scholarly journals Interannual temporal patterns of DeSoto Canyon macrofauna and evaluation of influence from the Deepwater Horizon

2021 ◽  
Author(s):  
Arvind K Shantharam ◽  
Amy Baco-Taylor
Keyword(s):  
Time Series ◽  
Temporal Variability ◽  
Benthic Communities ◽  
Deepwater Horizon ◽  
Time Range ◽  
Feeding Guild ◽  
Time Points ◽  
Quick Recovery ◽  
Time Series Studies ◽  
Low Levels

Submarine canyons are highly dynamic and productive ecosystems, but time-series studies of metazoan benthic communities in canyons are scarce. Deep-sea macrofauna from the DeSoto Canyon in the northern Gulf of Mexico were sampled annually from 2012 through 2014 from five stations within the Canyon and from two stations in 2013 and 2014 on the adjacent open slope, for analysis of interannual dynamics, temporal variability, and potential influence of the Deepwater Horizon oil spill (DwH), which occurred nearby in 2010. At a few sites, elevated abundance was observed at the start of the time-series for overall macrofauna and for deposit feeder abundance. However, diversity metrics showed no difference within stations among time points. Community and feeding guild structure varied by station, as expected, but showed no statistical difference among time points within a station. Some temporal variability was visible in temporal trajectory overlays. Cluster analyses showed grouping more by station than by time point. Metrics utilized for measuring potential oil contamination impact and overall community stress including the benthic polychaete/amphipod ratio, feeding guild abundance, macrofaunal indicators designed from the DwH, and community dispersion, generally exhibited a paucity of evidence of impact, both yearly and with site-to-site comparisons. This suggests low levels of impact in the canyon consistent with the low deposition of hydrocarbons, the timing of sampling, and quick recovery of canyon foraminifera. Taken together these results suggest relatively low levels of temporal variability within the DeSoto Canyon macrofauna with little evidence of oil influence on these sites within the studied time range.

scholarly journals A Simple Method of Residential Electricity Load Forecasting by Improved Bayesian Neural Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Shubin Zheng ◽  
Qianwen Zhong ◽  
Lele Peng ◽  
Xiaodong Chai
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Load Forecasting ◽  
Forecast Model ◽  
Time Range ◽  
Simple Method ◽  
Load Forecast ◽  
Bayesian Neural Networks ◽  
Electricity Load ◽  
Electricity Load Forecasting

Electricity load forecasting is becoming one of the key issues to solve energy crisis problem, and time-series Bayesian Neural Network is one popular method used in load forecast models. However, it has long running time and relatively strong dependence on time and weather factors at a residential level. To solve these problems, this article presents an improved Bayesian Neural Networks (IBNN) forecast model by augmenting historical load data as inputs based on simple feedforward structure. From the load time delays correlations and impact factors analysis, containing different inputs, number of hidden neurons, historic period of data, forecasting time range, and range requirement of sample data, some advices are given on how to better choose these factors. To validate the performance of improved Bayesian Neural Networks model, several residential sample datasets of one whole year from Ausgrid have been selected to build the improved Bayesian Neural Networks model. The results compared with the time-series load forecast model show that the improved Bayesian Neural Networks model can significantly reduce calculating time by more than 30 times and even when the time or meteorological factors are missing, it can still predict the load with a high accuracy. Compared with other widely used prediction methods, the IBNN also performs a better accuracy and relatively shorter computing time. This improved Bayesian Neural Networks forecasting method can be applied in residential energy management.

scholarly journals Extracting Features from Time Series

2018 ◽  
pp. 85-100 ◽  
Author(s):  
Christian Herff ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Feature Extraction ◽  
Clinical Data ◽  
Noise Filtering ◽  
Time Intervals ◽  
Time Points ◽  
Biomedical Systems ◽  
Patient Weight ◽  
Short Time

AbstractClinical data is often collected and processed as time series: a sequence of data indexed by successive time points. Such time series can be from sources that are sampled over short time intervals to represent continuous biophysical wave-(one word waveforms) forms such as the voltage measurements representing the electrocardiogram, to measurements that are sampled daily, weekly, yearly, etc. such as patient weight, blood triglyceride levels, etc. When analyzing clinical data or designing biomedical systems for measurements, interventions, or diagnostic aids, it is important to represent the information contained within such time series in a more compact or meaningful form (e.g., noise filtering), amenable to interpretation by a human or computer. This process is known as feature extraction. This chapter will discuss some fundamental techniques for extracting features from time series representing general forms of clinical data.

