Composition minérale du lait de femme en milieu rural au Cameroun : apports en minéraux chez le nourrisson de un à neuf mois

La composition minérale (P, Ca, Mg, Fe, Zn) de 357 échantillons de lait collectés au cours des neuf premiers mois de lactation a été déterminée au cours d'une enquête longitudinale chez 27 femmes primipares allaitantes. La concentration en calcium et en zinc ne varie pas au cours de la journée. Il existe une baisse significative, au cours de la lactation, du contenu du lait en phosphore et en calcium; la chute est moins sensible pour le magnésium, le fer et le zinc. Ainsi entre le premier et le dernier trimestre de lactation, les teneurs en phosphore, calcium, magnésium sont respectivement de 12.4-11.4, 20.7-17.1 et 2.53-2.0 mg/100 ml; celles en fer et en zinc, 65.1-54.8 et 283.7-233.1 microgr/100 ml. Des relations entre l'état nutritionnel des mères allaitantes, apprécié par le pourcentage du standard de Harvard du rapport poids-taille, et le contenu minéral de leur lait ont été démontrées. Les carences d'apports journaliers en ces différents minéraux sont importantes au cours de la lactation chez le nourrisson alimenté exclusivement au lait maternel. (Résumé d'auteur

Optimization and Prediction of Ultimate Tensile of TIG Mild Steel Welds Using ANN

TIG welding, is about the most popular welding method, which finds its applications in  the fabrication industry. The integrity and service life of engineering structures is a very important factor in the welding technology sector, one of the problem facing the fabrication industry is the control of the process input parameters to obtain a good welded joint . Research has shown that one of the practical ways to improving on weld qualities is to optimize the input process parameters. The aim of this study is to predict the ultimate tensile strength of TIG mild steel welds using ANN.In this study, twenty experimental runs were carried out, each experimental run comprising the current, voltage and gas flow rate, the TIG welding process was used to join two pieces of mild steel plates measuring 60 x40 x10 mm , the tensile strength was measured respectively. Thereafter the data collected from the experimental results was analysed with the ANN. The experimental results for the ultimate tensile strength was analyzed with the Artificial Neural Networks. The best validation performance is 0.48429 and occurred at epoch five (5). The R-value (coefficient of correlation ) for training shows of 99.9%closeness ,99.4% for validation and 89.8% for  testing respectively. The overall R-value is shown to be 98.7%. For ultimate tensile strength, both the artificial neural network and the Response surface methodology models fit well. However the RSM model Provided a better overall fit to the experimental data than the ANN.

