Evaluation of some basic traits of a promising coconut hybrid: Sri Lankan green dwarf crossed to Vanuatu tall (sgd x vtt)

The Lethal Yellowing Disease locally referred to as the “Cape St Paul Wilt Disease” is the single most important disease that has devastated several hectares of coconut plantations in Ghana. Two decades of coconut screening for tolerant planting material has identified the Sri LankanGreen Dwarf crossed Vanuatu Tall (SGD x VTT) coconut hybrid as the most promising planting material in the context of disease. To provide farmers with planting material that has high disease tolerance and also good agronomic characteristics, the study compared some basic traits ofthe coconut hybrid with other important coconut varieties with the objective of determining the suitability of the SGD x VTT as alternative planting material to revamp the coconut industry in Ghana. Mean sample size of 25 palms per coconut variety under the study was analyzed using two sample t-test procedure. The study indicated that the yield performance of the SGD x VTT coconut hybrid was better than the tall coconut types including the local West African Tall (WAT) and compared favourably with the Malayan Yellow Dwarf crossed Vanuatu Tall (MYD x VTT) coconut hybrid. The good agronomic characteristics of the SGD x VTT coupled with itshigh resistance to the CSPWD proved its suitability as alternative planting material to revamp the coconut industry in Ghana

Modeling Memorization and Forgetfulness Using Differential Equations

{\bf Research Context}: The aim of the study was to use differential equations to model memorization of students based on a given data taking into account forgetfulness.\\\noindent {\bf Research Methods}: The purpose of this paper was to decipher the rate at which students memorized the stuff that required memorization in the area of axioms and proofs of theorems as well as considering the fact that they will forget some of them along the way. The usage of differential equation was employed to model the trend. The paper contributes to the literature by documenting that students can memorize large number of stuff even beyond their perceived imaginations.\\\noindent {\bf Conclusion}:  This study employed the usage of differential equations to model the rate at which students could memorize a given number of axioms and proofs, considering the fact that they will forget some of them along the way. Persons who are able to absorb and retain more are able to recollect better than those who can absorb more and retain less.  On the other hand, those who can absorb less and retain more have an upper hand in recollection over those who can absorb more and retain less. Consequently it is better to have a higher retention constant than a higher absorption rate. Factors like the learning strategy, learning materials, learning environment, study mates have either a positive or negative influence on an individual's absorption and retention in the long term

High-resolution patterns and inequalities in ambient fine particle mass (PM2.5) and black carbon (BC) in the Greater Accra Metropolis, Ghana.

Growing cities in sub-Saharan Africa (SSA) experience high levels of ambient air pollution. However, sparse long-term city-wide air pollution exposure data limits policy mitigation efforts and assessment of the health and climate effects in growing cities. In the first study of its kind in West Africa, we developed high resolution spatiotemporal land use regression (LUR) models to map fine particulate matter (PM2.5) and black carbon (BC) concentrations in the Greater Accra Metropolitan Area (GAMA), one of the fastest sprawling metropolises in SSA. We conducted a one-year measurement campaign covering 146 sites and combined these data with geospatial and meteorological predictors to develop separate Harmattan and non-Harmattan season PM2.5 and BC models at 100 m resolution. The final models were selected with a forward stepwise procedure and performance was evaluated with 10-fold cross-validation. Model predictions were overlayed with the most recent census data to estimate the population distribution of exposure and socioeconomic inequalities in exposure at the census enumeration area level. The fixed effects components of the models explained 48-69 % and 63-71 % of the variance in PM2.5 and BC concentrations, respectively. Spatial variables related to road traffic and vegetation variables explained the most variability in the non-Harmattan models, while temporal variables were dominant in the Harmattan models. The entire GAMA population is exposed to PM2.5 levels above the World Health Organization guideline, including even the Interim Target 3 (15 μg/m3), with the highest exposures in poorer neighborhoods. The models can be used to support air pollution mitigation policies, health, and climate impact assessments. The measurement and modelling approach used in this study can be adapted to other African cities to bridge the air pollution data gap in the region

Repellency Potential, Chemical Constituents of Ocimum Plant Essential Oils, and Their Headspace Volatiles against Anopheles gambiae s. s., Malaria Vector

