Nutrition status among Children under 5 years in Ibalikoma Village Apac District. A Cross-sectional Study.

Abstract The study was conducted in Ibalikoma Village Apac District to assess the factors influencing the nutritional status of children under 5 years.  Objectives The objectives of the study were to assess the level of knowledge, practice, and maternal-related factors influencing nutrition status among children under 5 years in Ibalikoma village Apac district. Methodology The study design was a descriptive cross-sectional study where data was collected using self-administered questionnaires given by 100 respondents using random sampling. Results  Results showed that the majority 69(69%) knew that a child should be breastfed on demand. majority 85(85 %) knew that a child should spend at least 6 months on exclusive breastfeeding before initiating them on other feeds. About care taker’s practices, majority 56(56%) of the caregivers had stopped breastfeeding their children  Conclusion The study established that the majority of the respondents had knowledge about exclusive breastfeeding and complementary feeding However most of the mothers did not put all the knowledge into practice due to lack of resources and citing unfavorable environments to apply what they knew. The nature of the work of caretakers, their level of education, and their economic status had a great influence on the applicability of the required nutrition practices. Recommendations There is a need for increased sensitization and awareness campaigns by the government of Uganda through the ministry of health specifically about the practical ways which suit the lifestyle in rural areas that can help improve the nutritional status among children under 5 in rural areas.

Entrepreneurship, Unemployment and Economic Growth in Nigeria

This study examined the trend between entrepreneurship, unemployment and economic growth over the period 1981-2011. The study adopts secondary data as a source of data. Data for the purpose of this study was gleened from CBN Statistical Bulletin and National Bureau of Statistics (NBS) in Nigeria. This study made use of descriptive and econometric method of analysis. For the descriptive method, tables and/or graphs were used to achieve objective one while for the econometric method, Ordinary Least Square (OLS) method and Error Correction Model (ECM) was used to achieve objective two. The trend analysis showed that the variables are positively sloped which indicates that the stationary of the data. The econometric technique adopted showed that entrepreneurial activities, investment, and unemployment are statistically significant and positively related to economic growth. The result from this study also showed that there is a positive relationship between unemployment and economic growth. This study therefore recommended that developing countries such as Nigeria should create enabling environments for entrepreneurial activities which will consequently reduce unemployment while increasing both growth and standard of living

Intermittent Cathodic Protection for Steel Reinforced Concrete Bridges

Thermal-sprayed zinc anodes are used for impressed current cathodic protection (ICCP) systems on Oregon's reinforced concrete coastal bridges to prevent chloride-induced corrosion damage. Thermal-sprayed zinc performs well as an ICCP anode but the service life of the zinc anode is directly related to the average current density used to operate the systems. After a ICCP system is turned off, the rebar in the concrete remains passive and protected for a period of time. Intermittent operation of CP systems is possible when continuous corrosion rate monitoring is used to identify conditions when the CP system needs to be turned on to reestablish protection conditions for the rebar. This approach applies CP protection only when needed and reflects the fact that external protection may not be needed for a range of environmental conditions. In doing so, intermittent CP would lower the average current necessary to protect rebar, increase the anode service life, and reduce the lifetime costs for protecting reinforced concrete bridges

Biomécanique computationnelle pilotée par les données à l'aide de réseaux neuronaux profonds : application à la chirurgie augmentée

