Miracle-dz/UniFAR-FTUs huggingface dataset:
Polyamide Nanofibers Coating by Electrospinning Technique for Anti Corrosion Behavior

- Poly amide (PA-6) nanofiber coatings on aluminum surface using electrospinning technique under two different voltage (24 kV & 34 Kv) were prepared. The coating morphology , roughness and 3D structural properties tested by using Atomic force microscopes (AFM), surface characterized by (SEM) microscopy, and high resolution optical microscopy (HROM) , Contact angle for hydrophobic behavior was tested by shape drop analyzer , and FTIR analysis for changing in crystalline structure was performed . The AFM images showed that the PA coating on the aluminum surface have a tight and twisted nanofiber structure with some beads through its morphology. SEM images shows the morphology beads of surface nanofibers . The electrochemical corrosion of aluminum without and with PA coating studied by subjected it to aerobic solutions of 3.5 wt. % sodium chloride (NaCl) . It has been found that the PA coatings decrease the corrosion currents and corrosion rate as well as increase the corrosion resistance for aluminum in the NaCl solution .
Kinetics of Oscillating Neutrinos

In the context of core-collapse supernovae, Strack and Burrows (Phys. Rev. D 71, 093004 (2005)) have recently developed an extension of the classical Boltzmann kinetic formalism that retains all the standard neutrino oscillation phenomenology, including resonant flavor conversion (the MSW effect), neutrino self-interactions, and the interplay between neutrino-matter coupling and flavor oscillations. In this thesis, I extend the Strack & Burrows formalism to incorporate general relativity, spin degrees of freedom, and a possible neutrino magnetic-moment/magnetic-field interaction.
HRU-Net: A Transfer Learning Method for Carotid Artery Plaque Segmentation in Ultrasound Images

Carotid artery stenotic plaque segmentation in ultrasound images is a crucial means for the analysis of plaque components and vulnerability. However, segmentation of severe stenotic plaques remains a challenging task because of the heterogeneities of inter-plaques and intra-plaques, and obscure boundaries of plaques. In this paper, we propose an automated HRU-Net transfer learning method for segmenting carotid plaques, using the limited images. The HRU-Net is based on the U-Net encoder–decoder paradigm, and cross-domain knowledge is transferred for plaque segmentation by fine-tuning the pretrained ResNet-50. Moreover, a cropped-blood-vessel image augmentation is customized for the plaque position constraint during training only. Moreover, hybrid atrous convolutions (HACs) are designed to derive diverse long-range dependences for refined plaque segmentation that are used on high-level semantic layers to exploit the implicit discrimination features. The experiments are performed on 115 images; Firstly, the 10-fold cross-validation, using 40 images with severe stenosis plaques, shows that the proposed method outperforms some of the state-of-the-art CNN-based methods on Dice, IoU, Acc, and modified Hausdorff distance (MHD) metrics; the improvements on metrics of Dice and MHD are statistically significant (p < 0.05). Furthermore, our HRU-Net transfer learning method shows fine generalization performance on 75 new images with varying degrees of plaque stenosis, and it may be used as an alternative for automatic noisy plaque segmentation in carotid ultrasound images clinically.
Noise-immune oximetry employing a new expression for oxygen saturation in blood

Oximetry is a technique to detect differential changes of oxygenation in hemoglobin. In previous work, we extended the classical mathematical model of oxygen saturation to reduce noise, to consider amplification parameters and detector's responsivity. We proposed a new expression for oxygen saturation in blood. This expression is less sensitive to noise, isolates the attenuation coefficients, is independent of multiplicative noise, and increases modulation. In the present work, we evaluate the feasibility of this new expression in thick tissue.
Role of Supergiants in the Formation of Globular Clusters

