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Recent Submissions
EFFECTS OF LEGUME COVER CROPS ON SOIL PROPERTIES AND PRODUCTIVITY OF GRAFTED ORANGES (citrus sinensis) IN THE COASTAL LOWLANDS OF KENYA
(2017-07) JACKSON MUEMA MULINGE
Orange (Citrus sinensis) is an important food and cash crop in coastal lowland of Kenya. The
average orange production in Kenya is 12 tones/ha Compared to world production of 16
tones/ha due to low soil fertility, diseases and high costs of inputs. There is, therefore, a need
to develop a sustainable and low input production system for increased orange productivity
and improved fruit quality in coastal lowland of Kenya. This study was tconducted at
KALRO-Matuga, Ganda and Vitengeni within the coastal region of Kenya from May 2012 to
April 2015. The effects of legume cover crops on soil moisture, orange feeder root
distribution, soil pH, plant nutrients, orange yield and fruit quality was evaluated. There were
four treatments; mucuna (Mucuna pruriens), cowpea (Vigna unguiculata), dolichos (Lablab
purpureus) cover crops and a fallow as the control. The experiment was laidout in a
randomized complete block design (RCBD) where the treatments were replicated four times
within four blocks in an existing grafted Valencia orange orchard. Soil and orange root
sampling was between 2m and 3m radius from the orange trees trunk at two depths topsoil
(0-20 cm) and sub-soil (20-40 cm). Fruit and leaf samples were taken from the orange trees.
Data collected was subjected to analysis of variance (ANOVA) at (P ≤ 0.05) using
procedures of R statistical analysis version 3.3.2. Mean separation was done using the least
significant difference (LSD) at (P ≤ 0.05) level of significance. Mucuna, dolichos and
cowpea increased soil moisture content in orange orchard for all the site topsoil while in the
sub-soil is only mucuna and dolichos increased moisture content in the soil. Mucuna and
dolichos increased orange root density in the top and sub-soils. Mucuna, cowpea and
dolichos increased soil nitrogen in the orange orchard top and sub-soil. Mucuna, cowpea and
dolichos increased soil organic carbon in the orange orchard top and sub-soil. Mucuna
increased phosphorous in the top and sub-soil of orange orchard. Dolichos increased
phosphorous in the topsoil of orange orchard.. Cowpea and dolichos increased phosphorous
in the sub-soil of orange orchard. Mucuna, dolichos and cowpea increased the potassium in
the topsoil of orange orchard while in the sub-soil, potassium increase due to mucuna and
cowpea. Mucuna dolichos and cowpea increased orange leaf chlorophyll content. The orange
fruit number increased due to mucuna and dolichos. Orange fruit weight increased due to
mucuna and dolichos. Fruits size increased due to mucuna and dolichos. Fruits juice
increased due to mucuna and dolichos. Orange fruit brix increased due to mucuna and
dolichos. In conclusion, mucuna, dolichos and cowpea are effective in improving soil
moisture, root distribution and nutrients in the soil and orange yield and fruit quality. The use
of mucuna however, had the highest increase and it is strongly recommended as a cover crop
in orange production. Further research is however recommended to evaluate the long term
(>3years) effect of the cover crops under different Agro-ecological zones.
EFFICACY AND KINETICS OF ADSORPTION OF SINGLE AND MULTIPLE HEAVY METAL CATIONS FROM AQUEOUS SOLUTIONS BY FRUIT WASTE PRODUCTS
(2016-12) NTHIGA ESTHER WANJA
Removal of toxic heavy metals from water has been a major challenge, especially in
rural areas. Various methods have been used for this purpose; among them is biosorption based technology. The technology has been recognized as an economical and
eco-friendly method for removal of toxic anions and cations from wastewater.
Moreover, the efficacy of biomass in adsorption of cations and anions can be improved
by different modifications, including treatment with sulphuric acid. Numerous
approaches have been developed for adsorption of single ions in solution. However,
toxic metallic or non-metallic ions rarely occur singly in wastewater. The presence of
multiple ions in solution may often have agonistic or antagonistic effect on the
efficiency of an adsorbent, and there is insufficient information on the efficacy of
different methods for removing multiple ions. This study assessed the efficacy and
mechanism of acid treated (modified) and raw (unmodified) biosorbents (derived from
lemon, sweet yellow passion, banana, watermelon peels, and avocado seeds) for
removal of toxic cations (Cd, Pb and Cu) from water. Functional groups of adsorbents
were identified by mid-infrared spectroscopy (MIR) and their surface morphology was
probed by scanning electron microscopy (SEM). the efficacy of each adsorbent was
evaluated by quantifying the kinetics and levels of cations adsorbed at different pH of
the solution, initial concentrations, contact time and adsorbent dose. Desorption
experiments were conducted to determine the possibility of recovering ions and reusing
the sorbents for next cycle of deployment. Experimental data of each metal ion was
described by either Freundlich isotherm or Langmuir isotherm. The acid treated fruit
peels and avocado seeds recorded higher efficacy as compared to raw adsorbents.
Generally, acid treated watermelon peels demonstrated the highest uptake of 130.23
mg/g of Pb (II), followed by 114.234 mg/g of Cu (II) and 97.14 mg/g of Cd (II) ions.
Further adsorption trials with binary and ternary metal blends on showed significant
reductions in metal uptake capacities of evaluated adsorbents as compared to single
metal systems. On account of metal preference, the selectivity order for metal ions
towards the all the studied biomass was Pb (II) > Cu (II) > Cd (II). Time-course
measurements indicated involvement of pseudo-second-order kinetics in adsorptions.
