The progenitors of type Ia supernovae in the semidetached binaries with red giant donors

Context. The companions of the exploding carbon-oxygen white dwarfs (CO WDs) for producing type Ia supernovae (SNe Ia) are still not conclusively confirmed. A red-giant (RG) star has been suggested to be the mass donor of the exploding WD, named as the symbiotic channel. However, previous studies on the this channel gave a relatively low rate of SNe Ia. Aims. We aim to systematically investigate the parameter space, Galactic rates and delay time distributions of SNe Ia from the symbiotic channel by employing a revised mass-transfer prescription. Methods. We adopted an integrated mass-transfer prescription to calculate the mass-transfer process from a RG star onto the WD. In this prescription, the mass-transfer rate varies with the local material states. Results. We evolved a large number of WD+RG systems, and found that the parameter space of WD+RG systems for producing SNe Ia is significantly enlarged. This channel could produce SNe Ia with intermediate and old ages, contributing to at most 5% of all SNe Ia in the Galaxy. Our model increases the SN Ia rate from this channel by a factor of 5. We suggest that the symbiotic systems RS Oph and T CrB are strong candidates for the progenitors of SNe Ia.Comment: 8 pages, 6 figure

A Study on Identity Construction of First Person Pronouns in Academic Papers from the Perspective of Evidentiality

The first person pronoun plays an important role in identity construction, however, there is few study on it from the perspective of evidentiality. This paper took the first person pronouns as evidentials, and conducted a comparable analysis on the frequency of them and the identities they constructed in academic papers between soft and hard sciences, aiming to find the differences between different discourse communities and explore their preferences for academic identity construction. The results showed that both fields prefer to use plural and subjective cases of first person pronouns, and they both prefer to construct the authorial identity of “researcher”, but scarcely construct the authorial identity of “responsible person”. Researchers in hard science use less evidentials than researchers in soft science, and they prefer to use evidentials “we” and “statement”, which weaken the authorial identity. Evidentials that embody authorial identity, including singular first person pronouns and “participation” evidentials, account for higher proportion in soft science than those in hard science

Evaluating ecohydrological modelling framework to link atmospheric CO2 and stomatal conductance

The establishment of an accurate stomatal conductance (gs) model in responding to CO2 enrichment under diverse environmental conditions remains an important issue as gs is the key to understand the plant–water–atmosphere interactions. A better representation of gs is important to reduce uncertainties in predicting the climate change impacts on various ecosystem functions. In this study, we evaluated three most commonly used gs formulations for the estimation of the stomatal response to environmental factors using in situ measurements under different environmental conditions. The three gs models were Leuning's modified Ball–Berry model and two specific cases of the optimization models (i.e., Rubisco limitation model and RuBP regeneration limitation model). On the basis of an analysis of 234 data points obtained from experiments under instantaneous, semicontrolled, and the free‐air CO2 experiment conditions, we found that Leuning's modified Ball–Berry model and RuBP‐limited optimization model showed similar performance, and both performed better than Rubisco limitation model. Functional groups (e.g., C3 vs. C4 species) and life form (e.g., annual vs. perennial species) play an important role in determining the gs model performance and thus pose a challenge for gs predictions in mixed vegetation communities

2d Regional Correlation Analysis Of Single-molecule Time Trajectories

We report a new approach of 2D regional correlation analysis capable of analyzing fluctuation dynamics of complex multiple correlated and anticorrelated fluctuations under a noncorrelated noise background. Using this new method, by changing and scanning the start time and end time along a pair of fluctuation trajectories, we are able to map out any defined segments along the fluctuation trajectories and determine whether they are correlated, anticorrelated, or noncorrelated; after which, a cross-correlation analysis can be applied for each specific segment to obtain a detailed fluctuation dynamics analysis. We specifically discuss an application of this approach to analyze single-molecule fluorescence resonance energy transfer (FRET) fluctuation dynamics where the fluctuations are often complex, although this approach can be useful for analyzing other types of fluctuation dynamics of various physical variables as well

