The rapid rise of electric vehicles combined with breakthroughs in autonomous driving technology is reshaping the future of transportation toward greater sustainability. Intelligent electric vehicles, particularly plug-in hybrid electric vehicles (PHEVs), hold immense potential to slash energy consumption and curb emissions through smarter, more coordinated control of motion and powertrain operations. Yet achieving this dual goal of uncompromising safety and superior energy efficiency remains challenging. Traditional approaches often treat driving safety, eco-friendly trajectory planning, and powertrain energy management as separate tasks, leading to trade-offs that limit overall performance in complex, real-world driving scenarios.

As urban centers grapple with escalating traffic congestion and the limitations of traditional two-dimensional road networks, the concept of the split flying car has emerged as a transformative solution for future intelligent transportation. These innovative vehicles, which consist of a flight module, a passenger capsule, and an intelligent chassis, offer the flexibility to switch between aerial and ground travel. However, the transition between these modes—specifically the autonomous docking of the chassis to the aircraft bracket—presents a formidable technical hurdle. Researchers at Wuhan University of Technology have recently addressed this challenge by developing a lightweight, vision-based detection model that ensures high-precision, real-time docking even in complex environments and on hardware with limited computing power.

Light-sheet fluorescence microscopy (LSFM), with its innovative design of selective plane illumination and orthogonal detection optics, significantly reduces phototoxicity and photobleaching inherent in conventional microscopy, providing a revolutionary tool for long-term dynamic imaging of living specimens.

A joint research team led by Dr. Young-Jun Jang and Dr. Jongkuk Kim of the Extreme Materials Research Institute, in collaboration with Dr. Sungmo Moon’s team from the Energy and Environment Materials Research Division at the Korea Institute of Materials Science (KIMS) has successfully developed Korea’s first high corrosion- and wear-resistant carbon coating technology to mitigate the severe corrosion and wear issues associated with ammonia fuel. This technology is expected to serve as a key enabling platform for accelerating the commercialization of eco-friendly ammonia-powered ships.

Direct air capture (DAC) is an emerging technology aimed at mitigating global warming. However, conventional DAC technologies and the subsequent utilization processes are complex and energy-intensive. An integrated system of direct air capture and utilization (IDACU) via in-situ catalytic conversion to fuels and chemicals is a promising approach, although it remains in the early stages of development. This review examines the current technical routes of IDACU, including solid-based dual-functional materials (DFMs) through thermo-catalysis, IDACU using liquid sorbents with thermo-catalysis, and non-thermal conversion methods. It covers the basic principles, reaction conditions, main products, material types, and the existing problems and challenges associated with these technical routes. Additionally, it discusses the recent advancements in solid-based DFMs for IDACU, with particular attention to the differences in material characteristics between carbon capture from flue gases (ICCU) and DAC. While IDACU technology holds significant promise, it still faces numerous challenges, especially in the design of advanced materials.

In a recent study published in Science China Earth Sciences, a team of researchers proposed using an orthogonal conditional nonlinear optimal perturbations (O-CNOPs) method to tackle the challenge of forecasting unusual tropical cyclone (TC) tracks. Their findings revealed that this method exhibits exceptional capability in generating ensemble members that accurately predict sharp TC turns. The O-CNOPs method holds potential as a transformative tool for addressing the forecasting challenge, offering a more precise and reliable solution for predicting TC behavior.

Forecasting unusual TC tracks has long been a persistent challenge in TC prediction, with limited progress made over the years. However, this study demonstrated that the O-CNOPs outperformed traditional methods [singular vectors (SVs) and bred vectors (BVs)] by providing more stable and reliable improvements in TC track forecasting skills. Notably, at lead times of one to five days, the O-CNOPs showed superior ability to generate ensemble members that accurately predict sharp TC turns. Thus, the study offers a new ensemble forecasting technology to enhance the accuracy of unusual TC track forecasts, with potential for becoming a valuable approach to address this forecasting challenge.