Industry Showcase

The increasing complexity of the implementation and operation of deep learning techniques hinders their reproducibility and deployment at scale, especially in healthcare. Pati and colleagues introduce a deep learning framework to analyse healthcare data without requiring extensive computational experience, facilitating the integration of artificial intelligence in clinical workflows.

Pierre-Alexis Mouthuy and colleagues report the actuation of cell-material constructs in flexible bioreactor chambers inserted into musculoskeletal humanoid robots. This opens up the opportunity of applying realistic biomechanical stresses to tissue engineered constructs during culture.

Alanine aminotransferase (ALT) activity is an important indicator to determine drug induced liver injury. However, its measurement is limited to centralized laboratories. Muratore, Zhou, and colleagues designed an ALT assay biosensor platform using silicon nanowire field effect transistor arrays. The biosensor could monitor ALT enzyme activity electrically, with comparable precision to spectrophotometric detection. This biosensor platform will enable routine liver function monitoring in settings both inside and outside of the clinic.

Anton Molina and colleagues demonstrate the fabrication of absorbent materials from the drought tolerant plant agave sisalana (sometimes known as sisal). The findings suggest a route toward local production of the absorbent component of menstrual pads in low- and middle-income countries, where there is more limited access to sanitary products.

Oscar Recalde-Benitez and colleagues report a FIB-based sample preparation process to limit current leakage during operando TEM experiments, thus improving the accuracy of device nanocharacterization under operating conditions. The methodology results in leakage currents that are small compared to device currents, which enables the analysis of operating stack devices inside the microscope.

Kevin Wyss and colleagues report the flash synthesis of graphene from end-of-life vehicle plastic waste. A polyurethane/flash graphene composite is also re-flashed back into more graphene. A life cycle assessment suggests environmental benefits compared to other graphene synthetic routes.

Jan Kloppenborg Møller & Goran Goranović and colleagues introduce a data-driven twin methodology which balances physical knowledge with uncertainty quantifications. The approach makes it suited to application of real world problems with inherent unknowns. They demonstrate its application in the modelling and control of membrane water ultrafiltration

Costa and co-authors detected an earthquake in Mexico using conventional polarisation optics within a trans-oceanic fibre-optic cable connecting Los Angeles, USA with Valparaiso, Chile. Their approach enables non-invasive monitoring and localization of seismic waves on the seabed.

Yao Xiao and colleagues report a graph-based machine learning algorithm for categorising and then more efficiently distributing code segments across different processing units in heterogeneous hardware platforms. The approach leads to more efficient processing, vital for future developments harnessing AI, for example in fields such as autonomous vehicles and machine vision.

5G network operators need data traffic predictions to plan network expansion schemes. Yuguang Yang and colleagues demonstrate performance improvement over state-of-the-art forecasting tools of a deep learning model, Diviner. They demonstrate detailed months-level forecasting for massive ports with complex flow patterns.

Murray and colleagues report strain distributions in Si/SiGe nanosheet heterostructures using synchrotron x-ray nanodiffraction techniques. The observations and underlying mechanisms provide insights for future nanodevice design and performance prediction.

Debuisschert and colleagues describe a radio frequency signal analyzer platform utilizing the quantum properties of the nitrogen-vacancy center in single crystal diamond. This platform enables millisecond detection of complex microwave signals over a broad tunable frequency range up to 25 GHz.

Tong Ye and colleagues use a spectrum analyzer and the probability-maintained noise power ratio method to estimate the performance of nonlinear communications systems. The method is verified in seven different nonlinear mechanism and application scenarios.

Tomoki Inoue and colleagues report a time-series data clustering algorithm using a quantum-inspired digital annealer technology to improve the clustering performance. The algorithm was implemented to cluster time-series data derived from benchmark problems and flow measurement images.

Analysis of the aircraft structural load needs costly and inefficient ground tests. Chenxi and coworkers report a deep learning based approach to predict aircraft strains and loads by identifying the key flight parameters in the load prediction, providing a more efficient and economical way for aircraft load monitoring.

Xin Xiong and colleagues estimate end-of life mass predictions of aerospace and rail vehicles in China and predict waste accumulation between 2000 and 2050. The study will aid in developing and managing effective technological solutions for a circular economy as well as formulating plans for governance.

Roberto Torelli and colleagues propose a numerical framework to characterize fuel injection in internal combustion engines at multiple length and time scales. The approach demonstrates potential for increased fidelity in the flow dynamics by means of an affordable end-to-end methodology that links realistic injection operation to fuel combustion and engine emissions.

Over-reliance on automation in transportation systems is known to cause accidents. To address this, here, Tomohiro Nakade and colleagues describe a collaborative strategy for autonomous steering, in which the driver can take over from the automation without its full deactivation.