Spectral observations of X Persei: Connection between H-alpha and X-ray emission

We present spectroscopic observations of the Be/X-ray binary X Per obtained during the period 1999 - 2018. Using new and published data, we found that during "disc-rise" the expansion velocity of the circumstellar disc is 0.4 - 0.7 km/s. Our results suggest that the disc radius in recent decades show evidence of resonant truncation of the disc by resonances 10:1, 3:1, and 2:1, while the maximum disc size is larger than the Roche lobe of the primary and smaller than the closest approach of the neutron star. We find correlation between equivalent width of H-alpha emission line ($W\alpha$) and the X-ray flux, which is visible when $15 \: \AA \: < W\alpha \le 40 \: \AA$. The correlation is probably due to wind Roche lobe overflow.Comment: Accepted for publication in Astronomy & Astrophysic

An ultra melt-resistant hydrogel from food grade carbohydrates

© 2017 The Royal Society of Chemistry. We report a binary hydrogel system made from two food grade biopolymers, agar and methylcellulose (agar-MC), which does not require addition of salt for gelation to occur and has very unusual rheological and thermal properties. It is found that the storage modulus of the agar-MC hydrogel far exceeds those of hydrogels from the individual components. In addition, the agar-MC hydrogel has enhanced mechanical properties over the temperature range 25-85 °C and a maximum storage modulus at 55 °C when the concentration of methylcellulose was 0.75% w/v or higher. This is explained by a sol-gel phase transition of the methylcellulose upon heating as supported by differential scanning calorimetry (DSC) measurements. Above the melting point of agar, the storage modulus of agar-MC hydrogel decreases but is still an elastic hydrogel with mechanical properties dominated by the MC gelation. By varying the mixing ratio of the two polymers, agar and MC, it was possible to engineer a food grade hydrogel of controlled mechanical properties and thermal response. SEM imaging of flash-frozen and freeze-dried samples revealed that the agar-MC hydrogel contains two different types of heterogeneous regions of distinct microstructures. The latter was also tested for its stability towards heat treatment which showed that upon heating to temperatures above 120 °C its structure was retained without melting. The produced highly thermally stable hydrogel shows melt resistance which may find application in high temperature food processing and materials templating

Landau quantization and neutron emissions by nuclei in the crust of a magnetar

Magnetars are neutron stars endowed with surface magnetic fields of the order of $10^{14}-10^{15}$~G, and with presumably much stronger fields in their interior. As a result of Landau quantization of electron motion, the neutron-drip transition in the crust of a magnetar is shifted to either higher or lower densities depending on the magnetic field strength. The impact of nuclear uncertainties is explored considering the recent series of Brussels-Montreal microscopic nuclear mass models. All these models are based on the Hartree-Fock-Bogoliubov method with generalized Skyrme functionals. They differ in their predictions for the symmetry energy coefficient at saturation, and for the stiffness of the neutron-matter equation of state. For comparison, we have also considered the very accurate but more phenomenological model of Duflo and Zuker. Although the equilibrium composition of the crust of a magnetar and the onset of neutron emission are found to be model dependent, the quantum oscillations of the threshold density are essentially universal.Comment: 7 pages, 2 figure

MoBYv2AL: Self-supervised Active Learning for Image Classification

Active learning(AL) has recently gained popularity for deep learning(DL) models. This is due to efficient and informative sampling, especially when the learner requires large-scale labelled datasets. Commonly, the sampling and training happen in stages while more batches are added. One main bottleneck in this strategy is the narrow representation learned by the model that affects the overall AL selection. We present MoBYv2AL, a novel self-supervised active learning framework for image classification. Our contribution lies in lifting MoBY - one of the most successful self-supervised learning algorithms to the AL pipeline. Thus, we add the downstream task-aware objective function and optimize it jointly with contrastive loss. Further, we derive a data-distribution selection function from labelling the new examples. Finally, we test and study our pipeline robustness and performance for image classification tasks. We successfully achieved state-of-the-art results when compared to recent AL methods