Essays in empirical asset pricing

Cette thèse comprend trois chapitres dans lesquels sont développés des outils de comparaison et d’analyse dynamique des modèles linéaires d’évaluation d’actifs. Dans le premier chapitre, j’introduis la notion de facteurs inutiles par intermittence; il s’agit de facteurs dont la pertinence n’est pas figée dans le temps (parfois utiles, parfois inutiles). Sous ce nouveau cadre théorique, je développe une méthode d’inférence sur les primes de risque. A chaque période, ma méthode permet d’estimer de façon consistante la prime de risque des facteurs utiles, tout en étant robuste à la présence de facteurs inutiles par intermittence. Empiriquement, j’analyse le modèle Fama-French à cinq facteurs. Il apparait qu’à l’exception du marché, tous les facteurs de ce modèle sont inutiles par intermittence, même s’ils demeurent pertinents 90 pourcent du temps. Dans le second chapitre, je développe une méthode d’inférence sur les paramètres dynamiques d’un facteur d’actualisation stochastique (SDF) mal spécifié. J’étends au cadre des SDF conditionnels, l’analyse de Gospodinov, Kan & Robotti (2014); les coefficients et les covariances varient ici dans le temps. Cette nouvelle méthode permet d’éliminer les effets négatifs des facteurs inutiles, et de restaurer la pertinence des facteurs importants, le tout en étant robuste aux erreurs de spécification du modèle. Empiriquement, j’analyse l’évolution de 1963 à 2016, de la pertinence de certains modèles d’évaluation d’actifs. Il apparait que les modèles Fama-French à trois et à cinq facteurs sont les deux meilleurs modèles sur les 50 dernières années. Cependant depuis 2000, le meilleur modèle est le modèle à quatre facteurs de Carhart, suivi du modèle Fama-French à cinq facteurs. Une analyse des modèles possédant des facteurs non échangeables sur les marchés montre que certains de ces facteurs possèdent aussi un pouvoir explicatif sur les rendements observés. La pertinence d’un modèle à capital humain, inspiré de Lettau & Ludvigson (2001) et Gospodinov et al. (2014), est à ce propos mise en évidence. Le troisième chapitre propose une méthode de classification des modèles Fama-French, en fonction du niveau de préférence des investisseurs pour les moments d’ordre élevé. Les résultats indiquent que l’ajout de facteurs comme stratégie d’amélioration des performances des modèles d’évaluation, n’est efficace que lorsque les investisseurs ont un niveau de préférence assez faible pour les moments d’ordre élevé. Lorsque la préférence pour ces moments devient importante, le modèle à quatre facteurs de Carhart (1997) est plus performant que tous les modèles Fama-French. Les résultats indiquent par ailleurs que le modèle à capital humain analysé dans le deuxième chapitre possède un pouvoir explicatif sur les rendements observés, uniquement pour les investisseurs dont le niveau de préférence pour les moments d’ordre élevé est nul ou très faible.This thesis has three chapters in which I develop tools for comparisons and dynamic analysis of linear asset pricing models. In the first chapter, I introduce the notion of dynamically useless factors: factors that may be useless (uncorrelated with the assets returns) at some periods of time, while relevant at other periods of time. This notion bridges the literature on classical empirical asset pricing and the literature on useless factors, where both assume that the relevance of a factor remains constant through time. In this new framework, I propose a modified Fama-Macbeth procedure to estimate the time-varying risk premia from conditional linear asset pricing models. At each date, my estimator consistently estimates the conditional risk premium for every useful factor and is robust to the presence of the dynamically useless ones. I apply this methodology to the Fama-French five-factor model and find that, with the exception of the market, all the factors of this model are dynamically useless, although they remain useful 90 percent of the time. In the second chapter, I infer the time-varying parameters of a potentially misspecified stochastic discount factor (SDF) model. I extend the model of Gospodinov et al. (2014) to the framework of conditional SDF models, as the coefficients and the covariances are allowed to vary over time. The proposed misspecification-robust inference is able to eliminate the negative effects of potential useless factors, while maintaining the relevance of the useful ones. Empirically, I analyze the dynamical relevance of each factor in seven common asset pricing models from 1963 to 2016. The Fama-French’s three-factor model (FF3) and five factor model (FF5) have been the overall best SDFs in the last 50 years. However, since 2000, the best SDF is CARH (FF3 + momentum factor), followed by FF5 as the second best. Apart from traded factors, the results bring a nuance on non-traded factors. We analyze the relevance, for linear pricing, of a human capital model inspired by Lettau & Ludvigson (2001) and Gospodinov et al. (2014). The third chapter proposes a method for ranking Fama-French linear factor models according to investors’ preference for higher-order moments. I show that adding a new Fama- French factor to a prior Fama-French model systematically leads to a better model, only when the preference for higher-order moments is moderate (in absolute value). When the preference for higher-order moments is important or extreme, the four-factor model of Carhart (1997) has a better pricing ability than all the Fama-French models. An analysis of models with non-traded factors confirms the relevance, for linear pricing, of the human capital model analyzed in the second chapter. However, I show that this relevance is effective only for investors with null or very low preferences for higher-order moments

Prediction of Impact Energy of TIG Mild Steel Welds Using ANN

The present trend in the fabrication industries is the use of automated welding processes to obtain high production rates and high quality output.TIG welding, happens to be the best welding method employed in  the manufacturing industry. one of the problem facing the fabrication industry is the control of the process input parameters to obtain a good welded joint . however it is essential to establish the relationship between process parameters and weld quality output to predict and control weld bead quality .The aim of this study is to predict the impact energy of TIG mild steel welds using ANN.In this study, twenty experimental runs were carried out, each experimental run comprising the current, voltage and gas flow rate, the TIG welding process was used to join two pieces of mild steel plates measuring 60 x40 x10 mm , the impact energy was measured respectively. Thereafter the data collected from the experimental results was analysed with the ANN. The experimental results for the impact energy was analyzed with the Artificial Neural Networks. The overall R-value is shown to be 98.7%.  The best validation performance is 0.48429 and occurred at epoch five (5). The coefficient of correlation for training shows of 99.9%closeness ,99.4% for validation and 89.8% for  testing respectively

