Thinkers such as Dr. Kenneth Cooper well aware of the sedentary nature of people, especially women, who are victims of obesity have set out ways and means to provide a solution. They encourage people to enjoy and practice physical activity through a rhythmic music called aerobics.Today, there is a proliferation of fitness centers in Senegal. There is a large female clientele. Some want to lose weight and improve their physical condition. Others go to the gyms to keep their figure.VO2 max and resting heart rate are among some objective criteria to assess physical fitness. However, we found no studies reporting positive effects of aerobics on body composition and cardiorespiratory variables in sedentary Senegalese women.Objective: To study the effects of a 2-month aerobics program on the weight, body mass index, resting heart rate, blood pressure and VO2 max of sedentary Senegalese women aged between 20 and 50.Methods: The above variables in 12 sedentary Senegalese women aged 20-50 years were assessed before and after a 2-month aerobics program consisting of 3 one-hour sessions per week.Results: After 8 weeks of aerobic exercise, among all the parameters studied, the VO2 max, was the only one that underwent a slight reduction which is not statistically significant.Conclusion: 3 weekly one-hour aerobics sessions over 2 months would appear to be insufficient to significantly modify these variables in sedentary Senegalese women aged 20 to 50.

Objective:Toevaluatetheinfluenceofthemenstrualcycleonthephysicalqualitiesofsome INSEPS students. Method: 15 female students participated in our study. Each subject performed the verticalrelaxation, reaction speed, abdominal endurance and back endurance tests on the third day ofmenstruationand the third oneaftercessation of bleeding. Results: On average, the subjects had better vertical expansion after menstruation (45.13cm) than during menstruation (43.33 cm), with no significant difference (p=0.57). In terms ofreactionspeed,thegirlsperformedbetteronaverageduringmenstruationthanafter(111versus147), with no significant difference (p=0.06). The best average performance in abdominalendurance was achieved during menstruation (390.6 repetitions versus 367.73 repetitions) butthis difference was statistically insignificant (p=0.56). In back endurance, the best averageperformanceforgirlswasachievedaftermenstruation(342.67repetitionsversus337.53repetitions).Thedifferencewas statisticallyinsignificant (p=0.92). Conclusion:Thisstudy showsthatthe menstrualcycle doesnothave a significantinfluence on the vertical relaxation, reaction speed, abdominal endurance and back enduranceofthe INSEPS students inour sample.

The Sahara desert is a major global source of dust that is mostly transported southwest over the ocean off West Africa. The presence of this dust impacts the remote sensing of ocean surface properties. These aerosols have absorbing properties that are poorly accounted for in the standard ocean color data processing algorithm. This can result in an overestimation of the atmospheric contribution to the ocean color signal and consequently an underestimation of the oceanic contribution. A two-step algorithm initially applied to the Sea-viewing Wide field-of-view Sensor (SeaWiFS) data was adapted to the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) sensor in the Northwest African region. The Northwest African region is a very productive region, where pelagic resources are an important socio-economic sector. Improving atmospheric correction of ocean color products is, thus, of particular interest for this oceanic region. The two-step approach of classifying the top-of-atmosphere radiance spectra for a better estimate of aerosol type on the one hand, and using an optimization method to fit the parameters of these aerosols and chlorophyll-a concentration (Chla) on the other hand, allows for a better representation of the optical thickness, a correction of the marine reflectance spectrum, and an increase in the spatio-temporal coverage of the area. To the extent that the properties of the water color signal are improved by this data processing, the Chla estimates should also be improved by this approach. However, it is difficult to conclude on this point from the available in situ observations.

