Data scientists and developers at Jefferson Lab are exploring some of the latest artificial intelligence techniques to help make high-performance computers more reliable and less costly to run. The study deploys machine learning models that are trained to monitor and predict the behavior of a scientific computing cluster. The goal is to help system administrators quickly identify and respond to troublesome computing jobs or hardware behavior, reducing downtime for scientists processing data from their experiments.

A new review in Engineering explores vacuum glazing, a technology with potential for improving building energy efficiency. With buildings consuming a large portion of society’s energy, this research delves into its fabrication, performance assessment, and energy-saving capabilities, aiming to contribute to low-carbon building development.

A new study in Engineering introduces the Machine Learning on Blockchain (MLOB) framework. This innovation combines machine learning and blockchain technology to boost computational security in engineering. It addresses the shortcomings of existing methods that overlook computational security. Tests show it improves security, maintains accuracy, and has potential for real-world applications.

A study published in Engineering offers a new network dynamic approach for evaluating aeroengine performance. This method addresses the limitations of traditional evaluation methods, providing a more comprehensive and accurate way to assess aeroengines, which is crucial for flight safety and the development of related engineering fields.

New research published in Engineering by Ju-Hyung Kim and Yail J. Kim explores the behavior of reinforced concrete beams strengthened with CFRP and UHPC under thermocyclic loading. Through experiments and analysis, the study examines aspects like uncertainty, hysteresis, and pinching mechanisms. The findings, including a proposed performance degradation factor, provide useful information for engineers working on building structures to withstand multi-hazard situations.

The project has the potential to boost economic growth and job creation throughout the greater Appalachian region.

Quantum dot light-emitting diodes (QLEDs) have made rapid progress in luminescence, efficiency, and stability, making them promising candidates for displays and solid-state lighting applications. However, achieving high-performance QLEDs with high color purity remains a persistent challenge, particularly red QLEDs, thus limiting the popularity of ultra-high definition devices. Recently, Soochow University, in collaboration with Macau University of Science and Technology and other research institutes, reported a facile high-temperature successive ion layer adsorption and reaction (HT-SILAR) strategy for the growth of high-quality, large-particle, alloyed red QDs. These QDs exhibit a near-unity photoluminescence quantum yield (PLQY), and narrow emission with a full width at half maximum (FWHM) of 17.1 nm. As a result, a record external quantum efficiency (EQE) of 38.2%, luminance over 120,000 cd m⁻², and exceptional operational stability T₉₅ (tested at 1,000 cd m⁻²) of 24,100 hours were achieved for QLEDs. This work opens new avenues for synthesizing high-quality QDs with high color purity and was published in Science Bulletin.