Characteristics and uses of dual-spiral dual-energy CT in radiotherapy of the head and neck

Dual-Spiral Dual-Energy Computed Tomography is an emerging technology that is being introduced in Radiotherapy Departments of hospitals because of its cost and simplicity. There is still a lack of literature about its features and applications. This research fills that gap in the case of Head and Neck Radiotherapy, by means of a thorough study of the technique. This research is divided in three parts: 1) imaging quality, 2) utility in tumor delineation and 3) utility in plan dose calculations. The study combines in-phantom and in-patient investigations. From the observed results, it can be concluded that Dual-Spiral Dual-Energy brings good image quality in Virtual Monoenergetic Images in the range between 45 and 55 keV and is not inferior to other types of Dual-Energy. Moreover, its capability of virtually removing iodine from contrast-enhanced CT images can be safely used for radiotherapy calculations with a high accuracy if some controlled conditions are fulfilled.La Tomografia Computada de Doble Energia i Doble Espiral és una tecnologia emergent que està sent introduïda en els serveis de Radioteràpia dels hospitals pel seu cost i simplicitat. Hi ha un buit de literatura sobre les seves característiques i aplicacions. Aquesta recerca omple aquest buit en el cas de la radioteràpia del cap i coll, mitjançant un estudi complet de la tècnica. Està dividit en tres parts: 1) Qualitat de les imatges, 2) utilitat en la delineació de tumors i 3) utilitat en els càlculs de dosi dels plans. El treball combina recerques en maniquí i en pacients. A partir dels resultats observats, es pot concloure que la Doble Energia de Doble Espiral proporciona unes images virtuals monoenergètiques de bona qualitat en l’interval 45-55keV, i no és inferior a d’altres tipus de Doble Energia. Més encara, la seva capacitat de retirar virtualment l’iode d’imatges amb contrast injectat pot ser usada amb seguretat i alta precisió per a càlculs de radioteràpia sempre que es compleixin determinades condicions.La Tomografía Computarizada de Doble Energía y Doble Espiral es una tecnología emergente que está siendo introducida en los servicios de Radioterapia de los hospitales por su coste y simplicidad. Existe un vacío en la literatura sobre sus características y aplicaciones. Este estudio llena dicho vacío en el caso de la radioterapia de cabeza y cuello mediante un completo estudio de la técnica. El trabajo está dividido en tres partes: Calidad de las imágenes, 2) utilidad en delineación de tumores y 3) utilidad en el cálculo de dosis de los planes. La investigación comprende estudios sobre maniquí y sobre pacientes. A partir de los resultados observados, se puede concluir que la Doble Energía de Doble Espiral proporciona unas imágenes virtuales monoenergéticas de buena calidad en el intervalo 45-55keV, no siendo inferior a otros tipos de Doble Energía. Más aún, su capacidad de extraer virtualmente el yodo de las imágenes con contraste inyectado puede ser usada con seguridad y alta precisión para cálculos de radioterapia, siempre que se cumplan determinadas condiciones

El valor percibido de las entidades bancarias españolas

[ES] Tras la profunda crisis financiera que ha experimentado la economía española y las consecuencias sobre la estructura del sistema bancario español en un contexto de transformación del negocio bancario, este trabajo de fin de máster estudia la valoración que realizan los clientes de las entidades sobre un conjunto de productos bancarios así como su valor percibido del servicio bancario recibido. Para alcanzar los objetivos de este TFM se parte de una revisión de la literatura científica así como de un trabajo empírico basado en un cuestionario administrado a individuos residentes en Castelló de la Plana.Mateu Cepria, L. (2018). El valor percibido de las entidades bancarias españolas. http://hdl.handle.net/10251/110444TFG

Distribution and mobility of arsenic species in solids and leachate composts

Because arsenic can exist in toxic and non-toxic forms, it is important to identify them in compost. This organic material prepared from urban and agricultural wastes, is often used for recovering or remedying soils. As a matter of fact, it’s important to control the mobility of arsenic and its different forms present in leachate composts. In this work total arsenic was determined in compost samples, previously submitted to acid digestion using Inductively Coupled Plasma Mass Spectroscopy (ICP–MS). Total inorganic arsenic and As(III) results were compared with those directly determined in solids samples by Square Wave Voltammetry (SWV). It was found that two thirds of arsenic present in solids are in inorganic forms. As(III) is a minor component in the solid, detectable only in agricultural composts. In leachates, the inorganic arsenic was mobilized in his majority as As(III)

Deep learning-based embedded system for carabao mango (Mangifera indica L.) sorting

©BEIESP. This paper presents the design and development of an embedded system for ‘Carabao’ or Philippine mango sorting utilizing deep learning techniques. In particular, the proposed system initially takes as input a top view image of the mango, which is consequently rolled over to evaluate every sides. The input images were processed by Single Shot MultiBox Detector (SSD) MobileNet for mango detection and Multi-Task Learning Convolutional Neural Network (MTL-CNN) for classification/sorting ripeness and basic quality, running on an embedded computer, i.e. Raspberry Pi 3. Our dataset consisting of 2800 mango images derived from about 270 distinct mango fruits were annotated for multiple classification tasks, namely, basic quality (defective or good) and ripeness (green, semi-ripe, and ripe). The mango detection results achieved a total precision score of 0.92 and a mean average precision (mAP) of over 0.8 in the final checkpoint. The basic quality classification accuracy results were 0.98 and 0.92, respectively, for defective and good quality, while the ripeness classification for green, ripe, and semi-ripe were 1.0, 1.0, and 0.91, respectively. Overall, the results demonstrated the feasibility of our proposed embedded system for image-based Carabao mango sorting using deep learning techniques

Vision-based machine-mediated mango sorting system using OpenCV and convolutional neural network with tensor flow

A large part of the Philippine economy depends on the export of fresh produce to other countries. Among these major products is the mango, a yellow-coloured sweet fruit that is prone to bruises and other skin defects and are thus carefully scrutinized and screened by farmers to ensure the quality of their output. The standard of what constitutes an export-quality mango however can vary significantly among farmers throughout the day. The group therefore seeks to develop a system that would aid farmers in examining their mango produce to increase their output and to ensure standardization in the quality of the mangoes they export. The system would be equipped with OpenCV to analyze the images taken from the mango and tensor flow neural network to countercheck the features of the skin of the subject with the store desired characteristics to determine the status of the mango that is being examined by the system. The improvement in the mango selection process would translate into higher quality outputs of farmers and in turn would increase the demand for local products outside of the country, creating a better economy for the Philippines and for Filipinos