The impact of observational sampling on time series of global 0-700 m ocean average temperature: a case study

2016 ◽  
Vol 37 (5) ◽  
pp. 2260-2268 ◽  
Author(s):  
Simon A. Good
Keyword(s):  
Time Series ◽  
Average Temperature ◽  
The Impact

scholarly journals Trade Openness and Economic Growth Nexus: A Case Study of BRICS

2018 ◽  
Vol 54 (1) ◽  
pp. 1-15 ◽  
Author(s):  
L. G. Burange ◽  
Rucha R. Ranadive ◽  
Neha N. Karnik
Keyword(s):  
Economic Growth ◽  
Time Series ◽  
Causal Relationship ◽  
Trade Openness ◽  
Gdp Per Capita ◽  
Jel Codes ◽  
Per Capita ◽  
The Impact ◽  
Econometric Technique

The article analyses a causal relationship between trade openness and economic growth for the member countries of BRICS by using an econometric technique of time series analysis. Member countries of BRICS adopted a series of liberalization reforms, almost simultaneously, from the late 1980s. The article attempts to study the impact of trade openness on their growth in GDP per capita. It captures structural composition of GDP and openness of trade in four aspects, that is, merchandise exports, merchandise imports, services export and services import. In India, the study found growth-led trade in services hypothesis. The article supports the growth-led export and growth-led import hypothesis for China and export- and import-led growth for South Africa. However, no causal relationship was evident for Brazil and Russia. JEL Codes: F43, C22

scholarly journals Time Series Modeling of Air Pollution and Its Association with Season and Climate Variables in Istanbul, Turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Wind Speed ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using NARX method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

scholarly journals Do changing medical admissions practices in the UK impact on who is admitted? An interrupted time series analysis

BMJ Open ◽  
2018 ◽  
Vol 8 (10) ◽  
pp. e023274 ◽  
Author(s):  
Shona Fielding ◽  
Paul Alexander Tiffin ◽  
Rachel Greatrix ◽  
Amanda J Lee ◽  
Fiona Patterson ◽  
...  
Keyword(s):  
Time Series ◽  
Medical Students ◽  
Medical School ◽  
Interrupted Time Series ◽  
Interrupted Time Series Analysis ◽  
Target Groups ◽  
Lower Socioeconomic ◽  
The Uk ◽  
The Impact

IntroductionMedical admissions must balance two potentially competing missions: to select those who will be successful medical students and clinicians and to increase the diversity of the medical school population and workforce. Many countries address this dilemma by reducing the heavy reliance on prior educational attainment, complementing this with other selection tools. However, evidence to what extent this shift in practice has actually widened access is conflicting.AimTo examine if changes in medical school selection processes significantly impact on the composition of the student population.Design and settingObservational study of medical students from 18 UK 5-year medical programmes who took the UK Clinical Aptitude Test from 2007 to 2014; detailed analysis on four schools.Primary outcomeProportion of admissions to medical school for four target groups (lower socioeconomic classes, non-selective schooling, non-white and male).Data analysisInterrupted time-series framework with segmented regression was used to identify the impact of changes in selection practices in relation to invitation to interview to medical school. Four case study medical schools were used looking at admissions within for the four target groups.ResultsThere were no obvious changes in the overall proportion of admissions from each target group over the 8-year period, averaging at 3.3% lower socioeconomic group, 51.5% non-selective school, 30.5% non-white and 43.8% male. Each case study school changed their selection practice in decision making for invite to interview during 2007–2014. Yet, this within-school variation made little difference locally, and changes in admission practices did not lead to any discernible change in the demography of those accepted into medical school.ConclusionAlthough our case schools changed their selection procedures, these changes did not lead to any observable differences in their student populations. Increasing the diversity of medical students, and hence the medical profession, may require different, perhaps more radical, approaches to selection.

scholarly journals Time series modeling of air pollution and its association with season and climate variables in istanbul, turkey

2020 ◽  
Author(s):  
hazem al-najjar ◽  
Nadia Al-Rousan ◽  
Ismail A. Elhaty
Keyword(s):  
Air Pollution ◽  
Time Series ◽  
Feature Selection ◽  
Air Pollutants ◽  
Time Series Modeling ◽  
Climate Variables ◽  
Different Seasons ◽  
The Impact ◽  
Nonlinear Autoregressive

