Flowable and Stable Concrete : Design, Characterization and Performance Evaluation

Flowable concretes, while possessing remarkable workability properties, are inherently susceptible to sedimentation and segregation, especially under the influence of external stress such as vibration and pumping pressure. This situation is further aggravated by the fact that the concrete production, transportation and casting processes are liable to fluctuations in the quality of the raw materials and the environmental conditions. Consequently, their application in the concrete construction sector is currently very limited. However, when the geometrical complexity, reinforcement density and dimensional enormity of today’s modern structures are considered, there remains no plausible option other than to use flowable concretes. Hence, dealing with the bottlenecks beforehand is of paramount importance for a reliable application of such concretes. Within the scope of this dissertation, three main aspects with regard to flowable and stable concrete are addressed: mix-design, characterization and performance evaluation. The newly developed Water Balance Mix-Design method (WBMD) guarantees not only the flowability and pumpability but also stability (under vibration and pressure) and robustness of concrete. This is achieved through a systematic design strategy which includes optimization of the aggregate compositions to enhance the lattice effect, determination of the minimum paste demand of the aggregates and quantifying the effective water demand of fines compositions by integrating the effects of superplasticizers (SP). The water balanced concretes (WBC) were composed by making use of different paste and aggregate compositions. Moreover, extra water was added to the mixtures in order to evaluate their robustness. The characterization of the fresh concrete properties was carried out using standard and new investigation methods. The flowability was investigated using slump flow tests (with and without tapping). The stability under vibration was evaluated using a modified wash-out test (WT), sedimentation - sieve - test (SST) and visual assessment of the sedimentation behavior on hardened concrete specimen. The pumpability and pump-stability were quantified by means of a pumping resistance simulator (PuReSi) and high pressure filter press (HPFP). Moreover, rheological characterization of the concretes was conducted using a rotational rheometer Viskomat XL while the extracted mortar and paste compositions were tested using Viskomat NT. A high level of shear loading was applied for the rheological investigations to reproduce the structural breakdown process that takes place when concretes are exposed to external stress. Based on the results of the investigations, a detailed analysis is presented with regard to the effects of the different constituent materials and design parameters on the fresh concrete as well as the rheological properties. Moreover, through a systematic assessment of the rheological properties of the subsequent phases of paste, mortar and concrete, a multiscale rheological model is developed for quantifying the structural breakdown process. The rheological studies are also applied for the characterization of the sedimentation behavior during the structural breakdown process and the quantification of the pumpability and pump-stability properties. Furthermore, new performance evaluation criteria are defined for flowable concretes on the basis of the results of the stability, rheological and flowability investigations, especially with regard to the stability properties under vibration and pressure. To this end, the rheological performance criteria as applied to the paste, mortar and concrete phases are integrated with the performance criteria derived from the stability and flowability investigations to produce a multiscale performance evaluation strategy. A combined analysis of the water balance criteria (WB) with the performance evaluation criteria has confirmed the adequacy of the WBMD for designing flowable concretes of reliable stability. Finally, a comprehensive model for flowable and stable concrete comprising micro, meso and macro scales is presented that encompasses the WBMD, the relevant characterization methods and the corresponding performance evaluation criteria

BLOOD PRESSURE DISTRIBUTION AND HYPERTENSION IN TWO RURAL COMMUNITIES OF GONDAR REGION, ETHIOPIA

ABSTRACT: The purpose of this study was to document the distribution of blood pressures and the prevalence of hypertension in two rural communities located in the northwestern Gondar Region of Ethiopia. Based upon a systematic random sampling, 226 households (724 persons) were selected. Mean systolic and diastolic blood pressures (SBP and DBP) were calculated separately for male and female children (5 -15 years) and adults. Among male and female children the mean SBP's were 110.0 +/- 9.5 and 113.4 =/- 10.0, while the mean DBP's were 73.8 +/- 8.2 and 73.8 +/- 7.9, respectively. The difference in mean SBP's was statistically significant (p<0.05). Among adult males and females the mean SBP was 118 +/- 13.3 and 114.0 +/- 14.5, while the mean DDP was 73.5 +/- 8.2 and 72.7 +/- 9.2 respectively. The difference in mean SBP's was statistically significant (p<0.05). Blood pressure was found to rise with age. The prevalence of hypertension in children was 4.3% and in adults 2.7%. Prevalence rates were not significantly different in females and males

