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Extreme sea levels may cause damage and the disruption of activities in coastal areas. Thus, predicting extreme sea levels is essential for coastal management. Statistical inference of robust return level estimates critically depends on the length and quality of the observed time series. Here, we compare two different methods for extending a very short (∼ 10-year) time series of tide gauge measurements using a longer time series from a neighbouring tide gauge: linear regression and random forest machine learning. Both methods are applied to stations located in the Kattegat Basin between Denmark and Sweden. Reasonable results are obtained using both techniques, with the machine learning method providing a better reconstruction of the observed extremes. By generating a set of stochastic time series reflecting uncertainty estimates from the machine learning model and subsequently estimating the corresponding return levels using extreme value theory, the spread in the return levels is found to agree with results derived by more physically based methods.

Unmanned Aerial Systems (UAS) have rapidly gained attraction in recent years as a promising solution to revolutionize numerous applications and meet the growing demand for efficient and timely delivery services due to their highly automated operation framework. Beyond Visual Line of Sight (BVLOS) operations, in particular, offer new means of delivering added-value services via a wide range of applications. This "plateau of productivity" holds enormous promise, but it is challenging to equip the drone with affordable technologies which support the BVLOS use case. To close this gap, this work showcases the convergence of the automotive and aviation industries to advance BVLOS aviation for UAS in a practical setting by studying a combination of Commercial Off-The-Shelf (COTS) technologies and systems. A novel risk-based approach of investigating the key technological components, architectures, algorithms, and protocols is proposed that facilitate highly reliable and autonomous BVLOS operations, aiming to enhance the alignment between market and operational needs and to better identify integration requirements between the different capabilities to be developed., Signal Processing Systems

Background Essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (MF) are myeloproliferative neoplasms (MPN). Inflammation is involved in the initiation, progression, and symptomology of the diseases. The gut microbiota impacts the immune system, infection control, and steady-state hematopoiesis. Methods We analyzed the gut microbiota of 227 MPN patients and healthy controls (HCs) using next-generation sequencing. We expanded our previous results in PV and ET patients with additional PV, pre-MF, and MF patients which allowed us to compare MPN patients collectively, MPN sub-diagnoses, and MPN mutations (separately and combined) vs. HCs (N = 42) and compare within MPN sub-diagnoses and MPN mutation. Results MPN patients had a higher observed richness (median, 245 [range, 49–659]) compared with HCs (191.5 [range, 111–300; p = .003]) and a lower relative abundance of taxa within the Firmicutes phylum; for example, Faecalibacterium (6% vs. 14%, p < .001). The microbiota of CALR-positive patients (N = 30) resembled that of HCs more than that of patients with JAK2V617F (N = 177). In JAK2V617F-positive patients, only minor differences in the gut microbiota were observed between MPN sub-diagnoses, illustrating the importance of this mutation. Conclusion The gut microbiota in MPN patients differs from HCs and is driven by JAK2V617F, whereas the gut microbiota in CALR patients resembles HCs more., Background: Essential thrombocythemia (ET), polycythemia vera (PV), and primary myelofibrosis (MF) are myeloproliferative neoplasms (MPN). Inflammation is involved in the initiation, progression, and symptomology of the diseases. The gut microbiota impacts the immune system, infection control, and steady-state hematopoiesis. Methods: We analyzed the gut microbiota of 227 MPN patients and healthy controls (HCs) using next-generation sequencing. We expanded our previous results in PV and ET patients with additional PV, pre-MF, and MF patients which allowed us to compare MPN patients collectively, MPN sub-diagnoses, and MPN mutations (separately and combined) vs. HCs (N = 42) and compare within MPN sub-diagnoses and MPN mutation. Results: MPN patients had a higher observed richness (median, 245 [range, 49–659]) compared with HCs (191.5 [range, 111–300; p =.003]) and a lower relative abundance of taxa within the Firmicutes phylum; for example, Faecalibacterium (6% vs. 14%, p <.001). The microbiota of CALR-positive patients (N = 30) resembled that of HCs more than that of patients with JAK2V617F (N = 177). In JAK2V617F-positive patients, only minor differences in the gut microbiota were observed between MPN sub-diagnoses, illustrating the importance of this mutation. Conclusion: The gut microbiota in MPN patients differs from HCs and is driven by JAK2V617F, whereas the gut microbiota in CALR patients resembles HCs more.

Increasing renewable sources in the energy mix is essential to mitigate climate change, not least in countries where the energy demand is likely to rise over the coming decades to reduce or even skip durations of time where fossils dominate. For Africa, solar photovoltaic (PV) and inland wind energy, combined with hydropower, provide significant and untapped potentials, whereas trends and robustness measures need further investigation. This study aims to gain insight into distributed trends in solar PV and wind energy potentials over Africa. This study employs relevant metrics, including relative change, model agreement, robustness, bias, and absolute levels for every available model combination and two climate scenarios, with energy planning purposes in mind. The study finds that regional climate models were the primary control of spatio-temporal patterns over their driving global climate model. Solar PV potentials show more coherence between models, a lower bias and general high potentials in most African regions than wind potentials. Favourable locations for inland wind energy include mainly the regions of greater Sahara and the Horn region. For wind and solar potentials combined, scattered locations within Sahara stand out as the most favourable across scenarios and periods. The analysis of minimum energy potentials shows stable conditions despite low potentials in certain regions. The results demonstrate a potential for solar and wind power in most of the African regions and highlight why solar and wind power or synergies of energy mix should be considered for local energy planning and storage solutions.

Extreme sea levels may cause damage and the disruption of activities in coastal areas. Thus, predicting extreme sea levels is essential for coastal management. Statistical inference of robust return level estimates critically depends on the length and quality of the observed time series. Here, we compare two different methods for extending a very short (∼10-year) time series of tide gauge measurements using a longer time series from a neighbouring tide gauge: linear regression and random forest machine learning. Both methods are applied to stations located in the Kattegat Basin between Denmark and Sweden. Reasonable results are obtained using both techniques, with the machine learning method providing a better reconstruction of the observed extremes. By generating a set of stochastic time series reflecting uncertainty estimates from the machine learning model and subsequently estimating the corresponding return levels using extreme value theory, the spread in the return levels is found to agree with results derived by more physically based methods.