs for identifying future adverse cardiac events. We used data from 977 patients who experienced CVD within a 5-year interval post-image acquisition, alongside 1,877 control participants without CVD, totaling 2,854 subjects. We propose a novel binary classification network based on a Multi-channel Variational Autoencoder (MCVAE), which learns a latent embedding of patients' fundus and OCT images to classify individuals into two groups: those likely to develop CVD in the future and those who are not. Our model, trained on both imaging modalities, achieved promising results (AUROC 0.78 +/- 0.02, accuracy 0.68 +/- 0.002, precision 0.74 +/- 0.02, sensitivity 0.73 +/- 0.02, and specificity 0.68 +/- 0.01), demonstrating its efficacy in identifying patients at risk of future CVD events based on their retinal images. This study highlights the potential of retinal OCT imaging and fundus photographs as cost-effective, non-invasive alternatives for predicting cardiovascular disease risk. The widespread availability of these imaging techniques in optometry practices and hospitals further enhances their potential for large-scale CVD risk screening. Our findings contribute to the development of standardized, accessible methods for early CVD risk identification, potentially improving preventive care strategies and patient outcomes., Comment: Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15155))

Perturbative guiding center theory adequately describes the slow drift motion of charged particles in the strongly-magnetized regime characteristic of thermal particle populations in various magnetic fusion devices. However, it breaks down for particles with large enough energy. We report on a data-driven method for learning a non-perturbative guiding center model from full-orbit particle simulation data. We show the data-driven model significantly outperforms traditional asymptotic theory in magnetization regimes appropriate for fusion-born $\alpha$-particles in stellarators, thus opening the door to non-perturbative guiding center calculations., Comment: 23 pages, 4 figures, single column, double spaced