A Comparison of Chitosan Adhesion to KOH and H2O2 Pre-Treated Electrospun Poly(3-Hydroxybutyrate) Nanofibers

Chitosan coatings could effectively increase the biostability and biocompatibility of biomaterials while maintaining their structural integrity. In this study, electrospun fibrous polyhydroxybutyrate (PHB) membranes were pre-treated with potassium hydroxide (KOH) or hydrogen peroxide (H2O2) and then modified with dopamine (DA) and glutaraldehyde (GA) to improve their adhesion with chitosan (CS). Scanning electron microscopy (SEM), water contact angles (WCA), and Fourier transform infrared spectroscopy (FTIR) were used to demonstrate the successful generation of DA and GA-modified PHB fibers. KOH pre-treated PHB membranes exhibited superior binding efficiency with CS at low concentrations compared to their H2O2 pre-treated counterparts. The thermal analysis demonstrated a considerable decrease in the degradation temperature and crystallinity of KOH pre-treated membranes, with temperatures dropping from 309 °C to 265.5 °C and crystallinity reducing from 100% to 25.59% as CS concentration increased from 0 to 2 w/v%. In comparison, H2O2 pre-treated membranes experienced a mild reduction in degradation temperature, from 309 °C to 284.4 °C, and a large decrease in crystallinity from 100% to 43%. UV-vis analysis using Cibacron Brilliant Red 3B-A dye (CBR) indicated similar binding efficiencies at low CS concentrations for both pre-treatments, but decreased stability at higher concentrations for KOH pre-treated membranes. Mechanical testing revealed a considerable increase in Young’s modulus (2 to 14%), toughness (31 to 60%), and ultimate tensile stress (UTS) (14 to 63%) for KOH-treated membranes compared with H2O2 pre-treated membranes as CS concentration increased from 0 to 2 w/v%

The Effect of Blended Instruction on Student Performance: A Meta-Analysis of 106 Empirical Studies from China and Abroad

Blended instruction integrating off-line and on-line teaching has become an important instrument for promoting educational reform and innovation. However, the results of current empirical studies diverge on the effect of blended instruction on student performance, which necessitates further research on the effectiveness of blended instruction and related factors. This study, using an evidence-based meta-analytical approach, conducts a quantitative analysis of 106 experimental and quasi- experimental studies published from January 2000 to September 2021 in China and abroad, and systematically examines the effectiveness of blended instruction. The research finds that: i) The summary effect size (ES) of the included sample is 0.669 (n=142), indicating that blended instruction has above-moderate positive effects on student performance, especially on student learning motivation and academic emotions and attitude; ii) In terms of education levels, experimental periods and class sizes, blended instruction has the most significant positive effect on junior and senior secondary school students, on a teaching period from one to three months, and on a class size of 51 to 100 students; iii) Regarding the proportion and interactive patterns of online teaching, 50% composition of online teaching and synchronous or synchronous + asynchronous interaction exert the most significant positive effects on student learning. iv) Teaching methods including task-driven learning, role-playing, inquiry-based teaching, and case-based teaching have greater positive effects on student performance than other methods. Group study yields a greater effect on promoting student learning compared to individual study. Based on the findings, the present study also makes suggestions for the effective practice of blended instruction

A Comparison of Chitosan Adhesion to KOH and H_{2}O_{2} Pre-Treated Electrospun Poly(3-Hydroxybutyrate) Nanofibers

