282 research outputs found
The Global Cohort of Doctoral Students: Building Shared Global Health Research Capacity in High-Income and Low- and Middle-Income Countries
No full textDoctoral students in high- and low-income countries pursuing careers in global health face gaps in their training that could be readily filled through structured peer-learning activities with students based at partnering institutions in complimentary settings. We share lessons learned from the Global Cohort of Doctoral Students, a community of doctoral students based at the Harvard T. H. Chan School of Public Health, Haramaya University. University of Gondar, University of Botswana, and University of Rwanda College of Medicine and Health Sciences. Students in the Global Cohort program engage in collaborative research, forums for constructive feedback, and professional development activities. We describe the motivation for the program, core activities, and early successes.This work was funded by the Rose Traveling Fellowship and Deborah Rose Service Learning Fellowship at the Harvard T. H. Chan School of Public Health. The funding sources had no role in the writing of the manuscript or decision to submit it for publication.Iyer, HS (corresponding author), Harvard TH Chan Sch Publ Hlth, Dept Epidemiol, Boston, MA 02115 USA.
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Improving Medium- and Long-Range Hydrological Forecasts with Ensemble Meteorological Forecasts and Climatic Information
No full textTitle: Improving Medium- and Long-Range Hydrological Forecasts with Ensemble Meteorological Forecasts and Climatic Information, Author: Getnet Y. Muluye, Location: MillsThe ability to provide reliable and accurate medium- and long-range hydrological
forecasts is fundamental for the effective operation and management of water resources
systems. The principal objectives of this thesis are (i) to develop a framework for
advancing the long-range forecasting skills of hydrological models by coupling pertinent
and leading climate information with regional hydro-meteorological variables; and (ii) to
develop effective mechanisms for integrating meteorological ensemble systems in a
hydrologic prediction system, which would be useful for risk analysis by policy makers
for operating both large-scale as well as small-scale water resources systems. This
research constitutes three principal components: long-range forecasts, downscaling, and
medium-range forecasts. For long-range hydrological forecasting, four data-driven models, including multilayer perceptron (MLP), time-lagged feedforward network (TLFN), Bayesian neural network (BNN) and recurrent multilayer perceptron (RMLP) were designed by
incorporating low-frequency climatic indices to forecast seasonal reservoir inflows. The
results indicated that the incorporation of modes of climatic indices in a hydrologic
forecasting model resulted in a considerable improvement in the seasonal forecast
accuracy. Furthermore, the extended Kalman filter approach was used to train the
recurrent multilayer perceptron for capturing the complexity associated with the long range
streamflow forecasting. Results showed that the proposed methodology was able to
provide a robust modeling framework capable of capturing the complex dynamics of the
hydrologic system. Different statistical methods were developed and evaluated for downscaling local scale information of precipitation and temperature from the numerical weather prediction model output. Three different methods were considered: (i) hybrids; (ii) neural networks; and (iii) nearest neighbor-based approaches. The findings revealed that the skills in the downscaled temperature forecasts were superior to those in the downscaled precipitation forecasts. In particular, for downscaling daily precipitation, the artificial neural network-logistic regression (ANN-Logst), partial least squares (PLS) regression and recurrent
multilayer perceptron trained with the extended Kalman filter (EKF) models yielded
greater skill values, and the conditional resampling method (SDSM) and K-nearest
neighbor (KNN) based models showed potential for characterizing the variability in daily
precipitation. For the case of medium-range hydrological forecasting, the downscaled and the raw numerical model outputs were forced into an HBV hydrologic model in order to
generate an ensemble of reservoir inflows. The simulation results indicated that the
downscaled-based flows had greater skill values, and yielded more accurate forecasts
than the raw-based flows. The potential economic values of flow forecasts were further
assessed based on a simple optimal decision-making, cost-loss analysis technique. The
principal outcomes emerging from the analyses included: (i) the economic benefits
associated with probabilistic flow forecasts were more useful than their deterministic
counterparts; and (ii) the downscaled-based flow forecasts offered greater benefits, which
are applicable to a much wider range of users, than the raw-based flow forecasts.ThesisDoctor of Philosophy (PhD
Adding Benzene to Fire: Overlapping Seasonality as a Pull Factor to Producer Prices in Ethiopia
No full textCoupled with the seasonal nature of agricultural production, seasonality of farmers\' cash demand influences the level of actual market supply and price of agricultural products. This study investigates the seasonal behaviours of producer prices and farmers\' cash demand for two crops (white teff and white wheat) that serve as staples and sources
of cash income around Ambo, Ethiopia. Descriptive studies on price time series show that producer prices for the two crops get low during the harvest and immediate post harvest seasons and survey results show that most farmers have a high demand for cash during same seasons and, as a result, sell a great proportion of their marketable stock of the two
crops during such seasons. This creates overlapping seasonality between agricultural production, on the one hand, and high cash demand of farmers, on the other. This overlapping seasonality due to the high cash demand of farmers is expected to aggravate the seasonal decline of producer prices already resulted from the seasonal supply of agricultural production. A most likely policy implication, to raise and stabilize producer prices, is therefore to influence the seasonal behaviour of farmers\' high cash demand in such a way that it coincides with the lean seasons of agricultural supply. This could be approached through rescheduling the time of fertilizer debt and land use tax payment, those