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

Citation: Peralta, N.R.; Assefa, Y.; Du, J.; Barden, C.J.; Ciampitti, I.A. Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield. Remote Sens. 2016, 8, 848.This technical note presents the first Sentinel-2 data service platform for obtaining atmospherically-corrected images and generating the corresponding value-added products for any land surface on Earth (http://s2.boku.eodc.eu/). Using the European Space Agency’s (ESA) Sen2Cor algorithm, the platform processes ESA’s Level-1C top-of-atmosphere reflectance to atmospherically-corrected bottom-of-atmosphere (BoA) reflectance (Level-2A). The processing runs on-demand, with a global coverage, on the Earth Observation Data Centre (EODC), which is a public-private collaborative IT infrastructure in Vienna (Austria) for archiving, processing, and distributing Earth observation (EO) data (http://www.eodc.eu). Using the data service platform, users can submit processing requests and access the results via a user-friendly web page or using a dedicated application programming interface (API). Building on the processed Level-2A data, the platform also creates value-added products with a particular focus on agricultural vegetation monitoring, such as leaf area index (LAI) and broadband hemispherical-directional reflectance factor (HDRF). An analysis of the performance of the data service platform, along with processing capacity, is presented. Some preliminary consistency checks of the algorithm implementation are included to demonstrate the expected product quality. In particular, Sentinel-2 data were compared to atmospherically-corrected Landsat-8 data for six test sites achieving a R2 = 0.90 and Root Mean Square Error (RMSE) = 0.031. LAI was validated for one test site using ground estimations. Results show a very good agreement (R2 = 0.83) and a RMSE of 0.32 m2/m2 (12% of mean value)

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at midgrowing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.Sociedad Argentina de Informática e Investigación Operativ

The impact of glaucoma on quality of life in Ethiopia: a case-control study.

BACKGROUND: Glaucoma is a chronic disease characterized by irreversible optic nerve damage and visual field loss that leads to visual impairment and blindness; ultimately limiting personal independence and compromising overall quality of life of affected individuals. There is paucity of information on how glaucoma affects the quality of life of patients in low and middle-income countries where resources for both diagnosis and treatment of such conditions are limited. In this study we investigate the impact of glaucoma on quality of life in Ethiopian patients. METHODS: The quality of life of 307 glaucoma patients and 76 normal controls that were frequency matched to the age and sex profiles of the cases was assessed using Amharic version of Glaucoma Quality of Life -15 questionnaire. Linear regression models and the t-test were employed to compare significant differences in GQL-15 scores and to generate mean and mean differences between cases and controls respectively. RESULTS: The mean GQL-15 score in the glaucoma cases was substantially higher (indicating poorer quality of life) than the controls [cases 46.3 (95% CI, 28.8-63.8) and controls 18.6 (95% CI, 15.2-22.0), p < 0.0001]. Cases with normal visual acuity and mild glaucoma had significantly higher scores than the controls. Poorer quality of life was associated with age ≥ 71 years old 51.1 (95%CI, 26.2-75.9), rural residence 55.7 (95%CI, 49.9-61.5), monthly income of <400 Birr (53.1; 95%CI, 50.5-55.6), diagnosis time 1-5 years (49.6; 95%CI, 41.2-57.9), severe visual impairment (70.5; 95%CI, 58.1-82.8), and advanced glaucoma (50.9; 95%CI, 43.6-58.3). CONCLUSION: These glaucoma patients, including those with normal visual acuity and early disease, had poorer quality of life compared to normal controls. Older age, rural residence, low income and more advanced disease were significantly associated with poorer quality of life. There is a need to increase awareness of the impact of glaucoma among clinicians, patients and their families, for a better understanding of the impact this disease has on a person's life

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

A timely and accurate crop yield forecast is crucial to make better decisions on crop management, marketing, and storage by assessing ahead and implementing based on expected crop performance. The objective of this study was to investigate the potential of high-resolution satellite imagery data collected at midgrowing season for identification of within-field variability and to forecast corn yield at different sites within a field. A test was conducted on yield monitor data and RapidEye satellite imagery obtained for 22 cornfields located in five different counties (Clay, Dickinson, Rice, Saline, and Washington) of Kansas (total of 457 ha). Three basic tests were conducted on the data: (1) spatial dependence on each of the yield and vegetation indices (VIs) using Moran’s I test; (2) model selection for the relationship between imagery data and actual yield using ordinary least square regression (OLS) and spatial econometric (SPL) models; and (3) model validation for yield forecasting purposes. Spatial autocorrelation analysis (Moran’s I test) for both yield and VIs (red edge NDVI = NDVIre, normalized difference vegetation index = NDVIr, SRre = red-edge simple ratio, near infrared = NIR and green-NDVI = NDVIG) was tested positive and statistically significant for most of the fields (p < 0.05), except for one. Inclusion of spatial adjustment to model improved the model fit on most fields as compared to OLS models, with the spatial adjustment coefficient significant for half of the fields studied. When selected models were used for prediction to validate dataset, a striking similarity (RMSE = 0.02) was obtained between predicted and observed yield within a field. Yield maps could assist implementing more effective site-specific management tools and could be utilized as a proxy of yield monitor data. In summary, high-resolution satellite imagery data can be reasonably used to forecast yield via utilization of models that include spatial adjustment to inform precision agricultural management decisions.Sociedad Argentina de Informática e Investigación Operativ

Depth of Moist Soil at Planting Affected Grain Sorghum Response to Nitrogen Fertilizer