Particularly interesting because of its socio-economic contribution, the Canary upwelling system encompasses a number of regions with very special characteristics. The wind that blow over this system induces a permanent upwelling off Mauritania and a seasonal upwelling in the south off Senegal, which boosts the development of phytoplankton. To refine the understanding of the phytoplankton in this region (its distribution, variability, response to physical forcings), we combine a number of tools and methods to arrive at a better estimate, and a better monitoring of the concentration of chlorophyll-a (Chl-a), an input parameter for primary production models. Remote sensing of ocean color has particularly interesting advantages, both in terms of global sampling and data acquisition frequency. This method is all the more interesting since ocean color algorithms can be adapted to reduce bias when standard methods have limitations. The regional ocean color algorithm called SOM-NV (Self-Organized Map-Neuro-variational) offers the advantage of making atmospheric correction in the presence of absorbent aerosols, especially desert dust, which sweeps this area permanently and which compels the standard algorithm to apply a mask when atmospheric optical thickness exceeds a threshold of 0.3. This contribution of SOM-NV in the process of atmospheric correction allowed us to 1 : obtain a better reflectance spectra, and as a consequence offer a better estimate of the Chl-a concentrations ; 2 : acquire a larger number of pixels by processing pixels with an optical thickness greater than 0.3 ; 3 : go beyond the general distribution towards the distribution of dominant groups according to the Physat spectral method. The synthesis of 16 years of data from the MODIS-Aqua sensor, allowed us to revisit the seasonality of Chl-a distribution and its cross-shore particularityand an extension towards the open sea which differs according to the season. The highest coastal values are measured in winter and spring, when upwelling intensifies, while the lowest values are measured in summer, when warm, nutrient-poor equatorial waters freplace upwelling waters along the Senegalese coast. This change in water masses impacts phytoplankton communities. According to the work of some authors, nanoplankton gradually replaces diatoms, known to be present during the upwelling season. This makes this region a particularly interesting zone for monitoring dominant groups of phytoplankton, knowing that the change in community impacts the upper levels of the marine food chain, with phytoplankton playing a leading role. Keywords: Phytoplankton, ocean color, upwelling, atmospheric correction, dust

In a global context of scarcity of water resources, accurate prediction of soil moisture is important for its rational use and management. Soil moisture is included in the list of Essential Climate Variables. Because of the complex soil structure, meteorological parameters and the diversity of vegetation cover, it is not easy to establish a predictive relationship of soil moisture. In this paper, using the large amounts of data obtained in West Africa, we set up a deep neural network to establish an estimation of soil moisture for the two first layers and its prediction temporally and spatially. We construct deep neural network model which predicts soil moisture layer 1 and layer 2 multiple days in the future. Results obtained for accuracy training and test are greater than 93 %. The mean absolute errors are very low and vary between 0,01 to 0,03 m(3)/m(3).

In this article, we evaluate different factors of performance in long-distance running. Anthropometric measurements, biomechanical and kinematic analyzes obtained using treadmill videos and physiological measurements performed under standardized conditions on athletes allowed us to evaluate variables related to the race to explain the performance achieved during international marathons. Fourteen (14) athletes participated. The goal of this work was to enable Senegalese athletes to better prepare and improve their performance in the hope of finally competing with the world elite. At the end of our study, we have highlighted some essential variables that can allow athletes to improve their level of performance. The results demonstrated that a suspension time of stride must be increased from 0.3 to 0.5 and cushioning time to be reduced from 0.35 to 0.15.

Total column water vapor is an important factor for the weather and climate.This study apply deep learning based multiple regression to map the TCWV with elements that can improve spatiotemporal prediction.In this study, we predict the TCWV with the use of ERA5 that is the fifth generation ECMWF atmospheric reanalysis of the global climate.We use an appropriate deep learning based multiple regression algorithm using Keras library to improve nonlinear prediction between Total Column water vapor and predictors as Mean sea level pressure, Surface pressure, Sea surface temperature, 100 metre U wind component, 100 metre V wind component, 10 metre U wind component, 10 metre V wind component, 2 metre dew point temperature, 2 metre temperature.The results obtained permit to build a predictor which modelling TCWV with a mean abs error (MAE) equal to 3.60 kg/m 2 and a coefficient of determination R 2 equal to 0.90.

In this work, we study spatial and temporal atmospherics parameters evolution retrieved by neuro-variationnal method from SeaWiFS observations measured off the west African coast. The SeaWiFS sensor measures the radiance above the top of atmosphere (TOA) solar irradiance. SeaWiFS use standard algorithm to invert the signal in order to retrieve weakly absorbing aerosol optical thickness (AOT) less than 0.3 whereas the Senegalese coasts are frequently crossed by desert dust plumes from large optical thickness. A neural algorithm, so-called SOM-NV, was developed to deal with absorbing aerosols and to retrieve their optical parameters, off the Senegalese coast, from SeaWiFS observations. The impact of meteorological variables on these restitutions was studied over the entire period of the observations that we analyzed and over the whole studied area, on the one hand, but also in a more thorough way on three "sub-area" located in north, south and center. The results obtained showed that the composition of aerosols in the atmosphere is a function of the seasons. High altitude zonal U winds are correlated with non-desert aerosols of -62.16% in winter and autumn. The correlation is -60.32% between dust aerosols and the zonal wind.