Multiple stellar populations are observed in almost all globular clusters, but the origin of this phenomenon is still debated. We investigate the role cool supergiants may have played. To do this, we combine two investigative methods: state-of-the-art massive stellar evolution and calculations of the hydrodynamic structure of the cluster gas. This approach allows us to study how star formation in young massive clusters depends on the energy and mass input of the first generation of stars, while predicting the chemical composition of the second generation. We find that the presence of massive (9–500 M⊙) metal-poor supergiants in the young cluster leads to a star formation episode within the first 4 Myr of the cluster’s lifetime, that is, before the first core-collapse supernovae explode or the gas is expelled. The stellar winds accumulate in the cluster center, forming the second generation there. Its composition is predicted to show variations in O and Na abundances, consistently with observations. The abundance of helium is, similarly to other scenarios involving massive stars, higher than what is inferred from observations. Supposing dynamical removal of stars from the outskirts of the cluster, or applying a top-heavy initial mass function, we can predict a number ratio of the second generation as high as 20%–80%. The effect of metallicity is shown to be important, as the most luminous supergiants are only predicted at low metallicity, thus limiting—but not excluding—the extent of a polluted second generation at high metallicity. These massive stars becoming black holes suggest globular clusters hosting gravitational-wave progenitors. Our scenario predicts a correlation between the mass of the cluster and the extent of the multiple-population phenomenon.
Bursts from GS 1826-238: A Clocked Thermonuclear Flashes Generator

The transient X-ray source GS 1826-238 was monitored during five different observing periods between 1996 August and 1998 October with the BeppoSAX Wide Field Camera instrument in the framework of a deep observation of the Galactic center region. During this time, a first detection of X-ray bursts from this source occurred, ruling out its previously suggested black hole candidacy and strongly suggesting the compact object to be a weakly magnetized neutron star. During the 2.5 yr of monitoring, corresponding to ~2.0 Ms of observing time, 70 bursts were detected from the source. Here we report a quasi-periodicity of 5.76 hr in the burst occurrence time that is present during all observations. This is the first example of quasi-periodic bursting over a period of years. It is in line with the history of a rather constant flux since the turn-on in 1988 and points to a very stable accretion.
Model-Based Reinforcement Learning Control of Electrohydraulic Position Servo Systems

Even though the unprecedented success of AlphaGo Zero demonstrated reinforcement learning as a feasible complex problem solver, the research on reinforcement learning control of hydraulic systems is still void. We are motivated by the challenges presented in hydraulic systems to develop a new model-based reinforcement learning controller that achieves high-accuracy tracking at performance level and with asymptotic stability guarantees at system level. In this article, the proposed design consists of two frameworks: A recursive robust integral of the sign of the error (RISE) control approach to providing closed-loop system stability framework, and a reinforcement learning approach with actor-critic structure to dealing with the unknown dynamics or more specifically, the actor neural network is used to reduce the high feedback gain of the recursive RISE control approach by compensating the unknown dynamic while the critic neural network is integrated to improve the control performance by evaluating the filtered tracking error. A theoretical guarantee for the stability of the overall dynamic system is provided by using Lyapunov stability theory. Simulation and experimental results are provided to demonstrate improved control performance of the proposed controller.
Closing the gap between Earth-based and interplanetary mission observations: Vesta seen by VLT/SPHERE

Context. Over the past decades, several interplanetary missions have studied small bodies in situ, leading to major advances in our understanding of their geological and geophysical properties. These missions, however, have had a limited number of targets. Among them, the NASA Dawn mission has characterised in detail the topography and albedo variegation across the surface of asteroid (4) Vesta down to a spatial resolution of ~20 m pixel−1 scale. Aims. Here our aim was to determine how much topographic and albedo information can be retrieved from the ground with VLT/SPHERE in the case of Vesta, having a former space mission (Dawn) providing us with the ground truth that can be used as a benchmark. Methods. We observed Vesta with VLT/SPHERE/ZIMPOL as part of our ESO large programme (ID 199.C-0074) at six different epochs, and deconvolved the collected images with a parametric point spread function (PSF). We then compared our images with synthetic views of Vesta generated from the 3D shape model of the Dawn mission, on which we projected Vesta’s albedo information. Results. We show that the deconvolution of the VLT/SPHERE images with a parametric PSF allows the retrieval of the main topographic and albedo features present across the surface of Vesta down to a spatial resolution of ~20–30 km. Contour extraction shows an accuracy of ~1 pixel (3.6 mas). The present study provides the very first quantitative estimate of the accuracy of ground-based adaptive-optics imaging observations of asteroid surfaces. Conclusions. In the case of Vesta, the upcoming generation of 30–40 m telescopes (ELT, TMT, GMT) should in principle be able to resolve all of the main features present across its surface, including the troughs and the north–south crater dichotomy, provided that they operate at the diffraction limit.
Outcomes from a pilot dose comparison study of naming therapy in aphasia

