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.

KUALA LUMPUR / GLASGOW — As the world races to meet ambitious climate targets, nature-based strategies are gaining unprecedented traction—and biomass is stepping into the spotlight not just as renewable fuel, but as a powerful carbon sink. On December 17, 2025, leading sustainability expert Prof. Dato’ Dr. Agamutu Pariatamby FASc, Senior Professor at the Jeffrey Sachs Center on Sustainable Development, Sunway University (Malaysia), will unveil groundbreaking insights into how bio-based carbon capture can deliver up to 6.7 gigatonnes of CO₂-equivalent (GtCO₂e) in annual mitigation potential by 2050—according to IPCC (2022) estimates.

Explore the future of clean hydrogen in this recorded webinar featuring Dr. Muhammad Aziz from the University of Tokyo. Discover how chemical looping technology can produce high-purity hydrogen, capture CO₂, and recover usable heat—all within a near-zero emission process.

Tiny charming immune cells called microglia protect the central nervous system in a multitude of ways: They provide innate immunity, shape neurodevelopment, maintain homeostasis and modulate neurological disorders. That functionality can be lost, however, when microglia acquire mutations. An concept to correct this by replacing the mutated microglia with genetically typical cells — now called microglia intervention strategy for therapy and enhancement by replacement, or MISTER — emerged five years ago and was successfully achieved in mice. This year, researchers successfully used this microglia replacement approach to halt a fatal neurological disease ALSP (adult-onset leukoencephalopathy with axonal spheroids and pigmented glia) in human patients. 

A study has revealed that the expression of the poplar sex-determining gene FERR can significantly enhance drought tolerance in plants. 

Postharvest losses in fruits, particularly apples, are a significant global issue. 

A growing body of scientific evidence reveals that primary producers—including plants and phytoplankton—possess a previously overlooked ability to internally break down and detoxify methylmercury, one of the most potent neurotoxins circulating through global food webs.

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.