, The Lancet Planetary Health, Volume 5, November 2021
COVID-19 is disrupting and transforming the world. We argue that transformations catalysed by this pandemic should be used to improve human and planetary health and wellbeing. This paradigm shift requires decision makers and policy makers to go beyond building back better, by nesting the economic domain of sustainable development within social and environmental domains.
, The Lancet, Volume 398, 6 November 2021
, The Lancet, Volume 398, 30 October 2021
, The Lancet Planetary Health, Volume 5, July 2021
Record climate extremes are reducing urban liveability, compounding inequality, and threatening infrastructure. Adaptation measures that integrate technological, nature-based, and social solutions can provide multiple co-benefits to address complex socioecological issues in cities while increasing resilience to potential impacts. However, there remain many challenges to developing and implementing integrated solutions.
, Biomedical Signal Processing and Control, Volume 62, September 2020
Objective imaging-based biomarker discovery for psychiatric conditions is critical for accurate diagnosis and treatment. Using a machine learning framework, this work investigated the utility of brain's functional network topology (complex network features) extracted from functional magnetic resonance imaging (fMRI) functional connectivity (FC) as viable biomarker of autism spectrum disorder (ASD). To this end, we utilized resting-state fMRI data from the publicly available ABIDE dataset consisting of 432 ASD patients and 556 matched healthy controls.
, Thermal Science and Engineering Progress, Volume 18, 1 August 2020
Quality attributes such as moisture content, colour parameters and shrinkage of apples change undesirably during the drying process. Drying is a highly dynamic process, thus, an effective optimisation in terms of product quality and process performance requires continuous non-invasive measurement of the parameters in question.
, Renewable and Sustainable Energy Reviews, Volume 113, October 2019
Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in “Big Data” analytics and related statistical/computational tools raised interest in data-driven battery health estimation. Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications.
, The Lancet Planetary Health, Volume 1, July 2017
, Renewable and Sustainable Energy Reviews, Volume 76, 2017
An effective response to climate change demands rapid replacement of fossil carbon energy sources. This must occur concurrently with an ongoing rise in total global energy consumption. While many modelled scenarios have been published claiming to show that a 100% renewable electricity system is achievable, there is no empirical or historical evidence that demonstrates that such systems are in fact feasible. Of the studies published to date, 24 have forecast regional, national or global energy requirements at sufficient detail to be considered potentially credible.
, Sustainable Cities and Society, Volume 27, 1 November 2016
Shortages of freshwater have become a serious issue in many regions around the world, partly due to rapid urbanisation and climate change. Sustainable city development should consider minimising water use by people living in cities and urban areas. The purpose of this paper is to improve our understanding of water-use behaviour and to reliably predict water use. We collected appropriate data of daily water use, meteorological parameters, and socioeconomic factors for the City of Brossard in Quebec, Canada, and analysed these data using multiple regression techniques.