ELECTROCHEMICAL SYNTHESIS, TRANSFORMATION, AND CHARACTERIZATION OF MnO2 NANOWIRE ARRAYS FOR SUPERCAPACITOR ELECTRODES

The utilization of MnO2 nanowire arrays for future light weight energy storage devices is investigated here. One of the more specific questions this work looks to answer is: Can ultra high density arrays of MnO2 nanowires really be used to create future flexible micro-supercapacitors with high energy density, high power density, and long cycle lives? This research investigates the energy storage properties of dense arrays of solely MnO2 nanowires and synergistic MnO2 nanowire composites consisting of two or more materials/architectures, where the composite materials are able to offset some of the detrimental intrinsic properties of the MnO2 nanowires. Accordingly, a complete flexible supercapacitor device was prepared utilizing a coaxial MnO2/poly (3, 4-ethylenedioxythiophene) (PEDOT) core/shell nanowire array cathode with a PEDOT nanowire array anode. This material demonstrated metrics considerably better than current devices even while being flexed. In addition, a hierarchical MnO2 nanofibril/nanowire array was synthesized by transformation of a bare MnO2 nanowire array. This material was investigated for its supercapacitor properties while altering the parameters of its nanowire and nanofibril architectures. Finally, MnO2 nanowires were investigated for their charge storage mechanism using ICP-AES to detect Li ion to Mn ion ratios during the charging and discharging process. Their charge storage process was found to differ depending on whether the electrolyte solvent used was aqueous or organic. These projects all help advance energy storage devices well beyond their current status as bulky, heavy energy sources toward their prospective use as light weight, flexible, micro- power sources

Dense deformation field estimation for atlas registration using the active contour framework

A key research area in computer vision is image segmentation. Image segmentation aims at extracting objects of interest in images or video sequences. These objects contain relevant information for a given application. For example, a video surveillance application generally requires to extract moving objects (vehicles, persons or animals) from a sequence of images in order to check that their path stays conformed to the regulation rules set for the observed scene. Image segmentation is not an easy task. In many applications, the contours of the objects of interest are difficult to delineate, even manually. The problems linked to segmentation are often due to low contrast, fuzzy contours or too similar intensities with adjacent objects. In some cases, the objects to be extracted have no real contours in the image. This kind of objects is called virtual objects. Virtual objects appear especially in medical applications. To draw them, medical experts usually estimate their position from surrounding objects. The problems related to image segmentation can be greatly simplified with information known in advance on the objects to be extracted (the prior knowledge). A widely used method consists to extract the needed prior knowledge from a reference image often called atlas. The goal of the atlas is to describe the image to be segmented like a map would describe the components of a geographical area. An atlas can contain three types of information on each object being part of the image: an estimation of its position in the image, a description of its shape and texture, and the features of its adjacent objects. The atlas-based segmentation method is rather used when the atlas can characterize a range of images. This method is thus especially adapted to medical images due to the existing consistency between anatomical structures of same type. There exist two types of atlas: the determinist atlas and the statistical atlas. The determinist atlas is an image which has been selected or computed, to be the most representative of an image category to be segmented. This image is called intensity atlas. The contours of the objects of interest (the objects to be extracted in images of the same type) have been traced manually on the intensity atlas, or by using a semi-automatic method. A label is often attributed to each one of these objects in order to differentiate them. In this way, we obtain a labeled version of the atlas called labeled atlas. The statistical atlas is an atlas created from a database of images in order to be the most representative of a certain type of images to be segmented. In this atlas, the position and the features of the objects of interest depend on statistical measures. In this thesis, we are focused on the use of determinist atlases for image segmentation. The segmentation process with a determinist atlas consists to deform the objects delineated in the atlas in order to better align them with their corresponding objects in the image to be segmented. To perform this task, we have distinguished two types of approaches in the literature. The first approach consists to reduce the segmentation problem in an image registration problem. First of all, a dense deformation field that registers (i.e. puts in point-to-point spatial correspondence) the atlas to the image to be segmented, is explicitly computed. Then, this transformation is used to project the assigned labels onto each atlas structure on the image to be segmented. The advantage of this approach is that the deformation field computed from the registration of visible contours allows to easily estimate the position of virtual objects or objects with fuzzy contours. However, the methods currently used for the atlas registration are often only based on the intensity atlas. That means that they do not exploit the object-based information that can be obtained by combining the intensity atlas with its labeled version. In the second approach, the atlas contours selected by the labeled atlas are directly deformed without using a geometrical deformation. For that, this approach is based on matching contour techniques, generally called deformable models. In this thesis, we are interested to a particular type of deformable models, which are the active contour segmentation models. The advantage of the active contour method is that this segmentation technique has been designed to exploit the image information directly linked to the object to be delineated. By using object-based information, active contour models are frequently able to extract regions where the atlas-based segmentation method by registration fails. On the other hand, the result of this local segmentation method is very sensitive to the initial atlas contour position regarding to the target contours. On the other hand, this local segmentation method is very sensitive to the initial position of the atlas contours: the closer they are to the contours to be detected, the more robust the active contour-based segmentation will be. Besides, this segmentation technique needs prior shape models to be able to estimate the position of virtual objects. The main objective of this thesis is to design an algorithm for atlas-based segmentation which combines the advantages of the dense deformation field computed by the registration algorithms, with local segmentation constraints coming from the active contour framework. This implies to design a model where the registration and segmentation by active contours are jointly performed. The atlas registration algorithm that we propose is based on a formulation allowing the integration of any segmentation or contour regularization forces derived from the theory of the active contours in a non parametric registration process. Our algorithm led us to introduce the concept of hierarchical atlas registration. Its principle is that the registration of the main image objects helps the registration of depending objects. This allows to bring progressively the atlas contours closer to their target and thus, to limit the risk to be stuck in a local minimum. Our model had been designed to be easily adaptable to various types of segmentation problems. At the end of the thesis, we present several examples of atlas registration applications in medical imaging. These applications highlight the integration of manual constraints in an atlas registration process, the modeling of a tumor growth in the atlas, the labelization of the thalamus for a statistical study on neuronal connections, the localization of the subthalamic nucleus (STN) for deep brain stimulation (DBS) and the compensation of intra-operative brain shift for neuronavigation systems

