Extinction of Taurus, Orion, Perseus and California Molecular Clouds Based on the LAMOST, 2MASS and Gaia surveys I: Three-dimensional Extinction and Structure

The three-dimensional extinction and structure are studied for the Taurus, Orion, Perseus and California molecular clouds based on the LAMOST spectroscopy. Stellar color excess is calculated with the intrinsic color index derived from the atmospheric parameters in the LAMOST DR8 catalog and the observed color index in the Gaia EDR3 and the 2MASS PSC. In combination with the distance from the Gaia EDR3 parallax, the three-dimensional dust extinction maps are retrieved in the color excesses $E_{\rm{G_{BP},G_{RP}}}$ and $E_{\rm{J,K_{S}}}$ with an uncertainty of $\sim$0.03mag and $\sim$0.07mag respectively. The extinction maps successfully separate the clouds that overlap in the sky area and manifest the structure of the individual cloud. Meanwhile, a bow-like structure is found with a distance range from 175pc to 250pc, half of which is a part of the Per-Tau Shell in similar coordinates and distance while the other half is not. Three low-extinction rings are additionally discovered and briefly discussed.Comment: 22 pages, 15 figures, 2 tables, accepted for publication in Ap

SPar: estimating stellar parameters from multi-band photometries with empirical stellar libraries

Modern large-scale photometric surveys have provided us with multi-band photometries of billions of stars. Determining the stellar atmospheric parameters, such as the effective temperature (\teff) and metallicities (\feh), absolute magnitudes ($M_{G}$), distances ($d$) and reddening values (\ebr) is fundamental to study the stellar populations, structure, kinematics and chemistry of the Galaxy. This work constructed an empirical stellar library which maps the stellar parameters to multi-band photometries from a dataset with Gaia parallaxes, LAMOST atmospheric parameters, and optical to near-infrared photometry from several photometric surveys. Based on the stellar library, we developed a new algorithm, SPar (\textbf{S}tellar \textbf{P}arameters from multib\textbf{a}nd photomet\textbf{r}y), which fits the multi-band stellar photometries to derive the stellar parameters (\teff, \feh, $M_G$, $d$ and \ebr) of the individual stars. The algorithm is applied to the multi-band photometric measurements of a sample of stars selected from the SMSS survey, which have stellar parameters derived from the spectroscopic surveys. The stellar parameters derived from multi-band photometries by our algorithm are in good agreement with those from the spectroscopic surveys. The typical differences between our results and the literature values are 170\,K for \teff, 0.23\,dex for \feh, 0.13\,mag for $M_G$ and 0.05\,mag for \ebr. The algorithm proved to be robust and effective and will be applied to the data of future large-scale photometric surveys such as the Mephisto and CSST surveys.Comment: 16 pages, 10 figures, Accepted by The Astronomical Journal on 7/8/202

Satisfaction with care quality and anxiety among family members during nursing home visiting restrictions: The chain mediating effect of emotional regulation and perceived stress

IntroductionThis study aimed to investigate the psychological well-being (perceived stress and anxiety) of Chinese family members during nursing home visiting restrictions and to elucidate the relationships among satisfaction with care quality, emotion regulation, perceived stress, and anxiety.MethodsAn online survey was conducted with a cross-sectional study design. From 18 to 29 January 2022, a total of 571 family members of nursing home residents completed online questionnaires comprising socio-demographic characteristics, satisfaction with care quality, emotion regulation, perceived stress, and anxiety. Mediation analyses were performed to estimate the direct and indirect effects of satisfaction with care quality on anxiety using the PROCESS macro for SPSS.ResultsThe results showed that approximately one-quarter of Chinese family members had anxiety symptoms during nursing home visiting restrictions. Satisfaction with care quality affected anxiety via three mediating paths: (a) through cognitive reappraisal (effect = 0.028); (b) through cognitive reappraisal and perceived stress sequentially (effect = −0.057); and (c) through perceived stress (effect = −0.212). The chain mediating effect (path b) accounted for 23.7% of the total effect.ConclusionsThese findings corroborated our hypothesis that cognitive reappraisal (a kind of emotion regulation strategy) and perceived stress mediated the relationship between satisfaction with care quality and anxiety during nursing home visiting restrictions. Efforts to address family members’ psychological well-being by focusing on cognitive reappraisal should be considered

Enhanced γ-Glutamyltranspeptidase Imaging That Unravels the Glioma Recurrence in Post-radio/Chemotherapy Mixtures for Precise Pathology via Enzyme-Triggered Fluorescent Probe

