Space manipulators play an important role in the on-orbit services and planetary surface operation whose reliability is a key issue. In the extreme environment of space, space manipulators are susceptible to a variety of unknown disturbances. Since it is difficult for the manipulator to be repaired immediately, once it fails, it will mean that it cannot complete the mission as expected, which may cause serious losses and dangers. How to have a resilient guarantee, i.e., the manipulator’s ability to resiliently recover and continue to complete tasks, in failure or disturbance is the core capability of its future development. The motion planning unit is used as the computing terminal of the manipulator’s joint motion trajectory. Compared with traditional motion planning, learning-based motion planning has gradually become a hot spot in current research. However, no matter what kind of research ideas, the single robotic manipulator is studied as an independent agent, making it unable to provide sufficient flexibility under conditions such as external force disturbance, observation noise, and mechanical failure.

MendelR is a fully automated R package specifically developed for Mendelian randomization (MR) studies, designed to address the technical challenges of causal inference in biomedical research. As a powerful causal inference method, Mendelian randomization can effectively reduce confounding bias in observational studies. However, its complex data processing workflow, diverse analytical approaches, and stringent hypothesis-testing requirements often deter researchers. MendelR integrates the latest eQTL and pQTL databases, along with multi-omics resources, to provide a one-stop solution covering data acquisition, preprocessing, and instrumental variable selection, through to various MR analysis methods, thereby significantly lowering the technical barriers to research. This toolkit supports not only standard two-sample MR analysis but also encompasses advanced applications, including mediation analysis, multivariate MR, drug target analysis, and multi-omics integration. It also incorporates rigorous sensitivity analyses and multiple-testing correction mechanisms to ensure the reliability of research findings. With its user-friendly interface, fully automated workflow, and diverse visualization features, MendelR offers an efficient and accurate MR analysis platform for researchers at all levels, from beginners to seasoned professionals, thereby advancing the in-depth development of causal relationship research in the biomedical field.

Therapeutic cancer vaccines have experienced a remarkable resurgence over the past decade, representing a paradigm shift in oncology toward harnessing the immune system's intrinsic ability to combat malignancies. These innovative immunotherapeutic approaches are fundamentally designed to enhance specific stages of the cancer-immunity cycle, a conceptual framework that describes the sequential events required to mount effective anti-tumor immune responses. By strategically targeting critical phases including antigen release, presentation, T-cell priming, trafficking, infiltration, and cytotoxic execution, cancer vaccines aim to overcome immune resistance mechanisms and promote durable tumor regression.

This comprehensive review systematically links ML’s application in metal AM quality control, from multi-physics field prediction to real-time closed-loop control. It offers a novel roadmap for researchers, demonstrating how ML can decode complex process-structure-property relationships and enable adaptive, intelligent manufacturing.

Enhanced understanding of the interfacial electric field effect in stable magnesium anodes provides important insights for the development of electrochemically stable rechargeable magnesium batteries.

Generative AI (GenAI) is rapidly transforming higher education. This study explores the imperative for curriculum reform to effectively integrate these powerful tools of GenAI into education and prepare students for an AI-driven world.

This study examined the evolution of digital education policy in the United Kingdom (UK) from 2008 to 2024 based on a discourse analysis of 21 policy documents retrieved from the UK government’s official website, GOV.UK. 

The work introduces a large-language-model framework that predicts a student’s future performance and generates natural-language diagnoses from only a handful of activity records, thereby eliminating the data hunger and opacity that have long limited knowledge-tracing systems.

Our study profiled the dynamic epigenomic landscape, annotated regulatory DNA elements, and identified and validated BCL6B as a new transcription factor for EC differentiation and arterial-venous bifurcation. Our findings provide a valuable resource to study the epigenetic regulation of EC development.