Performance modeling of adaptive congestion control mechanisms for internet traffic

The need for access integrated applications such as video, voice and data with a defined quality of service parameter over the Internet by the users are currently increasing rapidly. Yet there are challenges on the Internet backbone to operate at its capacity to assure efficient service delivery to the users. One of the major challenges is called congestion collapse which results in issues like high packet delay, high packet loss and low packet throughput in the course of data transmission for various applications on the Internet. Now a day’s congestion prevention has become one of the most critical issues that must be confronted by the users. It is also a major challenge to researchers in the field of performance modelling. So far different researches have been carried out and remarkable achievements have been made in controlling congestion collapse and achieving minimum packets loss probability in both Single and Double Threshold analysis leading to Step and Linear reduction respectively. However, as it has been suggested by the researchers, improvements are still needed to achieve better performance results in this regard. The intentions of this project are: First formulating a new analytical model on different packet dropping function based on the previous model. Second derivations of performance metrics such as mean queue length, throughput, response time and probability of loss equations. Third validate the accuracy of the new analytical model through extensive experiment in MATLAB program. And to find out optimum packet dropping function which capture minimum packet loss probability and contribute to the research work in performance modelling. The results of the analysis show that exponential function is an optimum function which achieved lower probability of packet loss compared with others functions when the values of the threshold are increasing. And also a reasonable increment have been achieved in throughput, average queue length, and average queuing delay as expected with a change in threshold values

THE ROLE OF RISK ASSESSMENT IN MANAGING RISK

Risk and Uncertainty,

Nutritional, eco-physiological and symbiotic characteristics of rhizobia nodulating faba bean (Vicia faba L.) collected from acidic soils of Ethiopia

Characterization of root nodule bacteria is used for selecting and using them as inoculants to improve legume production. To this end, faba bean (Vicia faba L.) rhizobia were isolated from nodules collected from acidic soils of Central and Southern-Western parts of Ethiopia. A total of hundred rhizobial isolates were collected and characterized based upon their nutritional, ecophysiological and symbiotic characteristics. The isolates produced low to copious amount of extracellular polysaccharides (EPS) and attain colony sizes ranging from 2 to 7 mm with generation time ranging 0.75 to 3.9 h. Most of the isolates were grown at different pH levels ranging 4.5 to 7.0 and temperatures between 4 and 45°C. They were also capable of growing on many carbon sources and most of the nitrogen sources, and showed significant variations in resisting different types of antibiotics and heavy metals. Based on symbiotic efficiency (SE), 56% of the isolates were found to be very effective when applied with both Degaga and Dosha varieties. All taken together, two isolates, HUGAVf1 and HUCDVf5 were nutritionally versatile, showed a wide range of tolerance to the stress in many of the ecophysiological characters and very effective symbiotic performance should be utilized in future faba bean inoculants production.Key words: Rhizobium leguminosarum biovar. viceae, antibiotic tolerance, carbon utilization, fast growing, heavy metals, temperature tolerance

Vegetation Outlook (VegOut): Predicting Remote Sensing–Based Seasonal Greenness

Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing–based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006), which in turn may help to address agricultural development and food security issues, as well as improve early warning systems. Many studies have demonstrated the value of Vegetation Indices (VIs), such as the Normalized Difference Vegetation Index (NDVI), calculated from satellite observations for assessing vegetation cover and conditions (Tucker et al., 1985; Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005), and such data have become a common source of information for vegetation monitoring. The term vegetation condition in this chapter refers to vegetation greenness or vegetation health, as inferred from canopy reflectance values measured by satellite observations (Mennis, 2001; Anyamba and Tucker, 2005). The vegetation greenness metric is commonly calculated from time-series NDVI (Reed et al., 1994) and represents the seasonal, time-integrated NDVI at a specific date, which has been shown to be representative of indicators of general vegetation health including net primary production (NPP) and green biomass (Tucker et al., 1985; Reed et al., 1996; Yang et al., 1998; Eklundh and Olsson, 2003; Hill and Donald, 2003). As a result, VIs and VI derivatives such as time-integrated VI can be used to characterize the temporal and spatial relationships between climate and vegetation and improve our understanding of the lagged relationship between climate (e.g., precipitation and temperature) and vegetation response (Roerink et al., 2003; Anyamba and Tucker, 2005; Seaquist et al., 2005; Camberlin et al., 2007; Groeneveld and Baugh, 2007). Quantitative descriptions of climate-vegetation response lags can then be used to identify and predict vegetation stress during drought

Vitamin A supplementation on child morbidity

Objective: To determine the impact of vitamin A supplementation on child morbidity and nutritional status. Design: A community based follow-up (interventional) in nature. Setting: Two randomly selected Weredas (districts) of Tigray, North Ethiopia were studied between 1996 and 1997. Subjects: Four thousand seven hundred and seventy children aged between six and 72 months, selected using a multi-stage sampling procedure were enrolled and clinically assessed for xerophthalmia and nutritional status. A sub-sample of these children (n = 281) was further assessed for their serum retinol levels. Main outcome measures: The pre and post intervention data on xerophthalmia, morbidity, nutritional status and serum retinol levels were compared. Results: Vitamin A capsule coverage of 87% in all the villages of the Weredas and a statistically significant (p 0.7 µmole/L) has also improved significantly (from 36.8 to 56.2). Conclusion: In conclusion, the significant improvement in morbidity and nutritional status that followed the intervention programme although encouraging, it still indicates the importance of coupling periodic provision of Vitamin A capsules with nutrition education. (East African Medical Journal: 2003 80(1): 17-21