ABSTRACT Background People with aphasia vary considerably in response to aphasia treatments. Treatment dose is likely to be an important factor in understanding treatment response variability and optimising aphasia recovery; however, there is limited empirical evidence to guide dose prescription in post-stroke aphasia rehabilitation. In the present study, we used a novel approach to personalise dose prescription and explored the effect of dose on treatment response in chronic post-stroke aphasia. Aims Examine the effect of providing personalised doses of a cued picture naming treatment (Kendall et al., 2014) on acquisition and maintenance of picture naming outcomes. Method This pilot study used a multiple-baselines design with follow-up at 4- and 12-weeks with replication across four people with chronic post-stroke anomia. Prior to treatment, a comprehensive battery of cognitive and language tests was completed. Participants then undertook a period of cued picture naming treatment (45-minute sessions, five days per week for three weeks) totalling 15 sessions (11.25 hours). Participants were allocated four picture sets – one each for three treated conditions (low dose, moderate dose, and high dose) and one for an untreated control set. The number of naming opportunities provided per dose condition was calibrated against individuals’ pre-treatment picture naming accuracy and speed. Generalised linear mixed effects models were used to evaluate learning effects during treatment, maintenance of these effects, and dose-response relationships. Outcomes Participants received 99% of prescribed treatment doses (i.e., number of naming opportunities provided over the course of treatment). As anticipated, individual treatment responses varied substantially. Three participants demonstrated significantly improved picture naming accuracy on probed items during treatment, with varying response profiles by participant and dose. All participants were able to name more pictures accurately on a bespoke 298-item object picture naming test following treatment. However, no participant demonstrated significant pre-post treatment gains relative to untreated items, although one person demonstrated improved naming four weeks after treatment for items treated under the high dose condition. Dose-response relationships amongst these participants exhibited a greater number of significant results on naming probes in the high dose condition, possibly suggesting superiority of the high dose condition over lower doses of cued picture naming treatment. Conclusion Modest treatment effects and variable dose-response relationships were observed. We explore the role of dose, cognitive factors such as self-monitoring abilities, and linguistic factors such as underlying lexical-semantic and phonological processing that may have influenced treatment response in these participants. Avenues for future research are identified.
WR 20a Is an Eclipsing Binary: Accurate Determination of Parameters for an Extremely Massive Wolf-Rayet System

We present a high-precision I-band light curve for the Wolf-Rayet binary WR 20a, obtained as a subproject of the Optical Gravitational Lensing Experiment. Rauw et al. have recently presented spectroscopy for this system, strongly suggesting extremely large minimum masses of 70.7 ± 4.0 and 68.8 ± 3.8 M☉ for the component stars of the system, with the exact values depending strongly on the period of the system. We detect deep eclipses of about 0.4 mag in the light curve of WR 20a, confirming and refining the suspected period of P = 3.686 days and deriving an inclination angle of i = 74. °5 ± 2. °0. Using these photometric data and the radial velocity data of Rauw et al., we derive the masses for the two components of WR 20a to be 83.0 ± 5.0 and 82.0 ± 5.0 M☉. Therefore, WR 20a is confirmed to consist of two extremely massive stars and to be the most massive binary known with an accurate mass determination.
Overestimation of Relative Risk and Prevalence Ratio: Misuse of Logistic Modeling

