1,355,280 research outputs found
Reducing uncertainty in modeling the NDVI-precipitation relationship: A comparative study using global and local regression techniques
No full textThe spatial relationship between vegetation and rainfall in Central Kazakhstan was modeled using the Normalized Difference Vegetation Index (NDVI) and rainfall data from weather stations. The modeling is based on the application of two statistical approaches: conventional ordinary least squares (OLS) regression, and geographically weighted regression (GWR). The results support the assumption that the average impression provided by the OLS model may not accurately represent conditions locally. The GWR approach, dealing with spatial non-stationarity, significantly increases the model's accuracy and prediction power. The GWR provides a better solution to the problem of spatially autocorrelated errors in spatial modeling compared to the OLS modeling
Kappas, Attallah
No full textAttallah Kappas, 1987. Photo by Joyce Ravid
Kappas, Attallah (1926-2018) was a leading authority in diseases related to liver function and metabolism and in the development of diagnostics and treatments for those conditions.
One of Kappas’ most far-ranging and lasting contributions was in research uncovering the molecular mechanisms that instigate jaundice in newborn babies. Jaundice, which affects more than half of all babies to some degree, is caused by high levels of bilirubin, a yellow pigment in the blood that is normally metabolized by the liver. If the condition goes untreated, it can cause irreversible damage to the central nervous system.
Kappas was the physician-in-chief of Rockefeller’s hospital from 1974 to 1991 and served as a Rockefeller vice president from 1983 to 1991. He also had affiliations with Memorial Sloan Kettering Cancer Center, Weill Cornell Medical College, Johns Hopkins University Hospital, the Peter Bent Brigham Hospital, and the Karolinska Institute.
See also Endocrine-gene Interactions in the Hereditary Liver Disease, Acute Intermittent Porphyria, Management of Severe Newborn Jaundice: Changing the Clinical Paradigm from Treatment to Prevention, and Nutritional Pharmacology: Regulation of Drug and Hormone Metabolism by Components of the Human Diet
Years at The Rockefeller University: 1966-2018https://digitalcommons.rockefeller.edu/faculty-members/1092/thumbnail.jp
Category kappas for agreement between fuzzy classifications
No full textThe kappa statistic is a widely used as a measure for quantifying agreement between two nominal classifications. The statistic has been extended to the case of two normalized fuzzy classifications. In this paper we define category kappas for quantifying agreement on a particular category of two normalized fuzzy classifications. The overall fuzzy kappa is a weighted average of the proposed category kappas. Since the value of the overall kappa lies between the minimum and maximum values of the category kappas, the overall kappa, in a way, summarizes the agreement reflected in the category kappas. The overall kappa meaningfully reflects the degree of agreement between the fuzzy classifications if the category kappas are approximately equal. If this is not the case, it is more informative to report the category kappas. (C) 2016 Elsevier B.V. All rights reserved
Weighted Kappas for 3×3 Tables
No full textWeighted kappa is a widely used statistic for summarizing inter-rater agreement on a categorical scale. For rating scales with three categories, there are seven versions of weighted kappa. It is shown analytically how these weighted kappas are related. Several conditional equalities and inequalities between the weighted kappas are derived. The analytical analysis indicates that the weighted kappas are measuring the same thing but to a different extent. One cannot, therefore, use the same magnitude guidelines for all weighted kappas
Monitoring grassland carbon sequestration in central Kazakhstan with remote sensing and carbon cycle modelling
No full textAssessing Satellite-Observed Nighttime Lights for Monitoring Socioeconomic Parameters in the Republic of Kazakhstan
No full textThis paper describes an initial assessment of human-induced nighttime lights acquired by the Defence Meteorological Satellite Program (DMSP) Operational Linescan System (OLS) with respect to its applicability in monitoring settlement patterns, population, electricity consumption, gross domestic product (GDP), and carbon dioxide emissions at different spatial levels in the Republic of Kazakhstan. The results revealed the suitability of DMSP-OLS data to detect both urban expansion and contraction over last two decades caused by the new economic situation following the independence of Kazakhstan in 1991. Relationships between DMSP-OLS urban lit area and the socioeconomic parameters were quantified. The DMSP-OLS data proved to be an effective tool in the monitoring of both the spatial and temporal variability of the examined socioeconomic parameters