This thesis addresses the problem of soft tissue simulation for augmented reality applications in liver surgery assistance.In particular, we are implementing a non-rigid registration pipeline to generate interactive deformations of a patient-specific liver virtual representation.Traditional methods to compute realistic deformations cannot run at an interactive framerate (60 frames per second) with patient-specific data. Recently, researchers have used artificial neural networks to compute realistic deformations of resembling virtual objects in two milliseconds with accuracy and a relative stability.We propose using a similar approach but with a different artificial neural network architecture having equal precision while being ten times faster.With this proposition, we also present a new method to generate a dataset that minimizes user inputs but maintains control over the content using an analysis of the mechanical properties of the object.Furthermore, we show that it is possible to improve the reliability of the artificial neural network byusing its prediction as the initialization of the Newton-Rapshon algorithm used by the traditional methods.Using our previous contributions, we build a non-rigid registration pipeline using the optimal control framework and the backpropagation algorithm.This pipeline performs the computation multiple orders of magnitude faster than traditional methods at the cost of control reconstruction noise.Finally, we build a second non-rigid registration pipeline by implementing a fully differentiable soft-body physics engine that is slower than artificial neural networks but more flexible in the type of controls, reliable and precise.Cette thèse aborde le problème de la simulation des tissus mous pour les applications de réalité augmentée dans l'assistance à la chirurgie du foie.En particulier, nous mettons en œuvre un pipeline de recalage non rigide pour générer des déformations interactives d'une représentation virtuelle du foie des patients.Les méthodes traditionnelles de calcul de déformations réalistes ne peuvent pas fonctionner à une fréquence interactive (60 images par seconde) avec des données spécifiques au patient.Récemment, des chercheurs ont utilisé des réseaux neuronaux artificiels pour calculer des déformations réalistes d'objets virtuels similaires en deux millisecondes avec précision et une relative stabilité.Nous proposons d'utiliser une approche similaire, utilisant cependant une architecture de réseau neuronal artificiel différente offrant une précision égale tout en étant dix fois plus rapide.Avec cette proposition, nous présentons également une nouvelle méthode pour générer un ensemble de données qui minimise les entrées de l'utilisateur tout en maintenant le contrôle sur le contenu grâce à une analyse des propriétés mécaniques de l'objet.En outre, nous montrons qu'il est possible d'améliorer la fiabilité du réseau neuronal artificiel en utilisant sa prédiction comme initialisation de l'algorithme de Newton-Rapshon utilisé par les méthodes traditionnelles.A l'aide de nos contributions précédentes, nous construisons un pipeline de recalage non rigide en utilisant le cadre du contrôle optimal et l'algorithme de rétropropagation.Ce pipeline effectue le calcul plusieurs ordres de grandeur plus rapidement que les méthodes traditionnelles au prix d'un bruit de reconstruction de contrôle.Enfin, nous construisons un second pipeline de recalage non rigide en mettant en œuvre un moteurphysique de corps mou entièrement différentiable qui est plus lent que les réseaux neuronaux artificiels mais plus flexible dans le type de contrôles, fiable et précis

Biomécanique computationnelle pilotée par les données à l'aide de réseaux neuronaux profonds : application à la chirurgie augmentée