A Model of Talent Management in a Faith-Based Institution: An Appreciative Inquiry

Higher Educational Institutions (HEI) struggle to attract and retain talented employees to remain competitive. An appreciative inquiry using theory of attractive quality was conducted to develop a Talent Management Model (TM) to attract and retain talents. The themes that emerged included transparency in recruitment, workforce diversity, institutional mission, values and image, and work environment

Controlled-release of curcumin from poly(lactide-co-glycolide) acid/albumin/curcumin and silica/albumin/curcumin drug-delivery systems

In drug-delivery systems, the drug carriers should meet several prerequisites such as biocompatibility, biodegradability and lack of immune system activation, in order to play an effective role. In this study, a comprehensive attempt has been carried out to investigate the plausible intermolecular interactions of new drug-delivery systems, by correlating the drug release kinetic with the different types of carriers used. A hydrophilic metal oxide, silica (SiO2), was used as the inorganic carrier, while poly(lactide-co-glycolide) (PLGA), a hydrophobic polyester, was used as the organic carrier. Based on these materials, the designed drug-delivery systems were SiO2/albumin/curcumin (SiO2/Alb/Cur) and PLGA/albumin/curcumin (PLGA/Alb/Cur), where albumin was used as the co-carrier, while curcumin as the hydrophobic model drug. The release of curcumin was proved to be controlled by the addition of albumin in the systems. It was expected that by using different kinds of carriers, different drug release patterns will be obtained, since the properties of the carriers can then influence the intermolecular interactions within the systems. Thus, the study of the intermolecular interaction of SiO2/Alb/Cur systems was carried out by varying SiO2 and albumin composition, and using different sources of SiO2. Besides that, the study of the intermolecular interaction of PLGA/Alb/Cur was also done using different pretreatment methods and dispersion media of PLGA. The release experiments of albumin and curcumin were conducted via in-vitro procedures and phosphate buffer solution (pH 7) was used as the medium. The amounts of albumin and curcumin desorbed from the systems at different time intervals were monitored by UV-Visible spectroscopy (UV-Vis). The samples were characterized using diffuse reflectance UV-visible (DR-UV) spectroscopy, Fourier transform infrared spectroscopy (FTIR), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), specific surface area analysis and differential scanning calorimetry (DSC). The in-vitro studies show that the release of albumin and curcumin from SiO2/Alb/Cur system is dependent on the compositions of SiO2 and albumin, and the source of SiO2 used (tetraethoxysilane (TEOS) and fumed silica). The release of albumin and curcumin was correlated with the intermolecular interaction between SiO2, albumin, and curcumin. The addition of albumin as the co-carrier caused an increase in the total cumulative release amount of curcumin, suggesting that there was a competition between albumin and curcumin to interact with either SiO2 or PLGA. Here, it was demonstrated that the amount of curcumin released was strongly affected by the carriers used. The use of SiO2 as the carrier showed that release of curcumin followed pseudo-second order kinetics, while the use of PLGA showed a first-order kinetic at 49 h. It is concluded that a sustained and controlled drug release system can be achieved by using SiO2 as the carrier. The different strategies and intermolecular interactions described here may be useful in designing a sustainable and controlled drug release system that can meet the medical demands of pharmaceutical applications

Transitioning to Online Teaching in an Academy During COVID-19 Pandemic: A Case Study.

