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The scattering pattern of a crystal obeys the symmetry of the crystal structure through the corresponding Laue group. This is usually also true for the diffuse scattering, containing information about disorder, but here a case is reported where the diffuse scattering is of lower symmetry than the parent crystal structure. The mineral bixbyite has been studied by X-ray and neutron scattering techniques since 1928 with some of the most recent studies characterizing the low-temperature transition to a magnetically disordered spin-glass state. However, bixbyite also exhibits structural disorder, and here single-crystal X-ray and neutron scattering is used to characterize the different modes of disorder present. One-dimensional rods of diffuse scattering are observed in the cubic mineral bixbyite, which break the expected symmetry of the scattering pattern. It is shown that this scattering arises from epitaxial intergrowths of the related mineral, braunite. The presence of this disorder mode is found to be directly observable as well-defined residuals in the average structure refined against the Bragg diffraction. An additional three-dimensional diffuse scattering component is observed in neutron scattering data, which is shown to originate from the substitutional disorder on the Fe/Mn sites. This occupational disorder gives rise to local relaxations of the oxide sublattice, and the pattern of oxide displacements can be rationalized based on crystal-field theory. The combined use of neutron and X-ray single-crystal scattering techniques highlights their great complementarity. In particular, the large sample requirements for neutron scattering experiments prove to be an obstacle in solving the intergrowth disorder due to several growth orientations, whereas for X-ray scattering the one-dimensional nature of the intergrowth disorder renders solving this a more tractable task. On the other hand, the oxide relaxations cannot be resolved using X-rays due to the low Mn/Fe contrast. By combining the two approaches both types of disorder have been characterized.

A growing need for food is causing increased interest for seaweed farming globally. This requires the knowledge of the industry’s effects on the marine environment. We therefore aimed to explore the communities hosted by a kelp farm compared to that of wild kelp forests. The study was performed in mid-western Norway. Kelp associated fauna were collected from farmed kelp (Saccharina latissima and Alaria esculenta), in wild kelp forests (S. latissima, A. esculenta, and Laminaria hyperborea), and from fauna traps in the water column. The study showed that the kelp farm had lower taxa abundance and richness and a lower biodiversity than the wild kelp forests. Nonetheless, the farmed kelp hosted many associated species, with communities different from what was found on ropes without kelp (i.e., in the water column). The fauna communities among the farmed kelp were more similar to what was found in the wild L. hyperborea kelp forest than to its wild counterparts. The difference between the fauna communities of ‘old’ and ‘young’ farmed kelp (grown for 3 and 7 months, respectively) was not significant, but the fauna was dominated by the isopod species Idotea pelagica in the young forest and by amphipods, mainly belonging to the genus Caprella, in the older. The study contributes to our knowledge of kelp farms’ ecological role in the marine environment, which is of importance for today’s management as well as for ensuring a sustainable future development of the kelp farming industry.

Magnetic materials with the spinel structure (A2+B3+2O4) form the core of numerous magnetic devices, and ZnFe2O4 constitutes a peculiar example where the nature of the magnetism is still unresolved. Susceptibility measurements revealed a cusp around Tc = 13 K resembling an antiferromagnetic transition, despite the positive Curie–Weiss temperature determined to be ΘCW = 102.8(1) K. Bifurcation of field-cooled and zero-field-cooled data below Tc in conjunction with a frequency dependence of the peak position and a non-zero imaginary component below Tc shows it is in fact associated with a spin-glass transition. Highly structured magnetic diffuse neutron scattering from single crystals develops between 50 K and 25 K revealing the presence of magnetic disorder which is correlated in nature. Here, the 3D-mΔPDF method is used to visualize the local magnetic ordering preferences, and ferromagnetic nearest-neighbor and antiferromagnetic third nearest-neighbor correlations are shown to be dominant. Their temperature dependence is extraordinary with some flipping in sign and a strongly varying correlation length. The correlations can be explained by orbital interaction mechanisms for the magnetic pathways and a preferred spin cluster. This study demonstrates the power of the 3D-mΔPDF method in visualizing complex quantum phenomena thereby providing a way to obtain an atomic-scale understanding of magnetic frustration.

