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Upwelling processes bring nutrient-rich waters from the deep ocean to the surface. Areas of upwelling are often associated with high productivity, offering great economic value in terms of fisheries. The sensitivity of spring/summer-time coastal upwelling systems to climate change has recently received a lot of attention. Several studies have suggested that their intensity may increase in the future while other authors have shown decreasing intensity in their equatorward portions. Yet, recent observations do not show robust evidence of this intensification. The Senegalo-Mauritanian upwelling system (SMUS) located at the southern edge of the north Atlantic system (12°N–20°N) and most active in winter/spring has been largely excluded from these studies. Here, the seasonal cycle of the SMUS and its response to climate change is investigated in the database of the Coupled Models Inter comparison Project Phase 5 (CMIP5). Upwelling magnitude and surface signature are characterized by several sea surface temperature and wind stress indices. We highlight the ability of the climate models to reproduce the system, as well as their biases. The simulations suggest that the intensity of the SMUS winter/spring upwelling will moderately decrease in the future, primarily because of a reduction of the wind forcing linked to a northward shift of Azores anticyclone and a more regional modulation of the low pressures found over Northwest Africa. The implications of such an upwelling reduction on the ecosystems and local communities exploiting them remains very uncertain.

1 Faculty of Science and Technology, University Cheikh Anta Diop, Dakar-Senegal 2 Mining School of Niamey-Niger 3 Ecole Supeieure polytechnique de Daar-Senegal 4 Ecole Polytechnique of Thies, EPT, Thies, Senegal 5 Faculty of Science and Technology, University Cheikh Anta Diop, Dakar-Senegal 6 Ecole Supeieure polytechnique de Daar-Senegal 7 Faculty of Science and Technology, University Cheikh Anta Diop, Dakar-Senegal

Abstract In Senegal, soil is used in construction with a misunderstanding of the thermal and mechanical properties. The lifespan of building considerably depends of the mechanical properties of construction materials used. The thermal properties of construction materials have considerable influence on the energy performance of the building. This work deals with the determination of thermal and mechanical properties of soil bricks stabilized with cement produced by five handicraft manufacturers. For the mechanical characterization, we determine the compressive strength of bricks using a mechanical press. The thermal characterization is to determine thermal capacity and thermal conductivity of bricks by asymmetrical transient Hot Plate method. The results show that vibrated bricks have a compressive strength of about 1.6 MPa and a thermal conductivity of about 0.8 W.m-1.K-1. The compressed bricks have better compression strength of about 2 MPa and a thermal conductivity of about 0.7 W.m-1.K-1.

The theoretical study of chrysanthemin (cyanidin 3-glucoside) as a pigment for TiO2-based dye-sensitized solar cells (DSSCs) was performed with the GAUSSSIAN 09 simulation. The electronic spectra of neutral and anionic chrysanthemin molecules were calculated by density functional theory with B3LYP functional and DGDZVP basis set. A better energy level alignment was found for partially deprotonated molecules of chrysanthemin, with the excited photoelectron having enough energy in order to be transferred to the conduction band of TiO2 semiconductor in DSSCs. In addition, we used the raw aqueous extracts of roselle (Hibiscus sabdariffa) calyces as the source of chrysanthemin and the extracts with various pH values were tested in DSSCs. The extracts and photosensitized semiconductor layers were characterized by UV-Vis spectroscopy, and DSSCs based on raw extracts were characterized by current density-voltage measurements.

A theoretical and experimental study of a conventional boost converter is presented. Based on the real behavior of the components, the conventional boost converter model dealing with both inductive and capacitive losses as well as switching losses is introduced. From this model, the detailed analytical expressions of the voltage gain factor and the conversion efficiency are established taking into account the losses due to parasitic resistances and switching losses. The behavior of the converter is then analyzed by simulating the voltage gain factor and the conversion efficiency as a function of the duty cycle. The converter prototype was manufactured and a set of experimental measurements was made; these measurements made it possible to demonstrate that the proposed theoretical models were reliable for a large range of duty cycle for the boost converter.

AbstractThe aim of this study is to highlight the recent progress in mapping vector‐borne diseases in West Africa using modelling and field experiments. Based on climatic indicators, methods have been developed to map Rift Valley fever (RVF) and malaria risk. Modelling results corroborate that northern Senegal and southern Mauritania appear to be critical areas for RVF outbreaks and that the malaria epidemic fringe is located at the northern edge of the Sahel. Future projections highlight that the malaria risk decreases over northern Sahel. This is related to a southward shift of the potential epidemic belt in autumn. Copyright © 2010 Royal Meteorological Society

The quality of feedstock used in base oil processing depends on the source of the crude oil. Moreover, the refinery is fed with various blends of crude oil to meet the demand of the refining products. These circumstances have caused changes of quality of the feedstock for the base oil production. Often the feedstock properties deviate from the original properties measured during the process design phase. To recalculate and remodel using first principal approaches requires significant costs due to the detailed material characterizations and several pilot-plant runs requirements. To perform all material characterization and pilot plant runs every time the refinery receives a different blend of crude oil will simply multiply the costs. Due to economic reasons, only selected lab characterizations are performed, and the base oil processing plant is operated reactively based on the feedback of the lab analysis of the base oil product. However, this reactive method leads to loss in production for several hours because of the residence time as well as time required to perform the lab analysis. Hence in this paper, an alternative method is studied to minimize the production loss by reacting proactively utilizing machine learning algorithms. Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR) and Extreme Gradient Boosting (XGBoost) models are developed and studied using historical data of the plant to predict the base oil product kinematic viscosity and viscosity index based on the feedstock qualities and the process operating conditions. The XGBoost model shows the most optimal and consistent performance during validation and a 6.5 months plant testing period. Subsequent deployment at our plant facility and product recovery analysis have shown that the prediction model has facilitated in reducing the production recovery period during product transition by 40%.