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Sekondi-Takoradi is the most urbanized metropolis in the Western region of Ghana. Over the past two decades, it has experienced rapid shifts in both population density and land cover. Unfortunately, these transformations have significantly altered the natural landscape and expanded urban coverage. Previous studies have not established enough evidence on land use and land cover changes in the metropolis. For this reason, this study examined the existence, rate, and spatial distribution of land use and land cover as well as the future changes in land cover in the metropolis. Landsat imagery spanning from 1991, 2009, 2016, to 2023 provided the basis for evaluating the spatiotemporal dynamics of land use and cover within the metropolis. The Random Forest Classification algorithm was employed to categorize the images into three distinct classes: water, vegetation, and built-up areas. Analysis revealed a rapid growth in built-up areas by 63.08 km² (32.91 %), accompanied by a decrease in vegetation and water coverage by 60.99 km² (31.82 %) and 2.08 km² (1.09 %) respectively. This highlights the accelerating urbanization trend, emphasizing the critical need for vigilant monitoring and controlled urban expansion to mitigate potential adverse effects. Projections for 2030 indicate further changes with water areas decreasing to 1.21 km² (0.63 %), vegetation diminishing to 95.31 km² (49.73 %), and built-up areas expanding to 95.14 km² (49.64 %). This signifies a significant shift towards built-up areas, altering the metropolis' land cover composition and potentially impeding the achievement of Sustainable Development Goal 11. Understanding these changes in land use and land cover holds significant value for policymakers, enabling them to effectively monitor and safeguard natural resources.

Abstract Background African ancestry populations have the highest burden of stroke worldwide, yet the genetic basis of stroke in these populations is obscure. The Stroke Investigative Research and Educational Network (SIREN) is a multicenter study involving 16 sites in West Africa. We conducted the first-ever genome-wide association study (GWAS) of stroke in indigenous Africans. Methods Cases were consecutively recruited consenting adults (aged > 18 years) with neuroimaging-confirmed ischemic stroke. Stroke-free controls were ascertained using a locally validated Questionnaire for Verifying Stroke-Free Status. DNA genotyping with the H3Africa array was performed, and following initial quality control, GWAS datasets were imputed into the NIH Trans-Omics for Precision Medicine (TOPMed) release2 from BioData Catalyst. Furthermore, we performed fine-mapping, trans-ethnic meta-analysis, and in silico functional characterization to identify likely causal variants with a functional interpretation. Results We observed genome-wide significant (P-value < 5.0E−8) SNPs associations near AADACL2 and miRNA (MIR5186) genes in chromosome 3 after adjusting for hypertension, diabetes, dyslipidemia, and cardiac status in the base model as covariates. SNPs near the miRNA (MIR4458) gene in chromosome 5 were also associated with stroke (P-value < 1.0E−6). The putative genes near AADACL2, MIR5186, and MIR4458 genes were protective and novel. SNPs associations with stroke in chromosome 2 were more than 77 kb from the closest gene LINC01854 and SNPs in chromosome 7 were more than 116 kb to the closest gene LINC01446 (P-value < 1.0E−6). In addition, we observed SNPs in genes STXBP5-AS1 (chromosome 6), GALTN9 (chromosome 12), FANCA (chromosome 16), and DLGAP1 (chromosome 18) (P-value < 1.0E−6). Both genomic regions near genes AADACL2 and MIR4458 remained significant following fine mapping. Conclusions Our findings identify potential roles of regulatory miRNA, intergenic non-coding DNA, and intronic non-coding RNA in the biology of ischemic stroke. These findings reveal new molecular targets that promise to help close the current gaps in accurate African ancestry-based genetic stroke’s risk prediction and development of new targeted interventions to prevent or treat stroke.