Online teaching and learning have been a steady trend in distance education as an alternative to face-to-face teaching methods in the recent past. The worldwide pandemic outbreak of novel coronavirus has forced educational institutions to resort to online teaching and learning mode. Consequently, schools that had not implemented online teaching before, needed to rethink transitioning from face-to-face to online learning mode. A qualitative case study research design was used to explore and describe the transitioning experience from face-to-face to online teaching and learning during covid-19 Pandemic at an academy in the Philippines. Purposive sampling with maximum variation was used to select the participants for the study. There were eight participants that included school administrators, teachers, school children and their parents. Prior to data collection, the study sought for approval from Ethics Review Board, the school administration, and informed consent from the participants and the parents of the participating children. The data were collected through audio and video recorded interviews, observations, and document review. The data were transcribed and analyzed using HyperResearch software to obtain codes, categories and themes. The findings show the following themes: Leadership experiences, preparations needed, teacher experiences, online learning platforms utilized, and the challenges. The study has implications on adequate teacher preparation and training on online teaching, curriculum design and development, and classroom management that requires new paradigm. Further studies should focus on post pandemic teaching and learning implications

Prediction of tungsten inert gas welding process parameter using design of experiment and fuzzy logic

The focus of this study is to predict tungsten inert gas (TIG) welding process parameter such as heat input for stabilizing heat and removing post weld crack formation in mild steel weldment. The main input parameters examined are the welding current, voltage and speed whereas the measured (response) parameter is heat input. Statistical design of experiment was done by means of central composite design method using the range and levels of independent variables. The experiment was carried out 20 times (with 5 specimens per run) using 60 mm x 40 mm x 10 mm mild steel coupons. The plate samples were cut longitudinally with a Single-V joint preparation, with the edges beveled. The welding process utilizes 100% pure argon as a protecting gas to shield the weld specimen from external interaction. The interaction between the input and response variables was analyzed using a fuzzy logic system. The result showed that for a welding current, voltage and speed of 190 A, 21 V, and 2.0 mm/s respectively, the predicted heat input was 0.912 kJ/mm whereas for input parameters of (170 A, 25 V, and 2.0 mm/s) and (180 A, 23 V, 0.98 mm/s), the predicted heat inputs were 1.07 kJ/mm and 1.380 kJ/mm, respectively

Optimization of the Tungsten Inert Gas Process Parameters using Response Surface Methodology

Optimization is a very important techniques applied in the manufacturing industry that utilizes mathematical and artificial intelligence methods. The complexity associated with most optimization techniques have resulted to search for new ones. This search has led to the emergence of response surface methodology (RSM). The paper aims to optimize tungsten inert gas process parameters required to eliminate post-weld crack formation and stabilize heat input in mild steel weldment using RSM. The main input variables considered are voltage, current and speed whereas the response parameter is Brinell hardness number (BHN). The statistical design of experiment was done using the central composite design technique. The experiment was implemented 20 times with 5 specimens per experiment. The responses were measured, recorded and optimized using RSM. From the results, it was observed that a voltage of 21.95 V, current of 190.0 A, and welding speed of 5.00 mm/s produced a weld material with the following optimal properties; BHN (200.959 HAZ), heat input (1.69076 kJ/mm), cooling rate (72.07 /s), preheat temperature (150.68 ) and amount of diffusible hydrogen (12.36 mL/100g). The optimal solution was selected by design expert with a desirability value of 95.40 %

Optimization of Impact Energy of TIG Mild Steel Welds Using RSM

TIG welding, is about the most popular welding method, which finds its applications in the fabrication industry. The integrity and service life of engineering structures is a very important factor in the welding technology sector, one of the problem facing the fabrication industry is the control of the process input parameters to obtain a good welded joint. Research has shown that one of the practical ways to improving on weld qualities is to optimize the input process parameters. The aim of this study is to optimize the impact energy of TIG mild steel welds using RSM with the purpose of achieving the highest impact energy.In this study, twenty experimental runs were carried out, each experimental run comprising the current, voltage and gas flow rate, the TIG welding process was used to join two pieces of mild steel plates measuring 60 x40 x10 mm, the tensile strength was measured respectively. Thereafter the data collected from the experimental results was analysed with the RSM Analysis of variance (ANOVA) a p-value of 0.0001 which is <0.005 indicates that the model is significant. To validate the significance and adequacy of the model based on its ability to optimize the ultimate impact energythe goodness of fit statistics showns that the model posses an R2 value of 0.705989 and R2 adjusted of 0.537617a noise to signal ratio of 7.89717 was realized, a ratio greater tha 4 is desired  indicating thatt the model possesed adequate signal  to predict the target response.the result shows that a combination of current 90 amps,voltage 22volts and gas flow rate 13lit/min will produce an optimum UTS of 381Mpa and impact energyof 116.6898J  with a desiribility value of 0.889