Lighting condition is essential to human performance. With the widespread use of computer-based learning, the performance measurements become difficult, and the effects of artificial lighting conditions towards the new learning forms are not investigated extensively. The current study conducts a subject-within experiment with a 45-min-long online learning along with electroencephalogram (EEG)-based measurements, and a post-interview under five lighting setups respectively (300 lx, 3000 K; 300 lx, 4000 K; 300 lx, 6500 K; 500 lx, 4000 K; 1000 lx, 4000 K). Attention is chosen as the key factor to represent the learning performance. The results show that the attention of people aged in the 20s is not affected by the experimental lighting conditions. The results also demonstrate that people in high illumination at 1000 lx are more inclined to sustain attention despite the discomfort and dissatisfaction. Taking the EEG-based attention measurements and post-interview answers into consideration, lighting conditions at 300 lx, 4000 K are the recommended set points for university architectures among the investigated conditions, providing a practical basis when adjusting the lighting standard for its advantage in energy saving.

Localized heating systems are an effective approach to improve thermal comfort while reducing energy consumption in a cold indoor environment. Furthermore, localized heating devices have found widespread application in the hot-summer and cold-winter zones of China. This study investigates the heating characteristics of the local heating device in a low-temperature environment, as well as its effects on subjective perception and physiological responses, and develops a personalized control system for the device based on the experimental findings. We conducted experimental tests and questionnaires in a test room with air temperature set at 12 °C and 14 °C and a relative humidity of 55%. A total of six experimental conditions were designed using five types of heating equipment (heating wrist straps, heating insoles, heating leg straps, warm air blower, electric radiant heater), each employing different heat transfer methods. The results demonstrate that the head, hands, legs, and feet are susceptible to feeling cold in a low-temperature environment, and the use of a warm air blower and electric radiant heater can significantly enhance the thermal sensation of these parts, improve thermal acceptability, and raise local skin temperature. The electrocardiogram data indicate that heart rate variability can be utilized to assess thermal sensation in a cold environment with localized heating. Additionally, the relationship between thermal response and skin temperature was investigated, leading to the development of a control strategy for the local heating device in a low-temperature conditions.

BIM is one of the main technical ways to realize building informatization, and the model’s texture is essential to its style design during BIM construction. However, the texture maps provided by mainstream BIM software are not realistic enough and monotonous to meet the actual needs of users for the model style. Therefore, an interior furniture BIM style restoration method was proposed based on image retrieval and object location using convolutional neural network. First, two types of furniture images, namely grayscale contour images from BIM software and real images from the Internet, were collected to train the following network model. Second, a multi-feature weighted fusion neural network model based on an attention mechanism (AM-rVGG) was proposed, which focused on the structural information of furniture images to retrieve the most similar real image, and then some furniture image patches from the retrieved one were generated with object location and random cropping techniques as the candidate texture maps of the furniture BIM. Finally, the candidate ones were fed back into the BIM software to realize the restoration of the furniture BIM style. The experimental results showed that the average retrieval accuracy of the proposed network model was 83.1%, and the obtained texture maps could effectively restore the real style of the furniture BIM. This work provides a new idea for restoring the realism in other BIM.