scholarly journals The “Seili-index” For The Prediction of Chlorophyll-α Levels In The Archipelago Sea of The Northern Baltic Sea, Southwest Finland

2021 ◽  
Author(s):  
Jari Hänninen ◽  
Katja Mäkinen ◽  
Klaus Nordhausen ◽  
Jussi Laaksonlaita ◽  
Olli Loisa ◽  
...  
Keyword(s):  
Mixed Model ◽  
Water Layer ◽  
Monitoring Data ◽  
Short Term ◽  
Data Set ◽  
Chl A ◽  
Generalized Additive Mixed Model ◽  
Temperature Forecast ◽  
Archipelago Sea ◽  
Marine Environmental

Abstract To build a forecasting tool for the state of eutrophication in the Archipelago Sea, we fitted a Generalized Additive Mixed Model (GAMM) to marine environmental monitoring data, which were collected over the years 2011-2019 by an automated profiling buoy at the Seili ODAS-station. The resulting “Seili-index” can be used to predict the chlorophyll-α (chl-a) concentration in the seawater a number of days ahead by using the temperature forecast as a covariate. An array of test predictions with two separate models on the 2019 data set showed that the index is adept at predicting the amount of chl-a especially in the upper water layer. The visualization with 10 days of chl-a level predictions are presented online at https://saaristomeri.utu.fi/seili-index/. We also applied GAMMs to predict abrupt blooms of cyanobacteria on the basis of temperature and wind conditions and found the model to be feasible for short-term predictions. The use of automated monitoring data and the presented GAMM model in assessing the effects of natural resource management and pollution risks is discussed.

scholarly journals The Association Between the Baseline and the Change in Neutrophil-to-Lymphocyte Ratio and Short-Term Mortality in Patients With Acute Respiratory Distress Syndrome

2021 ◽  
Vol 8 ◽  
Author(s):  
Wei Zhang ◽  
Yadan Wang ◽  
Weijie Li ◽  
Guizuo Wang
Keyword(s):  
Mixed Model ◽  
Distress Syndrome ◽  
Multivariable Logistic Regression Analysis ◽  
Short Term ◽  
Survival Group ◽  
Generalized Additive Mixed Model ◽  
Short Term Mortality ◽  
The Difference ◽  
The Relationship ◽  
Over Time

Background: Two previous studies have shown that increased neutrophil to lymphocyte ratio (NLR) is associated with short-term prognosis in patients with acute respiratory distress syndrome (ARDS), but it is usually assessed as a single threshold value at baseline. We investigated the relationship between the baseline and the early change in NLR and 30-day mortality in patients with ARDS to evaluate the prognostic value of NLR baseline and NLR changes during the first 7 days after ICU admission.Methods: This is a retrospective cohort study, with all ARDS patients diagnosed according to the Berlin definition from the Medical Information Mart for Intensive Care III (MIMIC-III) database. We calculated the NLR by dividing the neutrophil count by the lymphocyte count. The multivariable logistic regression analysis was used to investigate the relationship between the baseline NLR and short-term mortality. Then the generalized additive mixed model was used to compare trends in NLR over time among survivors and non-survivors after adjusting for potential confounders.Results: A total of 1164 patients were enrolled in our study. Multivariable logistic regression analysis showed that after adjusting for confounders, elevated baseline NLR was a significant risk factor predicting 30-day mortality (OR 1.02, 95%CI 1.01, 1.03, P = 0.0046) and hospital mortality (OR 1.02, 95%CI 1.01, 1.03, P = 0.0003). The result of the generalized additive mixed model showed that the NLR decreased in the survival group and increased in the non-survival group gradually within 7 days after ICU admission. The difference between the two groups showed a trend of increase gradually and the difference increased by an average of 0.67 daily after adjusting for confounders.Conclusions: We confirmed that there was a positive correlation between baseline NLR and short-term mortality, and we found significant differences in NLR changes over time between the non-survival group and the survival group. The early increase in NLR was associated with short-term mortality in ARDS patients.

Short-Term Flood Prediction System

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Konrad Nering
Keyword(s):  
Flash Floods ◽  
Prediction System ◽  
Short Term ◽  
Data Set ◽  
Flood Prediction ◽  
Full Operation

AbstractThis paper describes a fully functional short-term flood prediction system. Its effect has been tested on watershed of Lubieńka river in Małopolska. To use this system it must have a data set also described in this paper. A modification of the system to adopt for predicting flash floods was described. Full operation of the system is shown on example of real flood on Lubieńka river in June 2011.