The extensive use of logistic regression models in analytical epidemiology as well as in randomized clinical trials, often creates inflated estimates of the relative risk (RR). Particularly, in cases where a binary outcome has a high or moderate incidence in the studied population (>10%), the bias in assessing the relative risk may be very high. Meta-analysis studies have estimated that about 40% of the relative risk estimates in prospective investigations, through binary logistic models, lead to extensive bias of the population parameters. The problem of risk inflation also appears in cross-sectional studies with binary outcomes, where the parameter of interest is the prevalence ratio. As an alternative to the use of logistic regression models in both longitudinal and cross-sectional studies, the modified Poisson regression model is proposed.
Age-Related Sex Differences in Glucose Tolerance by 75 g Oral Glucose Tolerance Test in Japanese

To elucidate the age-related sex difference in glucose tolerance, we conducted 75 g oral glucose tolerance tests in 1156 participants. Participants were divided into four groups, namely, young (22–29) males, young females, middle-aged (>50) males, and middle-aged females. According to the Japanese Clinical Practice Guideline for Diabetes 2019, the prevalence of normal glucose tolerance (NGT) was significantly lower in middle-aged than in young participants. The prevalence of high-normal fasting plasma glucose (FPG) was higher, and NGT was lower in young males (high-normal FPG 15.2%, NGT 82.0%) than young females (high-FPG 3.9%, NGT 94.3%). Combined glucose intolerance (CGI) was higher and NGT was lower in middle-aged males (CGI 10.2%, NGT 25.2%) than in middle-aged females (CGI 3.3%, NGT 39.8%). FPG and body mass index (BMI) were the lowest and Homeostatic model assessment beta cell function (HOMA-β) was the highest in young females, followed by young males, middle-aged females, and middle-aged males. Multiple linear regression analysis revealed that BMI weakly correlated with HOMA-β and Matsuda index in all subjects except young females. The superior glucose tolerance in females was apparent in young, but attenuated in middle-aged females. The differences are due to the higher insulin secretion potential and lower BMI in young females.
Hierarchical inference of the relationship between Concentration and Mass in Galaxy Groups and Clusters

Mass is a fundamental property of galaxy groups and clusters. In theory weak gravitational lensing will enable an approximately unbiased measurement of mass, but parametric methods for extracting cluster masses from data require the additional knowledge of concentration. Measurements of both mass and concentration are limited by the degeneracy between the two parameters, particularly in low mass, high redshift systems where the signal-to-noise is low. In this paper we develop a hierarchical model of mass and concentration for mass inference we test our method on toy data and then apply it to a sample of galaxy groups and poor clusters down to masses of $\sim$ 1e13 M$_\odot$. Our fit and model gives a relationship among masses, concentrations and redshift that allow prediction of these parameters from incomplete and noisy future measurements. Additionally the underlying population can be used to infer an observationally based concentration-mass relation. Our method is equivalent to a quasi- stacking approach with the degree of stacking set by the data. We also demonstrate that mass and concentration derived from pure stacking can be offset from the population mean with differing values depending on the method of stacking.
Optomechanics and nonlinear mechanics of suspended photonic crystal membranes

namic backaction is elucidated. Static displacement by the repulsive force of 1nm/mW is also demonstrated. In the second part of the thesis, the nonlinear mechanical signatures upon a strong coherent drive are reported. By resonantly driving the photonic crystal membrane with a piezo-actuator and an optical gradient force, we observed mechanical frequency mixing, mechanical bistability and non-trivial interactions of the Brownian peak with the driving signal. Finally we present our recent progress in establishing electrostatic control of individual photonic crystal membranes to reduce and calibrate the electrostatic artifact which plagues Casimir measurements. The results discussed in this thesis point towards an auspicious future of a complete realization of a Casimir optomechanical structure and novel applications with nonlinearity afforded by the Casimir force and the optical gradient force.
Void Formation and Crack Propagation in a Cr–Mn–N Metastable Austenitic Stainless Steel During Bending

