TencentPretrain: A Scalable and Flexible Toolkit for Pre-training Models of Different Modalities

Recently, the success of pre-training in text domain has been fully extended to vision, audio, and cross-modal scenarios. The proposed pre-training models of different modalities are showing a rising trend of homogeneity in their model structures, which brings the opportunity to implement different pre-training models within a uniform framework. In this paper, we present TencentPretrain, a toolkit supporting pre-training models of different modalities. The core feature of TencentPretrain is the modular design. The toolkit uniformly divides pre-training models into 5 components: embedding, encoder, target embedding, decoder, and target. As almost all of common modules are provided in each component, users can choose the desired modules from different components to build a complete pre-training model. The modular design enables users to efficiently reproduce existing pre-training models or build brand-new one. We test the toolkit on text, vision, and audio benchmarks and show that it can match the performance of the original implementations

A Comparison of the Fine-Grinding Performance between Cylpebs and Ceramic Balls in the Wet Tumbling Mill

This study investigated the effect of grinding media on the fine-grinding performance in the wet tumbling mill. Comparative experiments between cylpebs and ceramic balls were conducted in a laboratory wet ball mill under various conditions, such as different total masses, total surface areas, and total numbers. The results indicated that ceramic balls produce a slightly smaller instantaneous breakage rate than cylpebs due to their small bulk density at the same charge volume. However, a larger instantaneous breakage rate can be obtained by ceramic balls with the same total mass due to the larger surface area. According to experiments related to the total surface area and total number of ceramic balls, it was found that the fine grinding of ceramic balls also needs to meet a certain break force, which is mainly determined by the diameter of the ceramic balls. Moreover, two models were developed to use ceramic balls instead of cylpebs as fine-grinding media in the wet tumbling mill based on the ball size and the ratio of the total mass of cylpebs. Cylpebs can be replaced by ceramic balls with 1.16–1.75 times the diameter of cylpebs, taking up only 76%–83% of the total weight of cylpebs. When the charge volume of cylpebs is between 20% and 35% in the wet ball mill, cylpebs can be replaced by ceramic balls of the same single mass, with the total weight of the ceramic balls being only 74%–77% of that of the cylpebs

High Power Factor of Ag2Se/Ag/Nylon Composite Films for Wearable Thermoelectric Devices

A flexible thermoelectric device has been considered as a competitive candidate for powering wearable electronics. Here, we fabricated an n-type Ag2Se/Ag composite film on a flexible nylon substrate using vacuum-assisted filtration and a combination of cold and hot pressing. By optimising the Ag/Se ratio and the sequential addition and reaction time of AA, an excellent power factor of 2277.3 μW∙m−1 K−2 (corresponding to a ZT of ~0.71) at room temperature was achieved. In addition, the Ag2Se/Ag composite film exhibits remarkable flexibility, with only 4% loss and 10% loss in electrical conductivity after being bent around a rod of 4 mm radius for 1000 cycles and 2000 cycles, respectively. A seven-leg flexible thermoelectric device assembled with the optimised film demonstrates a voltage of 19 mV and a maximum power output of 3.48 μW (corresponding power density of 35.5 W m−2) at a temperature difference of 30 K. This study provides a potential path to design improved flexible TE devices

使用列线图筛查2型糖尿病轻度认知障碍

Abstract Background Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram‐based model to detect mild cognitive impairment (MCI) in T2DM patients. Methods Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well‐qualified investigators conducted face‐to‐face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM‐related information, and history of depression and anxiety. Cognitive function was assessed using the Mini‐Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively. Results A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%–38.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815–0.883), and the threshold probability ranged from 35.0% to 85.0%. Conclusions Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients

Sleep quality and its associated factors among patients with type 2 diabetes mellitus in Hunan, China: a cross-sectional study

Objectives Type 2 diabetes mellitus (T2DM) is a serious public health issue. Compared with the general population, patients with T2DM have a higher risk of poor sleep quality, which could ultimately result in poor prognosis. Therefore, this study aimed to evaluate sleep quality and its associated factors among patients with T2DM in Hunan, China.Design This was a cross-sectional study.Setting A tertiary hospital in Hunan, China.Participants Patients with T2DM hospitalised at the Endocrinology Department were consecutively enrolled between March 2021 and December 2022. Sociodemographic characteristics, lifestyle factors and T2DM-related information were collected retrospectively.Primary and secondary outcome measures Sleep quality was evaluated using the Pittsburgh Sleep Quality Index, with a cut-off value of >7 suggesting poor sleep quality. Multivariate logistic regression analysis was used to determine factors associated with poor sleep quality.Results Of the 1039 participants included, 1001 provided complete data. The mean age of the study sample was 60.24±10.09 years, and 40.5% (95% CI 37.5% to 43.5%) of patients had poor sleep quality. Multivariate logistic regression analysis showed that female sex (adjusted OR (aOR) 1.70, 95% CI 1.25 to 2.29), unmarried status (aOR 1.72, 95% CI 1.05 to 2.83), diabetic retinopathy (aOR 1.38, 95% CI 1.04 to 1.83), diabetic foot (aOR 1.80, 95% CI 1.11 to 2.93) and a per capita monthly household income of >5000 RMB (aOR 0.66, 95% CI 0.47 to 0.93) were associated with poor sleep quality.Conclusions Nearly two-fifths of patients with T2DM reported poor sleep quality in Hunan, China. Sex, marital status, diabetic retinopathy, diabetic foot and household income were independently associated with sleep quality among patients with T2DM in Hunan, China