Magnetic materials with the spinel structure (A

Preoperative anxiety is a common issue in surgery. Preoperative anxiety may lead to increased mortality, pain and dissatisfaction with surgery. Melatonin is a drug with only mild adverse effects and has previously been shown to reduce anxiety and pain in the perioperative setting. The aim of study was to investigate the anxiolytic effect of melatonin in the preoperative setting.This study was a randomised, double-blinded, placebo-controlled clinical trial. Included patients were allocated in the ratio 1:1 to either the placebo group or the melatonin group. Patients received 10 mg melatonin or placebo the evening before surgery, 2 h before surgery, immediately after surgery and the evening after surgery. Preoperative anxiety was examined 1 h before surgery, using the State Trait Anxiety Inventory (STAI) questionnaire, along with a Visual Analogue Scale (VAS) of anxiety. Furthermore, participants filled out a sleep log each morning, where we examined sleep quality, comfort and drowsiness.Sixteen patients received placebo treatment and 17 patients received melatonin. Median state anxiety on the STAI scale at baseline was 27 (interquartile range [IQR] 22-34) for the melatonin group and 27 (IQR 23-31) for the placebo group. In the primary outcome of preoperative anxiety 1 h before surgery, the median state anxiety on the STAI scale was 27 (IQR 24-34) for the melatonin group and 28 (24-33) for the placebo group, p = .814. VAS anxiety was median 7 (0-28) for the melatonin group and median 7 (1-13) for the placebo group, p = .813. Furthermore, there were no statistically significant differences between the two groups in the secondary outcomes of sleep quality and consumption of analgesics.Melatonin did not reduce preoperative anxiety in patients undergoing hernia repair. Preoperative anxiety levels were low, which limits the generalisability of our findings.

The transition to renewable energy sources such as solar energy has increased the interest in predicting cloud masks from remote sensing data. Even though deep learning methods have achieved great success on multiple meteorological tasks, only limited research has been conducted on nowcasting cloud masks based on high temporal and spatial resolution satellite data.This study investigates forecasting cloud masks over Germany six frames into the future based on satellite images. We compare predictions between three deep learning architectures (ConvLSTM, U-Net, and MetNet) relative to two baseline models (optical flow and persistence). We train and evaluate our models using two years of the ICARE SAFNWC Cloud Mask dataset1 , with a temporal resolution of 15 minutes per frame and a spatial resolution of 3×3 km per pixel. In our experiments we use a larger area of 256×256 pixels to predict the target area of size 128×128 pixels, leading to overall better performance compared to using an input size equal to the output size. Besides comparing different network architectures, we also investigate the effect of varying the temporal input size and output size for ConvLSTM. Finally, we examine the effect of adding more features (land/sea mask and elevation map) and changing the loss function.In summary, we have performed a comprehensive study investigating cloud mask nowcasting using 70,000 spatially and temporally aligned data frames, examining three loss functions, six evaluation metrics, and three deep learning models.During the presentation, we will highlight the main results from the study and present details of the model architectures, datasets, and how space and time affect the performance of the models.1) Kniffka, Stengel, and Hollmann, “SEVIRI Cloud Mask Dataset - Edition 1.”

