Solid Oxide Electrolysis Cells (SOECs) have proven to be a highly efficient key technology for producing valuable chemicals and fuels from renewably generated electricity at temperatures between 600 ...°C and 900 °C, thus providing a carbon-neutral method for energy storage. The successful implementation of this technology on an industrial level in particular requires the long-term stability of all system components with a concurrent overall degradation rate of a maximum of 0.75% kh
−1
or even better 0.5% kh
−1
, corresponding to a performance loss of 20% over approx. five years under constant operating parameters. However, the materials currently used for SOEC systems have been developed and optimized in recent decades for fuel cell operation. The degradation of these Solid Oxide Fuel Cell (SOFC) materials used in SOECs, however, slows down the technology and market ramp-up. Accordingly, a selection and development of materials specifically for use in SOEC operation, must therefore be based not only on the highest performance but also on the lowest achievable degradation rate. In general, the systematic development of new SOEC materials must be driven towards key performance parameters such as mechanical, thermal, and chemical stability as well as an application-oriented assessment (cost effectiveness, simple manufacturing). This review presents the state-of-the-art materials in current industrial use for planar SOECs as well as future challenges regarding materials design and degradation. Recent advances in material compositions are discussed and evaluated in terms of their performance, stability, and potential for industrial implementation. In addition, a materials selection for interconnects, coatings, and sealants is briefly listed to outline current developments in these areas.
The review article covers all state-of-the art materials related to high-temperature electrolyzers based on oxygen-ion conductors. The focus lies on the cell materials, materials of additional components like interconnects and sealants are briefly described.
Our objective was to identify weather-based variables in pre- and post-anthesis time windows for predicting major Fusarium head blight (FHB) epidemics (defined as FHB severity ≥ 10%) in the United ...States. A binary indicator of major epidemics for 527 unique observations (31% of which were major epidemics) was linked to 380 predictor variables summarizing temperature, relative humidity, and rainfall in 5-, 7-, 10-, 14-, or 15-day-long windows either pre- or post-anthesis. Logistic regression models were built with a training data set (70% of the 527 observations) using the leaps-and-bounds algorithm, coupled with bootstrap variable and model selection methods. Misclassification rates were estimated on the training and remaining (test) data. The predictive performance of models with indicator variables for cultivar resistance, wheat type (spring or winter), and corn residue presence was improved by adding up to four weather-based predictors. Because weather variables were intercorrelated, no single model or subset of predictor variables was best based on accuracy, model fit, and complexity. Weather-based predictors in the 15 final empirical models selected were all derivatives of relative humidity or temperature, except for one rainfall-based predictor, suggesting that relative humidity was better at characterizing moisture effects on FHB than other variables. The average test misclassification rate of the final models was 19% lower than that of models currently used in a national FHB prediction system.
Dietary intake of methyl donors, such as folic acid and methionine, shows considerable intra-individual variation in human populations. While it is recognized that maternal departures from the ...optimum of dietary methyl donor intake can increase the risk for mental health issues and neurological disorders in offspring, it has not been explored whether paternal dietary methyl donor intake influences behavioral and cognitive functions in the next generation. Here, we report that elevated paternal dietary methyl donor intake in a mouse model, transiently applied prior to mating, resulted in offspring animals (methyl donor-rich diet (MD) F1 mice) with deficits in hippocampus-dependent learning and memory, impaired hippocampal synaptic plasticity and reduced hippocampal theta oscillations. Gene expression analyses revealed altered expression of the methionine adenosyltransferase Mat2a and BK channel subunit Kcnmb2, which was associated with changes in Kcnmb2 promoter methylation in MD F1 mice. Hippocampal overexpression of Kcnmb2 in MD F1 mice ameliorated altered spatial learning and memory, supporting a role of this BK channel subunit in the MD F1 behavioral phenotype. Behavioral and gene expression changes did not extend into the F2 offspring generation. Together, our data indicate that paternal dietary factors influence cognitive and neural functions in the offspring generation.
Predicting Winter Wheat Heading Date Zhao, H. D.; Sassenrath, G. F.; Zambreski, Z. T. ...
