Fashion Design Education and Sustainability: Towards an Equilibrium between Craftsmanship and Artistic and Business Skills?

The analysis of art design under improved convolutional neural network based on the Internet of Things technology

This work aims to explore the application of an improved convolutional neural network (CNN) combined with Internet of Things (IoT) technology in art design education and teaching. The development of IoT technology has created new opportunities for art design education, while deep learning and improved CNN models can provide more accurate and effective tools for image processing and analysis. In order to enhance the effectiveness of art design teaching and students' creative expression, this work proposes an improved CNN model. In model construction, it increases the number of convolutional layers and neurons, and incorporates the batch normalization layer and dropout layer to enhance feature extraction capabilities and reduce overfitting. Besides, this work creates an experimental environment using IoT technology, capturing art image samples and environmental data using cameras, sensors, and other devices. In the model application phase, image samples undergo preprocessing and are input into the CNN for feature extraction. Sensor data are concatenated with image feature vectors and input into the fully connected layers to comprehensively understand the artwork. Finally, this work trains the model using techniques such as cross-entropy loss functions and L2 regularization and adjusts hyperparameters to optimize model performance. The results indicate that the improved CNN model can effectively acquire art sample data and student creative expression data, providing accurate and timely feedback and guidance for art design education and teaching, with promising applications. This work offers new insights and methods for the development of art design education.

Regulation for Promoting Sustainable, Fair and Circular Fashion

Over the past few decades, the production and consumption of clothing has increased exponentially, leading to a dramatic increase in the negative environmental consequences produced by the fashion industry. Given the rising pace of global warming and the rising concern about the fashion industry’s contribution to the climate crisis and its exploitative social dimensions, decision makers, politicians and government officials have begun to promote sustainable fashion through public policy. This article reviews the main barriers facing a circular economy in general and the fashion industry in particular. It considers nascent regulations emerging throughout the world in the field of sustainable fashion, applying circular economic principles to the fashion industry. Four categories of policy proposals are evaluated: Command and Control Interventions, Educational Initiatives, Incentives and Certification, grading them according to criteria of effectiveness, sustainability, feasibility, equity and compliance. The ranking process was based on an elicitation of expert judgement among a panel with expertise in the areas of sustainable fashion from the business sector, academia and civil society. Findings suggest that while some policy options appear to meet all criteria successfully, when selecting an optimal strategy for promoting sustainable fashion, there are frequently trade-offs between different alternatives.