Online yarn hairiness- Loop & protruding fibers dataset.

This paper introduces an online dataset focused on detecting hairiness in yarn, including loop and protruding fibers. The dataset is designed for use in assessing artificial intelligence algorithms. The dataset consists of 684 original images. Through augmentation, this number increases to 1644, with 11,037 annotations derived from videos featuring 56.4tex purple cotton yarn. The videos were captured during the winding and unwinding processes of the purple yarn coil. An image acquisition system capable of capturing high-resolution images while the yarn is in motion was used, reaching speeds of up to 4.2 m/s and producing images with a resolution of 1.6M pixels. This dataset containing 100m of purple cotton yarn images was recorded and is available for download in various formats, including, among others, YOLOv8, YOLOv5, YOLOv7, MT-YOLOv6, COCO JSON, YOLO Darknet, Pascal VOC XML, TFRecord, CreateML JSON. Within an interface developed for a designed mechatronic prototype, users can choose to gather images or videos of yarn. Various characteristics of the yarn, such us: diameter, linear mass, volume, twist orientation, twist step, number of cables, hairiness index, number of loose fibers, thin places, thick places, neps (mass parameters) and U, CV and sH (statistical parameters) can be obtained. Recently, this online yarn spinning dataset was employed to validate artificial neural network models for real-time detection of hairiness in yarns, including loop fibers and protruding fibers. The dataset presented, with its clear annotations and wide array of augmentation techniques, serves as a foundational resource for prospective studies in textile engineering, enabling progress in the analysis and comprehension of yarn analysis.

University Dropout in Engineering: Motives and Student Trajectories.

BACKGROUND: Dropout in higher education is a concern for students, families, educational institutions, and society. Tertiary education is an important mechanism for empowering people and STEM courses are vital to countries' development. METHOD: The study combined quantitative and qualitative data. Step 1 was an analysis of personal and contextual variables in a comprehensive examination of dropout in a sample of 1,016 engineering students. In step 2 a short interview by telephone were conducted with 82 students who dropped out, identifying their reasons and their academic/professional situation. In step 3 in-depth interviews were conducted with six students in order to understand the dynamic process of their decisions to leave. RESULTS: The academic/professional situations of students who dropped out were quite varied, for example the same course or a different course at another institution, starting a job, not working or studying, or attending another level of professional training. There were three main reasons for dropping: vocational, learning process and achievement, and reconciling roles. CONCLUSIONS: Engineering student dropout is related to vocational and academic achievement. This should be considered in the implementation of programs to reduce the rate of dropout.

University Dropout in Engineering: Motives and Student Trajectories / Abandono Universitario en Ingeniería: Motivos y Trayectorias de los Estudiantes

Background: Dropout in higher education is a concern for students, families, educational institutions, and society. Tertiary education is an important mechanism for empowering people and STEM courses are vital to countries’ development. Method: The study combined quantitative and qualitative data. Step 1 was an analysis of personal and contextual variables in a comprehensive examination of dropout in a sample of 1,016 engineering students. In step 2 a short interview by telephone were conducted with 82 students who dropped out, identifying their reasons and their academic/professional situation. In step 3 in-depth interviews were conducted with six students in order to understand the dynamic process of their decisions to leave. Results: The academic/professional situations of students who dropped out were quite varied, for example the same course or a different course at another institution, starting a job, not working or studying, or attending another level of professional training. There were three main reasons for dropping: vocational, learning process and achievement, and reconciling roles. Conclusions: Engineering student dropout is related to vocational and academic achievement. This should be considered in the implementation of programs to reduce the rate of dropout. (AU)