important factors that put farmers into selling a large proportion of their marketable crops during such seasons of low producer prices. By raising and stabilizing farmers\' income from crop sales, such policy will promote the economic incentive of smallholder farmers to increase their productivity. Keywords: Producer Price; Farmers, Cash Demand; Overlapping Seasonality; Sub-Saharan Africa; Ethiopia East African Journal of Sciences Vol. 1 (1) 2007: pp. 79-8
Léonce Ndikumana and James K. Boyce. 2011. Africa’s Odious Debts: How Foreign Loans and Capital Flight Bled a Continent. London/New York: Zed Books
No full textIrreducible polynomials in Z[x]
No full textAny irreducible polynomial f(x) in [special characters omitted][x] such that the set of values f([special characters omitted]) has no common divisor larger than 1 represents prime numbers infinitely often. The idea is to produce prime numbers from irreducible polynomials. The similarity between prime numbers and irreducible polynomials has been a dominant theme in the development of number theory. There are certain conjectures indicating that the connection goes well beyond analogy. For example, there is a famous conjecture of Buniakowski formulated in 1854 to the effect that any irreducible polynomial f(x) in [special characters omitted][x] such that the set of values f([special characters omitted]) has no common divisor larger than 1 represents prime numbers infinitely often. This conjecture is still open and one of the major unsolved problems in number theory when the degree of f is greater than one. When the degree of f is one (i.e., when f is linear), the result is true following Dirichlet\u27s theorem on primes in arithmetic progressions. In other words the irreducible polynomial f( x) = k x + l ∈ [special characters omitted][x] where k and l are fixed relatively prime integers, and x takes on the values 0, 1, 2… contains infinitely many primes
DESIGN of FUZZY SLIDING MODE CONTROL of CONTINUOUS STIRRED TANK REACTOR
No full textCSTR (Continuous Stirred Tank Reactor) is an important state of affairs in a chemical reaction
and performs different kinds of searches in the fields of control and instrumentation engineering.
The method (CSTR) utilizes various controllers to regulate the temperature and concentration.
The presence of model uncertainty and external disturbances are two difficult problems in the
control of a CSTR. The objective of this research was to control the temperature and concentration
of the saponification reaction in a continuous stirred tank reactor (CSTR). This CSTR study also
does a comparison of the response of the sliding mode controller and compares it with the fuzzy
sliding mode process variable controller of CSTR in the presence of perturbations performed on
it. A computational model of the CSTR has been developed, and a plant model has been designed.
MATLAB/Simulink are utilized for simulation. Ultimately, the results of the simulation and
comparison study demonstrate the continuously stirred tank reactor with FSMC controllers. The
result for the product concentration of the saponification reaction was 0.639 mol/L and the product
temperature was 297k in a fuzzy sliding mode controller performance steady state at 10 seconds,
while the sliding mode controller result for the product concentration of the saponification
reaction was 0.639 mol/L and the product temperature was around 300 k in a steady state at 304
seconds. The results obtained in this research may help control the ethyl acetate and sodium
hydroxide saponification reactions in a CSTR
Outcome Evaluation of the work of the CGIAR Research Program on Water, Land and Ecosystems (WLE) on soil and water management in Ethiopia
No full textIn 2019, the CGIAR Research Program on Water, Land and Ecosystems (WLE) Leadership chose to evaluate WLE’s work in Ethiopia as one of its countries where it has had most success. The objectives of the evaluation are: To determine how and in what ways WLE contributed to the achievement of intended/unintended outcomes; Based on the findings of the evaluation, make recommendations of how WLE (and its partners) can become more effective in supporting soil and water management in Ethiopia; To serve as a participatory learning experience for WLE and its partners. This report describes the evaluation process, findings, conclusions and recommendations
Ethnobotanical study of medicinal plants used against human ailments in Gubalafto District, Northern Ethiopia
No full textAbstract Background Traditional medicinal plant species documentation is very crucial in Ethiopia for biodiversity conservation, bioactive chemical extractions and indigenous knowledge retention. Having first observed the inhabitants of Gubalafto District (Northern Ethiopia), the author gathered, recorded, and documented the human traditional medicinal plant species and the associated indigenous knowledge. Methods The study was conducted from February 2013 to January 2015 and used descriptive field survey design. Eighty-four informants were selected from seven study kebeles (sub-districts) in the District through purposive, snowball, and random sampling techniques. Both quantitative and qualitative data were collected through semi-structured interviews, guided field walks, demonstrations, and focus group discussions with the help of guided questions. Data were organized and analyzed by descriptive statistics with SPSS version 20 and Microsoft Office Excel 2007. Results A total of 135 medicinal plant species within 120 genera and 64 families were documented. Among the species, Ocimum lamiifolium and Rhamnus prinoides scored the highest informant citations and fidelity level value, respectively. In the study area, Asteraceae with 8.1% and herbs with 50.4% plant species were the most used sources for their medicinal uses. A total of 65 ailments were identified as being treated by traditional medicinal plants, among which stomachache (abdominal health problems) was frequently reported. Solanum incanum was reported for the