In a significant stride towards cleaner water, researchers at the School of Resources and Environment, Northeast Agricultural University, Harbin, China, have developed a novel material that effectively removes harmful pollutants from water. Professors Jianhua Qu and Ying Zhang lead the team behind the study titled "Synthesis of Polyvinyl Chloride Modified Magnetic Hydrochar for Effective Removal of Pb(II) and Bisphenol A from Aqueous Phase: Performance and Mechanism Exploration." This innovative research introduces a powerful new tool in the fight against water pollution.

Acid rain from fossil fuel pollution may be quietly training soil bacteria to become longer-lived, more transmissible, and more deadly, according to a new study in the journal New Contaminants that tracks how a notorious foodborne pathogen rapidly evolved under simulated acid deposition.​​

Researchers have unveiled a promising new method that could transform how uranium is recovered from challenging wastewater streams. By combining a specially engineered covalent organic framework with an indirect electrochemical process, the approach delivers high efficiency, long term stability, and strong tolerance to chemically complex environments. The findings provide fresh insight into how advanced functional materials and optimized operating conditions can work together to support cleaner and more sustainable nuclear energy development.

A new study published in Nature on November 26 has shed light on the origins, population structures, and kinship systems of the people of Shimao—one of China's most significant late Neolithic settlements. Analyses of ancient DNA from individuals who inhabited the site reveal genetic links to southern settlements, while also providing evidence of male mass burials associated with human sacrifice.

The aroma of wine is shaped largely by volatile compounds originating from grape berries, among which β-damascenone is a major contributor to floral and fruity notes. 

Image reconstruction—the process of recovering clear images from incomplete or noisy data—has been advancing rapidly through deep learning. Yet most existing approaches rely on costly supervised training and lack theoretical transparency. A new survey maps the rise of unsupervised deep learning for image reconstruction, from traditional denoising-based priors to modern diffusion models. These methods learn structured visual information directly from unlabeled data, and have achieved impressive performance across various fields, including biomedical imaging and remote sensing. The study shows how unsupervised learning based image reconstruction unites neural network efficiency with solid mathematical foundations to achieve both interpretability and flexibility, offering a blueprint for next-generation imaging systems.

Researchers at the University of Melbourne have developed a new AI-based traffic signal control system called M²SAC that improves both fairness and efficiency at urban intersections. Unlike traditional systems focused only on cars, M²SAC accounts for pedestrians, buses, and other users. A key innovation is the phase mask mechanism, which dynamically adjusts green light timings to reduce delays. Tested on real Melbourne traffic data, the model outperformed existing methods, cutting congestion and balancing traffic flow more equitably. The approach supports smarter, fairer, and more inclusive transport systems for modern cities.

To address this challenge, researchers at Korea Advanced Institute of Science and Technology (KAIST) and Donghai Laboratory developed a new model called ProChunkFormer, which reconstructs vehicle trajectories from sparse and noisy GPS data, enabling more accurate mobility analysis and intelligent transportation planning.

The heterogeneity causes spatiotemporal inconsistencies in multimodal data, posing challenges for existing methods in multimodal feature extraction and alignment. First, in the temporal dimension, the microsecond-level temporal resolution of event data is significantly higher than the millisecond-level resolution of RGB data, resulting in temporal misalignment and making direct multimodal fusion infeasible. To address this issue, the researchers design an Event Correction Module (ECM) that temporally aligns asynchronous event streams with their corresponding image frames through optical-flow-based warping. The ECM is jointly optimized with the downstream object detection network to learn task-ware event representations.