Potential use of forage-legume intercropping technologies to adapt to climate-change impacts on mixed crop-livestock systems in Africa: a review

Regional Environmental Change - This paper summarizes effects of forage-legume intercropping on grain and fodder yield, land equivalent ratio, residual soil fertility, disease and insect pest...     

Planning STI/HIV prevention among refugees and mobile populations: situation assessment of Sudanese refugees

This article reflects an investigation of knowledge, attitudes and behaviours and HIV/STI prevalence of Sudanese refugees and Ethiopian sex workers in 1992. It represents one of the earliest such investigations within an African refugee population. The investigation took place in the Dimma refugee settlement in south-western Ethiopia and study participants included Sudanese refugee men and women and Ethiopian female sex workers. Methods used for this investigation included focus group discussions, behavioural surveys and serologic testing. The main outcome measures of the investigation were HIV/STI knowledge, attitudes and behaviours and biological markers for HIV, syphilis and herpes simplex 2. The study findings indicate that in the early 1990s, knowledge about AIDS and condom use was low among Sudanese refugee women and not one reported having ever used a condom. Furthermore, sexual contact between refugee men and sex workers was frequent during the time of this study and the prevalence of HIV and other STIs was high. The results confirm a widely held assumption that highly mobile and transient populations in Africa are susceptible to STIs and HIV, in large part due to their knowledge, attitudes and behaviours.     

Contributions of adsorption,bioreduction and desorption to uranium immobilization by extracellular polymeric substances

● EPS immobilizes U(VI) via adsorption, bioreduction and desorption. ● This work provides a framework to quantify the three immobilization processes. ● The non-equilibrium adsorption of U follows pseudo-second-order kinetics. ● The equilibrium adsorption of U followed Langmuir and Freundlich isotherms. Hexavalent uranium (U(VI)) can be immobilized by various microbes. The role of extracellular polymeric substances (EPS) in U(VI) immobilization has not been quantified. This work provides a model framework to quantify the contributions of three processes involved in EPS-mediated U(VI) immobilization: adsorption, bioreduction and desorption. Loosely associated EPS was extracted from a pure bacterial strain, Klebsiella sp. J1, and then exposed to H₂ and O₂ (no bioreduction control) to immobilize U(VI) in batch experiments. U(VI) immobilization was faster when exposed to H₂ than O₂ and stabilized at 94% for H₂ and 85% for O₂, respectively. The non-equilibrium data from the H₂ experiments were best simulated by a kinetic model consisting of pseudo-second-order adsorption (k_a = 2.87 × 10⁻³ g EPS·(mg U)⁻¹·min⁻¹), first-order bioreduction (k_b = 0.112 min⁻¹) and first-order desorption (k_d = 7.00 × 10⁻³ min⁻¹) and fitted the experimental data with R² of 0.999. While adsorption was dominant in the first minute of the experiments with H₂, bioreduction was dominant from the second minute to the 50^th min. After 50 min, adsorption was negligible, and bioreduction was balanced by desorption. This work also provides the first set of equilibrium data for U(VI) adsorption by EPS alone. The equilibrium experiments with O₂ were well simulated by both the Langmuir isotherm and the Freundlich isotherm, suggesting multiple mechanisms involved in the interactions between U(VI) and EPS. The thermodynamic study indicated that the adsorption of U(VI) onto EPS was endothermic, spontaneous and favorable at higher temperatures.     

Risk analysis for oil & gas pipelines: A sustainability assessment approach using fuzzy based bow-tie analysis

Vast amounts of oil & gas (O&G) are consumed around the world everyday that are mainly transported and distributed through pipelines. Only in Canada, the total length of O&G pipelines is approximately 100,000 km, which is the third largest in the world. Integrity of these pipelines is of primary interest to O&G companies, consultants, governmental agencies, consumers and other stakeholder due to adverse consequences and heavy financial losses in case of system failure. Fault tree analysis (FTA) and event tree analysis (ETA) are two graphical techniques used to perform risk analysis, where FTA represents causes (likelihood) and ETA represents consequences of a failure event. ‘Bow-tie’ is an approach that integrates a fault tree (on the left side) and an event tree (on the right side) to represent causes, threat (hazards) and consequences in a common platform. Traditional ‘bow-tie’ approach is not able to characterize model uncertainty that arises due to assumption of independence among different risk events. In this paper, in order to deal with vagueness of the data, the fuzzy logic is employed to derive fuzzy probabilities (likelihood) of basic events in fault tree and to estimate fuzzy probabilities (likelihood) of output event consequences. The study also explores how interdependencies among various factors might influence analysis results and introduces fuzzy utility value (FUV) to perform risk assessment for natural gas pipelines using triple bottom line (TBL) sustainability criteria, namely, social, environmental and economical consequences. The present study aims to help owners of transmission and distribution pipeline companies in risk management and decision-making to consider multi-dimensional consequences that may arise from pipeline failures. The research results can help professionals to decide whether and where to take preventive or corrective actions and help informed decision-making in the risk management process. A simple example is used to demonstrate the proposed approach.     

Predicting copper concentrations in acid mine drainage: a comparative analysis of five machine learning techniques

Acid mine drainage (AMD) is a global problem that may have serious human health and environmental implications. Laboratory and field tests are commonly used for predicting AMD, however, this is challenging since its formation varies from site-to-site for a number of reasons. Furthermore, these tests are often conducted at small-scale over a short period of time. Subsequently, extrapolation of these results into large-scale setting of mine sites introduce huge uncertainties for decision-makers. This study presents machine learning techniques to develop models to predict AMD quality using historical monitoring data of a mine site. The machine learning techniques explored in this study include artificial neural networks (ANN), support vector machine with polynomial (SVM-Poly) and radial base function (SVM-RBF) kernels, model tree (M5P), and K-nearest neighbors (K-NN). Input variables (physico-chemical parameters) that influence drainage dynamics are identified and used to develop models to predict copper concentrations. For these selected techniques, the predictive accuracy and uncertainty were evaluated based on different statistical measures. The results showed that SVM-Poly performed best, followed by the SVM-RBF, ANN, M5P, and KNN techniques. Overall, this study demonstrates that the machine learning techniques are promising tools for predicting AMD quality.