Background Baseline stroke severity is probably partly responsible for poor stroke outcomes in sub‐Saharan Africa. However, there is a paucity of information on determinants of stroke severity among indigenous Africans. We sought to identify the factors associated with stroke severity among West Africans in the SIREN (Stroke Investigative Research and Educational Networks) study. Methods and Results Stroke was diagnosed clinically and confirmed with brain neuroimaging. Severe stroke was defined as a Stroke Levity Scale score of ≤5. A multivariate logistic regression model was constructed to identify factors associated with stroke severity at 95% CI and a nominal cutoff of 5% type 1 error. A total of 3660 stroke cases were included. Overall, 50.7%% had severe stroke, including 47.6% of all ischemic strokes and 56.1% of intracerebral hemorrhage. Factors independently associated with severe stroke were meat consumption (adjusted odds ratio [aOR], 1.97 [95% CI, 1.43–2.73]), low vegetable consumption (aOR, 2.45 [95% CI, 1.93–3.12]), and lesion volume, with an aOR of 1.67 (95% CI, 1.03–2.72) for lesion volume of 10 to 30 cm3 and aOR of 3.88 (95% CI, 1.93–7.81) for lesion volume >30 cm3. Severe ischemic stroke was independently associated with total anterior circulation infarction (aOR, 3.1 [95% CI, 1.5–6.9]), posterior circulation infarction (aOR, 2.2 [95% CI, 1.1–4.2]), and partial anterior circulation infarction (aOR, 2.0 [95% CI, 1.2–3.3]) compared with lacunar stroke. Increasing age (aOR, 2.6 [95% CI, 1.3–5.2]) and lesion volume >30 cm3 (aOR, 6.2 [95% CI, 2.0–19.3]) were independently associated with severe intracerebral hemorrhage. Conclusions Severe stroke is common among indigenous West Africans, where modifiable dietary factors are independently associated with it. These factors could be targeted to reduce the burden of severe stroke.

Water security has been a major challenge in the semi-arid area of West Africa including Northern Ghana, where climate change is projected to increase if appropriate measures are not taken. This study assessed rainfall and temperature projections and its impact on the water resources in the Vea catchment using an ensemble mean of four bias-corrected Regional Climate Models and Statistical Downscaling Model-Decision Centric (SDSM-DC) simulations. The ensemble mean of the bias-corrected climate simulations was used as input to an already calibrated and validated Soil and Water Assessment Tool (SWAT) model, to assess the impact of climate change on actual evapotranspiration (ET), surface runoff and water yield, relative to the baseline (1990–2017) period. The results showed that the mean annual temperature and actual ET would increase by 1.3 °C and 8.3%, respectively, for the period 2020–2049 under the medium CO2 emission (RCP4.5) scenario, indicating a trend towards a drier climate. The surface runoff and water yield are projected to decrease by 42.7 and 38.7%, respectively. The projected decrease in water yield requires better planning and management of the water resources in the catchment. HIGHLIGHTS Variations in the mean annual rainfall projection over the catchment were found, with 66 and 33% of the models indicating an increase and decrease, respectively, during the rainy season.; The near future (2020–2049) climate under RCP4.5 scenario shows a warmer climate in the Vea catchment, with dry season temperature projections higher than the rainy season.; Surface runoff and water yield are projected to decrease by 42.7 and 38.7%, respectively, in the future period 2020–2049. This would require water resource commission to plan and manage the water resources well at the catchment.;

The economic implications of extreme climate changes are found to impact sub-Saharan Africa negatively. This study aimed to analyze projected changes in length of rainy season (LRS), and rainfall extreme indices at the Vea catchment, Ghana. The analysis was performed using high-resolution simulated rainfall data from the Weather Research and Forecasting (WRF) model under moderate greenhouse gas emission scenario for the period 2020–2049 relative to the 1981–2010 period. LRS was computed from the difference between rainfall onset and cessation dates, and its trends were assessed using Mann–Kendall test and Sen's slope estimator. Annual rainfall intensity and frequency indices were computed. Results showed an increase in mean LRS from 168 to 177 days, which was at a rate of 1 day/year in the future (2020–2049). The LRS increase would be more significant at northern and south-western parts of the catchment. Rainfall intensity and frequency indices are projected to increase at spatial scale across the catchment. Projected changes in rainfall extremes could increase the frequency and intensity of drought and flood events. Thus, it is necessary to integrate suitable climate change adaptation measures such as rainwater harvesting, flood control measures, and development of early warning systems in the planning process by decision-makers at the catchment. HIGHLIGHTS The spatio-temporal changes in length of rainy season and rainfall extreme indices were analyzed under moderate greenhouse gas emission scenario for the vea catchment.; High resolution regional climate model was employed in this study.; Rainfall intensity and frequency indices are projected to increase at the spatial scale across the catchment.; Mann–Kendall test, onset and cessation of rainfall were used for the data analsysis.;

Land Use Land Cover (LULC) changes are the direct and indirect consequences of human actions to secure essential resources for a successful livelihood. Therefore, it has become essential to analyze such changes for effective management of natural resources and protection of our environment. Anthropogenic factors such as urbanization, mining, and population increase in the Prestea Huni-Valley municipality are causing rapid changes to the land cover. These factors among others are putting a lot of pressure on the vegetation and this has dire consequences on the availability and protection of the vegetative cover. Given this, the study focused on assessing the impact of mining on vegetation cover with a particular focus on Prestea Huni-Valley municipality. Multi-spectral satellite images of the study area from 1986 to 2016 were spatially analyzed to identify the LULC change patterns. Modeling and analysis of these images were performed using IDRISI and R software. Six land cover classes were identified including forest, open vegetation, cultivated areas, bare lands, built-up, and mine sites. The result showed that the forest, open vegetation, and bare areas decreased by 14.59 %, 3.86 %, and 8.36 % respectively whereas cultivated, built up and mines increased by 6.31 %, 3.14 %, and 7.36 % respectively.

