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Correction to : Global scenarios for significant water use reduction in thermal power plants based on cooling water demand estimation using satellite imagery (Nature Energy, (2019), 4, 12, (1040-1048), 10.1038/s41560-019-0501-4)
(2025-03) Lohrmann, Alena; Farfan, Javier; Caldera, Upeksha; Lohrmann, Christoph; Breyer, Christian; Department of Computer Science
Correction to: Nature Energyhttps://doi.org/10.1038/s41560-019-0501-4, published online 25 November 2019.
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Troubled waters : Estimating the role of the power sector in future water scarcity crises
(2023-11-01) Lohrmann, Alena; Farfan, Javier; Lohrmann, Christoph; Kölbel, Julian Fritz; Pettersson, Frank; Department of Computer Science
One of the effects of climate change is on freshwater availability. The widespread drought in the summer of 2022 impeded access to freshwater, putting into question the reliability of the current and future energy generation and evoking concerns of competition of different industries for water. In response to climate change, energy transition scenarios represent pathways to a more sustainable energy system, but often overlook the water footprint of the energy sector. Therefore, this study uses machine learning for the identification of thermal power plants’ cooling systems to estimate the water footprint of the current and future energy system using six energy transition scenarios. It is built on published data on thermal power plants announced globally, with a total capacity of 3277 GW, which are planned to be installed between 2020 and 2050. The results demonstrate that the water consumption of the global power sector may increase by up to 50% until 2050, compared to the 2020 level. The findings also emphasize that every new thermal power plant installed in the future will be associated with a higher average water demand per unit of generated electricity. Hence, the rising stress on water systems becomes another argument supporting the transition towards renewables.
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NCOurd : modelling length distributions of NCO events and gene conversion tracts
(2023-08-01) Hardarson, Marteinn T.; Palsson, Gunnar; Halldorsson, Bjarni V.; Department of Engineering
Motivation: Meiotic recombination is the main driving force of human genetic diversity, along with mutations. Recombinations split into crossovers, separating large chromosomal regions originating from different homologous chromosomes, and non-crossovers (NCOs), where a small segment from one chromosome is embedded in a region originating from the homologous chromosome. NCOs are much less studied than mutations and crossovers as NCOs are short and can only be detected at markers heterozygous in the transmitting parent, leaving most of them undetectable. Results: The detectable NCOs, known as gene conversions, hide information about NCOs, including their number and length, waiting to be unveiled. We introduce NCOurd, software, and algorithm, based on an expectation-maximization algorithm, to estimate the number of NCOs and their length distribution from gene conversion data.
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Unorthodox technique in sensing with the metamaterial-based resonator sensor at millimetre frequencies
(2023-08) Qureshi, Suhail Asghar; Abidin, Zuhairiah Zainal; Majid, Huda A.; Ashyap, Adel Y.I.; Esmail, Bashar A.F.
A metamaterial-based resonator is presented in this paper for liquid-sensing applications. The designed sensor operates at millimetre-wave (mm-w) frequencies, and it can characterise the samples that may possess identical characteristics. This paper relies on the extracted permittivity of the structure in the characterisation of the samples, mainly liquids. The sensor requires a very small amount of samples for sensing and it is used in distinguishing oil, ethanol, methanol, glycerol and water. A shift in the resonance frequency of about 200 MHz per unit increase in the epsilon value of samples was achieved. The oil sample showed the lowest value in the extracted permittivity value, while water showed the nearest to zero extracted permittivity. This relationship of variation in extracted permittivity parameter with the change in the sample’s epsilon value is found to be linear and reliable regardless of the change in thickness of the sample.
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Identification of Parkinson's Disease Subtypes from Resting-State Electroencephalography
(2023-08) Yassine, Sahar; Gschwandtner, Ute; Auffret, Manon; Duprez, Joan; Verin, Marc; Fuhr, Peter; Hassan, Mahmoud
Background: Parkinson's disease (PD) patients present with a heterogeneous clinical phenotype, including motor, cognitive, sleep, and affective disruptions. However, this heterogeneity is often either ignored or assessed using only clinical assessments. Objectives: We aimed to identify different PD sub-phenotypes in a longitudinal follow-up analysis and their electrophysiological profile based on resting-state electroencephalography (RS-EEG) and to assess their clinical significance over the course of the disease. Methods: Using electrophysiological features obtained from RS-EEG recordings and data-driven methods (similarity network fusion and source-space spectral analysis), we have performed a clustering analysis to identify disease sub-phenotypes and we examined whether their different patterns of disruption are predictive of disease outcome. Results: We showed that PD patients (n = 44) can be sub-grouped into three phenotypes with distinct electrophysiological profiles. These clusters are characterized by different levels of disruptions in the somatomotor network (Δ and β band), the frontotemporal network (α2 band) and the default mode network (α1 band), which consistently correlate with clinical profiles and disease courses. These clusters are classified into either moderate (only-motor) or mild-to-severe (diffuse) disease. We showed that EEG features can predict cognitive evolution of PD patients from baseline, when the cognitive clinical scores were overlapped. Conclusions: The identification of novel PD subtypes based on electrical brain activity signatures may provide a more accurate prognosis in individual patients in clinical practice and help to stratify subgroups in clinical trials. Innovative profiling in PD can also support new therapeutic strategies that are brain-based and designed to modulate brain activity disruption.

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