Analysis of selected medicinal plants used in the treatment of malaria and typhoid fever in Ebonyi State, Nigeria

The study was aimed at evaluating selected medicinal plants used in the treatment of malaria and typhoid fever. Materials used include medicinal plants used locally in treating malaria and typhoid fever sourced from different villages in Ebonyi State. The analysis of the medicinal plants was conducted using random amplified polymorphic DNA (RAPD) markers and NTSYSpc software version 2.02. Different RAPD markers including OPB-1, OPB-2, OPB-3, OPB-5, OPB-12 and OPH-12 were used to amplify the DNA of these plants. These markers were found to be polymorphic except OPB-3 which did not produce any band. It was observed that RAPD markers can effectively amplify DNA sequences of different medicinal plants. The data matrix of RAPD profiles obtained from fragments of each amplicon were scored as 1 (presence of alleles) or 0 (absence of alleles). A dendrogram of the plants using unweighted pair group mean (UPGMA) clustered the genotypes into groups. The dissimilarity values were 0.26 and 1 as minimum and maximum with an edge length of 1.32. Principal component analysis of the generated amplicons resulted to clusters with unique genetic identity. The polymorphism detected among the plants genotypes will be useful in selecting genetically diverse species in future breeding programme.Keywords: Medicinal plants, Malaria/Typhoid, RAPD, Ebonyi State, Nigeri

Extreme gradient boosting: A machine learning technique for daily global solar radiation forecasting on tilted surfaces

Enhancing solar irradiance and accurate forecasting is required for improved performance of photovoltaic and solar thermal systems. In this study, Extreme Gradient Boosting (XGBoost) model was developed using three input parameters (time, day number, and horizontal solar radiation) and was utilized to forecast daily global solar radiation on tilted surfaces. The proposed model was built using XGBRegressor with five generations, 100 n estimators, and a learning rate of 0.1. Three statistical metrics, such as the coefficient of determination (R2 ), root mean square error (RMSE), and mean absolute error (MAE), were used to compare the model’s results to observed solar radiation data from the Nation Centre for Energy, Research and Development, University of Nigeria, Nsukka. The results showed improved prediction accuracy and XGBoost capability to estimate daily global solar radiation on tilted surfaces. In the training section, the proposed model had a statistical performance of R2 = 0.9977, RMSE = 1.6988, and MAE = 1.081, and in the testing section, R2 = 0.9934, RMSE = 2.8558, and MAE = 2.033. XGBoost model demonstrated a better performance when compared with other models in the literature. As a result, the proposed model provides an effective approach for estimating solar radiation

Machine learning approach for solar irradiance estimation on tilted surfaces in comparison with sky models prediction

In this study, two supervised machine learning models (Extreme Gradient Boosting and K-nearest Neighbour) and four isotropic sky models (Liu and Jordan, Badescu, Koronakis, and Tian) were employed to estimate global solar radiation on daily data measured for one year period at the National Center for Energy, Research and Development (NCERD) at the University of Nigeria, Nsukka. Two solarimeters were employed to measure solar radiation: one measured solar radiation on a tilted surface at a 15° angle of tilt, facing south, and the other measured global horizontal solar radiation. The measured global horizontal solar radiation and the time and day number were used as input for the prediction process. Python computational software was used for model prediction, and the performance of each model was assessed using statistical methods such as mean bias error (MBE), mean absolute error (MAE), and root mean square error (RMSE) (RMSE). Compared to the measured data, it was discovered that the Extreme Gradient Boosting (XGBoost) algorithm offered the best performance with the least inaccuracy to sky models