INTRODUCTION: The availability and utilization of MRI units across sub-Saharan Africa countries remain poor and its distribution is largely unknown. A cross-sectional survey was conducted to determine the distribution and utilization of MRI facilities across the West African sub-region. METHODS: An interview and online search survey was conducted from September 2015 to September 2016, to determine the MRI facilities (Government/Public and Private) available in the West African sub-region. In Nigeria and Ghana, face-to-face interviews were conducted while for other West African countries, telephone interviews with radiologists and other health professionals as well as a Google online search were conducted to ascertain the distribution of the MRI facilities in the region. The number of MRI units in West Africa per million population was calculated and compared with other parts of the world from available published data. RESULTS: Eighty-four MRI units serve a combined population of 372,551,411 in the West African sub-region at the time of this report. Nigeria accounted for more than two-thirds (58 (69%)) of the available units. Of these, 45 (77.6%) of the units were low-field strength systems. Ghana's 14 MRI units were fairly equally distributed between the private (57%) and the public sectors (43%). Ghana with 0.48 units/million population had the highest number of MRI units/million population followed by Nigeria with 0.30 units/million population. CONCLUSION: Though there is an increase in the number of available MRI units in the West African sub region in the last decade, the numbers remain appallingly small for the population. Infrastructural and maintenance limitations constitute a major impediment to the use of high filed systems in the region. There may be need for greater cooperation between public and private enterprises for future improvement of MRI utilization in the region.

Summary: Background: Sub-Saharan Africa has the highest incidence, prevalence, and fatality from stroke globally. Yet, only little information about context-specific risk factors for prioritising interventions to reduce the stroke burden in sub-Saharan Africa is available. We aimed to identify and characterise the effect of the top modifiable risk factors for stroke in sub-Saharan Africa. Methods: The Stroke Investigative Research and Educational Network (SIREN) study is a multicentre, case-control study done at 15 sites in Nigeria and Ghana. Cases were adults (aged ≥18 years) with stroke confirmed by CT or MRI. Controls were age-matched and gender-matched stroke-free adults (aged ≥18 years) recruited from the communities in catchment areas of cases. Comprehensive assessment for vascular, lifestyle, and psychosocial factors was done using standard instruments. We used conditional logistic regression to estimate odds ratios (ORs) and population-attributable risks (PARs) with 95% CIs. Findings: Between Aug 28, 2014, and June 15, 2017, we enrolled 2118 case-control pairs (1192 [56%] men) with mean ages of 59·0 years (SD 13·8) for cases and 57·8 years (13·7) for controls. 1430 (68%) had ischaemic stoke, 682 (32%) had haemorrhagic stroke, and six (

This study proposes a system that, using Internet access, should enable remote fuel level checks independent of the location of the vehicle. The system uses a sensor that continuously measures the gasoline level in a vehicle tank and connects to a microcontroller input. The Internet of Things (IoT) implementation provides real-time fuel monitoring with the aid of the sensor, which in turn is processed by the microcontroller and then sent through the Internet to the smartphone module of the vehicle owner. The IoT-based vehicle fuel theft detection system offers a reliable and efficient solution for monitoring and detecting fuel theft, to vehicle owners and fleet management companies. The system's ability to detect unauthorized fuel flow and alert the user through the Internet makes it an ideal solution to the problem of vehicle fuel theft. [ABSTRACT FROM AUTHOR]