a challenge for interdisciplinary research
No full textHuman, animal and plant health is a field of work which offers opportunities for inter- and trans-disciplinary research. The whole topic bridges the natural and social sciences. Today, in a world of global environmental change it is widely recognized that human societies and their wellbeing depend on a sustainable equilibrium of ecosystem services and the possibility of cultural adaptation to global environmental change. The need to identify and quantify health risks related to global environmental change is now one of the most important challenges of humankind. Describing spatial (geographic, intra/inter-population) and temporal differences in health risks is an urgent task to understand societies’ vulnerabilities and priorities for interventions better. The Göttingen International Health Network (GIHN) is a research and teaching network in relation to this cross-cutting topic. The book provides a collection of articles which contribute to this issue of overriding importance and presents an overview of the GIHN launch event
Modeling Net Ecosystem Exchange for Grassland in Central Kazakhstan by Combining Remote Sensing and Field Data
No full textCarbon sequestration was estimated in a semi-arid grassland region in Central Kazakhstan using an approach that integrates remote sensing, field measurements and meteorological data. Carbon fluxes for each pixel of 1 × 1 km were calculated as a product of photosynthetically active radiation (PAR) and its fraction absorbed by vegetation (fPAR), the light use efficiency (LUE) and ecosystem respiration (Re). The PAR is obtained from a mathematical model incorporating Earth-Sun distance, solar inclination, solar elevation angle, geographical position and cloudiness information of localities. The fPAR was measured in field using hemispherical photography and was extrapolated to each pixel by combination with the Normalized Difference Vegetation Index (NDVI) obtained by the Vegetation instrument on board the Satellite Pour l’Observation de la Terra (SPOT) satellite. Gross Primary Production (GPP) of the aboveground and belowground vegetation of 14 sites along a 230 km west-east transect within the study region were determined at the peak of growing season in different land cover types and linearly related to the amount of PAR absorbed by vegetation (APAR). The product of this relationship is LUE = 0.61 and 0.97 g C/MJ APAR for short grassland and steppe, respectively. The Re is estimated using complex models driven by climatic data. Growing season carbon sequestration was calculated for the modelling year of 2004. Overall, the short grassland was a net carbon sink, whereas the steppe was carbon neutral. The evaluation of the modelled carbon sequestration against independent reference data sets proved high accuracy of the estimations
Assessment of vegetation vulnerability to ENSO warm events over Africa
No full textThe study investigates the vulnerability of vegetation over Africa to El-Nino Southern Oscillation (ENSO) events using the moving window statistical correlation analysis technique The correlation analysis was done between Normalized Difference Vegetation Index (NDVI) data from Advanced Very High Resolution Radiometer (AVHRR) and an ENSO index, namely Multivariate ENSO Index (MEI) The Study develops a new monitoring approach (ENSO vulnerability assessment system) to quantify the relationships between monthly maximum NDVI anomalies and their month-to-month correlations with the ENSO indices for the vegetative land areas of Africa The data Used in this Study was for the period 1982-2006 The new monitoring approach was used in the assessment of the long-time vegetation sensitivity to the ENSO warm events that Occurred during the Study period A map of Africa indicating the vegetation Vulnerability to ENSO is produced Different areas of vegetation vulnerability are Identified within the main vegetation cover classes For the African vegetative land. 16% of the total area was characterized by moderate vulnerability of vegetation to El-Nino, whereas 1.18% showed high Vulnerability Results suggest that the vulnerability of vegetative land surfaces across Africa to climate extremes, such as ENSO depends considerably oil the vegetation type In Particular, results show that areas of equatorial rainforest are more resistant to drought stress than the wooded and non-wooded vegetation categories. (C) 2009 Elsevier B V All rights reserve
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