Balanced! is a hybrid physical-digital board game that helps children reflect on the complexity of sustainable futures through collective play. It combines idea cards based on children's own ideas, using a shared budgeting mechanism, multi-dimensional impact routes, and speculative future outcomes to support critical reflection and systems thinking across Individuals, Society, and Environment in sustainability, while incorporating economic considerations through budgeting mechanics. The paper is contextualized in 40 ideas from children around the world who facilitated our design thinking.

The increasing performance of large language models (LLMs) encourages research on using LLM-based multiagent systems (MAS) to simulate and predict human activities in the real world, especially in the context of negotiation simulation. Meanwhile, the consensus-reaching process (CRP) research has been developed to simulate people’s perception of consensus in practice, particularly the development of consensus models to address specific consensus-reaching issues. However, current LLM negotiation studies largely rely on prompts and outcome scores, offering limited guarantees on stability, salience-aware behaviour, or meaningful termination, whereas CRP provides explicit consensus indices, update logic, and stopping rules. Bridging these lines of work enables negotiation that is measurable rather than anecdotal and reliable rather than round-cap dependent. This paper argues that a bridge between these lines of work is timely and introduces a systematic framework to bring the language of consensus—quality, stability, fairness, and robustness—into LLM-based MAS, allowing different agent designs to be compared on common standards. We introduce and combine methods of the fuzzy logic theory to quantify people’s preferences. We also design a general workflow for LLM-based agents to reach potential consensus in two predefined CRP scenarios. Finally, we introduce and define several cross-paradigm metrics to evaluate the performance of three different agent designs. The experimental results indicate that incorporating consensus models improves stability and fairness. Overall, the paper reframes LLM negotiation as consensus-aware, stability-measurable negotiation, providing a practical bridge between CRP theory and LLM-based agents, and offering a reproducible toolkit for transparent, comparable assessment of multi-issue negotiation.

Accurately delineating the connection between short nucleolar RNA (snoRNA) and disease is crucial for advancing disease detection and treatment. While traditional biological experimental methods are effective, they are labor-intensive, costly and lack scalability. With the ongoing progress in computer technology, an increasing number of deep learning techniques are being employed to predict snoRNA-disease associations. Nevertheless, the majority of these methods are black-box models, lacking interpretability and the capability to elucidate the snoRNA-disease association mechanism. In this study, we introduce IGCNSDA, an innovative and interpretable graph convolutional network (GCN) approach tailored for the efficient inference of snoRNA-disease associations. IGCNSDA leverages the GCN framework to extract node feature representations of snoRNAs and diseases from the bipartite snoRNA-disease graph. SnoRNAs with high similarity are more likely to be linked to analogous diseases, and vice versa. To facilitate this process, we introduce a subgraph generation algorithm that effectively groups similar snoRNAs and their associated diseases into cohesive subgraphs. Subsequently, we aggregate information from neighboring nodes within these subgraphs, iteratively updating the embeddings of snoRNAs and diseases. The experimental results demonstrate that IGCNSDA outperforms the most recent, highly relevant methods. Additionally, our interpretability analysis provides compelling evidence that IGCNSDA adeptly captures the underlying similarity between snoRNAs and diseases, thus affording researchers enhanced insights into the snoRNA-disease association mechanism. Furthermore, we present illustrative case studies that demonstrate the utility of IGCNSDA as a valuable tool for efficiently predicting potential snoRNA-disease associations. The dataset and source code for IGCNSDA are openly accessible at: https://github.com/altriavin/IGCNSDA.

The escalating severity of antibiotic resistance poses substantial challenges across diverse sectors, encompassing everyday life, agriculture, and clinical medical interventions. Conventional methods for investigating antibiotic resistance genes (ARGs), such as culture-based techniques and whole-genome sequencing, often suffer from demands of time, labor, and limited accuracy. Moreover, the fragmented nature of existing datasets hampers a comprehensive analysis of antibiotic resistance gene sequences. In this study, we introduce an innovative computational framework known as TGC-ARG, designed to predict potential ARGs. TGC-ARG harnesses protein sequences as input, retrieves protein structures through SCRATCH-1D, and employs a feature extraction module to deduce feature representations for both protein sequences and structures. Subsequently, we integrate a siamese network to establish a contrastive learning paradigm, thus augmenting the model’s representational capabilities. The resultant sequence embeddings and structure embeddings are merged and directed into a Multilayer Perceptron (MLP) for predicting ARG presence. To assess the performance, we curate a pioneering publicly available dataset named ARSS (Antibiotic Resistance Sequence Statistics). Our extensive comparative experimental outcomes underscore the superiority of our approach over the current state-of-the-art (SOTA) methodology. Furthermore, through comprehensive case analyses, we demonstrate the efficacy of our approach in predicting potential ARGs. The dataset and source code are accessible at https://github.com/angel1gel/TGC-ARG.