Estimation of global solar radiation using empirical models

The dearth of solar radiation data availability has necessitated the development of several mathematical models for estimating global solar radiation (GSR) of regions using the readily available meteorological data of the region. This study was centered on estimating the GSR of the Ihiala region in Sub-Saharan Africa using empirical models. For the last ten years, meteorological data from the Nigerian Meteorological Agency (NIMET) were used. The sunshine-based equation, temperature-based equation, and multivariate polynomial equations were the empirical models employed to estimate the GSR of the region. The performance of the seven models was determined using statistical measures. From the results obtained, the seven models had their respective P-values all less than 5 % significant level for a confidence interval of 95 %. Thereby attesting their suitability for GSR estimation of the region is needed. Also, from the other statistical tools employed, the considered multivariate model had better estimation performance than the other models. Therefore, the considered multivariate model is suitable for estimating the GSR of the Ihiala region in Sub-Saharan Africa

Calidad de Vida: a systematic review of quality of life in Latino cancer survivors in the USA

Background: Cancer is the leading cause of death among Hispanics/Latinos. Thus, understanding health-related quality of life (HRQOL) needs among this diverse racial/ethnic group is critical. Using Ferrell’s multidimensional framework for measuring QOL, we synthesized evidence on HRQOL needs among Hispanic/Latino cancer survivors. Methods: We searched MEDLINE/PubMed, EMBASE, CINAHL, and PsycINFO, for English language articles published between 1995 and January 2020, reporting HRQOL among Hispanic/Latino cancer survivors in the USA. Results: Of the 648 articles reviewed, 176 met inclusion criteria, with 100 of these studies focusing exclusively on breast cancer patients and no studies examining end-of-life HRQOL issues. Compared with other racial/ethnic groups, Hispanics/Latinos reported lower HRQOL and a higher symptom burden across multiple HRQOL domains. Over 80% of studies examining racial/ethnic differences in psychological well-being (n = 45) reported worse outcomes among Hispanics/Latinos compared with other racial/ethnic groups. Hispanic/Latino cancer survivors were also more likely to report suboptimal physical well-being in 60% of studies assessing racial/ethnic differences (n = 27), and Hispanics/Latinos also reported lower social well-being relative to non-Hispanics/Latinos in 78% of studies reporting these outcomes (n = 32). In contrast, reports of spiritual well-being and spirituality-based coping were higher among Hispanics/Latinos cancer survivors in 50% of studies examining racial/ethnic differences (n = 15). Discussion: Findings from this review point to the need for more systematic and tailored interventions to address HRQOL needs among this growing cancer survivor population. Future HRQOL research on Hispanics/Latinos should evaluate variations in HRQOL needs across cancer types and Hispanic/Latino subgroups and assess HRQOL needs during metastatic and end-of-life disease phases

A review on the factors affecting the properties of natural fibre polymer composites

Improved quality natural fibre composites cannot be achieved without considering certain factors such as the degree of uniformity of the fibre, wettability of the fibre, fibre length, fibre volume fraction, type of matrix, interfacial bond strength, fibre orientation, compatibility of the fibre with the resin, processing parameters and manufacturing techniques among others. Their influences on the properties of the composites with typical examples from previous works were highlighted. The exact or approximate volume fractions of specific fibres in specific resins for optimal performance in composites are lacking. Epoxy, low density polyethylene, polystyrene and polyester resins were mostly used as matrix for natural fibre composites. Epoxy resins possess higher tensile and flexural strengths than polyester resins. Significant differences in the tensile strength and Young’s modulus of natural fibre polymer composites were observed with changes in the orientation and length of the fibres particularly when the differences in length are significant. Other relevant issues affecting natural fibre composites were buttressed with the aim of improving the properties of natural fibre polymer composites for advanced applications

Tensile and microstructural properties of unidirectional coir bio-derived epoxy composites

The study investigates the possibilities of reinforcing bio-derived epoxy (bio-epoxy) polymer with unidirectional arrays of coir fibre using hand layup. The coir fibres were unidirectionally prepared in two layers. The tensile properties such as strength, Young’s modulus and elongation at break were found to be 43.83 MPa, 2.4 GPa and 2.72 % respectively. The tensile strength of coir/bio-epoxy composite was higher than the unreinforced bio-epoxy resin. The fibre volume fraction of the composites obtained using ImageJ on an 8-bit grey scale was 34%. The optical microstructure of the composites shows the distribution of the fibres within the bio-epoxy matrix