Most African countries have in-use steel stocks below 1 tonne per capita—less than one-twentieth of industrialised levels. Meanwhile, €10 billion in European subsidies for domestic green iron has yielded only one project reaching final investment decision. A new article in Technology Review for Carbon Neutrality argues these are not separate problems: they share a solution. Green iron produced in developing country "sweetspots" could supply European steelmakers at 27% lower cost—delivering a competitive decarbonised EU steel industry while providing the bankable anchor investment that developing countries need to build their own steel industries and infrastructure in parallel.

Many promising drug molecules fail to reach patients because they do not dissolve well enough in water, limiting their effectiveness when taken orally. Now, researchers from Japan investigated an innovative method that uses sublimation to load drugs into a mesoporous silica carrier without relying on organic solvents. Using ibuprofen as a model compound, they showed that this approach can produce formulations with significantly enhanced solubility, offering a cleaner and more sustainable strategy for drug development.

Scientists from the Research Center for Materials Nanoarchitectonics (MANA) developed a liquid-repellent particle coating that allows pico- and nanoliter liquid droplets to be handled like dry powder, enabling precise control of ultra-small liquid volumes in microfluidics applications.

How comprehensive is our healthcare system, and who is being left behind? In this study, The University of Tokyo researchers synthesized the patients’ real-world experiences with complex genetic disorders into a single case. The study reveals how compartmentalized care leads to treatment refusal and patient harm, while coordinated interdisciplinary teams can restore well-being. It highlights the urgent need for reforms in medical education, care continuity, and health policy to create more inclusive, patient-centered healthcare systems.

Abstract

Purpose – We investigate latent higher-order dependencies in Chinese sectoral risk connectedness networks, characterize their topology and quantify resilience at both the system and sector levels, thereby offering new insights for mitigating systemic risk and preserving financial stability.

Design/methodology/approach – Employing the RHOSTS approach, we construct higher-order risk connectedness networks for Chinese stock sectors and analyze their structure with network-topology metrics. These metrics are then embedded in a coupled-map-lattice model to track the time-varying resilience of the overall network and its constituent sectors.

Findings – The sectoral network exhibits pronounced higher-order interactions, with four-sector synchronous resonance as the prevailing motif. Shock-specific core resonance clusters emerge and although system-wide resilience increases over time, marked heterogeneity across sectors persists.

Originality/value – By moving beyond traditional pairwise spillover models, our higher-order financial network reveals collective risk resonance spanning multiple sectors. The topology-based metrics we propose enable simultaneous assessment of system-level and sector-specific resilience and its evolution.

Abstract

Purpose – This paper aims to enhance the predictability of stock returns. Existing studies have used investor sentiment to forecast stock returns. However, it is unclear whether high-frequency intraday investor sentiment can enhance the forecasting performance of low-frequency stock returns.

Design/methodology/approach – Thus, we employ the MIDAS model and the high-frequency intraday sentiment extracted from the Internet stock forum to forecast Chinese A-shares returns at daily frequency.

Findings – The results illustrate that high-frequency sentiment data are better than daily sentiment data in predicting daily stock returns, and the sentiment in non-trading hours has been proved superior to those in trading hours.

Originality/value – First, our study adds to the growing literature on investor sentiment. We are the first to construct a proxy for high-frequency investor sentiment using intraday postings collected from Chinese Internet stock forum. Second, we confirm that sentiment in non-trading hours has a stronger predictive ability than those in trading hours. Third, we also contribute to the performance comparison of MIDAS-class models. The good performance of U-MIDAS is confirmed in our empirical applications.