To support children’s food interaction and enhance their understanding of food through morphing food technology, we develop a design exploration through the Research through Design (RtD) methodology. Our exploration integrates four stages: (1) defining design objectives through empathy with stakeholders, (2) investigating morphing food materials to understand their deformation mechanisms, (3) designing and iteratively developing tools based on user feedback, and (4) conducting a workshop-based evaluation. Our design outcome is a toolkit, comprising a morphing food library, trigger tools, and instructional interfaces. The workshop showed that through interaction with morphing food, children learned not only scientific principles but also developed culinary skills, as well as the diversity of food forms and functions. We discussed the detailed findings, insights, and implications for future design.

In this work, we present MorphingScents, a thin-film, five-layer, flexible, shape-changing odor-emitting mechanism that uses Joule heating to drive both scent release and morphological changes, aiming to provide lightweight, intricate, dynamic, and aesthetic olfactory displays and experiences. Based on the direction of scent release and shape change, we propose two fundamental scent release mechanisms: Inward Gathering Release and Outward Dispersing Release. Combining four deformation mechanisms and three multi-scent release methods, we compiled a shape-changing olfactory interfaces library and further provided a detailed design and fabrication pipeline. We conducted a series of technical tests, including relation to heating and deformation angles and olfactory concentration across different areas, thicknesses, and directions of the odor layer. We demonstrated five applications. We conducted a user-involved workshop to validate MorphingScents’s usability and feasibility. Finally, we discuss the potential for expansion, usage precautions, and design iterations of MorphingScents.

The Transformer-convolutional neural network (CNN) hybrid learning approach is gaining traction for balancing deep and shallow image features for hierarchical semantic segmentation. However, they are still confronted with a contradiction between comprehensive semantic understanding and meticulous detail extraction. To solve this problem, this article proposes a novel Transformer-CNN hybrid hierarchical network, dubbed contourlet transformer (CoT). In the CoT framework, the semantic representation process of the Transformer is unavoidably peppered with sparsely distributed points that, while not desired, demand finer detail. Therefore, we design a deep detail representation (DDR) structure to investigate their fine-grained features. First, through contourlet transform (CT), we distill the high-frequency directional components from the raw image, yielding localized features that accommodate the inductive bias of CNN. Second, a CNN deep sparse learning (DSL) module takes them as input to represent the underlying detailed features. This memory- and energy-efficient learning method can keep the same sparse pattern between input and output. Finally, the decoder hierarchically fuses the detailed features with the semantic features via an image reconstruction-like fashion. Experiments demonstrate that CoT achieves competitive performance on three benchmark datasets: PASCAL Context [57.21% mean intersection over union (mIoU)], ADE20K (54.16% mIoU), and Cityscapes (84.23% mIoU). Furthermore, we conducted robustness studies to validate its resistance against various sorts of corruption. Our code is available at: https://github.com/yilinshao/CoT-Contourlet-Transformer.

Existing methods in human-computer interaction to enhance children’s eating habits predominantly rely on digital interactive technologies, which pose the risk of increasing sensory stimulation and diverting children’s attention away from the food itself. Drawing inspiration from shape-changing food research, we propose an approach that combines deformable wafer paper for food preparation. We summarize the principles of wafer paper controllable deformation and develop a toolkit to facilitate its use. We support children in creating personalized, transformable food items using this method, aiming to provide a playful, convenient, and safe food-making experience tailored for children, thereby enhancing children’s mealtime engagement and habits.

Orthoses with electronic functions have emerged as a promising medical product in response to the increasing demand for rehabilitation training, therapy assistance, and health monitoring. However, fabricating this “smart orthosis” often requires long development cycles and exorbitant prices. We introduce E-Orthosis, an integrated fabrication approach with construction toolkits for healthcare professionals to quickly embed electronics in off-the-shelf orthoses with customized functions cost-effectively and time-efficiently. Specifically, we develop components with magnets and pogo pins to support rapid attachment and sustainable use, and textile-based electrodes with snap installation to improve the wearing experience. We also provide a circuit iron tool to apply circuit traces on complex surfaces of orthoses directly and a hot punch tool to embed magnet ports and electrodes. Three application examples, technical evaluations, and expert reviews demonstrate the functionality of E-Orthosis and the potential for democratizing rapid-developed and low-cost smart orthoses for patients.

Deep Learning-based (DL) methods have dominated the task of semantic segmentation of remote sensing images. However, the sizes of different objects vary widely, and there is a great deal of label-noise due to the inevitable shadows. Therefore, there is an urgent need for a method that can precisely handle complex ground data. In this paper, we propose an Inter-Class Enhanced Network (ICEN) for representing features of varying sizes. It comprises two branches: Sparse Representation Network (SPN) and Feature Extraction Network (FEN). Then, a Class-Perception Block is inserted between the two branches to instruct the SPN’s low-level semantic features to be merged into the deeper network. Such a block can reduce label-noise in remote sensing image segmentation. In addition, the proposed EIRI provides a more precise classification process for target edges containing many misclassified points without requiring excessive computational overhead. The experimental results of our proposed Class-Perception Network (C-PNet) achieve competitive performance on the Vaihingen, Potsdam, LoveDA, and UAVid datasets.