Characterization of the Spindle Morphology Nanomicelles Assembled from Sericin and Gelatin

Complex nanomicelles were prepared by sericin and type A gelatin with molecular weight of 5789 Da and 128664 Da separately. The assembling conditions were as follows: mass ratio (sericin/gelatin) was 1 : 1, protein concentration was 0.5%, temperature was 35°C, and assembling time was 18 hours. Scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), Fourier transform infrared (FT-IR) spectroscopy, differential scanning calorimetry (DSC), and dynamic light scattering (DLS) were conducted to observe and characterize the complex nanomicelles. Results showed that the complex sericin/gelatin micelles was a kind of nanospindle micelles. The micelles had high electrochemical stability, thermal stability, antidilution stability, and storage stability

Optimization of a continuous flow electrocoagulation as pretreatment for membrane distillation of the waste stream in vinyl ester resin production

Vinyl ester resin production wastewater (VERW) contains high concentrations of organics particularly, methacrylic acid and bisphenol A, which are hazardous chemicals and harmful to the aquatic environment. Therefore, there is an urgent need to properly treat the effluent before discharge into the aquatic system. In this work, direct contact membrane distillation (DCMD) was explored as an advanced treatment of the VERW pre-treated by a continuous flow electrocoagulation (EC) and peroxi-electrocoagulation (PEC) processes. Optimization of EC and PEC processes were investigated and the DCMD performance was evaluated. Results showed that the optimal value of current density and polyacrylamide (PAM) dosage was 15 mA/cm2 and 1 mg/L, respectively in the EC process. For the PEC process, the optimal addition of hydrogen peroxide (H2O2) dosage was four times of the chemical oxygen demand (COD) concentration of EC effluent. The COD of VERW was effectively removed via EC followed by PEC (EC-PEC), resulting in the significant alleviation of membrane fouling during DCMD filtration of VERW. The initial flux of DCMD filtration of VERW pre-treated via EC-PEC improved by 35%, compared that only pre-treated by EC. Moreover, the concentration factor (CF) of the DCMD system reached up to 8.1 and the conductivity of distillate was less than 33.2 μS/cm. Hence, the EC and membrane distillation hybrid process paves a new way for the effective treatment of waste steam from resin production.</p

Optimization of a continuous flow electrocoagulation as pretreatment for membrane distillation of the waste stream in vinyl ester resin production

Vinyl ester resin production wastewater (VERW) contains high concentrations of organics particularly, methacrylic acid and bisphenol A, which are hazardous chemicals and harmful to the aquatic environment. Therefore, there is an urgent need to properly treat the effluent before discharge into the aquatic system. In this work, direct contact membrane distillation (DCMD) was explored as an advanced treatment of the VERW pre-treated by a continuous flow electrocoagulation (EC) and peroxi-electrocoagulation (PEC) processes. Optimization of EC and PEC processes were investigated and the DCMD performance was evaluated. Results showed that the optimal value of current density and polyacrylamide (PAM) dosage was 15 mA/cm2 and 1 mg/L, respectively in the EC process. For the PEC process, the optimal addition of hydrogen peroxide (H2O2) dosage was four times of the chemical oxygen demand (COD) concentration of EC effluent. The COD of VERW was effectively removed via EC followed by PEC (EC-PEC), resulting in the significant alleviation of membrane fouling during DCMD filtration of VERW. The initial flux of DCMD filtration of VERW pre-treated via EC-PEC improved by 35%, compared that only pre-treated by EC. Moreover, the concentration factor (CF) of the DCMD system reached up to 8.1 and the conductivity of distillate was less than 33.2 μS/cm. Hence, the EC and membrane distillation hybrid process paves a new way for the effective treatment of waste steam from resin production.</p

Workplace learning in China: transferring training into practice to improve performance