Accurate weather forecasts play an important role in our daily lives, ranging from everyday conveniences such as precipitation and temperature estimates to more critical and potentially life-threatening scenarios of hazardous and extreme weather. Due to the importance of weather forecasting, considerable advances have been achieved for the underlying engine producing weather forecasts, namely numerical weather prediction models. However, the unprecedented amounts of atmospheric observations available today pose considerable challenges since traditional methods cannot assimilate all this data in a timely fashion. To overcome these issues, data-driven techniques offer an attractive alternative given their recent success in multiple scientific disciplines.This thesis investigated data-driven methods for short-term and medium-term weather forecasting based on deep learning. The problem was addressed in three parts: cloud type forecasting, solar irradiance and power forecasting, and atmospheric flow forecasting. In addition, a novel satellite-based dataset called CloudCast was presented to provide a suitable benchmark and stimulate future research in cloud forecasting studies.Three methods were proposed for short-term cloud type forecasting based on the CloudCast dataset. The models showed superior performance relative to a meteorological benchmark with static motion, but they also exhibited a rapid decline in accuracy across time. Next, a two-stage pipeline was proposed for solar irradiance and power production forecasting. The first-stage model, IrradianceNet, focused exclusively on improved modeling of cloud-induced irradiance and displayed superior performance to all competing benchmark models, including the NWP-based ERA5 reanalysis dataset. The second-stage model displayed promising albeit preliminary results for a case study of power production forecasting in Germany. Finally, two methods were proposed for medium-term atmospheric flow forecasting. The first method improved accuracy compared to several meteorological benchmarks for forecasting large-scale circulation weather regimes in the North Atlantic-European region. The second method showed subpar results for geopotential height forecasting at 500 hectopascal, arguably the ultimate challenge between data-driven and numerical weather prediction methods. The proposed methods contribute to the rapidly growing field of data-driven weather forecasting using deep learning and multi-modal atmospheric data. More specifically, the study has shown promising results using purely data-driven approaches for several short-term and medium-term weather forecasting tasks, even compared to some of the best meteorological and numerical weather prediction benchmarks. Accurate weather forecasts play an important role in our daily lives, ranging from everyday conveniences such as precipitation and temperature estimates to more critical and potentially life-threatening scenarios of hazardous and extreme weather. Due to the importance of weather forecasting, considerable advances have been achieved for the underlying engine producing weather forecasts, namely numerical weather prediction models. However, the unprecedented amounts of atmospheric observations available today pose considerable challenges since traditional methods cannot assimilate all this data in a timely fashion. To overcome these issues, data-driven techniques offer an attractive alternative given their recent success in multiple scientific disciplines.This thesis investigated data-driven methods for short-term and medium-term weather forecasting based on deep learning. The problem was addressed in three parts: cloud type forecasting, solar irradiance and power forecasting, and atmospheric flow forecasting. In addition, a novel satellite-based dataset called CloudCast was presented to provide a suitable benchmark and stimulate future research in cloud forecasting studies.Three methods were proposed for short-term cloud type forecasting based on the CloudCast dataset. The models showed superior performance relative to a meteorological benchmark with static motion, but they also exhibited a rapid decline in accuracy across time.Next, a two-stage pipeline was proposed for solar irradiance and power production forecasting. The first-stage model, IrradianceNet, focused exclusively on improved modeling of cloud-induced irradiance and displayed superior performance to all competing benchmark models, including the NWP-based ERA5 reanalysis dataset. The second-stage model displayed promising albeit preliminary results for a case study of power production forecasting in Germany. Finally, two methods were proposed for medium-term atmospheric flow forecasting. The first method improved accuracy compared to several meteorological benchmarks for forecasting large-scale circulation weather regimes in the North Atlantic-European region. The second method showed subpar results for geopotential height forecasting at 500 hectopascal, arguably the ultimate challenge between data-driven and numerical weather prediction methods. The proposed methods contribute to the rapidly growing field of data-driven weather forecasting using deep learning and multi-modal atmospheric data. More specifically, the study has shown promising results using purely data-driven approaches for several short-term and medium-term weather forecasting tasks, even compared to some of the best meteorological and numerical weather prediction benchmarks.