Cyberchondria among Filipino teacher education students

The internet-dense ecosystem has dramatically influenced the health-seeking behaviors of various population groups, including education students. Exposure to massive and readily accessible online health information increases health anxiety resulting in cyberchondria, a phenomenon characterized by excessive health search on the internet. This cross-sectional research study examined the level of cyberchondria among 179 teacher education students. The findings revealed that excessiveness, distress, reassurance, and compulsion subscales of cyberchondria were at moderate levels. However, higher correlations existed between excessiveness and compulsion subscales and between distress and compulsion subscales. Gender and programs enrolled have very weak relationships with cyberchondria subscales. The weakest association was between the programs enrolled by the students and all cyberchondria subscales. Meanwhile, students' age and year level have slightly higher but weak associations to the cyberchondria subscales, especially the excessiveness and distress subscales. As an intervention, an interdisciplinary collaboration between teacher education programs and the health-related institutes is recommended to promote awareness about cyberchondria, its prevention, and management

Sudden Changes and Their Associations with Quality of Life during COVID-19 Lockdown: A Cross-Sectional Study in the French-Speaking Part of Switzerland.

The lockdown due to the COVID-19 pandemic has led to various sudden changes in a large number of individuals. In response, the question of how individuals from different social and economic strata cope with those changes has arisen, as well as how much they have affected their mental well-being. Choosing strategies that cope with both the pandemic and the well-being of the population has also been a challenge for different governments. While a large number of studies have investigated the mental health of people from different populations during the COVID-19 pandemic, few have explored the number and type of changes experienced during lockdown by the general population, alongside their relationships with health-related quality of life (HRQoL). To fill this research gap, an observational cross-sectional study on those associations was conducted in the French-speaking part of the Swiss general population. Data were collected from 431 participants during the first four weeks of lockdown due to COVID-19. Multivariate regressions were used to identify the sociodemographic profile of the population that experienced different types and numbers of changes during this period, the association of those changes with the HRQoL-mental and physical-and infection beliefs, and the perception of the governmental measures. We show that the more changes people experienced, the lower their mental HRQoL; however, adherence to governmental measures has helped people to cope with the imposed changes, even though the number of unexpected and unwished changes have strained their mental HRQoL. The low-income population experienced financial difficulties and changes in their food intake more frequently, while dual-citizenship or non-Swiss individuals declared conflictual situations more frequently. Sport practice had a positive association with mental HRQoL; nevertheless, a decrease in sport practice was frequently reported, which correlated with a lower mental HRQoL. Risk perception of COVID-19 increased with lower physical HRQoL score, which supports the efficiency of governmental communication regarding the pandemic. Our results support that government measures should be accompanied by effective and targeted communication about the risk of infection, in order to encourage all strata of the general population to follow such measures and adapt to the changes without unduly affecting their mental health. The usage of such tools might help to reduce the impact of policy-imposed changes on the mental HRQoL of the general population, by inducing voluntary changes in informed and engaged populations

Mémoire, traumatisme et histoire dans le cinéma sud-coréen contemporain : Mother et Peppermint Candy

Le film Mother (Corée du Sud, 2009) du réalisateur Bong Joon-ho a été diffusé lors du 25e Festival International du Film de Fribourg dans le cadre d’un panorama dédié à la figure de la femme dans le film noir. L’objectif de cette rétrospective était d’interroger le caractère supposément misogyne de ce genre cinématographique en proposant des productions qui se distinguent par leur manière de représenter les personnages féminins. En développant une intrigue policière presqu’exclusivement centr..

