5,783 research outputs found
Contaminación por microplásticos en cuatro especies de peces en playas de Punta de Bombón, Islay- Arequipa
Los microplásticos en los océanos son un problema amenazante para las especies
que lo habitan, por confundirlo como alimento, por ello esta investigación busca
determinar la contaminación por microplásticos en cuatro especies de peces en
playas de Punta de Bombón, Islay-Arequipa. La investigación de tipo aplicada y no
experimental, consideró como población a cuatro especies de peces costeras,
demersales: Cabinza (Isacia conceptionis), Lisa (Mugil cephalus), Pejerrey
(Odontesthes regia regia) y Pintadilla (Cheilodactylus variegatus), con un muestreo
de cuarenta individuos, diez de cada especie, clasificados en grandes y pequeños
todos capturados en playas y peñas con ayuda de pescadores artesanales. Se
tomaron datos morfológicos y se extrajo el tracto gastrointestinal de los peces para
luego en laboratorio pasar una etapa de digestión con KOH para la eliminación de
materia orgánica y poder observar la forma, tamaño, color y peso de los
microplásticos, luego mediante el método FT-IR obtener el tipo de plástico. Los
resultados arrojaron presencia de microplásticos en tres especies de cuatro y el tipo
de plástico no pudo ser determinado con precisión. Concluyendo que la presencia
de microplásticos en peces está relacionada con las actividades antropogénicas
realizadas en la zona
Color characterization comparison for machine vision-based fruit recognition
In this paper we present a comparison between three color characterizations methods applied for fruit recognition, two of them are selected from two related works and the third is the authors’ proposal; in the three works, color is represented in the RGB space. The related works characterize the colors considering their intensity data; but employing the intensity data of colors in the RGB space may lead to obtain imprecise models of colors, because, in this space, despite two colors with the same chromaticity if they have different intensities then they represent different colors. Hence, we introduce a method to characterize the color of objects by extracting the chromaticity of colors; so, the intensity of colors does not influence significantly the color extraction. The color characterizations of these two methods and our proposal are implemented and tested to extract the color features of different fruit classes. The color features are concatenated with the shape characteristics, obtained using Fourier descriptors, Hu moments and four basic geometric features, to form a feature vector. A feed-forward neural network is employed as classifier; the performance of each method is evaluated using an image database with 12 fruit classes
Just Drop My Body on the Steps of the FDA: Emotion & Activism at ACT UP’s “Seize Control of the FDA” Action
The outbreak of AIDS politicized and radicalized the gay community in New York City, which is when ACT UP emerged. The anger and hopelessness felt by the gay community due to the government’s inaction was vigorously channeled towards activism and disruption, which in turn created visibility and enabled changes that would make living with AIDS manageable. I will be focusing on the emotional aspect that drove ACT UP activists to channel their anger and frustration into something productive that ultimately lead to tangible changes. Changes related to how AIDS and people with AIDS were represented in the media and most importantly in terms of treatment availability. By looking at one particular action that ACT UP/NY was involved in, I hope to illustrate how the movement was inspired and driven by emotion, which in a lot ways made the movement more militant. Ultimately, we will see how the raw emotions and drive felt by a lot of the activists within ACT UP enabled them to become extremely interested in research and data gathering which opened the door for activists to become involved in the quest for new and fairer trials and treatments
Computing the number of groups for color image segmentation using competitive neural networks and fuzzy c-means
Se calcula la cantidad de grupos en que los vectores de color son agrupados usando fuzzy c-meansFuzzy C-means (FCM) is one of the most often techniques employed for color image segmentation; the drawback with this technique is the number of clusters the data, pixels’ colors, is grouped must be defined a priori. In this paper we present an approach to compute the number of clusters automatically. A competitive neural network (CNN) and a self-organizing map (SOM) are trained with chromaticity samples of different colors; the neural networks process each pixel of the image to segment, where the activation occurrences of each neuron are collected in a histogram. The number of clusters is set by computing the number of the most activated neurons. The number of clusters is adjusted by comparing the similitude of colors. We show successful segmentation results obtained using images of the Berkeley segmentation database by training only one time the CNN and SOM, using only chromaticity data
Color image segmentation using saturated RGB colors and decoupling the intensity from the hue
Although the RGB space is accepted to represent colors, it is not adequate for color processing. In related works the colors are usually mapped to other color spaces more suitable for color processing, but it may imply an important computational load because of the non-linear operations involved to map the colors between spaces; nevertheless, it is common to find in the state-of-the-art works using the RGB space. In this paper we introduce an approach for color image segmentation, using the RGB space to represent and process colors; where the chromaticity and the intensity are processed separately, mimicking the human perception of color, reducing the underlying sensitiveness to intensity of the RGB space. We show the hue of colors can be processed by training a self-organizing map with chromaticity samples of the most saturated colors, where the training set is small but very representative; once the neural network is trained it can be employed to process any given image without training it again. We create an intensity channel by extracting the magnitudes of the color vectors; by using the Otsu method, we compute the threshold values to divide the intensity range in three classes. We perform experiments with the Berkeley segmentation database; in order to show the benefits of our proposal, we perform experiments with a neural network trained with different colors by subsampling the RGB space, where the chromaticity and the intensity are processed jointly. We evaluate and compare quantitatively the segmented images obtained with both approaches. We claim to obtain competitive results with respect to related works
Funcionamiento familiar, personalidad y satisfacción vital en parejas casadas.