Accurate pathological diagnosis of gliomas recurrence is crucial for the optimal management and prognosis prediction. The study here unravels that our newly developed γ-glutamyltranspeptidase (GGT) fluorescence probe (Figure 1A) imaging in twenty recurrent glioma tissues selectively recognizes the most malignant portion from treatment responsive tissues induced by radio/chemo-therapy (Figure 1B). The overexpression of GGT in recurrent gliomas and low level in radiation necrosis were validated by western blot analysis and immunohistochemistry. Furthermore, the ki-67 index evaluation demonstrated the significant increase of malignancy, aided by the GGT-responsive fluorescent probe to screen out the right specimen through fast enhanced imaging of enzyme activity. Importantly, our GGT-targeting probe can be used for accurate determination of pathologic evaluation of tumor malignancy, and eventually for guiding the following management in patients with recurrent gliomas

DNA Nanotechnology on Bio-Membranes

In recent years, DNA nanotechnology, including both structural and dynamic DNA nanotechnology, has emerged as a powerful tool for various analytical and biomedical applications in biological membranes [...

A Frustratingly Easy Improvement for Position Embeddings via Random Padding

Position embeddings, encoding the positional relationships among tokens in text sequences, make great contributions to modeling local context features in Transformer-based pre-trained language models. However, in Extractive Question Answering, position embeddings trained with instances of varied context lengths may not perform well as we expect. Since the embeddings of rear positions are updated fewer times than the front position embeddings, the rear ones may not be properly trained. In this paper, we propose a simple but effective strategy, Random Padding, without any modifications to architectures of existing pre-trained language models. We adjust the token order of input sequences when fine-tuning, to balance the number of updating times of every position embedding. Experiments show that Random Padding can significantly improve model performance on the instances whose answers are located at rear positions, especially when models are trained on short contexts but evaluated on long contexts. Our code and data will be released for future research

Optimization Design of the Torsion Characteristic of Diesel Vehicle Clutch Driven Plate

To study idle noise of diesel vehicle transmission,the equivalent mechanical model of the diesel vehicle transmission system is established. The engine flywheel wave function is obtained by test. The influence trend of characteristic of clutch torsion on the idle noise is studied and the characteristic of clutch torsion is optimized. The real vehicle test shows that idle noise problem is well solved through the optimal design

Investigation of the Adaptability of Paper Sludge with Wood Fiber in Cement-Based Insulation Mortar

Paper sludge generated from the paper industry is classified as solid waste, comprising primarily wood fiber with excellent toughness and CaCO3 with low thermal conductivity. The purpose of this work was to investigate the adaptability of paper sludge with wood fiber into cement-based insulation mortar. The addition of paper sludge with wood fiber was found to be beneficial for optimizing the performance of cement-expanded polystyrene (EPS)/paper sludge (CEP) mortar. In detail, the addition of paper sludge with low fiber content in the range of 2.5% to 7.5% improved the toughness and softening coefficient of CEP mortar. In comparison, an increase of wood fiber content notably improved the properties of CEP mortar when its addition level reached 15%. Additionally, paper sludge with different fiber contents decreased the thermal conductivity of CEP mortar, ranging from 0.0897 to 0.0885 W/(m·K). In conclusion, paper sludge with wood fiber exhibited good adaptability in CEP mortar

Near-Surface Defects Identification of Polyethylene Pipes Based on Synchro-Squeezing Transform and Deep Learning

To conduct the ultrasonic weld inspection of polyethylene pipes, it is necessary to use low-frequency transducers due to the high sound energy attenuation of polyethylene. However, one of the challenges in this process is that the blind zone of the ultrasonic transducer may cover a part of the workpiece being tested. This leads to a situation where if a defect appears near the surface of the workpiece, its signal will be buried by the blind zone signal. This hinders the early identification of defects, which is not favorable in such a scenario. To address this issue, we propose a new approach to detect and locate the near-surface defects. We begin by performing a synchro-squeezing transform on the original A-scan signal to obtain an accurate time-frequency distribution. While successful in detecting and localizing near-surface defects, the method alone fails to identify the specific type of defect directly: a limitation shared with other signal processing methods. Thus, an effective and lightweight defect identification model was established that combines depth-wise separable convolution and an attention mechanism. Finally, the performance of the proposed model was compared and visually analyzed with other models. This paper successfully achieves the detection, localization, and identification of near-surface defects through the synchro-squeezing transform and the defect identification model. The results show that our model can identify both general and near-surface defects with an accuracy of 99.50% while having a model size of only 1.14 MB