Nonlinearities in bilingual visual word recognition: An introduction to generalized additive modeling

2021 ◽  
pp. 1-8
Author(s):  
Koji Miwa ◽  
Harald Baayen
Keyword(s):  
Lexical Decision ◽  
Tensor Product ◽  
Nonlinear Interaction ◽  
Mixed Model ◽  
Nonlinear Effects ◽  
Regression Splines ◽  
Generalized Additive Mixed Model ◽  
Generalized Additive Modeling ◽  
Additive Modeling ◽  
Cross Language

Abstract This paper introduces the generalized additive mixed model (GAMM) and the quantile generalized additive mixed model (QGAMM) through reanalyses of bilinguals’ lexical decision data from Dijkstra et al. (2010) and Miwa et al. (2014). We illustrate how regression splines can be used to test for nonlinear effects of cross-language similarity in form as well as for controlling experimental trial effects. We further illustrate the tensor product smooth for a nonlinear interaction between cross-language semantic similarity and word frequency. Finally, we show how the QGAMM helps clarify whether the effect of a particular predictor is constant across distributions of RTs.

scholarly journals Investigating the Broken-Heart Effect: a Model for Short-Term Dependence between the Remaining Lifetimes of Joint Lives

2012 ◽  
Vol 7 (2) ◽  
pp. 236-257 ◽  
Author(s):  
Jaap Spreeuw ◽  
Iqbal Owadally
Keyword(s):  
Mortality Rates ◽  
Short Term ◽  
Numerical Work ◽  
Data Set ◽  
Multiple State ◽  
Multiple State Model ◽  
Insurance Data ◽  
Over Time ◽  
Assurance Contracts

AbstractWe analyze the mortality of couples by fitting a multiple state model to a large insurance data set. We find evidence that mortality rates increase after the death of a partner and, in addition, that this phenomenon diminishes over time. This is popularly known as a “broken-heart” effect and we find that it affects widowers more than widows. Remaining lifetimes of joint lives therefore exhibit short-term dependence. We carry out numerical work involving the pricing and valuation of typical contingent assurance contracts and of a joint life and survivor annuity. If insurers ignore dependence, or mis-specify it as long-term dependence, then significant mis-pricing and inappropriate provisioning can result. Detailed numerical results are presented.

Functional Delineation of Prefrontal Networks Underlying Working Memory in Schizophrenia: A Cross-data-set Examination

2021 ◽  
pp. 1-29
Author(s):  
Nicole Sanford ◽  
Todd S. Woodward
Keyword(s):  
Working Memory ◽  
Mixed Model ◽  
Data Sets ◽  
Generation Task ◽  
Data Set ◽  
Set Switching ◽  
Dorsolateral Prefrontal ◽  
Task Conditions ◽  
Experiment Analysis ◽  
Internal Attention

Abstract Background: Working memory (WM) impairment in schizophrenia substantially impacts functional outcome. Although the dorsolateral pFC has been implicated in such impairment, a more comprehensive examination of brain networks comprising pFC is warranted. The present research used a whole-brain, multi-experiment analysis to delineate task-related networks comprising pFC. Activity was examined in schizophrenia patients across a variety of cognitive demands. Methods: One hundred schizophrenia patients and 102 healthy controls completed one of four fMRI tasks: a Sternberg verbal WM task, a visuospatial WM task, a Stroop set-switching task, and a thought generation task (TGT). Task-related networks were identified using multi-experiment constrained PCA for fMRI. Effects of task conditions and group differences were examined using mixed-model ANOVA on the task-related time series. Correlations between task performance and network engagement were also performed. Results: Four spatially and temporally distinct networks with pFC activation emerged and were postulated to subserve (1) internal attention, (2) auditory–motor attention, (3) motor responses, and (4) task energizing. The “energizing” network—engaged during WM encoding and diminished in patients—exhibited consistent trend relationships with WM capacity across different data sets. The dorsolateral-prefrontal-cortex-dominated “internal attention” network exhibited some evidence of hypoactivity in patients, but was not correlated with WM performance. Conclusions: Multi-experiment analysis allowed delineation of task-related, pFC-anchored networks across different cognitive constructs. Given the results with respect to the early-responding “energizing” network, WM deficits in schizophrenia may arise from disruption in the “energization” process described by Donald Stuss' model of pFC functions.

Nowcasting heavy precipitation over the Netherlands using a 13-year radar archive: a machine learning approach

2021 ◽  
Author(s):  
Eva van der Kooij ◽  
Marc Schleiss ◽  
Riccardo Taormina ◽  
Francesco Fioranelli ◽  
Dorien Lugt ◽  
...  
Keyword(s):  
Machine Learning ◽  
The Netherlands ◽  
Heavy Rainfall ◽  
Predictive Performance ◽  
Heavy Precipitation ◽  
Early Warning Systems ◽  
Training Data ◽  
Short Term ◽  
Data Set ◽  
Radar Images