Desorption efficacies were high on acid-treated adsorbents: 99.97 % ofPb(II) ions from
acid treated avocado seeds; and 99.79 % of Cu(II) and 99.23 % of Cd (II) from acid
treated watermelon peels. The results show good performance of the fruit peels and
avocado seeds in adsorbing single and multiple metal ions, and the potential of using
such wastes for purifying drinking and cooking water at household level.
Machine Learning Prediction Models for Postpartum Depression, a Review of Literature
(International Journal of Computer Applications Technology and Research, 2022) George Kimwomi, Mvurya Mgala, Fullgence Mwakondo, Pamela Kimeto
Postpartum depression is a medical condition which continue to affect many mothers after delivery even though
the disease can be prevented. It consequently exposes mothers and family members to illness and even death. Families,
governments and other stakeholders incur heavy expenditure in the management of the disease. Research studies have been
done to develop machine learning models for prediction of mothers at risk of postpartum depression during pregnancy for
preventive measures. This paper presents a literature review of the machine learning prediction models which have been
developed for the condition with specific focus on feature selection methods, algorithms used and the resulting performance.
Literature review was done with google scholar integrated to an online institutional account for e-resources from e-databases
accessed by subscription or free access. Inclusion involved all articles with the key words “machine learning, prediction model,
postpartum depression” in the articles dated from 2018 to 2022 and sorted by relevance. A total of 3430 articles were listed
while only 17 which were accessible with full text were eligible and therefore selected for the study. Analyzes were done
using Microsoft Excel and descriptive analysis. Findings and conclusions will inform scientists on the status of research in the
area to guide new studies, and inform the market on the potential benefits of integrating machine learning models in their
systems.
An Appropriate Feature Selection Technique for Use on Socio-Demographic Predictor Variables to Enable Early Detection of Preeclampsia: A Review of Literature
(Computer Engineering and Intelligent Systems, 2022-08-31) Arina A. Jamwa, Mgala Mvurya, Antony Luvanda, Pamela Kimetto
Preeclampsia is categorized by the World Health Organization as one of the leading causes of high morbidity
and mortality in infant and mothers around the world. It accounts for between 3% to 5% of all pregnancy related
complications reported worldwide. This condition is much higher among women aged between 30 and 40 years
in developing nations especially those in the sub-Saharan region, where the figures range between 5.6% to 6.5%
of all reported pregnancies. Preeclampsia is a condition normally detected in the third trimester of pregnancy that
is characterized by high risk factors such as sudden High Blood Pressure, High levels of protein in Urine,
Chronic kidney disease and Type 1 or 2 diabetes. If preeclampsia is not detected early, it can advance to
eclampsia or result to maternal and fetal death. This study sought to identify the optimal features as predictors to
enable early detection of preeclampsia through a systematic review of relevant literature. The predictors under
consideration were; Maternal age, Occupation, Education, ANC Attendance, BMI, Blood Pressure, Medical
History, Urine dipstick, Gravida, Ethnicity, Gestation weeks as identified from literature.
REPARAMETERIZATION OF AUTOREGRESSIVE DISTRIBUTED LAG TO ERROR CORRECTION MODEL TO STUDY YOUTH UNEMPLOYMENT IN KENYA
(2019-07) Shem Otio Odhiambo Sam
The research provides statistical basis for assessing and prioritizing investment policies, initiatives and projects to maximise youth employment by scrutinizing in uence of macroeconomic variables. The macroeconomic variables considered are gross domestic product (GDP), external debt (ED), foreign domestic investment (FDI), private investment(PI), youth unemployment(YUN), literacy rate (LR), and youth population (POP). The research approach taken uses predictive analytics such as impulse response functions and variance decomposition from vector error corrections model (VECM) and cointegration regression in autoregressive distributed lag (ARDL) to identify key determinants of youth unemployment to prioritize investment. This research analyzes reparameterization of ARDL to VECM through cointegration of time series. First, the time series data undergo logarithm transformation to reduce outlier e ects and have elasticity interpreted in terms of percentage. The study scrutinizes the e ects of macroeconomic shocks on youth unemployment in Kenya. For this purpose, the Augmented Dickey-Fuller test is conducted to assess stationarity of the variables used. Then Johansen Cointegration test is carried out to establish the rank at which the series are cointegrated. The unit root test has been performed on YUN, GDP, ED, FDI, PI, and LR, and POP to assess stationarity. The cointegrated dynamic ARDL model is estimated using ordinary least squares (OLS) and e ects of variables and their lags interpreted. The results reveal that Gross Domestic Product (GDP) and its second lag have negative e ect on youth unemployment, that is, one unit increase in (GDP) and GDP lag 2 reduce youth unemployment by 0.207922% and 0.2052705% respectively. Also, one unit of External Debt (ED) and ED lag 2 reduce youth unemployment by 0.07303% and 0.009116% respectively. Furthermore, unit increase in one year lag of youth literacy rate reduces youth unemployment by 0.0892691%. Lastly, lag one and three of population reduce youth unemployment by 0.2590455% and 4.3093119% respectively. The Johansen Cointegration Analysis has revealed three long run relationships which can be interpreted as a GDP e ect; External Debt e ect and Foreign Direct Investment e ect relations. A structural VECM has been described through restrictions taken from the Cointegration Analysis. Based on the results of the Impulse-Response Function and variance decomposition analyses of the Structural VECM, it is concluded that GDP, literacy level, population, and FDI shocks have signi cant iii e ects on Kenyan youth unemployment in the long run. On the superiority of the two models, whereas ARDL captures the in uence of past shocks through coe cients of lags, VECM predicts the e ects of current shocks and resulting movement of variables more than 10 unit steps ahead. Also, Granger causality present in ARDL does not exist in reparameterized VECM. The F-test and t-test reveal that the two models are signi cant at 95% c
