Influence of Molding Water Content on Shear Strength Characteristic of Compacted Cement Kiln Dust Treated Lateritic Soils for Liners and Covers

A laboratory investigation was carried out to determine the shear strength characteristics of compacted cement kiln dust treated lateritic soils for use in liners and covers with up to 12.5% cement kiln dust by dry weight of soil. Specimens were prepared at molding water contents of -2, 0, +2 and +4% of the optimum moisture content at the compactive energy levels of the British standard light (BSL), West African Standard (WAS) or “intermediate” and British standard heavy (BSH). Specimens treated with cement kiln dust met the minimum UCS value of 200 kN/m2 requirement. Generally, the recorded UCS values for lateritic soil – cement kiln dust mixtures produced satisfactory UCS values for all the compactive energy levels considered. However, due to the unavailability of water to meet the hydration demand of the  cement kiln dust pozolana  no significant improvement in UCS values was recorded beyond 2.5% for cement kiln dust treated specimens compacted at BSH energy level and especially on the dry side of optimum moisture content.http://dx.doi.org/10.4314/njt.v34i2.

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

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

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

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

A national survey of medical education fellowships

Purpose: The purpose of our study was to determine the prevalence, focus, time commitment, graduation requirements and programme evaluation methods of medical education fellowships throughout the United States. Medical education fellowships are defined as a single cohort of medical teaching faculty who participate in an extended faculty development programme. Methods: A 26-item online questionnaire was distributed to all US medical schools (n=127) in 2005 and 2006. The questionnaire asked each school if it had a medical education fellowship and the characteristics of the fellowship programme. Results: Almost half (n=55) of the participating schools (n=120, response rate 94.5 %) reported having fellowships. Duration (10–584 hours) and length (<1 month–48 months) varied; most focused on teaching skills, scholarly dissemination and curriculum design, and required the completion of a scholarly project. A majority collected participant satisfaction; few used other programme evaluation strategies. Conclusions: The number of medical education fellowships increased rapidly during the 1990s and 2000s. Across the US, programmes are similar in participant characteristics and curricular focus but unique in completion requirements. Fellowships collect limited programme evaluation data, indicating a need for better outcome data. These results provide benchmark data for those implementing or revising existing medical education fellowships

High-resolution spatiotemporal measurement of air and environmental noise pollution in sub-Saharan African cities: Pathways to Equitable Health Cities Study protocol for Accra, Ghana

Introduction: Air and noise pollution are emerging environmental health hazards in African cities, with potentially complex spatial and temporal patterns. Limited local data is a barrier to the formulation and evaluation of policies to reduce air and noise pollution. Methods and analysis: We designed a year-long measurement campaign to characterize air and noise pollution and their sources at high-resolution within the Greater Accra Metropolitan Area, Ghana. Our design utilizes a combination of fixed (year-long, n = 10) and rotating (week-long, n = ~130) sites, selected to represent a range of land uses and source influences (e.g. background, road-traffic, commercial, industrial, and residential areas, and various neighbourhood socioeconomic classes). We will collect data on fine particulate matter (PM2.5), nitrogen oxides (NOx), weather variables, sound (noise level and audio) along with street-level time-lapse images. We deploy low-cost, low-power, lightweight monitoring devices that are robust, socially unobtrusive, and able to function in the Sub-Saharan African (SSA) climate. We will use state-of-the-art methods, including spatial statistics, deep/machine learning, and processed-based emissions modelling, to capture highly resolved temporal and spatial variations in pollution levels across Accra and to identify their potential sources. This protocol can serve as a prototype for other SSA cities. Ethics and dissemination: This environmental study was deemed exempt from full ethics review at Imperial College London and the University of Massachusetts Amherst; it was approved by the University of Ghana Ethics Committee. This protocol is designed to be implementable in SSA cities to map environmental pollution to inform urban planning decisions to reduce health harming exposures to air and noise pollution. It will be disseminated through local stakeholder engagement (public and private sectors), peer-reviewed publications, contribution to policy documents, media, and conference presentations

Outcome Assessment of a Dedicated HIV Positive Health Care Worker Clinic at a Central Hospital in Malawi: A Retrospective Observational Study