Journal of applied meteorology and climatology,
12/2021, Letnik:
60, Številka:
12
Journal Article
Recenzirano
Odprti dostop
Accurate prediction of winter wheat (Triticum aestivum L.) heading date is important for determining the potential incidence of diseases and abiotic stresses such as freeze or heat events. Wheat ...phenological modeling requires cultivar- and crop-zone-specific vernalization and photoperiod knowledge. Previous models applied in Kansas showed that the uncertainties of predicting heading date were large and could be improved. In this study, a modification to the Scientific Impact Assessment and Modeling Platform for Advanced Crop and Ecosystem Management (SIMPLACE) model was developed and implemented to improve the accuracy of winter wheat heading date estimation. The cultivar- and crop-zone-specific model parameters were calculated using a Markov chain Monte Carlo simulation. The modified models were calibrated by using the longest observation site to cover all cultivars planted in each crop zone. Model performance was then evaluated for seven winter wheat cultivars at eight experiment sites distributed across four crop zones in Kansas. Our modified model (MS) had a root-mean-square error (RMSE) between predicted and observed heading date of 4 days, which reflects an improved accuracy by 5–8 days on average compared to the Agricultural Production Systems Simulator (APSIM) or the original SIMPLACE models. There was a clear correlation between the uncertainty of the modeled heading date and the sowing date in previous models. Our modified model demonstrates that integrating nonlinear temperature response functions, temperature stress factors, and sowing date information improved prediction of the heading date in winter wheat across Kansas.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
This book gives a comprehensive account of the development and present status of the field of soft (i.e. non-perturbative) phenomena encountered in the production of (multi-) hadronic final states by ...the collision of various types of particles at high energies. Phenomenological models used to describe the data are in general inspired by Quantum Chromo Dynamics (QCD) and the book repeatedly crosses the border — if at all existent — between soft (non-perturbative) and hard (perturbative) QCD.
ABSTRACT Logistic regression models for wheat Fusarium head blight were developed using information collected at 50 location-years, including four states, representing three different U.S. ...wheat-production regions. Non-parametric correlation analysis and stepwise logistic regression analysis identified combinations of temperature, relative humidity, and rainfall or durations of specified weather conditions, for 7 days prior to anthesis, and 10 days beginning at crop anthesis, as potential predictor variables. Prediction accuracy of developed logistic regression models ranged from 62 to 85%. Models suitable for application as a disease warning system were identified based on model prediction accuracy, sensitivity, specificity, and availability of weather variables at crop anthesis. Four of the identified models correctly classified 84% of the 50 location-years. A fifth model that used only pre-anthesis weather conditions correctly classified 70% of the location-years. The most useful predictor variables were the duration (h) of precipitation 7 days prior to anthesis, duration (h) that temperature was between 15 and 30 degrees C 7 days prior to anthesis, and the duration (h) that temperature was between 15 and 30 degrees C and relative humidity was greater than or equal to 90%. When model performance was evaluated with an independent validation set (n = 9), prediction accuracy was only 6% lower than the accuracy for the original data sets. These results indicate that narrow time periods around crop anthesis can be used to predict Fusarium head blight epidemics.
Epidemics are often triggered by specific weather patterns favouring the pathogen on susceptible hosts. For plant diseases, models predicting epidemics have therefore often emphasized the ...identification of early season weather patterns that are correlated with a disease outcome at some later point. Toward that end, window-pane analysis is an exhaustive search algorithm traditionally used in plant pathology for mining correlations in a weather series with respect to a disease endpoint. Here we show, with reference to Fusarium head blight (FHB) of wheat, that a functional approach is a more principled analytical method for understanding the relationship between disease epidemics and environmental conditions over an extended time series. We used scalar-on-function regression to model a binary outcome (FHB epidemic or non-epidemic) relative to weather time series spanning 140 days relative to flowering (when FHB infection primarily occurs). The functional models overall fit the data better than previously described standard logistic regression (lr) models. Periods much earlier than heretofore realized were associated with FHB epidemics. The findings were used to create novel weather summary variables which, when incorporated into lr models, yielded a new set of models that performed as well as existing lr models for real-time predictions of disease risk. This article is part of the theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes'. This issue is linked with the subsequent theme issue 'Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control'.
