This global study investigates the dynamic impact of Financial Technology (FinTech) on financial stability, introducing the novel dimension of green finance as a key moderating factor. Analysing panel data from 148 advanced and emerging economies (2005-2022) with advanced econometric models, the research finds that both FinTech and the overall composite of green finance significantly enhance financial stability. The study further deconstructs green finance into its core dimensions, revealing that while environmental, resource, and financial dimensions are positive drivers, the economic dimension presents a short-term challenge. Crucially, the synergy between FinTech and most green finance dimensions acts as a powerful positive force for stability. However, the COVID-19 pandemic exerted a consistent negative spillover effect. The findings provide actionable insights for policymakers to design integrated FinTech and green finance frameworks, fostering resilient financial systems and a sustainable economic future.

This study investigates the under-explored impact of banking deregulation on bank risk-taking. Analyzing China's 2009 deregulation as a natural experiment, we find that deregulated banks significantly increase their risk-taking. Mechanism analysis identifies the bank balance sheet capacity channel: deregulation boosts bank net interest margins, strengthening their financial capacity and thus their risk appetite. While this policy successfully improves long-term credit access for firms in underserved regions, especially smaller ones, it creates a critical trade-off for policymakers between supporting the real economy and safeguarding financial stability.

A new study published in Biochar reports that sulfur-modified wheat straw biochar can dramatically reduce the toxic effects of vanadium contamination in rice, offering a promising and sustainable strategy to protect global food production in polluted regions. Vanadium is a heavy metal increasingly found in agricultural soils due to mining, smelting, fertilizer overuse, and wastewater irrigation. Even moderate exposure can stunt plant growth, damage photosynthesis, and impair the antioxidant defenses that keep crops healthy.

Mathematical reasoning is a fundamental aspect of intelligence, encompassing a spectrum from basic arithmetic to intricate problem-solving. Recent investigations into the mathematical abilities of large language models (LLMs) have yielded inconsistent and incomplete assessments. In response, we introduce MathEval, a comprehensive benchmark designed to methodically evaluate the mathematical problem-solving proficiency of LLMs in various contexts, adaptation strategies, and evaluation metrics. MathEval consolidates 22 distinct datasets, encompassing a broad spectrum of mathematical disciplines, languages (including English and Chinese), and problem categories (ranging from arithmetic and competitive mathematics to higher mathematics), with varying degrees of difficulty from elementary to advanced. To address the complexity of mathematical reasoning outputs and adapt to diverse models and prompts, we employ GPT-4 as an automated pipeline for answer extraction and comparison. Additionally, we trained a publicly available DeepSeek-LLM-7B-Base model using GPT-4 results, enabling precise answer validation without requiring GPT-4 access. To mitigate potential test data contamination and truly gauge progress, MathEval incorporates an annually refreshed set of problems from the latest Chinese National College Entrance Examination (Gaokao-2023, Gaokao-2024), thereby benchmarking genuine advancements in mathematical problem solving skills.

With the rapid development of online education, cognitive diagnosis has become a key task in intelligent education, particularly for student ability assessments and resource recommendations. However, existing cognitive diagnosis models face the diagnostic system cold-start problem, whereby there are no response logs in new domains, making accurate student diagnosis challenging. This research defines this task as zero-shot cross-domain cognitive diagnosis (ZCCD), which aims to diagnose students’ cognitive abilities in the target domain using only the response logs from the source domain without prior interaction data. To address this, a novel paradigm, large language model (LLM)-guided cognitive state transfer (LCST) is proposed, which leverages the powerful capabilities of LLMs to bridge the gap between the source and target domains. By modelling cognitive states as natural language tasks, LLMs act as intermediaries to transfer students’ cognitive states across domains. The research uses advanced LLMs to analyze the relationships between knowledge concepts and diagnose students’ mastery of the target domain. The experimental results on real-world datasets shows that the LCST significantly improves cognitive diagnostic performance, which highlights the potential of LLMs as educational experts in this context. This approach provides a promising direction for solving the ZCCD challenge and advancing the application of LLMs in intelligent education.

As a data-driven analysis and decision-making tool, student portraits have gained significant attention in education management and personalized instruction. This research systematically explores the construction process of student portraits by integrating knowledge graph technology with advanced data analytics, including clustering, predictive modelling, and natural language processing. It then examines the portraits’ applications in personalized learning, such as student-centric adaptation of content and paths, and personalized teaching, especially the educator-driven instructional adjustments. Through case studies and quantitative analysis of multimodal datasets, including structured academic records, unstructured behavioural logs, and socio-emotional assessments, the research demonstrates how student portraits enable academic early warnings, adaptive learning path design, and equitable resource allocation. The findings provide actionable insights and technical frameworks for implementing precision education.