Collective reflection strategy for moderating conformity tendency and promoting reflective judgment performance

Reflective judgement is crucial for medical-related practitioners in dealing with controversial issues. However, the conformity phenomenon is likely to occur and interfere with reflective judgement learning during interactive activities. Effective strategies are required to moderate the conformity behaviour tendency (CBT) and improve reflective judgement performance (RJP). This study demonstrates two significant results: (a) Compared with the guided self-reflection learning strategy, the online collective reflection (OCR) learning strategy effectively weakened the learners' general CBT while dealing with professional controversial issues; and (b) a significantly negative correlation between the RJP achieved and the change of CBT in online environment was detected in the OCR group. The implications and potential applications in higher education were discussed. Further studies are needed to confirm the long-term effects and the extending application to other professional studies.     

Static pentane extraction as a potential alternative to Soxhlet extraction for polypropylene olive oil overall migration limit measurements

Static pentane extraction of olive oil from polypropylene was evaluated as a safer, higher throughput alternative to pentane Soxhlet extraction for the recovery of olive oil from polypropylene for overall migration limit determinations. Static, ambient temperature, pentane extraction achieved quantitative recovery of olive oil from polypropylene plaques prepared by using the OM‐5 overall migration limit protocol. In addition to validation data for static extractions, a modified esterification procedure is presented as a means to accommodate the larger sample sets that are enabled with static extraction.     

The control of paracetamol particle size and surface morphology through crystallisation in a spray dryer

The parameters governing the crystallisation of paracetamol using various conventional techniques has been extensively studied, however the factors influencing the drug crystallisation using spray drying is not as well understood. The aim of this work was to investigate the crystallisation of an active pharmaceutical ingredient through evaporative crystallisation using a spray dryer to study the physicochemical properties of the drug and to use semi-empirical equations to gain insight into the morphology and particle size of the dried powder. Paracetamol solutions were spray dried at various inlet temperatures ranging from 60 °C to 120 °C and also from a series of inlet feed solvent compositions ranging from 50/50% v/v ethanol/water to 100% ethanol and solid-state characterisation was done. The size and morphology of the dried materials were altered with a change in spray drying parameters, with an increase in inlet temperature leading to an increase in particle Sauter mean diameter (from 3.0 to 4.4 µm) and a decrease in the particle size with an increase in ethanol concentration in the feed (from 4.6 to 4.4 µm) as a result of changes in particle density and atomised droplet size. The morphology of the dried particles consisted of agglomerates of individual crystallites bound together into larger semi-spherical agglomerates with a higher tendency for particles having crystalline ridges to form at higher ethanol concentrations of the feed.     

基于卷积神经网络的葡萄叶片氮含量识别方法

为实现对葡萄叶片氮素含量快速、便捷的识别，在卷积神经网络VGG-16网络结构基础上，将数据增广后的图像按不同梯度划分进行模型训练，通过十折交叉验证法探究最佳的训练集与验证集分配比例，并构建4个不同深度的网络模型进行训练对比，采用全局平均池化代替全连接层约简网络参数量。训练结果表明，氮含量梯度设为0.70%、0.35%和0.175%时，室内简单背景识别准确率分别为85.9%、76.2%和71.1%；晴天室外复杂背景下识别准确率分别为44.6%、35.0%和30.4%。研究结果表明利用VGG-16建立的网络学习模型对葡萄叶片氮含量识别提供了一种新的便捷方法，对农业信息化和智能化技术应用具有一定促进作用。     

Lewis acid-oxygen vacancy interfacial synergistic catalysis over SO42−/Ce0.84Zr0.16O2–WO3–ZrO2 for N,N-diethylation of aniline with ethanol

Herein, a new mechanism involving Lewis acid-oxygen vacancy interfacial synergistic catalysis for aniline N,N-diethylation with ethanol was proposed, and the SO₄²⁻/Ce_0.84Zr_0.16O₂–WO₃–ZrO₂ catalyst (SCWZ) with both Lewis acid sites and oxygen vacancies was synthesized by the hydrothermal method, which shows better catalytic activity than the reported solid acidic catalysts. Besides, the SO₄²⁻/ZrO₂ (SZ) and SO₄²⁻/WO₃–ZrO₂ (SWZ) catalysts were also prepared and compared with SCWZ to investigate the synergistic effect of each component. The SO₄²⁻ and WO₃ mainly generate Lewis acid by bonding with ZrO₂, which is beneficial for the fracture of the N–H bond in aniline. The Ce_0.84Zr_0.16O₂ solid solution mainly plays a vital role in generating the oxygen vacancies as the interface active species, which can participate in stripping –OH from ethanol, then the carbocation will also be released, which only needs 1.3805 kcal/mol energy, calculated by density functional theory (DFT), to be input. In comparison, the traditional reaction mechanism needs the Brønsted acidic sites to promote the protonation of ethanol, then dehydration and subsequent formation of carbocation followed, and 108.6846 kcal/mol energy needs to be input, which is far higher than that of the new mechanism. The apparent activation energy (E_a) over SCWZ was measured by experiment to be 34.09 kJ/mol, which is much lower than that of SWZ (47.10 kJ/mol) and SZ (54.37 kJ/mol), illustrating comparatively preferable kinetics for SCWZ than that of SWZ and SZ. Besides, the conversion of aniline and selectivity to N,N-diethylaniline over SCWZ reach almost 100% and 73%, respectively. The SCWZ can be renewed for 4 times without rapid deactivation, and the longevity of SCWZ is longer than that of SWZ and SZ, as the loaded SO₄²⁻ and tetragonal ZrO₂ are stabilized by Ce_0.84Zr_0.16O₂ and WO₃, respectively.     

Adaptive neuro-fuzzy algorithm applied to predict and control multi-phase flow rates through wellhead chokes

A Takagi-Sugeno adaptive neuro-fuzzy inference system (TSFIS) model is developed and applied to a dataset of wellhead flow-test data for the Resalat oil field located offshore southern Iran, the objective is to assist in the prediction and control of multi-phase flow rates of oil and gas through the wellhead chokes. For this purpose, 182 test data points (Appendix 1) related to the Resalat field are evaluated. In order to predict production flow rate (Q_L) expressed as stock-tank barrels per day (STB/D), this dataset includes four selected input variables: upstream pressure (Pwh); wellhead choke sizes (D64); gas to liquid ratio (GLR); and, base solids and water including some water-soluble oil emulsion (BS&W). The test data points evaluated include a wide range of oil flow rate conditions and values for the four input variables recorded. The TSFIS algorithm applied involves five data processing steps: a) pre-processing, b) fuzzification, c) rules base and adaptive neuro-fuzzy inference engine, d) defuzzification, and e) post-processing of the fuzzy model. The developed TSFIS model for the Resalat oil field database predicted oil flow rate to a high degree of accuracy (root mean square error = 247 STB/D, correlation coefficient = 0.9987), which improves substantially on the commonly used empirical algorithms used for such predictions. TSFIS can potentially be applied in wellhead choke fuzzy controllers to stabilize flow in specific wells based on real-time input data records.