Computer‐based technologies are changing at an accelerating pace and becoming increasingly complex. Smart and context‐aware devices and Internet access enable children and adult learners to gain factual and procedure knowledge about many things anywhere at any time. These computer‐based technologies, if well‐utilized in support of learning, can promote personalized and collaborative education, which research suggests are valuable pedagogies. However, new technologies create a burden on designers and educators to use them effectively in support of learning and instruction. Emphasis on 21st century skills suggests that education should emphasize the development of critical thinking and complex problem‐solving skills. Meanwhile, many educators encounter students who lack motivation and are ill‐prepared in reading and mathematics. In addition, engineering educators face another challenge—namely, the need to frequently update curricula so that students can attain marketable skills and adapt to rapidly changing technologies in the workplace. As it happens, proper use of educational technologies can provide solutions to these challenges. This paper reports one such approach to integrate new technologies in two hybrid synchronous courses using technology‐enabled scaffolds in support of deep learning and enhanced problem‐solving competence in engineering education at small colleges in the United States. The emphasis here is on the developmental approach rather than research analysis.

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Machine learning technology has become a hot topic and is being applied in many fields. However, in the prediction of thermal sensation in the elderly, there is not enough research on the neural network to predict the effect of human thermal comfort. In this paper, two neural network algorithms were used to predict the thermal expectation of the elderly, and the accuracy of the two algorithms was compared to find a suitable neural network algorithm to predict human thermal comfort. The dataset was collected from the laboratory study and included 10 local skin temperatures of the subjects, thermal perception voted at three temperatures (28/30/32°C), different wind speeds, and two forms of wind. Thirteen subjects with an average age of 63.5 years old were recruited for the subjective survey. These subjects sat for long periods of summer working conditions, wore uniform thermal resistance clothing, and collected votes on thermal sensation, as well as skin temperature. The results showed that the prediction accuracy of the two algorithms was related to the added influence factors, and the RBF neural network algorithm was the most accurate in predicting thermal sensation of the elderly. The main influencing factors were average skin temperature, wind speed and body fat rate.

Hough transform is an effective way in object recognition and applied to many industrial processes. Based on the principle of Hough transform, a new algorithm which can detect objects through an affine transform was proposed in this paper. First, application of Hough transform to extract straight lines in a model image and a scene image, got these coordinates of the lines, sorted according to the direction angle. Because of affine transform and the periodic direction angle, the direction order of the lines on scene image were different from those on the model image, these lines on scene image were expanse a cycle. Finally affine transform parameters were applied to objects detection. The results showed the effectiveness of the algorithm.