Study uncovers neural mechanisms underlying social status link to addiction
Peer-Reviewed Publication
Updates every hour. Last Updated: 30-Jun-2025 12:10 ET (30-Jun-2025 16:10 GMT/UTC)
In a paper published in the Journal of Geo-Information Science, a Chinese team of scientists presents a cost-efficient and equitable facility location problem (CEEFLP) for public service. The new model is simple yet effective in selecting spatially equitable facility locations while considering facility costs and travel efficiency.
Scientists from China have developed a breakthrough asynchronous optical computing accelerator based on wavelength encoding. This architecture overcomes synchronization challenges in conventional optical recurrent processors, significantly reducing both the energy consumption of electronic components and the complexity of optical layout design. The chip enables efficient computing for large-scale AI tasks, such as DNA analysis and speech recognition, while achieving high energy efficiency.
Focusing on the spatial cognitive capabilities of large language models (LLMs), researchers led by Prof. Danhuai Guo from Beijing University of Chemical Technology have introduced SRT4LLM, a standardized framework for testing the spatial cognition of LLMs. The framework systematically assesses LLMs across three key dimensions—spatial object types, spatial relations, and prompt engineering strategies—and establishes a unified testing process to support the development of native geographic LLMs.
Falls are a serious public health problem globally, particularly in acute care hospitals, where hospitalized patients are at high risk for falls and may experience adverse outcomes such as prolonged hospital stays. Multifactorial fall prevention programs can reduce the risk of falls and related injuries, but successful implementation requires active patient participation.
In the rapidly evolving field of quantum computing, silicon spin qubits are emerging as a leading candidate for building scalable, fault-tolerant quantum computers. A new review titled "Single-Electron Spin Qubits in Silicon for Quantum" published May 2 in Intelligent Computing, a Science Partner Journal, highlights the latest advances, challenges and future prospects of silicon spin qubits for quantum computing.
Point-of-Interest (POI) recommendation is crucial in the recommendation system field. Graph neural networks are used for POI recommendations, but data sparsity affects GNN training. Existing GNN-based methods have two flaws. Firstly, they have coarse granularity for modelling heterogeneity, overlooking complex relationships due to time and space factors. Although some work constructs complex graphs, it may reduce performance by introducing noise. Secondly, they insufficiently consider interaction sparsity issues, with little attention in POI recommendations. To solve these problems, a novel method HestGCL is proposed. It builds a heterogeneous spatio-temporal graph with three node types and three relations to model heterogeneity at a finer granularity. Inspired by self-supervised learning, it uses a cross-view contrastive learning technique, splitting the graph into spatial and temporal views, designing specific graph neural networks, and using node representations for contrastive learning. Experiments on three datasets show that HestGCL outperforms state-of-the-art methods, with relative improvements in Recall@50, and ablation studies prove its effectiveness and robustness.
Researchers from Chinese Academy of Sciences and Peking University introduce DFFPA—a novel method that enhances detection capabilities for new object classes with limited data. DFFPA leverages dual-domain feature fusion and patch-level attention to achieve superior performance. This breakthrough holds good potential for applications in autonomous driving and robotics.
Researchers from the Shanghai Advanced Research Institute and collaborating institutions have achieved high-precision measurements of the 27Al(γ,n)26Al photoneutron cross section using quasi-monochromatic γbeams at the Shanghai Laser-Electron Gamma Source (SLEGS). By employing a novel flat-efficiency neutron detector array, the team reduced measurement uncertainties to below 4%, resolving discrepancies in existing experimental data and improving theoretical models for nuclear astrophysics. The results, spanning 13.2–21.7 MeV, provide critical insights into the production of 26Al in cosmic environments and address inconsistencies with prior datasets.