scholarly journals Generalized linear mixed models in time series studies of air pollution

2011 ◽  
Vol 2 (4) ◽  
pp. 428-435 ◽  
Author(s):  
Ya–Hsiu Chuang ◽  
Sati Mazumdar ◽  
Taeyoung Park ◽  
Gong Tang ◽  
Vincent. C. Arena ◽  
...  
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Mixed Models ◽  
Generalized Linear Mixed Models ◽  
Linear Mixed Models ◽  
Time Series Studies

NDVI dynamics as reflected in climatic variables: spatial and temporal trends – a case study of Hungary

2019 ◽  
Vol 56 (4) ◽  
pp. 624-644 ◽  
Author(s):  
Szabó ◽  
Elemér ◽  
Kovács ◽  
Püspöki ◽  
Kertész ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Local Level ◽  
Temporal Trends ◽  
Maximum Temperature ◽  
Time Range ◽  
Climatic Data ◽  
Spatial Pattern Analysis ◽  
Short Period ◽  
Temporal Variance

Understanding climate change and revealing its future paths on a local level is a great challenge for the future. Beside the expanding sets of available climatic data, satellite images provide a valuable source of information. In our study we aimed to reveal whether satellite data are an appropriate way to identify global trends, given their shorter available time range. We used the CARPATCLIM (CC) database (1961–2010) and the MODIS NDVI images (2000–2016) and evaluated the time period covered by both (2000–2010). We performed a regression analysis between the NDVI and CC variables, and a time series analysis for the 1961–2008 and 2000–2008 periods at all data points. The results justified the belief that maximum temperature (TMAX), potential evapotranspiration and aridity all have a strong correlation with the NDVI; furthermore, the short period trend of TMAX can be described with a functional connection with its long period trend. Consequently, TMAX is an appropriate tool as an explanatory variable for NDVI spatial and temporal variance. Spatial pattern analysis revealed that with regression coefficients, macro-regions reflected topography (plains, hills and mountains), while in the case of time series regression slopes, it justified a decreasing trend from western areas (Transdanubia) to eastern ones (The Great Hungarian Plain). This is an important consideration for future agricultural and land use planning; i.e. that western areas have to allow for greater effects of climate change.

Use of Age-Adjusted Suicide Rates in Time Series Studies in Canada

1997 ◽  
Vol 81 (2) ◽  
pp. 490-490
Author(s):  
David Lester
Keyword(s):  
Time Series ◽  
Suicide Rates ◽  
Time Series Studies ◽  
Divorce Rates

For 1950–1985 age adjusted suicide rates were associated with marriage, birth, and divorce rates in Canada in the same way as were crude suicide rates.

scholarly journals DRAFT: Young Children’s Prosocial Behavior Protects Against Academic Risk in Neighborhoods Facing Adversity

2020 ◽  
Author(s):  
Emma Armstrong-Carter ◽  
Jonas G. Miller ◽  
Liam Hill ◽  
Benjamin Domingue
Keyword(s):  
Academic Achievement ◽  
At Risk ◽  
Prosocial Behavior ◽  
Neighborhood Disadvantage ◽  
Prosocial Behaviors ◽  
Academic Risk ◽  
Time Points ◽  
Disadvantaged Neighborhoods ◽  
Low Levels ◽  
Diverse Children

Children born into neighborhood adversity are at risk for low academic achievement. Identifying factors that help children from disadvantaged neighborhoods thrive is critical for reducing inequalities. We investigated whether children’s prosocial behavior buffers concurrent and subsequent academic risk in disadvantaged neighborhoods in Bradford, UK. Diverse children (N = 1,185) were followed from before birth to age seven, with measurements taken at four time points. We used governmental indexes of neighborhood adversity, teachers observations of prosocial behaviors, and direct assessments of academic achievement. Neighborhood disadvantage was associated with lower academic achievement only among children who displayed low levels of prosocial behavior. Findings were robust to sensitivity and sub-group analyses. Prosocial behavior may mitigate early academic risk in contexts of neighborhood disadvantage.

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