Held on 8 May 2025, the 10th UN Multi-stakeholder Forum on Science, Technology and Innovation for the Sustainable Development Goals side event, titled “One Health for All: Synergistic Solutions Advancing SDG3 through Sustainable Science & Inclusive Innovation”, convened global experts to explore integrated strategies for advancing human, animal, and environmental health within the 2030 Agenda. The event highlighted innovations like artificial intelligence (AI)-driven surveillance and low-carbon diagnostics, emphasizing equity and Sustainable Development Goals (SDGs) acceleration. One milestone was the launch of an expert consensus promoting the global One Health index (GOHI), a roadmap to align research and experimental development (R&D) with SDG3 targets. Discussions centered on three key issues covering: the One Health approach for SDGs, the role of GOHI as a scientific tool addressing data fragmentation and capacity disparities, and GOHI's potential to enhance cross-sectoral governance, exemplified by case studies from Japan, Cambodia, and Singapore. A consensus emerged to promote GOHI at the sub-national level, recognizing its value as a comprehensive, structured framework offering practical tools, data transformation capabilities, economic analysis, and global knowledge sharing, despite implementation challenges. Six actionable recommendations were proposed, focusing on strengthening institutional coordination, bridging data gaps, integrating GOHI into governance, piloting localized interventions, mobilizing funding, and building capacity through global partnerships. The event marked a significant step forward, positioning the One Health framework, facilitated by tools like GOHI, as essential for achieving the SDGs and ensuring a healthier, more sustainable future for all.

Background: After an apparent absence of disease, 79 cases of canine leptospirosis were reported in New South Wales (NSW), Australia between 2017 and 2023. Between 2016 and 2023, 281 human leptospirosis cases were reported in NSW. Our aim was to compare the geospatial distribution and causative serovars of canine and human cases to investigate if cases are possibly associated. Methods: Human data (n = 190) included Statistical Area Level 3 (SA3)-location, place of acquisition, and serovar; cases acquired outside NSW were excluded. Canine data comprised postcodes (allocated to corresponding SA3) and serovar. Spatial patterns for human and canine cases were mapped, and correlations between human and canine cases were examined. Results: In dogs, serovar Australis (n = 23, 29 %) and Copenhageni (n = 14, 18 %) were most common, whereas in humans it was serovar Arborea (n = 111, 58 %) and Hardjo (n = 13, 7 %). Serovars causing disease in both humans and dogs were Australis, Copenhageni, Hardjo, Pomona, and Robinsoni. In southeastern NSW, serovar Australis infections increased in both dogs and humans. Canine cases were significantly clustered in Greater Sydney and the South Coast whereas human cases were mainly centered around the North Coast with no significant clustering. Overall, there were nine SA3s where both canine (n = 38) and human (n = 51) cases were reported. There was no evidence of correlation between numbers of human and canine cases at the SA3 level (Spearman's rank correlation coefficient [r] = − 0.053, P = 0.641) and no overlap between specific serovars at the spatial level. Conclusion: Our results do not support dog− human transmission or common sources of infection.

Classic AI technologies are disembodied, and insufficient to make robots intelligently behave in the real world. In contrast, embodied artificial intelligence (Embodied AI) enables artificial agents with physical embodiment to achieve intelligent behavior through interactions with environments. Prof. Weinan Zhang and his team from Harbin Institute of Technology provide a comprehensive survey on Embodied AI. The survey proposes a structured research framework for Embodied AI from the perspective of robot behavior.

This study proposes a mechanism-data dual-driven framework to address the challenge of balancing water conservation, carbon emission reduction, and aquatic ecosystem preservation in China's industrial sector at minimal cost. It involves developing hybrid models for water-use and treatment processes and establishing a superstructure optimization model. This model identifies the optimal pathway for simultaneous water saving and carbon mitigation, supporting cost-effective decisions for industrial park water network optimization. Case studies confirm the framework's effectiveness in balancing economic and environmental benefits.

Ultrawide bandgap semiconductor optoelectronic synapses can perform high-parallel computing with low false alarm rate, making them ideal for building deep-ultraviolet (DUV) neuromorphic visual system (NVS). Scientists in China developed a breakthrough DUV optoelectronic synapse using Ga₂O₃-based cascade heterojunctions. The device mimics biological vision with 4,096 conductance states and ultrahigh linearity, enabling high-accuracy fingerprint recognition and fault-tolerant logic operations. This innovation paves the way for energy-efficient neuromorphic vision systems in military, environmental monitoring, and secure communications.

Scientists in Korea developed a photopatterning approach for emissive layer (EML) patterns using prepatterned photoresist based on a molecular crosslinking strategy. This approach enables ultra-high resolution up to 3000 ppi—fulfilling AR display requirements—without direct exposure of EML to etchants or UV irradiation, unlike conventional photolithography. The simple method offers a promising route for high-resolution OLEDs for VR/AR applications

Overcoming fundamental power limitations in single-frequency fiber lasers—long constrained by stimulated Brillouin scattering effect (SBS) and transverse mode instability effect (TMI)—Chinese researchers combined optic-acoustic-thermodynamic multi-physics comprehensive analysis and innovatively proposed the insight of using a regionally refractive index design to suppress SBS and TMI. Their ‘bat-type’ refractive-index fiber achieved the first kilowatt breakthrough in single-frequency fiber lasers internationally, which could provide a novel laser source for next-generation advanced detection and sensing systems.

Flexible electronics face critical challenges in achieving monolithic three-dimensional (3D) integration, including material compatibility, structural stability, and scalable fabrication methods. Inspired by the tactile sensing mechanism of the human skin, we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste, where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor. The in-plane mesopores of MXene significantly improve ion accessibility, mitigate the self-stacking of nanosheets, and allow the holey MXene to multifunctionally act as a sensing material, an active electrode, and a conductive interconnect, thus drastically reducing the interface mismatch and enhancing the mechanical robustness. Furthermore, we fabricate a large-scale device using a blade-coating and stamping method, which demonstrates excellent mechanical flexibility, low-power consumption, rapid response, and stable long-term operation. As a proof-of-concept application, we integrate our sensing array into a smart access control system, leveraging deep learning to accurately identify users based on their unique pressing behaviors. This study provides a promising approach for designing highly integrated, intelligent, and flexible electronic systems for advanced human–computer interactions and personalized electronics.