Antecedentes: la deserción en la educación superior es una preocupación para los estudiantes, las familias, las instituciones educativas y la sociedad. La educación terciaria es un mecanismo importante para el empoderamiento de las personas, y los cursos STEM son vitales para el desarrollo de los países. Método: estudio que combina datos cuantitativos y cualitativos. En el paso 1 analizamos variables personales y contextuales y la deserción de 1.016 estudiantes de ingeniería. En el paso 2 se realizó una breve entrevista telefónica a 82 estudiantes que desertaron, identifi cando sus motivos y situación académica/profesional. En el paso 3 se realizaron entrevistas en profundidad a seis estudiantes para comprender el proceso dinámico de decisión de abandonar. Resultados: la situación académica/profesional de los estudiantes que desertaron es bastante diferente, por ejemplo cursar la misma o otra carrera en otra institución, empezar trabajando, no trabajar ni estudiar y cursar otro nivel de formación profesional. Los estudiantes informaron tres razones principales para la deserción: vocacional, proceso de aprendizaje y logro, y reconciliación de roles. Conclusiones: la deserción de los estudiantes de ingeniería está relacionada con el rendimiento académico y vocacional. Esto debe tenerse en cuenta en la implementación de programas para reducir la deserción. (AU)

Importancia del seguimiento de lesiones melanocíticas con dermatoscopia digital para el diagnóstico temprano de melanoma / Importance of monitoring melanocytic lesions with digital dermatoscopy for diagnosis early melanoma

La dermatoscopia digital es una herramienta que permite el diagnóstico de melanomas en estadios tempranos, por medio del seguimiento de las lesiones pigmentarias a largo plazo. Se comunican tres casos de pacientes con alto riesgo de melanoma, en los cuales ‒a través del seguimiento con dermatoscopia digital‒ se realizó el diagnóstico de la enfermedad mediante la detección de cambios morfológicos, arquitecturales y de pigmentación de las lesiones estudiadas. (AU)

Digital dermoscopy is a tool that allows the early diagnosis of melanomas, through the long-term follow up of pigmentary skin lesions. We report three cases of patients with high-risk of melanoma, in which the diagnosis had been made by morphological, arquitectural and pigmentary changes observed by the digital dermoscopy follow-up. (AU)

Abordagens à aprendizagem, autorregulação e motivação: convergência no desempenho acadêmico excelente / Approaches to learning, self-regulation and motivation: their convergence on excellent academic performance / Enfoques del aprendizaje, la autorregulación y la motivación: convergencia en el desempeño académico excelente

Este estudo exploratório teve por objetivo identificar os fatores diferenciadores e preditivos do rendimento acadêmico excelente. Para tal, foi aplicada a Escala de Competências de Estudo e o Questionário de Motivação para a Prática Deliberada junto de um grupo de alunos excelentes (N = 33) e de um grupo de alunos com rendimento médio (N = 200, aproximadamente), do 2º ao 5º ano de cursos de engenharia. Os resultados apontam diferenças a favor dos alunos excelentes na abordagem profunda ao estudo e nas motivações relacionadas com o futuro profissional. Verificou-se ainda que o rendimento surge negativamente associado com o tempo de trabalho em grupo, com alguns comportamentos específicos do estudo, e com a orientação estratégica do estudo para o processo de avaliação.(AU)

In this exploratory study, we aimed to identify the factors that differentiate highly- achieving students and also analyze the variables predicting high performance. We applied the Scale of Study Skills and the Motivation for Deliberate Practice Questionnaire to a group of high achievers (N = 33) and a group of students with average achievement (N = 200), from 2nd to 5th grade in engineering courses. The results showed that high achievers have a positive statistical difference concerning a deeper approach to learning and motivations related to their future career. We also verified that high achievement is negatively associated with the time spent with team work, some specific study behaviors, and with a strategic orientation of the study towards the evaluation process.(AU)