Cette thèse aborde le problème de la simulation des tissus mous pour les applications de réalité augmentée dans l'assistance à la chirurgie du foie.En particulier, nous mettons en œuvre un pipeline de recalage non rigide pour générer des déformations interactives d'une représentation virtuelle du foie des patients.Les méthodes traditionnelles de calcul de déformations réalistes ne peuvent pas fonctionner à une fréquence interactive (60 images par seconde) avec des données spécifiques au patient.Récemment, des chercheurs ont utilisé des réseaux neuronaux artificiels pour calculer des déformations réalistes d'objets virtuels similaires en deux millisecondes avec précision et une relative stabilité.Nous proposons d'utiliser une approche similaire, utilisant cependant une architecture de réseau neuronal artificiel différente offrant une précision égale tout en étant dix fois plus rapide.Avec cette proposition, nous présentons également une nouvelle méthode pour générer un ensemble de données qui minimise les entrées de l'utilisateur tout en maintenant le contrôle sur le contenu grâce à une analyse des propriétés mécaniques de l'objet.En outre, nous montrons qu'il est possible d'améliorer la fiabilité du réseau neuronal artificiel en utilisant sa prédiction comme initialisation de l'algorithme de Newton-Rapshon utilisé par les méthodes traditionnelles.A l'aide de nos contributions précédentes, nous construisons un pipeline de recalage non rigide en utilisant le cadre du contrôle optimal et l'algorithme de rétropropagation.Ce pipeline effectue le calcul plusieurs ordres de grandeur plus rapidement que les méthodes traditionnelles au prix d'un bruit de reconstruction de contrôle.Enfin, nous construisons un second pipeline de recalage non rigide en mettant en œuvre un moteurphysique de corps mou entièrement différentiable qui est plus lent que les réseaux neuronaux artificiels mais plus flexible dans le type de contrôles, fiable et précis.This thesis addresses the problem of soft tissue simulation for augmented reality applications in liver surgery assistance.In particular, we are implementing a non-rigid registration pipeline to generate interactive deformations of a patient-specific liver virtual representation.Traditional methods to compute realistic deformations cannot run at an interactive framerate (60 frames per second) with patient-specific data. Recently, researchers have used artificial neural networks to compute realistic deformations of resembling virtual objects in two milliseconds with accuracy and a relative stability.We propose using a similar approach but with a different artificial neural network architecture having equal precision while being ten times faster.With this proposition, we also present a new method to generate a dataset that minimizes user inputs but maintains control over the content using an analysis of the mechanical properties of the object.Furthermore, we show that it is possible to improve the reliability of the artificial neural network byusing its prediction as the initialization of the Newton-Rapshon algorithm used by the traditional methods.Using our previous contributions, we build a non-rigid registration pipeline using the optimal control framework and the backpropagation algorithm.This pipeline performs the computation multiple orders of magnitude faster than traditional methods at the cost of control reconstruction noise.Finally, we build a second non-rigid registration pipeline by implementing a fully differentiable soft-body physics engine that is slower than artificial neural networks but more flexible in the type of controls, reliable and precise

Ifrågasätta Genus : En lärares guide till att öka genusmedvetenhet i klassrummet - Exemplifierat genom Stephanie Meyers Twilight

In the Swedish school one of the tasks is to work against gender stereotypes and towardsequality between the sexes. The purpose with this essay is to present ways of looking atliterature that teachers can either implement in their classroom or use to better preparethemselves, ways for both teachers and their pupils to gain a critical view towards literaturethat can strengthen the work towards such equality. The tools used in the essays are 1) readingprevious scholars’ analysis of the text, 2) the Bechdel-Wallace-Test, and 3) the Gender Stairs.My example text will be Stephanie Meyer’s Twilight from 2007. The results shows that thebook clearly presents stereotypes of males and females, since the male characters in the bookare strong, protective, and active, while the female characters are beautiful, dependent, andpassive. The novel also defends, preserves, and amplifies patriarchal structures. This analysisplaces Twilight as a minus three in Edwertz and Lundström’s Gender Stairs. The novel is thusa good book for teachers to use if they want their students to see a classical example of howgender myths are presented in literature. Showing a classical example of stereotypes inliterature may in turn help the students detect stereotypes, which is one step towards equalitybetween men and women, which is one of the tasks of the Swedish schools

DeepPhysics: a physics aware deep learning framework for real-time simulation

Real-time simulation of elastic structures is essential in many applications, from computer-guided surgical interventions to interactive design in mechanical engineering. The Finite Element Method is often used as the numerical method of reference for solving the partial differential equations associated with these problems. Yet, deep learning methods have recently shown that they could represent an alternative strategy to solve physics-based problems 1,2,3. In this paper, we propose a solution to simulate hyper-elastic materials using a data-driven approach, where a neural network is trained to learn the non-linear relationship between boundary conditions and the resulting displacement field. We also introduce a method to guarantee the validity of the solution. In total, we present three contributions: an optimized data set generation algorithm based on modal analysis, a physics-informed loss function, and a Hybrid Newton-Raphson algorithm. The method is applied to two benchmarks: a cantilever beam and a propeller. The results show that our network architecture trained with a limited amount of data can predict the displacement field in less than a millisecond. The predictions on various geometries, topologies, mesh resolutions, and boundary conditions are accurate to a few micrometers for non-linear deformations of several centimeters of amplitude