Los estudios sobre las parejas casadas señalan que las características de personalidad de cada uno de los cónyuges, están relacionadas con la satisfacción en la pareja. Las investigaciones indican que Neuroticismo, Amabilidad y Responsabilidad son los factores de personalidad relacionados con la satisfacción matrimonial, que a su vez promueve la satisfacción vital de los cónyuges en general. Con respecto al funcionamiento familiar, la dimensión de cohesión familiar, definida como vínculos afectivos entre los miembros de la familia, ha sido iden- tificada como variable predictora del bienestar subjetivo. Las investigaciones específicas sobre la influencia que tienen los rasgos de personalidad de las parejas casadas sobre el funcionamiento familiar y marital son escasas. Tampoco existen resultados concluyentes sobre las diferencias de género, y la importancia de personalidades similares vs. diferentes como predictores de la satisfacción en la pareja. El objetivo del estudio es identificar la influencia de las características de personalidad y del funcionamiento familiar sobre la satisfacción con la vida en las relaciones de pareja, así como especificar las diferencias de género. Los participantes fueron 187 parejas casadas (N=374) con edades comprendidas entre los 27 y 54 años que complementaron el 'Big Five Inventory' (BFI-10), la Escala de la Cohesión y Adaptabilidad Familiar (CAF) y la Escala de Satisfacción con la Vida (SWLS). Se realizaron pruebas t de Student para muestras relacionadas, análisis de varianzas, correlaciones bivariadas de Pearson y análisis de regresión lineal múltiple. Los resultados indican que existen asociaciones significativas entre personalidad, funcionamiento familiar y satisfacción con patrones diferentes para mujeres y hombres. Así como que existen diferencias de género en la predicción de la variable satisfacción. Concluimos que las parejas se parecen entre sí a nivel psicológico, pero la relación entre las variables evaluadas tiene una mayor relevancia para las mujeres que para sus marido
Contrast enhacenment of RGB color images by histogram equalization of color vectors' intensities
Mejora del contraste de imagenes de color RGBThe histogram equalization (HE) is a technique developed for image contrast enhancement of grayscale images. For RGB (Red, Green, Blue) color images, the HE is usually applied in the color channels separately; due to correlation between the color channels, the chromaticity of colors is modified. In order to overcome this problem, the colors of the image are mapped to different color spaces where the chromaticity and the intensity of colors are decoupled; then, the HE is applied in the intensity channel. Mapping colors between different color spaces may involve a huge computational load, because the mathematical operations are not linear. In this paper we present a proposal for contrast enhancement of RGB color images, without mapping the colors to different color spaces, where the HE is applied to the intensities of the color vectors. We show that the images obtained with our proposal are very similar to the images processed in the HSV (Hue, Saturation, Value) and L*a*b* color spaces
Adicción a las redes sociales y procrastinación académica en estudiantes del nivel secundario de una institución educativa de Chiclayo, 2022
Objetivo: Determinar si existe relación significativa entre la adicción a las
redes sociales y procrastinación académica en los estudiantes de una institución
educativa del nivel secundario de Chiclayo – 2022. Método: estudio de tipo no
experimental con diseño transversal – correlacional realizado sobre 241
estudiantes de secundaria de ambos sexos, de entre 12 y 17 años de edad. A
quienes les fue aplicado el cuestionario de procrastinación adolescente de
Arévalo y la Escala de Adicción a Redes Sociales de Escurra y Salas. Se aplicó
estadística inferencial mediante el coeficiente de Spearman. Resultados: Se
encontró una correlación alta significativa (Rho=.659; p-valor = .05). Lo que quiere
decir que, a mayor nivel de adicción a las redes sociales, mayor el grado de
procrastinación de los adolescentes. De la misma forma, se halló que la mayoría
de las dimensiones de procrastinación adolescente se vinculan significativamente
con la variable adicción a redes sociales. Encontrando que la dimensión
dependencia es la única dimensión que no presenta relación. Conclusión: los
estudiantes de 1ero a 5to grado de secundaria evidencian adicción a las
plataformas digitales de sociabilización lo cual se encuentra asociado a la
tendencia de postergar sus deberes académicos.TesisComunicación y desarrollo human
Competencias y habilidades para contribuir al desarrollo sostenible e inclusivo
XV Conferencia Internacional sobre Bibliotecas Universitarias "La Biblioteca hacia el 2030", evento realizado en el Centro de Exposiciones y Congresos de la UNAM, del 25 al 27 de Octubre de 2017, México.Conferencia que aborda las competencias y habilidades que requiere el profesional de la información para contribuir a un desarrollo sostenible e inclusivo
Data selection based on decision tree for SVM classification on large data sets
Support Vector Machine (SVM) has important properties such as a strong mathematical background and a better generalization capability with respect to other classification methods. On the other hand, the major drawback of SVM occurs in its training phase, which is computationally expensive and highly dependent on the size of input data set. In this study, a new algorithm to speed up the training time of SVM is presented; this method selects a small and representative amount of data from data sets to improve training time of SVM. The novel method uses an induction tree to reduce the training data set for SVM, producing a very fast and high-accuracy algorithm. According to the results, the proposed algorithm produces results with similar accuracy and in a faster way than the current SVM implementations.Proyecto UAEM 3771/2014/C
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