Dense Deformation Field Estimation for Atlas Registration using the Active Contour Framework

In this paper, we propose a new paradigm to carry outthe registration task with a dense deformation fieldderived from the optical flow model and the activecontour method. The proposed framework merges differenttasks such as segmentation, regularization, incorporationof prior knowledge and registration into a singleframework. The active contour model is at the core of ourframework even if it is used in a different way than thestandard approaches. Indeed, active contours are awell-known technique for image segmentation. Thistechnique consists in finding the curve which minimizesan energy functional designed to be minimal when thecurve has reached the object contours. That way, we getaccurate and smooth segmentation results. So far, theactive contour model has been used to segment objectslying in images from boundary-based, region-based orshape-based information. Our registration technique willprofit of all these families of active contours todetermine a dense deformation field defined on the wholeimage. A well-suited application of our model is theatlas registration in medical imaging which consists inautomatically delineating anatomical structures. Wepresent results on 2D synthetic images to show theperformances of our non rigid deformation field based ona natural registration term. We also present registrationresults on real 3D medical data with a large spaceoccupying tumor substantially deforming surroundingstructures, which constitutes a high challenging problem

Impacto de la implementación de paquetes preventivos en la incidencia de infecciones asociadas a la atención en salud. Unidad de Cuidados Intensivos Hospital General los Ceibos, julio a diciembre 2021.

El objetivo de la investigación fue conocer el impacto generado por la implementación de los paquetes preventivos en la incidencia de las Infecciones Asociadas a la Atención en Salud (IAAS) en la Unidad de Cuidados Intensivos del Hospital General Los Ceibos. El diseño cuasi experimental con el que se estructuró la investigación trabajo con dos grupos conformados previamente según disponibilidad de camas al momento del ingreso de pacientes, el alcance fue de tipo exploratorio, prospectivo y longitudinal desarrollado de julio a diciembre de 2021. Al grupo seleccionado (Sala de UCI A) se aplicó el paquete preventivo de neumonía en 1205 pacientes expuestos a la Ventilación Mecánica y el paquete preventivo de sepsis en 1297 pacientes expuestos al Catéter Venoso Central, obteniendo una incidencia global de 1,43 casos de IAAS por cada 100 egresos con una disminución de 45,0% en relación a los pacientes ingresados en la sala B en el mismo período de tiempo y una disminución de 35,4% de casos de IAAS en relación a los pacientes ingresados en la misma sala en el primer semestre del año. Se aplicó el coeficiente de Pearson con correlación negativa muy alta, también se aplicó la prueba T de student, para comparar la media de incidencia de IAAS de la sala A y Sala B, existiendo una diferencia de 2,25 casos adicionales en la sala que no realizó la implementación de los paquetes preventivos, obteniendo un valor p de 0,000050 por lo que se rechaza la hipótesis nula.The goal of the research was to know the impact generated by the implementation of preventive packages on the incidence of Infections Associated with Healthcare (IAH) in the Intensive Care Unit of the General Hospital Los Ceibos. The quasi-experimental design with which the research was structured, worked with two groups previously formed according to bed availability at the time of patient admission, the scope was exploratory, prospective, and longitudinal developed from July to December 2021. The preventive package of pneumonia was applied to the selected group (ICU Room A) in 1205 patients exposed to Mechanical Ventilation and the preventive package of sepsis in 1297 patients exposed to the Central Venous Catheter, obtaining an overall incidence of 1.43 cases of IAH per 100 discharges with a decrease of 45.0% in relation to patients admitted to room B in the same period of time and a decrease of 35.4% of IAH cases in relation to patients admitted to the same ward in the first half of the year. Pearson's coefficient was applied with a very high negative correlation, and the student’s t-test was also applied to compare the mean incidence of IAHs in Ward A and Ward B. There was a difference of 2.25 additional cases in the ward that did not implement the preventive packages, obtaining a p-value of 0.000050, so the null hypothesis was rejected

Atlas-based segmentation of medical images locally constrained by level sets

Atlas-based segmentation has become a standard paradigm for exploiting prior knowledge in medical image segmentation. In this paper, we propose a method to exploit both the robustness of global registration techniques and the accuracy of a local registration based on level set tracking. First, the atlas is globally put in correspondence with the patient image by an affine and an intensity-based non rigid registration. Based on this rough initialisation, the level set functions corresponding to particular objects of interest of the deformed atlas are used to segment the corresponding objects in the patient image. We propose a technique to derive a dense deformation field from the motion of these level set functions. This is particularly important when we want to infer the position of invisible structures like the brain sub-thalamic nuclei from the position of visible surrounding structures. This can also be advantageously exploited to register an atlas following a hierarchical approach. Results are shown on 2D synthetic images and 2D real images extracted from brain and prostate MR volumes and neck CT volumes

gestión y resolución del conflicto en la empresa familiar

Este trabajo, supone un estudio sobre como afectan los conflictos, así como las vías que toman para manifestarse en la empresa familiar. Su naturaleza podría compararse a la de un estudio social, si atendemos al método de investigación escogido. Por otro lado, se trata de encontrar los nexos causales, así como definir el contexto y los roles ejercidos por los empleados y encargado o jefes, con el fin de delimitar también, aquellas variables extrínsecas y intrínsecas que dignifican y dan explicación al conflicto. Todo ello con el fin, de ofrecer una forma de resolverlos de forma eficaz, adaptando las herramientas tanto a la dimensión de la empresa, como al contexto y las características individuales de los implicados