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Anthropometric prevalence indicators such as stunting, wasting, and underweight are widely-used population-level tools used to track trends in childhood nutrition. Threats to the validity of these data can lead to erroneous decision making and improper allocation of finite resources intended to support some of the world's most vulnerable populations. It has been demonstrated previously that aggregated prevalence rates for these indicators can be highly sensitive to biases in the presence of non-directional measurement errors, but the quantitative relationship between the contributing factors and the scale of this bias has not been fully described. In this work, a Monte Carlo simulation exercise was performed to generate high-statistics z-score distributions with a wide range of mean and standard deviation parameters relevant to the populations in low- and middle-income countries (LMIC). With the important assumption that the distribution's standard deviation should be close to 1.0 in the absence of non-directional measurement errors, the shift in prevalence rate due to this common challenge is calculated and explored. Assuming access to a given z-score distribution's mean and standard deviation values, this relationship can be used to evaluate the potential scale of prevalence bias for both historical and modern anthropometric indicator results. As a demonstration of the efficacy of this exercise, the bias scale for a set of 21 child anthropometry datasets collected in LMIC contexts is presented.

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Water scarcity is a major challenge in the Sahel region of West Africa. Water scarcity in combination with prevalent soil degradation has severely reduced the land productivity in the region. The decrease in resiliency of food security systems of marginalized population has huge societal implications which often leads to mass migrations and conflicts. The U.S. Agency for International Development (USAID) and development organizations have made major investments in the Sahel to improve resilience through land rehabilitation activities in recent years. To help restore degraded lands at the farm level, the World Food Programme (WFP) with assistance from USAID's Bureau for Humanitarian Assistance supported the construction of water and soil retention structures called half-moons. The vegetation growing in the half-moons is vitally important to increase agricultural productivity and feed animals, a critical element of sustainable food security in the region. This paper investigates the effectiveness of interventions at 18 WFP sites in southern Niger using vegetative greenness observations from the Landsat 7 satellite. The pre - and post-intervention analysis shows that vegetation greenness after the half-moon intervention was nearly 50% higher than in the pre-intervention years. The vegetation in the intervened area was more than 25% greener than the nearby control area. Together, the results indicate that the half-moons are effective adaptations to the traditional land management systems to increase agricultural production in arid ecosystems, which is evident through improved vegetation conditions in southern Niger. The analysis shows that the improvement brought by the interventions continue to provide the benefits. Continued application of these adaptation techniques on a larger scale will increase agricultural production and build resilience to drought for subsistence farmers in West Africa. Quantifiable increase in efficacy of local-scale land and water management techniques, and the resulting jump in large-scale investments to scale similar efforts will help farmers enhance their resiliency in a sustainable manner will lead to a reduction in food security shortages.

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Pulsed nuclear magnetic resonance (NMR) is widely used in high-precision magnetic field measurements. The absolute value of the magnetic field is determined from the precession frequency of nuclear magnetic moments. The Hilbert transform is one of the methods that have been used to extract the phase function from the observed free induction decay (FID) signal and then its frequency. In this paper, a detailed implementation of a Hilbert-transform based FID frequency extraction method is described, and it is briefly compared with other commonly used frequency extraction methods. How artifacts and noise level in the FID signal affect the extracted phase function are derived analytically. A method of mitigating the artifacts in the extracted phase function of an FID is discussed. Correlations between noises of the phase function samples are studied for different noise spectra. We discovered that the error covariance matrix for the extracted phase function is nearly singular and improper for constructing the χ2 used in the fitting routine. A down-sampling method for fixing the singular covariance matrix has been developed, so that the minimum χ2-fit yields properly the statistical uncertainty of the extracted frequency. Other practical methods of obtaining the statistical uncertainty are also discussed.