Este estudio exploratorio tuvo el objeto de identificar los factores diferenciadores y predictivos del rendimiento académico excelente. Para eso, se aplicó la Escala de Competencias de Estudio y el Cuestionario de Motivación para la Práctica Deliberada a un grupo de alumnos excelentes (N = 33) y un grupo de alumnos con rendimiento medio (N = 200, aproximadamente), de 2º a 5º año de cursos de ingeniería. Los resultados indican diferencias a favor de los alumnos excelentes en el enfoque profundo al estudio y en las motivaciones relacionadas con el futuro profesional. Se verificó aun que el rendimiento surge negativamente asociado con el tiempo de trabajo en grupo, con algunos comportamientos específicos del estudio, y con la orientación estratégica del estudio para el proceso de evaluación.(AU)

Abordagens à aprendizagem, autorregulação e motivação: convergência no desempenho acadêmico excelente / Approaches to learning, self-regulation and motivation: their convergence on excellent academic performance / Enfoques del aprendizaje, la autorregulación y la motivación: convergencia en el desempeño académico excelente

Este estudo exploratório teve por objetivo identificar os fatores diferenciadores e preditivos do rendimento acadêmico excelente. Para tal, foi aplicada a Escala de Competências de Estudo e o Questionário de Motivação para a Prática Deliberada junto de um grupo de alunos excelentes (N = 33) e de um grupo de alunos com rendimento médio (N = 200, aproximadamente), do 2º ao 5º ano de cursos de engenharia. Os resultados apontam diferenças a favor dos alunos excelentes na abordagem profunda ao estudo e nas motivações relacionadas com o futuro profissional. Verificou-se ainda que o rendimento surge negativamente associado com o tempo de trabalho em grupo, com alguns comportamentos específicos do estudo, e com a orientação estratégica do estudo para o processo de avaliação.

In this exploratory study, we aimed to identify the factors that differentiate highly- achieving students and also analyze the variables predicting high performance. We applied the Scale of Study Skills and the Motivation for Deliberate Practice Questionnaire to a group of high achievers (N = 33) and a group of students with average achievement (N = 200), from 2nd to 5th grade in engineering courses. The results showed that high achievers have a positive statistical difference concerning a deeper approach to learning and motivations related to their future career. We also verified that high achievement is negatively associated with the time spent with team work, some specific study behaviors, and with a strategic orientation of the study towards the evaluation process.

Este estudio exploratorio tuvo el objeto de identificar los factores diferenciadores y predictivos del rendimiento académico excelente. Para eso, se aplicó la Escala de Competencias de Estudio y el Cuestionario de Motivación para la Práctica Deliberada a un grupo de alumnos excelentes (N = 33) y un grupo de alumnos con rendimiento medio (N = 200, aproximadamente), de 2º a 5º año de cursos de ingeniería. Los resultados indican diferencias a favor de los alumnos excelentes en el enfoque profundo al estudio y en las motivaciones relacionadas con el futuro profesional. Se verificó aun que el rendimiento surge negativamente asociado con el tiempo de trabajo en grupo, con algunos comportamientos específicos del estudio, y con la orientación estratégica del estudio para el proceso de evaluación.

Métodos de estudo em alunos do 1º ano da universidade / Study methods of freshment students

artigo analisa os métodos de estudo de uma amostra de alunos do 1º ano de uma Universidade de Portugal, no momento de ingresso na Universidade, maioritariamente de cursos de Engenharia. Utilizou-se o Inventário de Atitudes e Comportamentos Habituais de Estudo – IACHE, que contempla cinco sub-escalas: enfoque compreensivo; enfoque reprodutivo; percepções pessoais de competência; envolvimento no estudo; e organização das actividades de estudo. A análise considera a nota de candidatura ao ensino superior e o género. Os resultados mostram que alunos com melhores classificações ao nível do ensino secundário apresentam pontuações mais altas nos itens reportados a um enfoque mais compreensivo que memorístico no estudo, assim como nos itens que traduzem percepções pessoais mais positivas de competência e de realização académica. Os alunos do sexo feminino apresentam, ainda, resultados mais elevados nas várias sub-escalas, inferindo-se níveis superiores de profundidade compreensiva e de envolvimento no estudo, assim como melhor organização das actividades escolares(AU)