The depth of moist soil before planting is a critical factor for grain crop production in dryland cropping systems. We investigated depth of moist soil at planting and nitrogen (N) fertilizer application effects on continuous grain sorghum yields on a Crete silt loam soil over 32 years in western Kansas. Treatments were four N rates (0, 20, 40, and 60 lb/a) in a randomized complete block design with four replications and depth of moist soil at planting determined with a Paul Brown moisture probe. Grain sorghum yield response to N fertilizer application was -0.10, 14.4, 29.3, and 36.5 lb of grain/a for every lb of N applied in very low yielding (VLY), low yielding (LY), high yielding (HY), and very high yielding (VHY) environments, respectively. Grain yield increased with depth of moist soil at planting for each N rate, with yield increases of 217 to 461 lb/a per inch increase in depth of moist soil at planting for the unfertilized control through 60 lb N/a. Regardless of yield environment, net returns were negative when depth of moist soil at planting was less than 30 inches. These results suggest that continuous grain sorghum should not be planted when depth of moist soil measured with a Paul Brown probe is \u3c 30 inches. Results of this 32-year study showed the depth of moist soil at planting could be used to fine-tune N application rates for sorghum. Despite greater drought tolerance, sorghum N response is dependent on combination of soil water at planting and in-season precipitation. We need to continue this research to identify sorghum hybrids with improved drought tolerance and nitrogen use efficiency to increase probability of dryland sorghum production

The Magnitude of Neonatal Mortality and Its Predictors in Ethiopia:A Systematic Review and Meta-Analysis

Background. Although neonatal death is a global burden, it is the highest in sub-Saharan African countries such as Ethiopia. Moreover, there is disparity in the prevalence and associated factors of studies. Therefore, this study was aimed at providing pooled national prevalence and predictors of neonatal mortality in Ethiopia. Methods. The following databases were systematically explored to search for articles: Boolean operator, Cochrane Library, PubMed, EMBASE, Hinari, and Google Scholar. Selection, screening, reviewing, and data extraction were done by two reviewers independently using Microsoft Excel spreadsheet. The modified Newcastle-Ottawa Scale (NOS) and the Joanna Briggs Institute Prevalence Critical Appraisal tools were used to assess the quality of evidence. All studies conducted in Ethiopia and reporting the prevalence and predictors of neonatal mortality were included. Data were extracted using Microsoft Excel spreadsheet software and imported into Stata version 14s for further analysis. Publication bias was checked using funnel plots and Egger's and Begg's tests. Heterogeneity was also checked by Higgins's method. A random effects meta-analysis model with 95% confidence interval was computed to estimate the pooled effect size (i.e., prevalence and odds ratio). Moreover, subgroup analysis based on region, sample size, and study design was done. Results. After reviewing 88 studies, 12 studies fulfilled the inclusion criteria and were included in the meta-analysis. Pooled national prevalence of neonatal mortality in Ethiopia was 16.3% (95% CI: 12.1, 20.6, I2=98.8%). The subgroup analysis indicated that the highest prevalence was observed in the Amhara region, 20.3% (95% CI: 9.6, 31.1), followed by Oromia, 18.8% (95% CI: 11.9, 49.4). Gestational age [AOR: 1.32 (95% CI: 1.07, 1.58)], neonatal sepsis [AOR: 1.23 (95% CI: 1.05, 1.4)], respiratory distress syndromes (RDS) [AOR: 1.18 (95% CI: 0.87, 1.49)], and place of residency [AOR: 1.93 (95% CI: 1.13, 2.73)] were the most important predictors. Conclusions. Neonatal mortality in Ethiopia was significantly decreased. There was evidence that neonatal sepsis, gestational age, and place of residency were the significant predictors. RDS were also a main predictor of mortality even if not statistically significant. We strongly recommended that health care workers should give a priority for preterm neonates with diagnosis with sepsis and RDS

Spatio-temporal evaluation of plant height in corn via unmanned aerial systems

Detailed spatial and temporal data on plant growth are critical to guide crop management. Conventional methods to determine field plant traits are intensive, time-consuming, expensive, and limited to small areas. The objective of this study was to examine the integration of data collected via unmanned aerial systems (UAS) at critical corn (Zea mays L.) developmental stages for plant height and its relation to plant biomass. The main steps followed in this research were (1) workflow development for an ultrahigh resolution crop surface model (CSM) with the goal of determining plant height (CSM-estimated plant height) using data gathered from the UAS missions; (2) validation of CSM-estimated plant height with ground-truthing plant height (measured plant height); and (3) final estimation of plant biomass via integration of CSM-estimated plant height with ground-truthing stem diameter data. Results indicated a correlation between CSM-estimated plant height and ground-truthing plant height data at two weeks prior to flowering and at flowering stage, but high predictability at the later growth stage. Log–log analysis on the temporal data confirmed that these relationships are stable, presenting equal slopes for both crop stages evaluated. Concluding, data collected from low-altitude and with a low-cost sensor could be useful in estimating plant height.Sociedad Argentina de Informática e Investigación Operativ