Linking medical faculty stress/burnout to willingness to implement medical school curriculum change: a preliminary investigation

Rationale, aims and objectivesBalancing administrative demands from the medical school while providing patient support and seeking academic advancement can cause personal hardship that ranges from high stress to clinically recognizable conditions such as burnout. Regarding the importance of clinical faculties’ burnout and its effects on different aspects of their professional career, this study was conducted and aimed to evaluate the relationship between willingness to change teaching approaches as characterized by a modified stage‐of‐change model and measures of stress and burnout.MethodsThis descriptive analytic study was conducted on 143 clinical faculty members of Tehran University of Medical Sciences in Iran. Participants were asked to complete three questionnaires: a modified stages of change questionnaire the Maslach Burnout Inventory and the General Health Questionnaire. Data were analysed by SPSS: 16 using non‐parametric statistical tests such as multiple regression and ICC (intra‐class coefficient) and Spearman correlation coefficient test.ResultA significant relationship was found between faculty members’ readiness to change teaching approaches and the subscales of occupational burnout. Specifically, participants with low occupational burnout were more likely to be in the action stage, while those with high burnout were in the attitude or intention stage, which could be understood as not being ready to implement change. There was no significant correlation between general health scores and stage of change. ConclusionsWe found it feasible to measure stages of change as well as stress/burnout in academic doctors. Occupational burnout directly reduces the readiness to change. To have successful academic reform in medical schools, it therefore would be beneficial to assess and manage occupational burnout among clinical faculty members.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/135679/1/jep12439.pd

Scalable Joint Detection and Segmentation of Surgical Instruments with Weak Supervision

Computer vision based models, such as object segmentation, detection and tracking, have the potential to assist surgeons intra-operatively and improve the quality and outcomes of minimally invasive surgery. Different work streams towards instrument detection include segmentation, bounding box localisation and classification. While segmentation models offer much more granular results, bounding box annotations are easier to annotate at scale. To leverage the granularity of segmentation approaches with the scalability of bounding box-based models, a multi-task model for joint bounding box detection and segmentation of surgical instruments is proposed. The model consists of a shared backbone and three independent heads for the tasks of classification, bounding box regression, and segmentation. Using adaptive losses together with simple yet effective weakly-supervised label inference, the proposed model use weak labels to learn to segment surgical instruments with a fraction of the dataset requiring segmentation masks. Results suggest that instrument detection and segmentation tasks share intrinsic challenges and jointly learning from both reduces the burden of annotating masks at scale. Experimental validation shows that the proposed model obtain comparable results to that of single-task state-of-the-art detector and segmentation models, while only requiring a fraction of the dataset to be annotated with masks. Specifically, the proposed model obtained 0.81 weighted average precision (wAP) and 0.73 mean intersection-over-union (IOU) in the Endovis2018 dataset with 1% annotated masks, while performing joint detection and segmentation at more than 20 frames per second

Structuring and calorie control of bakery products by templating batter with ultra melt-resistant food-grade hydrogel beads

This journal is © The Royal Society of Chemistry. We report the use of a temperature insensitive, food-grade hydrogel to reduce the caloric density of pancakes that were prepared at temperatures much higher than the boiling point of water. This cheap, facile method utilises a mixed agar-methylcellulose hydrogel, which was blended to produce a slurry of hydrogel microbeads. The pancake batter was mixed with a controlled volume percentage of slurry of hydrogel beads and cooked. From bomb calorimetry experiments, the composites were found to have a reduced caloric density that reflects the volume percentage of hydrogel beads mixed with the batter. Using this procedure, we were able to reduce the caloric density of pancakes by up to 23 ± 3% when the volume percentage of hydrogel beads initially used was 25%. The method is not limited to pancakes and could potentially be applied to various other food products. The structure and morphology of the freeze-dried pancakes and pancake-hydrogel composites were investigated and pores of a similar size to the hydrogel beads were found, confirming that the gel beads maintained their structure during the cooking process. There is scope for further development of this method by the encapsulation of nutritionally beneficial or flavour enhancing ingredients within the hydrogel beads