<p>Accurate short-term forecasts, also known as nowcasts, of heavy precipitation are desirable for creating early warning systems for extreme weather and its consequences, e.g. urban flooding. In this research, we explore the use of machine learning for short-term prediction of heavy rainfall showers in the Netherlands.</p><p>We assess the performance of a recurrent, convolutional neural network (TrajGRU) with lead times of 0 to 2 hours. The network is trained on a 13-year archive of radar images with 5-min temporal and 1-km spatial resolution from the precipitation radars of the Royal Netherlands Meteorological Institute (KNMI). We aim to train the model to predict the formation and dissipation of dynamic, heavy, localized rain events, a task for which traditional Lagrangian nowcasting methods still come up short.</p><p>We report on different ways to optimize predictive performance for heavy rainfall intensities through several experiments. The large dataset available provides many possible configurations for training. To focus on heavy rainfall intensities, we use different subsets of this dataset through using different conditions for event selection and varying the ratio of light and heavy precipitation events present in the training data set and change the loss function used to train the model.</p><p>To assess the performance of the model, we compare our method to current state-of-the-art Lagrangian nowcasting system from the pySTEPS library, like S-PROG, a deterministic approximation of an ensemble mean forecast. The results of the experiments are used to discuss the pros and cons of machine-learning based methods for precipitation nowcasting and possible ways to further increase performance.</p>

scholarly journals Temporal Variation of Chlorophyll-a Concentrations in Highly Dynamic Waters from Unattended Sensors and Remote Sensing Observations

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2699 ◽  
Author(s):  
Jian Li ◽  
Liqiao Tian ◽  
Qingjun Song ◽  
Zhaohua Sun ◽  
Hongjing Yu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Chlorophyll Fluorescence ◽  
Chlorophyll A ◽  
Temporal Variations ◽  
Lake Area ◽  
Short Term ◽  
Chl A ◽  
Spatio Temporal

Monitoring of water quality changes in highly dynamic inland lakes is frequently impeded by insufficient spatial and temporal coverage, for both field surveys and remote sensing methods. To track short-term variations of chlorophyll fluorescence and chlorophyll-a concentrations in Poyang Lake, the largest freshwater lake in China, high-frequency, in-situ, measurements were collected from two fixed stations. The K-mean clustering method was also applied to identify clusters with similar spatio-temporal variations, using remote sensing Chl-a data products from the MERIS satellite, taken from 2003 to 2012. Four lake area classes were obtained with distinct spatio-temporal patterns, two of which were selected for in situ measurement. Distinct daily periodic variations were observed, with peaks at approximately 3:00 PM and troughs at night or early morning. Short-term variations of chlorophyll fluorescence and Chl-a levels were revealed, with a maximum intra-diurnal ratio of 5.1 and inter-diurnal ratio of 7.4, respectively. Using geostatistical analysis, the temporal range of chlorophyll fluorescence and corresponding Chl-a variations was determined to be 9.6 h, which indicates that there is a temporal discrepancy between Chl-a variations and the sampling frequency of current satellite missions. An analysis of the optimal sampling strategies demonstrated that the influence of the sampling time on the mean Chl-a concentrations observed was higher than 25%, and the uncertainty of any single Terra/MODIS or Aqua/MODIS observation was approximately 15%. Therefore, sampling twice a day is essential to resolve Chl-a variations with a bias level of 10% or less. The results highlight short-term variations of critical water quality parameters in freshwater, and they help identify specific design requirements for geostationary earth observation missions, so that they can better address the challenges of monitoring complex coastal and inland environments around the world.

scholarly journals Interactive effects of ambient ozone and climate measured on growth of mature loblolly pine trees

1996 ◽  
Vol 26 (4) ◽  
pp. 670-681 ◽  
Author(s):  
S.B. McLaughlin ◽  
D.J. Downing
Keyword(s):  
Soil Moisture ◽  
Loblolly Pine ◽  
Moisture Stress ◽  
Growth Patterns ◽  
Forest Growth ◽  
Interactive Effects ◽  
Forest Trees ◽  
Short Term ◽  
Data Set ◽  
Ambient Ozone

Seasonal growth patterns of mature loblolly pine (Pinustaeda L.) trees over the interval 1988–1993 have been analyzed to evaluate the effects of ambient ozone on growth of large forest trees. Patterns of stem expansion and contraction of 34 trees were examined using serial measurements with sensitive dendrometer band systems. Study sites, located in eastern Tennessee, varied significantly in soil moisture, soil fertility, and stand density. Levels of ozone, rainfall, and temperature varied widely over the 6-year study interval. Regression analysis identified statistically significant influences of ozone on stem growth patterns, with responses differing widely among trees and across years. Ozone interacted with both soil moisture stress and high temperatures, explaining 63% of the high frequency, climatic variance in stem expansion identified by stepwise regression of the 5-year data set. Observed responses to ozone were rapid, typically occurring within 1–3 days of exposure to ozone at ≥40 ppb and were significantly amplified by low soil moisture and high air temperatures. Both short-term responses, apparently tied to ozone-induced increases in whole-tree water stress, and longer term cumulative responses were identified. These data indicate that relatively low levels of ambient ozone can significantly reduce growth of mature forest trees and that interactions between ambient ozone and climate are likely to be important modifiers of future forest growth and function. Additional studies of mechanisms of short-term response and interspecies comparisons are clearly needed.

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