African malaria mosquitoes (Anopheles gambiae sensu stricto) transmit a malaria parasite (Plasmodium falciparum) to humans. The current control strategies for the vector have mainly focussed on synthetic products, which negatively impact the environment and human health. Given the potential use of environmentally friendly plant-derived volatiles as a control, this work aims to examine and compare the repellency potential of essential oils and headspace volatiles from Ocimum gratissimum, Ocimum tenuiflorum, and Ocimum basilicum and their chemical compositions. The repellency potential and chemical composition of the plants were achieved by using the protected arm-in-cage method and gas chromatography-mass spectrometry (GC-MS) analysis. Among the three Ocimum species, both the essential oils and the headspace volatiles from O. tenuiflorum achieved the longest repellency time lengths of 90–120 minutes. One hundred and one (101) chemical constituents were identified in the headspace volatiles of the three Ocimum spp. Nonetheless, (−)-camphor, (E)-γ-bisabolene, terpinolene, β-chamigrene, cubedol, (E)-farnesol, germacrene D-4-ol, viridiflorol, γ-eudesmol, tetracyclo [6.3.2.0 (2,5).0(1,8)] tridecan-9-ol, 4,4-dimethyl, α-eudesmol, isolongifolol, and endo-borneol were unique only to O. tenuiflorum headspace volatiles. Either essential oils or headspace volatiles from O. tenuiflorum could offer longer protection time length to humans against An. gambiae. Though field studies are needed to assess the complementarity between the chemical constituents in the headspace volatiles of O. tenuiflorum, our observations provide a foundation for developing effective repellents against An. gambiae

Quantifying within-city inequalities in child mortality across neighbourhoods in Accra, Ghana: a Bayesian spatial analysis

Objective Countries in sub-Saharan Africa suffer the highest rates of child mortality worldwide. Urban areas tend to have lower mortality than rural areas, but these comparisons likely mask large within-city inequalities. We aimed to estimate rates of under-five mortality (U5M) at the neighbourhood level for Ghana’s Greater Accra Metropolitan Area (GAMA) and measure the extent of intraurban inequalities. Methods We accessed data on >700 000 women aged 25–49 years living in GAMA using the most recent Ghana census (2010). We summarised counts of child births and deaths by five-year age group of women and neighbourhood (n=406) and applied indirect demographic methods to convert the summaries to yearly probabilities of death before age five years. We fitted a Bayesian spatiotemporal model to the neighbourhood U5M probabilities to obtain estimates for the year 2010 and examined their correlations with indicators of neighbourhood living and socioeconomic conditions. Results U5M varied almost five-fold across neighbourhoods in GAMA in 2010, ranging from 28 (95% credible interval (CrI) 8 to 63) to 138 (95% CrI 111 to 167) deaths per 1000 live births. U5M was highest in neighbourhoods of the central urban core and industrial areas, with an average of 95 deaths per 1000 live births across these neighbourhoods. Peri-urban neighbourhoods performed better, on average, but rates varied more across neighbourhoods compared with neighbourhoods in the central urban areas. U5M was negatively correlated with multiple indicators of improved living and socioeconomic conditions among peri-urban neighbourhoods. Among urban neighbourhoods, correlations with these factors were weaker or, in some cases, reversed, including with median household consumption and women’s schooling. Conclusion Reducing child mortality in high-burden urban neighbourhoods in GAMA, where a substantial portion of the urban population resides, should be prioritised as part of continued efforts to meet the Sustainable Development Goal national target of less than 25 deaths per 1000 live births

Exploring the knowledge and awareness of diabetes mellitus among inhabitants of Ho municipality in Ghana: A cross-sectional study

