In order to meet industrial demands, some colleges and universities have offered interdisciplinary programs that integrate design, engineering, and business. However, how many changes these programs have brought to students, and whether students participating in these programs have had better interdisciplinary ability than students involved in a single discipline study have always been questions that many researchers want to explore. In a university that offers an interdisciplinary program, we found that there is no significant difference in interdisciplinary integration ability between the students participating in the interdisciplinary program and the students involved in a single discipline study through quantitative comparisons of 91 student questionnaires and analyses of interviews with nine teachers of interdisciplinary courses and other related staff members. This may result from the students’ lack of motivation, lack of prior experience, the influence of individual traits, the increase of learning pressure and academic burden, and the interference of disciplinary factors during interdisciplinary learning. The research finding is intended to improve student interdisciplinary learning effectiveness by facilitating interdisciplinary teachers’ understanding of the influencing factors of student interdisciplinary learning, and by providing a reference for interdisciplinary teaching design.

This research addressed the selection of shoe lasts for footwear design to help relieve the pain associated with diabetic neuropathy and foot ulcers. A reverse engineering (RE) technique was used to convert point clouds corresponding to scanned shoe lasts and diabetic foot data into stereo lithograph (STL) meshes. A slicing algorithm was developed and was used to find relevant girth features of diabetic foot and the shoe lasts. An artificial neural network, termed self-organizing map (SOM), classified 60 sets of shoe lasts into similar groups. Foot shapes of three mild diabetic patients were entered into the SOM feature categories to match with suitable shoe lasts. By conducting expert questionnaire analysis of the characteristic girths featured data with analytic hierarchy process (AHP), the weights of the girths were obtained. Grey relational analysis (GRA) was then used to calculate the correlation between foot girth and the corresponding range of shoe lasts. The most suitable shoe last for each patient with a mild diabetic foot can be determined by calculating the relative fitness function for each patient. By correlating diabetic foot with suitable shoe lasts, this study demonstrated an effective strategy for designing shoes for patients with mild diabetes, which can then be manufactured to meet customized requirements.

The study applies Kansei engineering in analyzing the color and texture of leather footwear, utilizing neural network verification to mirror consumers’ visual and tactile imageries onto varieties of leather. This aids in the development of an advanced system for selecting leather footwear based on such impressions. Initially, representative word pairs denoting consumers’ visual and tactile perceptions of leather footwear were delineated. Post-evaluation of these perceptions through a sensibility assessment questionnaire was administered, using 54 samples of leather footwear provided by manufacturers, with each leather type codified in terms of visual and tactile sensibilities. Subsequently, a customized software algorithm was crafted to isolate the primary color and adhesiveness as color features from the leather sample images. Analyzing grayscale values of the images and using pixel neighborhood as a base, the associated calculation methods, such as LBP, SCOV, VAR, SAC, etc., were proposed to extract texture features from the images. The derived color and texture feature values were used as the input layer and the sensory vocabulary quantified values as the output layer. Backpropagation neural network training was conducted on 49 leather samples, with five leather samples used for testing, culminating in the verification of neural network training for three types and 17 combinations. The outcome was an optimal method for leather footwear Kansei engineering and neural network training, establishing a design process for leather footwear characteristics assisted by sensory vocabulary and a backpropagation neural network. Additionally, a computer-aided system for selecting leather footwear, based on these impressions, was designed and validated through footwear design. This study utilized symmetry in footwear design. By using the design of a single shoe to represent the imagery of a pair of symmetrical shoes, we verified whether the leather samples recommended by the leather imagery selection query system met the expected system input settings.

In this study, a method is devised that allows the intentions of autonomous vehicles to be effectively communicated to pedestrians and passengers via an efficient interactive interface. Visual and auditory factors are used as variables to investigate the effects of different autonomous vehicle signal factors on the judgment of pedestrians and to determine the main factors such that the best combination can be proposed. Two visual dimensions (i.e., color and flashing) and three auditory dimensions (i.e., rhythm, frequency, and melody) are used as the experimental signal variables. In addition, deceleration and waiting-to-restart scenarios are investigated. Multiple-choice questions and a subjective cognition scale are used for evaluation. The results show that the combination of green and slow rhythm can be used for the road-user-first case, whereas the combination of red and fast rhythm can be used for the vehicle-first case. Under the same intention, factors of color, flashing, rhythm, and melody are highly similar in terms of the combination mode, except for the frequency. In the deceleration and waiting-to-restart scenarios, the frequencies of the best signal are high and low frequencies, respectively. The results of this study can be used as a reference for the signal design of autonomous vehicles in the future and provide ideas for the interactions between autonomous vehicles and pedestrians.