Recently, Professor Lin Ma et al. from Nanjing University, China Agricultural University, and Hebei Agricultural University proposed a new agricultural system research method that combines “top-down” and “bottom-up” approaches, providing a viable pathway to address this dilemma. The related paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025628).

Recently, Professor Wenfeng Cong et al. from China Agricultural University proposed a solution called “green technology”, validated through over 12,000 field comparison trials conducted via a nationwide collaborative network. This research not only addresses the aforementioned challenges but also introduces a novel agricultural research paradigm—the “12345” model. This model emphasizes starting from actual production needs and resolving the dual contradictions between high yield and environmental protection, as well as economic growth and ecological preservation, through multidisciplinary collaboration and participation from multiple stakeholders. The relevant paper has been published in Frontiers of Agricultural Science and Engineering (DOI: 10.15302/J-FASE-2025630).

A high throughput synthesis method is established to produce ultrathin MOF nanosheets and nanosheet membranes within only 30 min, shedding light on 2D high-performance membrane customization for separation and purification requirements.

In a paper published in Frontiers of Engineering Management, researchers reveal significant regional disparities in China's WEEE generation: central/eastern regions exhibit higher stockpiles but slower growth, while western areas show lower baselines yet faster expansion—with computers and air conditioners displaying the most rapid obsolescence rates and institutional sources presenting high uncertainty. The findings underscore the urgent need for coordinated regional recycling strategies and prioritized category governance.

In a study published in Frontiers of Engineering Management, researchers from Huazhong University of Science and Technology present an online hidden Markov model (OHMM) for predicting geological risks during tunnel excavation.  Applied to a tunnel excavation project in Singapore, the OHMM outperformed conventional methods, accurately forecasting geological risks ahead of the tunnel boring machine using minimal historical data.

Macrocyclic compounds exhibiting narrowband emission are pivotal for advancing wide-gamut displays. Researchers report a BN-embedded cyclophane (BN-CP) possessing Multiple Resonance Thermally Activated Delayed Fluorescence (MR-TADF) character, synthesized efficiently via a one-pot triple intramolecular Bora-Friedel-Crafts reaction from an aza[14]cyclophane precursor. X-ray crystallographic analysis and DFT calculations demonstrate that cyclization-induced conformational rigidity significantly narrows the emission spectrum. Leveraging the synergistic MR effects of its three B/N centers, BN-CP achieves deep-blue emission with a remarkably narrow full width at half maximum (FWHM) of 24 nm. Corresponding OLED devices exhibit a peak external quantum efficiency (EQE) of 23.3%, ranking among the highest reported for deep-blue MR-OLEDs.

Participants in oil markets are increasingly aware of the climate risks posed by frequent extreme weather. This paper examines the role of extremely high-temperature weather information in predicting oil futures prices on the China International Energy Exchange (INE). An extreme high-temperature weather index (HTI) is developed on the basis of meteorological data at INE’s crude oil production and storage sites. The local interpretable model-agnostic explanations (LIME) and accumulated local effects (ALE) methods are used to compare the predictive contribution of the HTI with that of 15 common predictors.  

The results indicate that the HTI enhances the out-of-sample accuracy of five classical prediction models for INE oil prices. The recurrent neural network (RNN) model exhibits superior out-of-sample forecast performance, with an MAE of 14.379, an RMSE of 19.624, and a DS of 66.67%. The predictive importance of the HTI in the best RNN model ranks third in most test instances, surpassing conventional oil price predictors such as stock market indicators.  

The ALE analysis reveals a positive correlation between extremely high-temperature weather and INE oil prices. These findings can help investors and oil market regulators improve oil price forecast accuracy while also providing new evidence about the relationship between climate risk and oil prices.