One of the fast-growing major non-communicable diseases (NCD) that poses a danger to global public health is Diabetes mellitus (DM). Trends in  the incidence of DM indicate a disproportionate increase in developing countries due to current rapid demographic transitions from traditional to  more westernized and urbanized lifestyles. Knowledge of DM is vital for curbing or control. The objectives of this study were to evaluate the level of  knowledge and awareness of DM among the Ho municipality general population, identify areas of deficiency for targeted health education efforts,  and identify respondent characteristics that may be associated with knowledge of diabetes. A survey involving 132 respondents (age over 18 years)  was conducted in the Ho municipality of the Volta region of Ghana. A 42-item pre-tested questionnaire was administered to participants to evaluate  general and specific knowledge and awareness of DM. The Pairwise Multiple Comparison and Fisher’s Exact tests were used to test the hypotheses  and associations between the respondents’ knowledge level and groups respectively. Of the 132 respondents, 22% were in the age range of 40-46  years; 72.7% were female. Mean over all diabetes knowledge composite score was poor: 32.99% (CI; 27.5, 38.5). Respondents performed best in the  symptoms section: mean score was 36.247% (CI; 29.0, 43.4); and worst in the section on complications: mean score was 30.909% (CI; 23.6, 38.2). In  multiple linear regression analyses, education level, older age, own self having diabetes, and having a family member/relative/friend with diabetes  were significantly associated with knowledge of diabetes. Knowledge of diabetes among the inhabitants of Ho municipality respondents was  interpreted as being inadequate 32.99% (CI; 27.5, 38.5). Some deficient portions and factors associated with knowledge of diabetes were identified.  Relevant information for targeted health education programs in Ghana and beyond may be considered as one of such benefits of these findings.  &nbsp

Spatial-temporal patterns of ambient fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra

Background: Sub-Saharan Africa (SSA) is rapidly urbanizing, and ambient air pollution has emerged as a major environmental health concern in SSA cities. Yet, effective air quality management is hindered by limited data. We deployed robust, low-cost and low-power devices in a large-scale measurement campaign and characterized within-city variations in fine particulate matter (PM2.5) and black carbon (BC) pollution in Accra, Ghana. Methods: Between April 2019 and June 2020, we measured weekly gravimetric (filter-based) and minute-by-minute PM2.5 concentrations at 146 unique locations, comprising of 10 fixed (~1-year) and 136 rotating (7-day) sites covering a range of land-use and source influences. Filters were weighed for mass, and light absorbance (10−5m−1) of the filters was used as proxy for BC concentration. Year-long data at four fixed sites that were monitored in a previous study (2006-2007) were compared to assess change in PM2.5 concentrations. Results: The mean annual PM2.5 across the fixed sites ranged from 26 μg/m3 at a peri-urban site to 40 μg/m3 at commercial, business, and industrial (CBI) areas. CBI areas had the highest PM2.5 levels (mean: 37 μg/m3), followed by high-density residential neighborhoods (mean: 36 μg/m3), while peri-urban areas recorded the lowest (mean: 26 μg/m3). Both PM2.5 and BC levels were highest during the dry dusty Harmattan period (mean PM2.5: 89 μg/m3) compared to non-Harmattan season (mean PM2.5: 23 μg/m3). PM2.5 at all sites peaked at dawn and dusk, coinciding with morning and evening heavy traffic. We found about a ~50% reduction (71 vs 37 μg/m3) in mean annual PM2.5 concentrations when compared to measurements in 2006-2007 in Accra. Conclusion: Ambient PM2.5 concentrations in Accra may have plateaued at levels lower than those seen in large Asian megacities. However, levels are still 2- to 4-fold higher than the WHO guideline. Effective and equitable policies are needed to reduce pollution levels and protect public health

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Beyond here and now: Evaluating pollution estimation across space and time from street view images with deep learning

Advances in computer vision, driven by deep learning, allows for the inference of environmental pollution and its potential sources from images. The spatial and temporal generalisability of image-based pollution models is crucial in their real-world application, but is currently understudied, particularly in low-income countries where infrastructure for measuring the complex patterns of pollution is limited and modelling may therefore provide the most utility. We employed convolutional neural networks (CNNs) for two complementary classification models, in both an end-to-end approach and as an interpretable feature extractor (object detection), to estimate spatially and temporally resolved fine particulate matter (PM2.5) and noise levels in Accra, Ghana. Data used for training the models were from a unique dataset of over 1.6 million images collected over 15 months at 145 representative locations across the city, paired with air and noise measurements. Both end-to-end CNN and object-based approaches surpassed null model benchmarks for predicting PM2.5 and noise at single locations, but performance deteriorated when applied to other locations. Model accuracy diminished when tested on images from locations unseen during training, but improved by sampling a greater number of locations during model training, even if the total quantity of data was reduced. The end-to-end models used characteristics of images associated with atmospheric visibility for predicting PM2.5, and specific objects such as vehicles and people for noise. The results demonstrate the potential and challenges of image-based, spatiotemporal air pollution and noise estimation, and that robust, environmental modelling with images requires integration with traditional sensor networks