Purpose: The present study seeks to examine the efficacy of different training modalities on increasing workplace learning, representatives\u27 intent to transfer what they learned into their work, and importantly how training impacts actual work performance. These relationships are tested in the context of a Chinese division of a multinational pharmaceutical company, where pharmaceutical representatives are tasked with relaying relevant efficacy and safety information on pharmaceutical products to health care professionals who prescribe them to patients. Methods: The present study employed a three-group between-subjects experimental design. Representatives received varying forms of training (instruction only, instruction plus reflection, and instruction, reflection, plus direct feedback) based on experimental conditions. After three training sessions over the course of six weeks, representatives were assessed on how much they learned in the training and their actual work performance through observer assessment of meetings with health care professionals, facilitated by the representatives. Findings: In this study, it was found that the process of actively reflecting on what was learned in training led to increased learning, as well as increased performance, compared to simply studying the material. However, receiving direct feedback on training performance, combined with active reflection training, did not provide any further benefits in terms of learning or work performance. Notably, there were no differences in intent to transfer learned material to work, as all conditions reported high levels of transfer intention. Conclusion: The finding provides insightful evidence to support the benefits of fostering trainees\u27 active reflections for work-based learning in the Chinese industry training scenario. In contrast, receiving direct comments on how students performed from a manager or trainer, as well as advise on how do better in the future, had no effect on increasing learning or performance. Although the effect of direct feedback is not statistically significant in this context, further research should be done in understanding individuals\u27 thoughts and behaviors when received direct feedbacks received in workplace training. Relatively little workplace research has assessed both workplace learning and performance in the same study,specifically in the Chinese context. While training efficacy likely varies across cultures to begin with, compensation structures in China do not provide the same monetary incentives for workplace learning (i.e. chance to increase income) as Western culture. This means that any way to increase workplace learning should be of extra value, as employees otherwise may not engage in it at all. (DIPF/Orig.

Workplace Learning in China: Transferring Training Into Practice to Improve Performance

Purpose: The present study seeks to examine the efficacy of different training modalities on increasing workplace learning, representatives' intent to transfer what they learned into their work, and importantly how training impacts actual work performance. These relationships are tested in the context of a Chinese division of a multinational pharmaceutical company, where pharmaceutical representatives are tasked with relaying relevant efficacy and safety information on pharmaceutical products to health care professionals who prescribe them to patients. Methods: The present study employed a three-group between-subjects experimental design. Representatives received varying forms of training (instruction only, instruction plus reflection, and instruction, reflection, plus direct feedback) based on experimental conditions. After three training sessions over the course of six weeks, representatives were assessed on how much they learned in the training and their actual work performance through observer assessment of meetings with health care professionals, facilitated by the representatives. Findings: In this study, it was found that the process of actively reflecting on what was learned in training led to increased learning, as well as increased performance, compared to simply studying the material. However, receiving direct feedback on training performance, combined with active reflection training, did not provide any further benefits in terms of learning or work performance. Notably, there were no differences in intent to transfer learned material to work, as all conditions reported high levels of transfer intention. Conclusion: The finding provides insightful evidence to support the benefits of fostering trainees' active reflections for work-based learning in the Chinese industry training scenario. In contrast, receiving direct comments on how students performed from a manager or trainer, as well as advise on how do better in the future, had no effect on increasing learning or performance. Although the effect of direct feedback is not statistically significant in this context, further research should be done in understanding individuals' thoughts and behaviors when received direct feedbacks received in workplace training. Relatively little workplace research has assessed both workplace learning and performance in the same study, specifically in the Chinese context. While training efficacy likely varies across cultures to begin with, compensation structures in China do not provide the same monetary incentives for workplace learning (i.e. chance to increase income) as Western culture. This means that any way to increase workplace learning should be of extra value, as employees otherwise may not engage in it at all.

PlanarNeRF: Online Learning of Planar Primitives with Neural Radiance Fields

Identifying spatially complete planar primitives from visual data is a crucial task in computer vision. Prior methods are largely restricted to either 2D segment recovery or simplifying 3D structures, even with extensive plane annotations. We present PlanarNeRF, a novel framework capable of detecting dense 3D planes through online learning. Drawing upon the neural field representation, PlanarNeRF brings three major contributions. First, it enhances 3D plane detection with concurrent appearance and geometry knowledge. Second, a lightweight plane fitting module is proposed to estimate plane parameters. Third, a novel global memory bank structure with an update mechanism is introduced, ensuring consistent cross-frame correspondence. The flexible architecture of PlanarNeRF allows it to function in both 2D-supervised and self-supervised solutions, in each of which it can effectively learn from sparse training signals, significantly improving training efficiency. Through extensive experiments, we demonstrate the effectiveness of PlanarNeRF in various scenarios and remarkable improvement over existing works

Use of Traditional Chinese Medicine and Its Impact on Medical Cost among Urban Ischemic Stroke Inpatients in China: A National Cross-Sectional Study