In past efforts, input weather variables for Fusarium head blight (FHB) prediction models in the United States were identified after following some version of the window-pane algorithm, which ...discretizes a continuous weather time series into fixed-length windows before searching for summary variables associated with FHB risk. Functional data analysis, on the other hand, reconstructs the assumed continuous process (represented by a series of recorded weather data) by using smoothing functions, and is an alternative way of working with time series data with respect to FHB risk. Our objective was to functionally model weather-based time series data linked to 865 observations of FHB (covering 16 states and 31 years in total), classified as epidemics (FHB disease index ≥ 10%) and nonepidemics (FHB disease index < 10%). Altogether, 94 different time series variables were modeled by penalized cubic B-splines for the smoothing function, from 120 days pre-anthesis to 20 days post-anthesis. Functional mean curves, standard deviations, and first derivatives were plotted for FHB epidemics relative to nonepidemics. Function-on-scalar regressions assessed the temporal trends of the magnitude and significance of the mean difference between functionally represented weather time series associated with FHB epidemics and nonepidemics. The mean functional weather-variable curve for epidemics started to deviate, in general, from that for nonepidemics as early as 40 days pre-anthesis for several weather variables. The greatest deviations were often near anthesis, the period of maximum susceptibility of wheat to FHB-causing fungi. The most consistent separations between the mean functional curves were seen with the daily averages of moisture-related variables (such as average relative humidity) and with variables summarizing the daily variation in temperature (as opposed to the daily mean). Functional data analysis was useful for extending our knowledge of relationships between weather variables and FHB epidemics.
Studies conflict on the significance of burn-induced coagulopathy. We posit that burn-induced coagulopathy is associated with injury severity in burns. Our purpose was to characterize coagulopathy ...profiles in burns and determine relationships between % total burn surface area (TBSA) burned and coagulopathy using the International Normalized Ratio (INR). Burned patients with INR values were identified in the TriNetX database and analyzed by %TBSA burned. Patients with history of transfusions, chronic hepatic failure, and those on anticoagulant medications were excluded. Interquartile ranges for INR in the burned study population were 1.2 (1.0-1.4). An INR of ≥ 1.5 was used to represent those with burn-induced coagulopathy as it fell outside the 3rd quartile. The population was stratified into subgroups using INR levels <1.5 or ≥1.5 on the day of injury. Data are average ± SD analyzed using chi-square; p < .05 was considered significant. There were 7,364 burned patients identified with INR <1.5, and 635 had INR ≥1.5. Comparing TBSA burned groups, burn-induced coagulopathy significantly increased in those with ≥20% TBSA; p = .048 at 20-29% TBSA, p = .0005 at 30-39% TBSA, and p < .0001 for 40% TBSA and above. Age played a significant factor with average age for those with burn-induced coagulopathy 59 ± 21.5 years and 46 ± 21.8 for those without (p < .0001). After matching for age, TBSA, and demographics, the risk of 28 day-mortality was higher in those with burn-induced coagulopathy compared to those without (risk difference 20.9%, p < .0001) and the odd ratio with 95% CI is 4.45 (3.399-5.825). Investigation of conditions associated with burn-induced coagulopathy showed the effect of heart diseases to be significant; 53% of patients with burn-induced coagulopathy had hypertension (p < .0001). Burn-induced coagulopathy increases with %TBSA burned. The information gained firmly reflects a link between %TBSA and burn-induced coagulopathy, which could be useful in prognosis and treatment decisions.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
High-temperature electrolysis using solid oxide electrolysis cells (SOECs) is an innovative technology to temporarily store unused electrical energy from renewable energy sources. However, they show ...continuous performance loss during long-term operation, which is the main issue preventing their widespread use. In this work, we have performed the long-term stability tests up to 1000 h under steam and co-electrolysis conditions using commercial NiO-YSZ/YSZ/GDC/LSC single cells in order to understand the degradation process. The electrolysis tests were carried out at different temperatures and fuel gas compositions. Intermittent AC- and DC- measurements were performed to characterize the single cells and to determine the responsible electrode processes for the degradation during long-term operation. An increased degradation rate is observed at 800 °C compared to 750 °C under steam electrolysis conditions. Moreover, a lower degradation rate is noticed under co-electrolysis operation in comparison to steam electrolysis operation. Finally, the post-test analyses using SEM-EDX and XRD were carried out in order to understand the degradation mechanism. The delamination of LSC is observed under steam electrolysis conditions at 800 °C, however, such delamination is not observed during co-electrolysis operation. In addition, Ni-depletion and agglomeration are observed on the fuel electrode side for all the cells.
PDF