One of the most important sources of water supply is groundwater. However, the groundwater level (GWL) is significantly impacted by the global climate change. Therefore, under these more severe climate change conditions, the accurate and simple forecast of farmland GWL is a crucial component of agricultural water management. A hybrid model (HM) of Bayesian random forest (BRF), Bayesian support vector machine (BSVM), and Bayesian artificial neural network (BANN) is built in this study. The HM is made up of a Bayesian model averaging (BMA) and three machine learning models: random forest (RF), support vector machine (SVM), and artificial neural network. These three HMs are employed to help automate logical inference and decision‐making in business intelligence for groundwater management. For this purpose, data on 8 separate climatic factors that impact GWL changes in the study area were acquired. Nine distinct farming communities' GWL change data were utilised as the dependent variables for each model fit (community data). The effectiveness of the HM techniques was assessed using the evaluation metrics of mean absolute error (MAE), coefficient of determination (R2), mean absolute percent error (MAPE), and root mean square error (RMSE). The model fit in Suhum had the greatest performance with the highest accuracy (R2 varied from 0.9051 to 0.9679) and the lowest error scores (RMSE ranged from 0.0653 to 0.0727, and MAE ranged from 0.0121 to 0.0541), according to the models' evaluation results. The BRF delivered the greatest results when compared to the two independent HMs, the BSVM and BANN. Future GWL and climatic variable data may be trained using the trained HM techniques to determine the effects of climate change. Farmers, businesses, and civil society organisations might benefit from continuous monitoring of GWL data and education on climate change to help control and prevent excessive deteriorations of global climate change on GWL. [ABSTRACT FROM AUTHOR]

The Landsat mission which has existed over five decades has remained at the forefront of providing consistent moderate spatial and temporal resolution optical images of the earth. The failure of the scan line corrector (SLC) on board the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in May 2003 has permanently resulted in data gaps on each Landsat 7 scene. Due to the obvious negative impacts on the image usability, a number of methods have been developed to fill the no-data areas in the image. This study assessed the performance of four Landsat 7 ETM+ SLC-off gap-filling methods in a highly heterogeneous landscape of West Africa for two different seasons (dry and rainy). The methods considered are: (1) Weighted Linear Regression (WLR) integrated with Laplacian Prior Regularization Method (LPRM), (2) Localised Linear Histogram Matching (LLHM), (3) Neighbourhood Similar Pixel Interpolator (NSPI) and (4) Geostatistical Neighbourhood Similar Pixel Interpolator (GNSPI). All the images used were Landsat 7 ETM+ SLC-off images, temporally close and from the same season for each set of time step. Visual comparison, mean, and standard deviations of the histograms of all bands of only the filled areas were used to assess the results. Additionally, overall accuracy (OA), kappa coefficient (κ), and balanced accuracy (BA) per class were used to evaluate a land use/cover (LULC) classification based on the gap-filled images. Visually, all the four methods were able to completely fill the gaps in the Landsat 7 ETM+ SLC-off image. They all look similar and spatially continuous with no anomalies or artefacts on them. The histograms from each band for only the filled areas for all the four methods also gave similar means and standard deviations in most cases. All the four gap-filling methods provided satisfactory results (OA >96% and κ> 0.937 in all methods for images in the dry season and OA >93% and κ> 0.877 for the image in the rainy season) in the land cover classification considering the complexity of the study area. But the GNSPI was superiority in all cases with the highest OA of 97.1% and κ of 0.947 in the dry season and OA of 94.6% and κ of 0.899 in the rainy season. This implies that the GNSPI is more robust in gap filling of Landsat 7 ETM+ SLC-off images than the other three methods in a heterogeneous landscape of West Africa regardless of the season. This study suggests that gap filling of Landsat 7 ETM+ SLC-off images will help to increase the number of Landsat images needed to build time-series data for a data-scarce region such as West Africa. [ABSTRACT FROM AUTHOR]

This paper proposes an optimization-based Residential Energy Management System (REMS) to minimize not only the customer's energy cost but also customer dissatisfaction with Power Holding Company of Nigeria (PHCN). REMS is designed in such a way that when there is a person in a particular room, electrical power is supplied to that room from its main source, and once the person leaves and the room, power supply is cut off, shutting off all appliances and devices in that room.This is achieved by the usage of a passive infrared sensor (PIR) to detect motion and an ArduinoMicrocontrollerATMEGA-328Pto process input data from the PIR sensors to determine if electrical power is needed in a room or not. AMicrocontroller PIC16F877Ais also usedto carry out all processing activities whose result would be effected through the aid of five output devices with a Liquid Crystal Display (LCD) used to display the PIR and the microprocessor status. The current state of the prototype design is described and a brief introduction to the control approach is made. [ABSTRACT FROM AUTHOR]

Giant bladder diverticula are rare causes of bladder outlet obstruction in children and have rarely been reported.[1] In this paper, we present three children with giant bladder diverticula who presented with bladder outlet obstruction within a year. Micturating cystourethrogram is important for investigating bladder outlet obstruction in children and was used to confirm the diagnosis in all the patients. The relationship between the diverticula and ureters varied, with the ureters running either through the wall of the diverticulum and opening directly into the bladder, or opening into the diverticulum. In one patient, there was a recurrence, which was excised successfully. Excision is important to reduce the risk of recurrence. [ABSTRACT FROM AUTHOR]