Background. Traditional Chinese medicine (TCM) has long been widely adopted by the Chinese people and has been covered by China’s basic medical insurance schemes to treat ischemic stroke. Previous research has mainly highlighted the therapy effect of TCM on ischemic stroke patients. Some studies have demonstrated that employing TCM can reduce the medical burden on other diseases. But no research has explored whether using TCM could reduce inpatient medical cost for ischemic stroke in mainland China. The purpose of this study is to investigate the impact of the use of TCM on the total inpatient cost of ischemic stroke and to explore whether TCM has played the role of being complementary to, or an alternative for, conventional medicine to treat ischemic stroke. Methods. We conducted a national cross-sectional analysis based on a 5% random sample from claims data of China Urban Employee Basic Medical Insurance (UEBMI) and Urban Resident Basic Medical Insurance (URBMI) schemes in 2015. Mann–Whitney test was used to compare unadjusted total inpatient cost, conventional medication cost, and nonpharmacy cost estimates. Ordinary least square regression analysis was performed to compare demographics-adjusted total inpatient cost and to examine the association between TCM cost and conventional medication cost. Results. A total of 47321 urban inpatients diagnosed with ischemic stroke were identified in our study, with 92.6% (43843) of the patients using TCM in their inpatient treatment. Total inpatient cost for TCM users was significantly higher than TCM nonusers (USD 1217 versus USD 1036, P<0.001). Conventional medication cost was significantly lower for TCM users (USD 335 versus USD 436, P<0.001). The average cost of TCM per patient among TCM users was USD 289. Among TCM users, conventional medication costs were found to be positively associated with TCM cost after adjusting for confounding factors (Coef. = 0.144, P<0.001). Conclusion. Although the use of TCM reduced the cost of conventional medicine compared with TCM nonusers, TCM imposed an extra financial component on the total inpatient cost on TCM users. Our study suggests that TCM mainly played a complementary role to conventional medicine in ischemic stroke treatment in mainland China

Nickel Isotopic Evidence for Late-Stage Accretion of Mercury-Like Differentiated Planetary Embryos

© 2021, The Author(s). Earth’s habitability is closely tied to its late-stage accretion, during which impactors delivered the majority of life-essential volatiles. However, the nature of these final building blocks remains poorly constrained. Nickel (Ni) can be a useful tracer in characterizing this accretion as most Ni in the bulk silicate Earth (BSE) comes from the late-stage impactors. Here, we apply Ni stable isotope analysis to a large number of meteorites and terrestrial rocks, and find that the BSE has a lighter Ni isotopic composition compared to chondrites. Using first-principles calculations based on density functional theory, we show that core-mantle differentiation cannot produce the observed light Ni isotopic composition of the BSE. Rather, the sub-chondritic Ni isotopic signature was established during Earth’s late-stage accretion, probably through the Moon-forming giant impact. We propose that a highly reduced sulfide-rich, Mercury-like body, whose mantle is characterized by light Ni isotopic composition, collided with and merged into the proto-Earth during the Moon-forming giant impact, producing the sub-chondritic Ni isotopic signature of the BSE, while delivering sulfur and probably other volatiles to the Earth

Predicting Stick-Slips in Sheared Granular Fault Using Machine Learning Optimized Dense Fault Dynamics Data

Predicting earthquakes through reasonable methods can significantly reduce the damage caused by secondary disasters such as tsunamis. Recently, machine learning (ML) approaches have been employed to predict laboratory earthquakes using stick-slip dynamics data obtained from sheared granular fault experiments. Here, we adopt the combined finite-discrete element method (FDEM) to simulate a two-dimensional sheared granular fault system, from which abundant fault dynamics data (i.e., displacement and velocity) during stick-slip cycles are collected at 2203 “sensor” points densely placed along and inside the gouge. We use the simulated data to train LightGBM (Light Gradient Boosting Machine) models and predict the gouge-plate friction coefficient (an indicator of stick-slips and the friction state of the fault). To optimize the data, we build the importance ranking of input features and select those with top feature importance for prediction. We then use the optimized data and their statistics for training and finally reach a LightGBM model with an acceptable prediction accuracy (R2 = 0.94). The SHAP (SHapley Additive exPlanations) values of input features are also calculated to quantify their contributions to the prediction. We show that when sufficient fault dynamics data are available, LightGBM, together with the SHAP value approach, is capable of accurately predicting the friction state of laboratory faults and can also help pinpoint the most critical input features for laboratory earthquake prediction. This work may shed light on natural earthquake prediction and open new possibilities to explore useful earthquake precursors using artificial intelligence