UnboxAI is a tangible system designed to foster collaborative AI literacy between children (ages 8–11) and parents. To counter children’s overtrust and parents’ mediation challenges, UnboxAI utilizes a physical console with six NFC-enabled Strategy Cards that externalize AI appraisal into observable, physically enacted moves. By transforming passive interaction into a shared practice of inquiry, the system enables families to co-develop informed trust. UnboxAI promotes educational sustainability by establishing a resilient home-based ecosystem and equipping children with enduring critical thinking skills for a long-term, evolving AI landscape. Video link: https://youtu.be/3cqgYYTVExU?si=eM5NWEksnp_4BQBw

Most AI literacy activities for middle-school students focus on how AI works or how to use AI tools, while giving less attention to the energy, water, and material resources that make these systems possible. We present Eco-AI Campus, a cooperative digital game designed to help students explore the environmental and ethical tradeoffs involved in AI use across school settings. In the game, each player has limited water, electricity, and rare material resources and must decide how to deploy AI sustainably in different locations while considering resource costs and ethical risks. Eco-AI Campus contributes a cooperative design approach to AI literacy that aims to support discussion of AI’s limitations, resource costs, and ethical tradeoffs, while offering educators a scaffold for reflection on responsible AI use.

The rock-climbing fish is a benthic organism that can move rapidly and flexibly on rock surfaces in complex underwater environments. Studies have shown that this unique adhesion-sliding movement mechanism of the rock-climbing fish relies on the anisotropic friction exhibited by its sucker structure, which helps to reduce friction in the forward direction and defend against the impact of the flow field. In this work, inspired by the anisotropic friction phenomenon of the sucker of the rock-climbing fish, we designed the absorption module and pectoral and pelvic fin flapping module of the robotic fish to realize the contact switching from low friction to high friction. Meanwhile the propulsion module adopts a novel design of wire-driven caudal fin that can oscillate at high frequency (exceeding ~5 Hz). Resulting robotic fish can realize different motion modes such as adhesion-sliding movement (~0.5 BL/s) and wall-stabilized adsorption. This work will provide a new solution for the design pattern of underwater wall robots.

Fish can adaptively adjust their body kinematics and swimming modes by sensing to realize optimal propulsion. However, most soft robotic fish have an unchangeable swimming mode through simple structure design, making them difficult to adapt to dynamic and complex fluid environments. Here, inspired by the multiple muscle synergy and lateral line sensing function of fish, we developed a soft robotic fish with multiple actuating units and embedded sensing elements. By collaboratively controlling the amplitude and phase of excitation from the multiple flexible actuating units, the soft robotic fish can successfully realize various swimming modes very similar to those of natural fish. Additionally, the embedded flexible sensing elements enable the robotic fish to sense the swimming state and the surrounding fluid environment in real time. The multiple actuation and embedded sensing allow the soft robotic fish to adaptively switch to an optimal swimming mode in a certain fluid environment. The multimode swimming and perception capabilities proposed in this work not only make soft robotic fish more intelligent and adaptable to complex fluid environments, but also contribute to the future implementation of autonomous control capabilities for robotic fish.

Cluster assignment is crucial for analyzing single-cell RNA sequencing (scRNA-seq) data, essential for studying cell diversity and biological functions at the single-cell level. Deep learning-based clustering methods have been extensively applied in scRNA-seq data analysis. However, current deep models frequently overlook the deep exploration of potential gene correlation, resulting in networks lacking guidance from gene correlation information when learning hidden layer features. This paper proposes an adaptive feedback fuzzy clustering method based on deep gene correlation mining(scDCMAC). The deep gene correlation mining network employs contrast learning to compute the contrast loss on two cell similarity matrices, constructed from denoised autoencoder hidden layer features and original input features, to uncover potential gene correlation. The adaptive feedback clustering network further denoises the hidden layer features using a self-attention mechanism, computes the affiliation matrix, and supervises the denoising autoencoder’s dimensionality reduction using the affiliation matrix. Experiments on 13 real scRNA-seq datasets demonstrate that scDCMAC excels in clustering accuracy and stability, showing great potential in scRNA-seq sample analysis.

Single-cell sequencing technologies allow for a high-resolution examination of various biological activities, such as the genome, transcriptome, and epigenetic modifications, across thousands of cells. In contrast, single-omics approaches fall short in fully capturing the intricate molecular mechanisms. In this regrad, integrating multi-omics data can potentially provide a more comprehensive understanding of fundamental biological processes. Although sequencing technologies have emerged that can simultaneously obtain different omics from single cells, they are limited by throughput and the types of omics. A more common scenario is that data from different omics are measured independently, resulting in unpaired cell compositions. Therefore, efficient algorithms are needed to integrate and analyze unpaired multi-omics data.In this study, we propose a novel framework for integrating unpaired single-cell multi-omics data based on the Kolmogorov-Arnold deep embedding model (scKADE). This framework introduces Kolmogorov–Arnold Networks (KAN) to map different omics data to a unified common feature space for adversarial alignment. Furthermore, to assess the contribution of different cells in data integration, we employ an unsupervised weight estimation algorithm to enhance the alignment quality.

In recent years, bio-syncretic robots actuated by living materials have received widespread attention. Among the common living materials, engineered skeletal muscle tissue (eSKT) has been the focus of researchers due to its high contraction force and good controllability. However, the current performance of eSKT is far from that of natural skeletal muscle tissue. In this paper, an optimized design method for eSKTs has been proposed. By combining simulation analysis with experiments, the eSKTs with multiple strips have been developed. The results show that under a specific volume (250 μL), the optimized strip structures can enhance the stability of eSKT and facilitate the penetration of nutrients and oxygen, leading to improved fusion of myoblasts and the directional arrangement of myotubes, thus improving the performance of eSKT. The eSKT with multiple strips exhibits a significant contraction force and has been successfully utilized in a bio-syncretic robot to demonstrate its actuation capability. This work may provide insights into the development of the field of bio-syncretic robots and even tissue engineering.

It has been clinically proven that exoskeletons are effective self-training rehabilitation or daily living assistance devices for patients with hand dysfunctions. However, exoskeleton-assisted hand exercises with high degrees-of-freedom are considered as challenging tasks because the digit space, especially the thumb, cannot accommodate enough actuators. In this article, we report a tendon-driven soft hand exoskeleton with a hybrid configuration for thumb actuation. The soft hand exoskeleton system uses the least number of actuators to realize full degrees-of-freedom actuation for all digits. It is tested on a stroke patient with hemiplegia and a healthy subject. The experimental results show that the hand exoskeleton could assist the stroke patient to accomplish various training tasks, such as thumb encircling, grasping, pinching, releasing, and writing. It was found that digit trajectories and joint angle changes of the stroke patient were close to those of the healthy subject. Especially, the range of motion of the stroke patient shows significant improvement with the hand exoskeleton assistance compared to that without the hand exoskeleton assistance. The research in this article paves the way to develop fully actuated soft hand exoskeleton that can be eventually integrated with an electroencephalogram or electromyography for self-training rehabilitation or daily living assistance.