Microstructure evolution via deformation‐induced martensitic transformation, void formation, and crack propagation is investigated in a metastable austenitic Cr–Mn–N stainless steel for up to 90° bending using a combination of electron backscattering diffraction and parent grain reconstruction. Stress–strain heterogeneity and stress triaxiality studied using finite‐element analysis reveal that the inner and outer radii are in approximately uniaxial compressive and tensile stress states, respectively, with the outer radius showing higher values for von Mises and principal stresses and equivalent strain. Voids are observed at both austenite/α′‐martensite and α′/α′‐martensite interfaces. Parent grain reconstruction applied to the 90° bending sample reveal that the cracks at α′/α′‐martensite interfaces tend to propagate predominantly along intergranular parent austenite grain boundaries. It is also found that both intragranular and intergranular cracks in parent austenite tend to propagate between α′‐martensite child–child grains comprising the same crystallographic packets. This is the first study of its kind to comprehensively show this phenomenon. A representative example of intergranular crack propagation within a ⟨110⟩ ‖normal direction (ND)‐oriented parent austenite grain is also demonstrated.
A Novel Online State of Health Estimation Method for Electric Vehicle Pouch Cells Using Magnetic Field Imaging and Convolution Neural Networks

Lithium-ion batteries (LiBs) are used as the main power source in electric vehicles (EVs). Despite their high energy density and commercial availability, LiBs chronically suffer from non-uniform cell ageing, leading to early capacity fade in the battery packs. In this paper, a non-invasive, online characterisation method based on deep learning models is proposed for cell-level SoH estimation. For an accurate measurement of the state of health (SoH), we need to characterize electrochemical capacity fade scenarios carefully. Then, with the help of real-time monitoring, the control systems can reduce the LiB’s degradation. The proposed method, which is based on convolutional neural networks (CNN), characterises the changes in current density distributions originating from the positive electrodes in different SoH states. For training and classification by the deep learning model, current density images (CDIs) were experimentally acquired in different ageing conditions. The results confirm the efficiency of the proposed approach in online SoH estimation and the prediction of the capacity fade scenarios.
Recent Challenges and Opportunities in Video Summarization With Machine Learning Algorithms

The fast progress in digital technology has sparked the generation of the amount of voluminous data from different social media platforms like Instagram, Facebook, YouTube, etc. There are other platforms, as well which generate large data like News, CCTV videos, sports, entertainment, etc. Lengthy Videos typically contain a significant number of duplicate occurrences that are uninteresting to the viewer. Eliminating this unnecessary information and concentra only on the crucial events will be far more advantageous. This produces a summary of lengthy films, which can save viewers time and enable better memory management. The highlights of a lengthy video are condensed into a video summary. Video summarization is an essential topic today, since many industries have CCTV cameras installed for various reasons such as monitoring, security, and tracking. Because surveillance videos are taken 24 hours a day, enormous amounts of memory and time are required if one wishes to trace any incident or person from the full day’s video. The summary generated from multiple views is far more challenging, so more study and advancement in MVS is required. The conceptual basis of video summarizing approaches is thoroughly addressed in this paper. This paper addresses applications and technology challenges in Single view and Multi View summarization.
Band-gap-engineered spin-phonon, and spin-spin interactions with defect centers in diamond coupled to phononic crystals

We study a spin-phononic system where diamond defect centers are interfaced with a quasi-one-dimensional phononic crystal. We show that, a single defect center, coupled to the phonon modes of a phononic crystal waveguide near the band gap, can seed its own phononic cavity mode with an exponentially decaying envelope around the defect center's position. The spin-induced phononic cavity, with a greatly reduced and tunable mode volume, allows coherent phonon-mediated interactions between distant spins with a highly tunable range, enabling access to a variety of long-range interacting spin models. This work opens prospects for exploring quantum many-body physics and quantum information processing with defect centers and periodic phononic nanostructures.