A digital polar transmitter is presented that uses a digital-to-time-converter (DTC) for wide-channel phase modulation with multiband support. DTC-based phase modulation enables high-efficiency polar operation with high-efficiency RFDAC. The transmitter uses coarse division inherent in DTC operation to generate TX output frequencies from 0.7 to 2.2 GHz with a limited DCO tuning range of only 7.3–8.7 GHz (±8.7%). Phase modulation of narrowband signals (e.g., one resource block) using DTC could result in spurs in output spectrum due to the almost periodic nature of DTC errors. A theoretical analysis is presented that predicts the location of these spurs and a technique to eliminate such spurs by appropriately adjusting the PLL frequency is introduced. A new digitally controlled-edge-interpolator architecture is also presented to minimize dynamic errors, which is necessary in order to meet stringent cellular noise requirements. The DTC was implemented using digital synthesis and auto-place-and-route (APR) tools, requiring very limited analog layout resources. The TX was fabricated in 14-nm FinFET and it achieved a noise of −153/−150 dBc/Hz for LTE5/LTE20 in Band1 at +4-dBm output power with DTC power dissipation of 27 mW.

openaire: EC/H2020/633053/EU//EUROfusion Funding Information: The authors would like to thank Professor U. Fantz and Professor D. Reiter for valuable discussions enabling this work. This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under grant agreement number 633053. The views and opinions expressed herein do not necessarily reflect those of the European Commission. This work was supported by the Academy of Finland under grant no. 13330050. Publisher Copyright: © 2022 The Authors. Contributions to Plasma Physics published by Wiley-VCH GmbH. To evaluate the impact of transport of metastable, vibrationally excited states of the hydrogen molecule in dense and cold plasmas each vibrational state must be simulated as an individual species. Eirene neutral gas simulations of a one-dimensional flux-tube using a metastable-resolved model indicate a 30–50% decrease in the effective dissociation rate compared to simulations using a metastable-unresolved setup, which consider a single molecular species. Zero-dimensional Eirene simulations omitting transport effects predict a 25–65% decrease in the effective dissociation rate due to differences between the metastable-unresolved AMJUEL and the metastable-resolved H2VIBR rates available in Eirene. The exclusion of molecular hydrogen depletion via electronically excited states and vibrational transitions (Formula presented.) from the metastable-resolved rates reduce the effective dissociation rate. By accounting for the difference caused by the different collisional-radiative treatment of the metastable-resolved rates compared to the metastable-unresolved rates, transport effects are expected to be relevant under detached divertor conditions. It is, however, not possible to individually assess the role of the collisional-radiative processes and transport on the effective dissociation rate using the currently available atomic and molecular rates for the metastable-resolved and metastable-unresolved Eirene setups.

Data-driven methods for atmospheric and renewables forecasting using satellite images are becoming increasingly popular in the literature due to the massive amount of data combined with recent advances in deep learning methods. This paper proposes a two-stage machine learning model for short-term satellite-derived irradiance to solar power production forecasting 4-hours into the future. The proposed method extends the state-of-the-art satellite derived solar irradiance forecasting model IrradianceNet to the solar power production forecasting domain, using simulated power profiles and a second-stage machine learning model. Experiments show promising results for forecasting solar power production in Germany, but more experiments are needed to further validate the performance of the proposed methodology.

