Ship traffic in shallow areas, such as ports, can trigger large methane emissions by just moving through the water. The researchers in a study, led by Chalmers University of Technology in Sweden, observed twenty times higher methane emissions in the shipping lane compared to nearby undisturbed areas. Despite the fact that methane is a greenhouse gas that is 27 times as powerful as carbon dioxide, these emissions are often overlooked with today's measurement methods.

"Our measurements show that ship passages trigger clear pulses of high methane fluxes from the water to the atmosphere. This is caused by pressure changes and mixing of the water mass. Even if the pulses are short, the total amount during a day is significant," says Amanda Nylund, researcher at Chalmers University of Technology and the Swedish Meteorological and Hydrological Institute, SMHI.

The ability to analyze gene expression at the single-cell level — known as single-cell RNA sequencing (scRNA-seq) — has transformed life sciences, driving discoveries across immunology, oncology, and developmental biology. Over 40,000 studies have leveraged this technique to map the complex diversity of cells within tissues and organisms.

Yet beneath this explosive growth lies a persistent problem: clustering instability. When researchers attempt to group cells by expression patterns to identify cell types or disease states, they often face inconsistent results — even when analyzing the same dataset repeatedly.

Inaccurate clustering can lead to misclassifying normal cells as cancerous or missing rare but critical cell types — jeopardizing interpretation and therapeutic decisions. This “reliability crisis” forces scientists to rerun analyses or rely on computationally expensive pipelines to extract trustworthy insights.

Clean, safe water is vital for human health and well-being. It also plays a critical role in our food security, supports high-tech industries, and enables sustainable urbanisation. However, detecting contamination quickly and accurately remains a major challenge in many parts of the world. A groundbreaking new device developed by researchers at the National University of Singapore has the potential to significantly advance water quality monitoring and management.

Over recent decades, carbon-based chemical sensor technologies have advanced significantly. Nevertheless, significant opportunities persist for enhancing analyte recognition capabilities, particularly in complex environments. Conventional monovariable sensors exhibit inherent limitations, such as susceptibility to interference from coexisting analytes, which results in response overlap. Although sensor arrays, through modification of multiple sensing materials, offer a potential solution for analyte recognition, their practical applications are constrained by intricate material modification processes. In this context, multivariable chemical sensors have emerged as a promising alternative, enabling the generation of multiple outputs to construct a comprehensive sensing space for analyte recognition, while utilizing a single sensing material. Among various carbon-based materials, carbon nanotubes (CNTs) and graphene have emerged as ideal candidates for constructing high-performance chemical sensors, owing to their well-established batch fabrication processes, superior electrical properties, and outstanding sensing capabilities. This review examines the progress of carbon-based multivariable chemical sensors, focusing on CNTs/graphene as sensing materials and field-effect transistors as transducers for analyte recognition. The discussion encompasses fundamental aspects of these sensors, including sensing materials, sensor architectures, performance metrics, pattern recognition algorithms, and multivariable sensing mechanism. Furthermore, the review highlights innovative multivariable extraction schemes and their practical applications when integrated with advanced pattern recognition algorithms.

Discover how integrating decentralized water systems, source separation, and low-carbon technologies can transform urban water management. This comprehensive strategy aims to reduce carbon emissions while promoting resource conservation and recovery, offering a sustainable approach to meet global climate goals.

A novel real-time automated damage detection method leveraging reinforcement learning has been unveiled in a recent study published in Engineering. The research, spearheaded by Chengwen Zhang, Qing Chun, and Yijie Lin from Southeast University, China, introduces an innovative approach that significantly enhances the efficiency and accuracy of damage detection in structural health monitoring (SHM) systems, particularly for architectural heritage.