Abstract Air pollution depends on seasons, wind speed, temperature, wind direction and air pressure. The effect of different seasons on air pollution is not fully addressed in the reported works. The current study investigated the impact of season on air pollutants including SO2, PM10, NO, NOX, and O3 using Nonlinear AutoregRessive network with eXogenous inputs (NARX) method. In the applied methodology, a feature selection was used with each pollutant to find the most important season(s). Afterward, six models are designed based on the feature selection to show the impact of seasons in finding the concentration of pollutants. A case study is conducted on Esenyurt which is one of the most populated and industrialized places in Istanbul to validate the proposed framework. The performance of using all of the designed models with different pollutants showed that using season effect led to improving the performance of predictor and generating high R2 and low error functions.

scholarly journals Multivariate statistical modelling of extreme coastal water levels and the effect of climate variability: a case study in the Netherlands

2020 ◽  
Author(s):  
Victor M. Santos ◽  
Mercè Casas-Prat ◽  
Benjamin Poschlod ◽  
Elisa Ragno ◽  
Bart van den Hurk ◽  
...  
Keyword(s):  
Time Series ◽  
Water Level ◽  
Climate Model ◽  
Regional Climate ◽  
Water Levels ◽  
Multivariate Statistical ◽  
Inland Water ◽  
Impact Function ◽  
The Impact

Abstract. The co-occurrence of (not necessarily extreme) precipitation and surge can lead to extreme inland water levels in coastal areas. In a previous work the positive dependence between the two meteorological drivers was demonstrated in a case study in the Netherlands by empirically investigating an 800-year time series of water levels, which were simulated via a physical-based hydrological model driven by a regional climate model large ensemble. In this study, we present and test a multivariate statistical framework to replicate the demonstrated dependence and the resulting return periods of inland water levels. We use the same 800-year data series to develop an impact function, which is able to empirically describe the relationship between high inland water levels (the impact) and its driving variables (precipitation and surge). In our study area, this relationship is complex because of the high degree of human management affecting the dynamics of the water level. By event sampling and conditioning the drivers, an impact function was created that can reproduce the water levels maintaining an unbiased performance at the full range of simulated water levels. The dependence structure between the driving variables is modeled using two- and three-dimensional copulas. These are used to generate paired synthetic precipitation and surge events, transformed into inland water levels via the impact function. The compounding effects of surge and precipitation and the return water level estimates fairly well reproduce the earlier results from the empirical analysis of the same regional climate model ensemble. The proposed framework is therefore able to produce robust estimates of compound extreme water levels for a highly managed hydrological system. In addition, we present a unique assessment of the uncertainty when using only 50 years of data (what is typically available from observations). Training the impact function with short records leads to a general underestimation of the return levels as water level extremes are not well sampled. Also, the marginal distributions of the 50-year time series of the surge show high variability. Moreover, compounding effects tend to be underestimated when using 50 year slices to estimate the dependence pattern between predictors. Overall, the internal variability of the climate system is identified as a major source of uncertainty in the multivariate statistical model.

scholarly journals Temporal Progression: A case study in Porcine Survivability through Hemostatic Nanoparticles

2021 ◽  
Author(s):  
Chhaya Kulkarni ◽  
Nuzhat Maisha ◽  
Leasha J Schaub ◽  
Jacob Glaser ◽  
Erin Lavik ◽  
...  
Keyword(s):  
Time Series ◽  
Blood Loss ◽  
Series Representation ◽  
Armed Forces ◽  
Factors Affecting ◽  
Temporal Models ◽  
Temporal Modelling ◽  
The Impact ◽  
Analysis Of Time Series

This paper focuses on the analysis of time series representation of blood loss and cytokines in animals experiencing trauma to understand the temporal progression of factors affecting survivability of the animal. Trauma related grave injuries cause exsanguination and lead to death. 50% of deaths especially in the armed forces are due to trauma injuries. Restricting blood loss usually requires the presence of first responders, which is not feasible in certain cases. Hemostatic nanoparticles have been developed to tackle these kinds of situations to help achieve efficient blood coagulation. Hemostatic nanoparticles were administered into trauma induced porcine animals (pigs) to observe impact on the cytokine and blood loss experienced by them. In this paper we present temporal models to study the impact of the hemostatic nanoparticles and provide snapshots about the trend in cytokines and blood loss in the porcine data to study their progression over time. We utilized Piecewise Aggregate Approximation, Similarity based Merging and clustering to evaluate the impact of the different hemostatic nanoparticles administered. In some cases the fluctuations in the cytokines may be too small. So in addition we highlight situations where temporal modelling that produces a smoothed time series may not be useful as it may remove out the noise and miss the overall fluctuations resulting from the nanoparticles. Our results indicate certain nanoparticles stand out and lead to novel hypothesis formation.

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