Chitosan coatings could effectively increase the biostability and biocompatibility of biomaterials while maintaining their structural integrity. In this study, electrospun fibrous polyhydroxybutyrate (PHB) membranes were pre-treated with potassium hydroxide (KOH) or hydrogen peroxide (H2O2) and then modified with dopamine (DA) and glutaraldehyde (GA) to improve their adhesion with chitosan (CS). Scanning electron microscopy (SEM), water contact angles (WCA), and Fourier transform infrared spectroscopy (FTIR) were used to demonstrate the successful generation of DA and GA-modified PHB fibers. KOH pre-treated PHB membranes exhibited superior binding efficiency with CS at low concentrations compared to their H2O2 pre-treated counterparts. The thermal analysis demonstrated a considerable decrease in the degradation temperature and crystallinity of KOH pre-treated membranes, with temperatures dropping from 309 °C to 265.5 °C and crystallinity reducing from 100% to 25.59% as CS concentration increased from 0 to 2 w/v%. In comparison, H2O2 pre-treated membranes experienced a mild reduction in degradation temperature, from 309 °C to 284.4 °C, and a large decrease in crystallinity from 100% to 43%. UV-vis analysis using Cibacron Brilliant Red 3B-A dye (CBR) indicated similar binding efficiencies at low CS concentrations for both pre-treatments, but decreased stability at higher concentrations for KOH pre-treated membranes. Mechanical testing revealed a considerable increase in Young’s modulus (2 to 14%), toughness (31 to 60%), and ultimate tensile stress (UTS) (14 to 63%) for KOH-treated membranes compared with H2O2 pre-treated membranes as CS concentration increased from 0 to 2 w/v%

Constructing Sample-to-Class Graph for Few-Shot Class-Incremental Learning

Few-shot class-incremental learning (FSCIL) aims to build machine learning model that can continually learn new concepts from a few data samples, without forgetting knowledge of old classes. The challenges of FSCIL lies in the limited data of new classes, which not only lead to significant overfitting issues but also exacerbates the notorious catastrophic forgetting problems. As proved in early studies, building sample relationships is beneficial for learning from few-shot samples. In this paper, we promote the idea to the incremental scenario, and propose a Sample-to-Class (S2C) graph learning method for FSCIL. Specifically, we propose a Sample-level Graph Network (SGN) that focuses on analyzing sample relationships within a single session. This network helps aggregate similar samples, ultimately leading to the extraction of more refined class-level features. Then, we present a Class-level Graph Network (CGN) that establishes connections across class-level features of both new and old classes. This network plays a crucial role in linking the knowledge between different sessions and helps improve overall learning in the FSCIL scenario. Moreover, we design a multi-stage strategy for training S2C model, which mitigates the training challenges posed by limited data in the incremental process. The multi-stage training strategy is designed to build S2C graph from base to few-shot stages, and improve the capacity via an extra pseudo-incremental stage. Experiments on three popular benchmark datasets show that our method clearly outperforms the baselines and sets new state-of-the-art results in FSCIL

Mixing process of two miscible fluids in a lid-driven cavity

The authors gratefully acknowledge the financial support from the National and Key Research and Development Program of China (No.2016YFB0302801), National Natural Science Foundation of China (No.21676007) and Scientific Research and Technology Development Projects of China National Petroleum Corporation (No. 2016B-2605).Peer reviewedPostprin

Mechanisms for drawdown of floating particles in a laminar stirred tank flow

The authors gratefully acknowledge the financial support from the National Key R&D Program of China (2017YFB0306701) and from the National Natural Science Foundation of China (No.21676007).Peer reviewedPostprin

Sparsity-enhanced optimization for ejector performance prediction

Within a model of the ejector performance prediction, the influence of ejector component efficiencies is critical in the prediction accuracy of the model. In this paper, a unified method is developed based on sparsity-enhanced optimization to determine correlation equations of ejector component efficiencies in order to improve the prediction accuracy of the ejector performance. An ensemble algorithm that combines simulated annealing and gradient descent algorithm is proposed to obtain its global solution for the proposed optimization problem. The ejector performance prediction of a 1-D model in the literature is used as an example to illustrate and validate the proposed method. Tests results reveal that the maximum and average absolute errors for the ejector performance prediction are reduced much more when compared with existing results under the same experimental condition. Furthermore, the results indicate that the ratio of geometric parameters to operating parameters is a key factor affecting the ejector performance

Refractive Index-Matched PIV Experiments and CFD Simulations of Mixing in a Complex Dynamic Geometry

Acknowledgment The authors appreciatively acknowledge the financial support from the National Key Research and Development Program of China (No.2016YFB0302801), National Natural Science Foundation of China (No.21676007), the Fundamental Research Funds for the Central Universities (XK1802-1) and the China Scholarship Council.Peer reviewedPostprin