Sammendrag Industrialisering har gjort antropogen tungmetallforurensning til et globalt problem, da utslippet av disse elementene ofte representerer en alvorlig risiko for menneskehelse og miljøet. Til tross for at dette har mottatt mye oppmerksomhet de siste tiårene, er de nåværende teknologiene benyttet for fjerning av tungmetaller ansett som dyre, ineffektive under mange industrielle kondisjoner og ofte selv miljøskadelige. Bruken av organismer for å fjerne tungmetaller, kalt bioremediering, har dukket opp som et potensielt billig, effektivt og bærekraftig alternativ. Bioremediering som benytter seg av microalger, referert til som phycoremediering, er blant de mest lovende kandidatene for utviklingen av slik nestegenerasjons avfallsbehandling og har i de senere år mottatt mye oppmerksomhet. Mange mikroalgearter demonstrerer høye naturlige toleranser mot tungmetaller og evnen til å effektivt akkumulere dem. Fremskritt innenfor molekylær biologi og genetisk analyseteknologi har tillat forskere å bedre forstå det molekylære grunnlaget for disse trekkene, hvilket åpner muligheter for å videre forbedre phycoremedieringsteknologi og potensielt skape hypertolerante og hyperakkumulerende mutanter igjennom genteknologi. Målet for dette prosjektet var å undersøke rollene tre gener - VIT1, VIT2 og ATPase5-1B - som koder for antatte metaltransportør-proteiner i den naturlige tungmetalltoleransen til den marine diatomeen (kiselalgen) Phaeodactylum tricornutum, og dermed bidra til dette kunnskapsgrunnlaget. På grunn av deres sekvens-homologi med metatransportører beskrevet i andre organismer har det blitt forutsett at VIT1 og VIT2 transporterer tungmetaller fra cytosol inn i vakuoler, mens ATPase5-1B pumper tungmetaller ut av cellen. Deres funksjoner ble undersøkt i dette prosjektet ved å lage knockouts av disse genene ved hjelp av CRISPR/Cas9 gen editering, og deretter undersøke om mutantlinjene demonstrerte endringer i sink- og kadmiumtoleranse. Plasmider som kodet for CRISPR-Cas9 endonukleasen og et gRNA som gjenkjente enten ATPase5-1B eller både VIT1 og VIT2, ble skapt og benyttet til å transformere P. tricornutum cellelinjer via bakteriell konjugering og biolistisk partikkelbombardering. CRISPR/Cas9-induserte mutasjoner ble oppnådd, men isolasjonen av rene mutantlinjer måtte avbrytes tidlig på grunn av manglende tid. De fire mest lovende mutantlinjene ble valgt for videre undersøkelser: to linjer med knockout-mutasjoner for ATPase5-1B, og to linjer med mutasjoner som førte til knockout av VIT2 og knockdown av VIT1. Målinger av maksimum spesifikk vekstrate K’max variable klorofyll a fluorescens indikerte at verken kultivering i 5 mg/L Zn2+ eller 1.25 mg/L Cd2+ induserte uforholdsmessig toksisitet i noen av mutantlinjene. Langtidsveksten til ATPase5-1B-knockoutlinjene ved utsettelse for 1.25 mg/L Cd2+ var mer påvirket enn den for villtype, hvilket indikerer at denne transportøren kan være involvert i adaptiv tungmetalltoleranse i P. tricornutum. Videre undersøkelser, inkludert av endringer i genekspresjon og tungmetallakkumulasjon i slike knockoutmutanter, er nødvendige for å fastslå rollene disse metalltransportørene spiller i tungemetalltoleransen til P. tricornutum og hvilken betydning de kan ha for utviklingen av bærekraftig bioremedieringsteknologi. Abstract Industrialisation has led to heavy metal pollution from anthropogenic sources becoming a global problem, as these elements often represent a serious risk to human health and to the environment. Despite having received much attention in recent decades, currently utilized technologies for heavy metal waste removal are considered economically prohibitive, inefficient under many industrial conditions and often environmentally harmful themselves. The use of organisms to remove heavy metal pollutants, referred to as bioremediation, has emerged as a potentially cheap, effective and sustainably alternative. Bioremediation using microalgae, also referred to as phycoremediation, is among the most promising candidates for the development of such a next-generation waste management technology and has been the subject of much research in later years. Many microalgal species display high innate tolerance to heavy metals and the ability to effectively accumulate. Advancements in molecular biology and genetic analysis technology has allowed scientists to better understand the molecular basis of these traits, opening opportunities to further improve phycoremediation technologies and the potential of creating hypertolerant and hyperaccumulating mutant strains through genetic engineering. The goal of this project was to investigate the role of three genes - VIT1, VIT2 and ATPase5-1B - encoding predicted metal transporter proteins play in the innate heavy metal tolerance of the marine diatom Phaeodactylum tricornutum, and thereby contributing to this body of knowledge. Due to their sequence homology with metal transporters described in other organisms, it has been predicted that the VIT1 and VIT2 transporters help store cytosolic heavy metals in vacuoles while the ATPase5-1B transporter facilitates efflux from the cell. Their functions were investigated in this project by creating knockouts of these genes using the CRISPR/Cas9 gene editing technology and observing if mutant lines displayed any changes in tolerance to zinc and cadmium. Plasmids containing the CRISPR-Cas9 endonuclease and a gRNA targeting either ATPase5-1B or both VIT1 and VIT2 simultaneously, were generated and used to transform P. tricornutum by bacterial conjugation and biolistic particle bombardment. CRISPR/Cas9-induced mutations were thus obtained, but due to time constraints the screening and purification of mutant lines had to be concluded prematurely. The four most promising mutant lines were selected for further assays: two lines with knockout mutations for ATPase5-1B, and two lines with mutations leading to a knockout of VIT2 and knockdown of VIT1. Measurement of the maximum specific growth rates K’max and variable Chl a fluorescence indicated that cultivation in 5 mg/L Zn2+ or 1.25 mg/L Cd2+ did not induce disproportional toxicity in any of the mutant lines. The long-term growth of ATPase5-1B knockout when exposed to 1.25 mg/L Cd2+ was more affected than that of WT, indicating that this transporter might be involved in adaptive heavy metal tolerance in P. tricornutum. Further studies, including changes in gene expression and heavy metal accumulation in such knockout mutants, are however necessary to determine the role that these metal transporters play in the heavy metal tolerance of P. tricornutum and their potential relevance for the development of a sustainable bioremediation technology.