treatment of many of the reported diseases. The leaf, fresh parts, and crushed forms of the medicinal plants were the most preferred in remedy preparations. Oral application was the highest reported administration for 110 preparations. A majority of medicinal plant species existed in the wild without any particular conservation effort. Few informants (about 5%) had only brief notes about the traditional medicinal plants. Ninety percent of the respondents have learned indigenous medicinal plants knowledge from their family members and friends secretly. Orthodox Church schools were found the main place for 65% of healer’s indigenous knowledge origin and experiences. Elders, aged between 40 and 84 years, gave detailed descriptions about traditional medicinal plants. Conclusions Traditional medicinal plants and associated indigenous knowledge are the main systems to maintain human health in Gubalafto District. But minimal conservation measures were recorded in the community. Thus, in-situ and ex-situ conservation practices and sustainable utilization are required in the District
Electroluminescence from Silicon and Germanium Nanostructures
No full textSilicon (Si) and germanium (Ge) have an indirect band gap transitions; however when they are miniaturized to nanometer scale, the energy gap between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO) increases, and hence the transition changes to direct due to confinement. In this paper we explain photoluminescence (PL) and electroluminescence (EL) and formulate models to study electroluminescence from Si and Ge nanostructures. Using the models we got computational results to explain the dependence of EL on different parameters like size of the nanocluster, applied voltage, band gap energy, and wavelength for pure silicon nanocrystal (Si-nc) and for oxygen and hydrogen terminated Si-nc. The EL and PL intensities occurs at the same energy; however, the EL intensity has sharp Gaussian sub peaks and red shifted compared to the PL intensity. To get our result, we used the idea of quantum confinement model (QCM), that can explain PL and EL on pure Si nanostructures and Si-terminated with impurities
Dynamic Bayesian network modeling for longitudinal data on child undernutrition in Ethiopia (2002-2016)
No full textAbstract
Introduction:
Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002–2016 was used to analyze the complex relationships among various covariates influencing child undernutrition. We used a built-in Bayes server tool to identify potential features, followed by building the structure of the directed acyclic graph using a structural learning algorithm. The maximum posterior is determined using the relevance tree algorithm. The node with the highest values of mutual information and target entropy reduction, along with the lowest value of target entropy, was considered to have the strongest predictive power in the dataset.
Results:
This study revealed that long-term participation in programs increased the likelihood of children being in a normal nutritional state. Key factors influencing the nutritional status of children under two years of age include the mother’s education level, her subjective well-being, and the household’s wealth quintile. Children with educated parents were more likely to have a healthy nutritional status. Additionally, the causal pathway of intervention programs → wealth quintile → child nutritional status consistently exceeded 90% in Waves 3, 4, and 5, indicating a strong relationship. Similarly, the relationship between intervention programs → food security → child nutritional status was nearly perfect at 99.99% in Waves 4 and 5, indicating a strong association. Finally, the study revealed that household participation in intervention programs significantly reduces undernutrition in best-case scenarios, while the absence of support poses a higher risk in worst-case conditions.
Conclusion:
The comprehensive intervention program strongly improved household wealth, food security, and maternal well-being, which in turn affected children’s nutritional status.Abstract
Introduction:
Dynamic Bayesian networks improve the modeling of complex systems by incorporating continuous probabilistic relationships between covariates that change over time. This study aimed to analyze the complex causal links contributing to child undernutrition using dynamic Bayesian network modeling, examining both the best- and worst-case scenarios. The Young Cohort of the Ethiopian Young Lives dataset from 2002–2016 was used to analyze the complex relationships among various covariates influencing child undernutrition. We used a built-in Bayes server tool to identify potential features, followed by building the structure of the directed acyclic graph using a structural learning algorithm. The maximum posterior is determined using the relevance tree algorithm. The node with the highest values of mutual information and target entropy reduction, along with the lowest value of target entropy, was considered to have the strongest predictive power in the dataset.
Results:
This study revealed that long-term participation in programs increased the likelihood of children being in a normal nutritional state. Key factors influencing the nutritional status of children under two years of age include the mother’s education level, her subjective well-being, and the household’s wealth quintile. Children with educated parents were more likely to have a healthy nutritional status. Additionally, the causal pathway of intervention programs → wealth quintile → child nutritional status consistently exceeded 90% in Waves 3, 4, and 5, indicating a strong relationship. Similarly, the relationship between intervention programs → food security → child nutritional status was nearly perfect at 99.99% in Waves 4 and 5, indicating a strong association. Finally, the study revealed that household participation in intervention programs significantly reduces undernutrition in best-case scenarios, while the absence of support poses a higher risk in worst-case conditions.
Conclusion:
The comprehensive intervention program strongly improved household wealth, food security, and maternal well-being, which in turn affected children’s nutritional status
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