BACKGROUND: Malawi has one of the world's lowest densities of Health Care Workers (HCW) per capita. This study evaluates outcomes of a dedicated HCW HIV clinic in Malawi, created at Zomba Central Hospital in January 2007. METHODS AND FINDINGS: Retrospective cohort data was analyzed comparing HCW clinic patient baseline characteristics and treatment outcomes at 18 months after inception, against those attending the general HIV clinic. In-depth interviews and focus group discussions were conducted to explore perceptions of patients and caregivers regarding program value, level of awareness and barriers for uptake amongst HCW. 306 patients were enrolled on antiretroviral therapy (ART) in the HCW HIV clinic, 6784 in the general clinic. Significantly (p<0.01) more HCW clients were initiated on ART on the basis of CD4 as opposed to WHO Stage 3/4 (36% vs.23%). Significantly fewer HCW clients defaulted (6% vs.17%), and died (4% vs.12%). The dedicated HCW HIV clinic was perceived as important and convenient in terms of reduced waiting times, and prompt and high quality care. Improved confidentiality was an appreciated quality of the HCW clinic however barriers included fear of being recognized. CONCLUSIONS/SIGNIFICANCE: Outcomes at the HCW clinic appear better compared to the general HIV clinic. The strategy of dedicated clinics to care for health providers is a means of HIV impact mitigation within human resource constrained health systems in high prevalence settings

Reduced total energy expenditure and physical activity in cachectic patients with pancreatic cancer can be modulated by an energy and protein dense oral supplement enriched with n-3 fatty acids

The aim of the study was to assess the total energy expenditure (TEE), resting energy expenditure (REE) and physical activity level (PAL) in home-living cachectic patients with advanced pancreatic cancer. The influence of an energy and protein dense oral supplement either enriched with or without the n-3 fatty acid eicosapentaenoic acid (EPA) and administered over an 8-week period was also determined. In total, 24 patients were studied at baseline. The total energy expenditure was measured using doubly labelled water and REE determined by indirect calorimetry. Patients were studied at baseline and then randomised to either oral nutritional supplement. Measurements were repeated at 8 weeks. At baseline, REE was increased compared with predicted values for healthy individuals (1387(42) vs 1268(32) kcal day-1, P=0.001), but TEE (1732(82) vs 1903(48) kcal day-1, P=0.023) and PAL (1.24(0.04) vs 1.50) were reduced. After 8 weeks, the REE, TEE and PAL of patients who received the control supplement did not change significantly. In contrast, although REE did not change, TEE and PAL increased significantly in those who received the n-3 (EPA) enriched supplement. In summary, patients with advanced pancreatic cancer were hypermetabolic. However, TEE was reduced and this was secondary to a reduction in physical activity. The control energy and protein dense oral supplement did not influence the physical activity component of TEE. In contrast, administration of the supplement enriched with EPA was associated with an increase in physical activity, which may reflect improved quality of life

Rapid tests and urine sampling techniques for the diagnosis of urinary tract infection (UTI) in children under five years: a systematic review

Background: Urinary tract infection (UTI) is one of the most common sources of infection in children under five. Prompt diagnosis and treatment is important to reduce the risk of renal scarring. Rapid, cost-effective, methods of UTI diagnosis are required as an alternative to culture. Methods: We conducted a systematic review to determine the diagnostic accuracy of rapid tests for detecting UTI in children under five years of age. Results: The evidence supports the use of dipstick positive for both leukocyte esterase and nitrite (pooled LR+ = 28.2, 95% CI: 17.3, 46.0) or microscopy positive for both pyuria and bacteriuria (pooled LR+ = 37.0, 95% CI: 11.0, 125.9) to rule in UTI. Similarly dipstick negative for both LE and nitrite (Pooled LR- = 0.20, 95% CI: 0.16, 0.26) or microscopy negative for both pyuria and bacteriuria (Pooled LR- = 0.11, 95% CI: 0.05, 0.23) can be used to rule out UTI. A test for glucose showed promise in potty-trained children. However, all studies were over 30 years old. Further evaluation of this test may be useful. Conclusion: Dipstick negative for both LE and nitrite or microscopic analysis negative for both pyuria and bacteriuria of a clean voided urine, bag, or nappy/pad specimen may reasonably be used to rule out UTI. These patients can then reasonably be excluded from further investigation, without the need for confirmatory culture. Similarly, combinations of positive tests could be used to rule in UTI, and trigger further investigation