H-NS Regulation of IraD and IraM Antiadaptors for Control of RpoS Degradation

International audienceRpoS, the master sigma factor during stationary phase and under a variety of stress conditions, is regulated at multiple levels, including regulated degradation. Degradation is dependent upon ClpXP and the RssB adaptor protein. H-NS, a nucleoid-associated protein, affects the regulated degradation of RpoS; in the absence of H-NS, RpoS is stable. The mechanisms involved in this regulation were not known. We have found that H-NS inhibits the expression of iraD and iraM, the genes coding for two antiadaptor proteins that stabilize RpoS when overexpressed. The regulation by H-NS of iraM is independent from the previously demonstrated regulation by the PhoP/PhoQ two-component system. Moreover, differences in the behavior of several hns alleles are explained by a role for StpA, an H-NS-like protein, in the regulation of RpoS stability. This finding parallels recent observations for a role of StpA in regulation of RpoS stability in Salmonella

The Vegetation Outlook (VegOut): A New Method for Predicting Vegetation Seasonal Greenness

The vegetation outlook (VegOut) is a geospatial tool for predicting general vegetation condition patterns across large areas. VegOut predicts a standardized seasonal greenness (SSG) measure, which represents a general indicator of relative vegetation health. VegOut predicts SSG values at multiple time steps (two to six weeks into the future) based on the analysis of “historical patterns” (i.e., patterns at each 1 km grid cell and time of the year) of satellite, climate, and oceanic data over an 18-year period (1989 to 2006). The model underlying VegOut capitalizes on historical climate–vegetation interactions and ocean–climate teleconnections (such as El Niño and the Southern Oscillation, ENSO) expressed over the 18-year data record and also considers several environmental characteristics (e.g., land use/cover type and soils) that influence vegetation’s response to weather conditions to produce 1 km maps that depict future general vegetation conditions. VegOut provides regional level vegetation monitoring capabilities with local-scale information (e.g., county to sub-county level) that can complement more traditional remote sensing–based approaches that monitor “current” vegetation conditions. In this paper, the VegOut approach is discussed and a case study over the central United States for selected periods of the 2008 growing season is presented to demonstrate the potential of this new tool for assessing and predicting vegetation conditions

The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation

The development of new tools that provide timely, detailed-spatial-resolution drought information is essential for improving drought preparedness and response. This paper presents a new method for monitoring drought-induced vegetation stress called the Vegetation Drought Response Index (VegDRI). VegDRI integrates traditional climate-based drought indicators and satellite-derived vegetation index metrics with other biophysical information to produce a 1 km map of drought conditions that can be produced in near-real time. The initial VegDRI map results for a 2002 case study conducted across seven states in the north-central United States illustrates the utility of VegDRI for improved large-area drought monitoring

Developing a Remote Sensing-Based Combined Drought Indicator Approach for Agricultural Drought Monitoring over Marathwada, India

The increasing drought severities and consequent devastating impacts on society over the Indian semi-arid regions demand better drought monitoring and early warning systems. Operational agricultural drought assessment methods in India mainly depend on a single input parameter such as precipitation and are based on a sparsely located in-situ measurements, which limits monitoring precision. The overarching objective of this study is to address this need through the development of an integrated agro-climatological drought monitoring approach, i.e., combined drought indicator for Marathwada (CDI_M), situated in the central part of Maharashtra, India. In this study, satellite and model-based input parameters (i.e., standardized precipitation index (SPI-3), land surface temperature (LST), soil moisture (SM), and normalized difference vegetation index (NDVI)) were analyzed at a monthly scale from 2001 to 2018. Two quantitative methods were tested to combine the input parameters for developing the CDI_M. These methods included an expert judgment-based weight of each parameter (Method-I) and principle component analysis (PCA)-based weighting approach (Method-II). Secondary data for major types of crop yields in Marathwada were utilized to assess the CDI_M results for the study period. CDI_M maps depict moderate to extreme drought cases in the historic drought years of 2002, 2009, and 2015–2016. This study found a significant increase in drought intensities (p ≤ 0.05) and drought frequency over the years 2001–2018, especially in the Latur, Jalna, and Parbhani districts. In comparison to Method-I (r ≥ 0.4), PCA-based (Method-II) CDI_M showed a higher correlation (r ≥ 0.60) with crop yields in both harvesting seasons (Kharif and Rabi). In particular, crop yields during the drier years showed a greater association (r \u3e 6.5) with CDI_M over Marathwada. Hence, the present study illustrated the effectiveness of CDI_M to monitor agricultural drought in India and provide improved information to support agricultural drought management practices