INTRODUCTION We aimed to investigate how, changes in atmospheric pressure influence the human body. METHODS The study was an observational study, reported according to the STROBE-guideline (STrengthening the Reporting of OBservational studies in Epidemiology). Participants had their abdominal circumference measured on ground level, at the bottom of a pool, and during flight. This was used to investigate the relationship between atmospheric pressure and abdominal circumference. RESULTS We included 17 participants for the flight study and 12 participants for the pool study. Flying increased abdominal circumference from median 82 cm (range: 72-117 cm) at ground level to 86 cm (74-122 cm) in flight, p = 0.001. Submersion in water caused a decrease in abdominal circumference from median 82 cm (70-116 cm) at ground level to 79 cm (67-114 cm) under water, p = 0.003. Furthermore, flying resulted in a significant increase in self-reported flatulence and bloating from median 27 (0-69) to 50 (0-93), p = 0.02 (scale 0-100). Flying also caused an increase in abdominal pain from median 0 (0-68) to 3 (0-70), p = 0.02 (scale: 0-100). CONCLUSION Our findings confirm that changes to environment and thereby atmospheric pressure, influence abdominal circumference. This is likely due to expansion and compression of bowel gasses related to the ambient atmospheric pressure. Interestingly, our findings may help explain the physical appearance of mermaids. FUNDING none. TRIAL REGISTRATION none.

The presence of clouds is widely identified as the primary uncertainty in current surface solar global horizontal irradiance (GHI) forecasts. Despite a wealth of historical satellite-derived irradiance observations, only limited research has investigated this problem from a purely data-driven perspective, something that has seen tremendous success in related domains such as radar- and satellite-based precipitation short-term forecasting. This paper presents IrradianceNet, a novel satellite-based neural network for spatiotemporal forecasting of surface solar irradiance up to 4 h in the future over Europe. Our method is fully data-driven and needs no post-processing or calibration based on sparse ground-based measurements of irradiance. We demonstrate superior forecasting performance compared to several persistence models, the TV-L1 algorithm, and ERA5 reanalysis data for satellite-derived solar irradiance using the European SARAH-2.1 dataset. We also validate these results using ground-based pyranometer observations from the Baseline Surface Radiation Network. Our conclusions remain unchanged when we account for hourly and monthly seasonality. Finally, applying a simple cloud mask scheme, we demonstrate that our performance improvement arises due to a considerable reduction in cloudy pixel errors. This is initial evidence that purely data-driven methods might better approximate and infer future cloud dynamics and their impact on surface solar irradiance.