The process of selecting a nanofluid for a particular application requires determining the thermophysical properties of nanofluid, such as viscosity. However, the experimental measurement of nanofluid viscosity is expensive. Several closed-form formulas for calculating the viscosity have been proposed by scientists based on theoretical and empirical methods, but these methods produce inaccurate results. Recently, a machine learning model based on the combination of seven baselines, which is called the committee machine intelligent system (CMIS), was proposed to predict the viscosity of nanofluids. CMIS was applied on 3144 experimental data of relative viscosity of 42 different nanofluid systems based on five features (temperature, the viscosity of the base fluid, nanoparticle volume fraction, size, and density) and returned an average absolute relative error (AARE) of 4.036% on the test. In this work, eight models (on the same dataset as the one used in CMIS), including two multilayer perceptron (MLP), each with Nesterov accelerated adaptive moment (Nadam) optimizer; two MLP, each with three hidden layers and Adamax optimizer; a support vector regression (SVR) with radial basis function (RBF) kernel; a decision tree (DT); tree-based ensemble models, including random forest (RF) and extra tree (ET), were proposed. The performance of these models at different ranges of input variables was assessed and compared with the ones presented in the literature. Based on our result, all the eight suggested models outperformed the baselines used in the literature, and five of our presented models outperformed the CMIS, where two of them returned an AARE less than 3% on the test data. Besides, the physical validity of models was studied by examining the physically expected trends of nanofluid viscosity due to changing volume fraction.

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Reflective writing or exam wrappers appear to be a valuable exercise that may benefit students. This notion was evaluated in parallel within five engineering classrooms of different size and subject matter. The improvement or quantifiable impact of the reflective writing exercise differed from class to class. Despite this, overall results suggest that exam wrappers lend themselves well to improving learning, both for the students and instructors in the context of engineering classrooms.

Self-reflection and reflective writing are often used to promote self-regulated learning amongst students (Nilson, 2013). A number of engineering programs are incorporating greater opportunities for student reflection (Turns et al., 2014); at the same time, there is a growing need for additional research on the impact of selfreflection and reflective exercises in engineering education (Clark and Dickerson, 2019). We describe the implementation and examine the impact of two types of reflective writing exercisesan exam wrapper and selfevaluation in two Electrical and Computer Engineering courses, a fundamental first year course on signals and systems and a final year technical elective course on photonics.