Deep learning provide successful applications in many fields. Recently, machines learning are involved for oceans remote sensing applications. In this study, we use and compare about eight (8) deep learning estimators for retrieval of a mainly pigment of phytoplankton. Depending on the water case and the multiple instruments simultaneouslyobserving the earth on a variety of platforms, several algorithm are used to estimate the chlolophyll-a from marine reflectance. By using a long-term multi-sensor time-series of satellite ocean-colour data, as MODIS, SeaWifs, VIIRS, MERIS, etc, we make a unique deep network model able to establish a relationship between sea surface reflectance and chlorophyll-a from any measurement satellite sensor over West Africa. These data fusion take into account the bias between case water and instruments. We construct several chlorophyll-a concentration prediction deep learning based models, compare them and therefore use the best for our study. Results obtained for accuracy training and test are quite good. The mean absolute error are very low and vary between 0,07 to 0,13 mg/m3.

Identifying and characterizing the patient's blood samples is indispensable in diagnostics of malignance suspicious. A painstaking and sometimes subjective task is used in laboratories to manually classify white blood cells. Neural mathematical methods as deep learnings can be very useful in the automated recognition of blood cells. This study uses a particular type of deep learning i.e., convolutional neural networks (CNNs or ConvNets) for image recognition of the four (4) blood cell types (neutrophil, eosinophil, lymphocyte and monocyte) and to enable it to tag them employing a dataset of blood cells with labels for the corresponding cell types. The elements of the database are the input of our CNN and they allowed us to create learning models for the image recognition/classification of the blood cells. We evaluated the recognition performance and outputs learned by the networks in order to implement a neural image recognition model capable of distinguishing polynuclear cells (neutrophil and eosinophil) from those of mononuclear cells (lymphocyte and monocyte). The validation accuracy is 97.77%.

L'objectif de cette etude est de determiner et de predire la capacite de performance du coureur a 10 km des essais en laboratoire et sur le terrain. Les 10 km sont une distance suffisante pour etre un defi, mais ils ne necessitent pas une preparation aussi serieuse et minutieuse qu'une course de marathon. Cela signifie que la fatigue musculaire reste un probleme mineur d’autan plus que l'epuisement de la reserve de glycogene ne se produit pas.L’etude porte sur un nombre de vingt-sept (27) sujets, dont vingt (20) hommes et sept (07) femmes. Grâce a cette etude, nous avons pu evaluer certains des principaux determinants de la performance parmi les facteurs physiologiques, biomecaniques et anthropometriques. La prediction de la performance aerobie chez les coureurs d'endurance entraines utilisant la vitesse aerobie maximale sur une distance de 10 km a ete etablie et des relations mathematiques entre la performance et les parametres physiologiques et anthropometriques ont ete etablies. Les predictions des performances en fonction des parametres physiologiques et anthropometriques sont proches de 75%, voire 80% de correlation. ABSTRACT:The objective of this study is to determine and predict runner performance capability at 10 km from laboratory and field tests. The 10 km is a distance large enough to be a challenge but it does not require preparation as serious and thorough as a marathon race. This means that muscle fatigue remains a lesser problem caused by depletion of the glycogen store does not occur.The study is conducted on subjects of the number of twenty-seven (27) subjects including twenty (20) males and seven (07) females. Through this study, we have been able to evaluate some of the major determinants of performance among physiological, biomechanical and anthropometrical factors. The prediction of aerobic performance in trained endurance runners using the maximum aerobic speed over a distance of 10 km was made and mathematical relationships between performance and physiological and anthropometrical parameters were established. The predictions of the performances according to the physiological and anthropometric parameters are close to 75% or even 80% of correlation.

Aerosol optical thickness (AOT) was provided by SeaWiFS over oceans from October 1997 to December 2010. Weekly, monthly, and annually maps might help scientifics to better understand climate change and its impacts. Making average of several images to get these maps is not suitable on West African coast. A particularity of this area is that it is constantly traversed by desert dust. The algorithm used by SeaWiFS inverts the reflectance measurements to retrieve the aerosol optical thickness at 865 nm. For the poorly absorbing aerosol optical thickness less than 0.35, the standard algorithm works very well. On the west African coast that is often crossed by desert aerosol plumes characterized by high optical thicknesses. In this paper we study the spatial and temporal variability of aerosols on the West African coast during the period from December 1997 to November 2009 by using neural network inversion. The neural network method we used is mixed method of neuro-variational inversion called SOM-NV. It is an evolution of NeuroVaria that is a combination of a variational inversion and multilayer perceptrons, multilayer perceptrons (MLPs). This work also enables validation of the optical thickness retrieved by SOM-NV with AOT in situ measurements collected at AErosol RObotic NETwork (AERONET) stations.