Social Media Use Purposes and Psychological Wellbeing in Times of Crises

This study investigates the effect of social media (SM) use purposes and user characteristics on individual psychological wellbeing (PWB) during the coronavirus pandemic (COVID-19). Informed by the uses and gratifications theory and PWB research, this study analyzed survey data collected from 282 SM users aged 18 through 59 from a minority-serving university in the United States in March-April 2020. Our quantitative data analysis showed that social media can be used to improve the quality of personal experiences during the COVID-19 crisis through three mechanisms—connectedness (i.e., social), engagement (i.e., collaborative), and entertainment (i.e., hedonic). However, the effect varied by gender, SM usage level, and individual concern about COVID-19 risk. The findings contribute to the literature and offer implications in technology use for enhancing public mental health during crises

Probing Single-molecule Interfacial Geminate Electron-cation Recombination Dynamics

Interfacial electron-cation recombination in zinc-tetra (4-carboxyphenyl) porphyrin (ZnTCPP)/TiO(2) nanoparticle system has been probed at the single-molecule level by recording and analyzing photon-to-photon pair times of the ZnTCPP fluorescence. We have. developed a novel approach to reveal the hidden single-molecule interfacial electron-cation recombination dynamics by analyzing the autocorrelation function and a proposed convoluted single-molecule interfacial electron-cation recombination model. Our results suggest that the fluctuations of the interfacial electron transfer (ET) reactivity modulate the ET cycles as well as the interfacial electron-cation recombination dynamics. On the basis of this model, the single-molecule electron-cation recombination time of ZnTCPP/-TiO(2) system is deduced to be at time scale of 10(-5) s. The autocorrelation of photon-to-photon pair times as well as the convoluted ET model has been further demonstrated by simulation and interpreted in terms of the interfacial ET reactivity fluctuation and blinking. Our approach not only can effectively probe the single-molecule interfacial electron-cation dynamics but also can be applied to other single-molecule ground-state regeneration dynamics occurring at interfaces and within condensed phases

Cancellable Deep Learning Framework for EEG Biometrics

EEG-based biometric systems verify the identity of a user by comparing the probe to a reference EEG template of the claimed user enrolled in the system, or by classifying the probe against a user verification model stored in the system. These approaches are often referred to as template-based and model-based methods, respectively. Compared with template-based methods, model-based methods, especially those based on deep learning models, tend to provide enhanced performance and more flexible applications. However, there is no public research report on the security and cancellability issue for model-based approaches. This becomes a critical issue considering the growing popularity of deep learning in EEG biometric applications. In this study, we investigate the security issue of deep learning model-based EEG biometric systems, and demonstrate that model inversion attacks post a threat for such model-based systems. That is to say, an adversary can produce synthetic data based on the output and parameters of the user verification model to gain unauthorized access by the system. We propose a cancellable deep learning framework to defend against such attacks and protect system security. The framework utilizes a generative adversarial network to approximate a non-invertible transformation whose parameters can be changed to produce different data distributions. A user verification model is then trained using output generated from the generator model, while information about the transformation is discarded. The proposed framework is able to revoke compromised models to defend against hill climbing attacks and model inversion attacks. Evaluation results show that the proposed method, while being cancellable, achieves better verification performance than the template-based methods and state-of-the-art non-cancellable deep learning methods

Elevated CO2 as a driver of global dryland greening.

While recent findings based on satellite records indicate a positive trend in vegetation greenness over global drylands, the reasons remain elusive. We hypothesize that enhanced levels of atmospheric CO2 play an important role in the observed greening through the CO2 effect on plant water savings and consequent available soil water increases. Meta-analytic techniques were used to compare soil water content under ambient and elevated CO2 treatments across a range of climate regimes, vegetation types, soil textures and land management practices. Based on 1705 field measurements from 21 distinct sites, a consistent and statistically significant increase in the availability of soil water (11%) was observed under elevated CO2 treatments in both drylands and non-drylands, with a statistically stronger response over drylands (17% vs. 9%). Given the inherent water limitation in drylands, it is suggested that the additional soil water availability is a likely driver of observed increases in vegetation greenness