Multi-GCN: Graph Convolutional Networks for Multi-View Networks, with Applications to Global Poverty

With the rapid expansion of mobile phone networks in developing countries, large-scale graph machine learning has gained sudden relevance in the study of global poverty. Recent applications range from humanitarian response and poverty estimation to urban planning and epidemic containment. Yet the vast majority of computational tools and algorithms used in these applications do not account for the multi-view nature of social networks: people are related in myriad ways, but most graph learning models treat relations as binary. In this paper, we develop a graph-based convolutional network for learning on multi-view networks. We show that this method outperforms state-of-the-art semi-supervised learning algorithms on three different prediction tasks using mobile phone datasets from three different developing countries. We also show that, while designed specifically for use in poverty research, the algorithm also outperforms existing benchmarks on a broader set of learning tasks on multi-view networks, including node labelling in citation networks

Explorando las Barreras al Aprendizaje en Línea durante la Pandemia de COVID-19. Un Caso de Estudiantes Pakistaníes de IES [Instituciones de Educación Superior]

The main objective of this study is to explore various barriers that are preventing Pakistani HEIs (universities) students from learning online in this COVID-19 pandemic. Applying a qualitative research design, twelve (12) in-depth interviews were conducted with individual business school participants (students), selected at undergraduate and graduate levels to participate. Data were collected from six (06) universities in Islamabad, Pakistan. After collecting the data using a self-developed questionnaire, a thematic analysis method within the qualitative research was applied to uncover several barriers relating to the objective of this study. Eight themes emerged from the interview data: internet technology access, the content of digital slides, student’s perception towards online learning, power outages during COVID-19 pandemic, students’ fear of losing marks and impact on employment, faculty skills in using technology to teach online, student’s stress and health in the COVID-19 pandemic and student skills to use distance learning tools. Based on the findings, students who live in rural areas are more affected by online learning during the COVID-19 pandemic than students in urban areas due to identified barriers and, most importantly, lack of technology infrastructure. Opportunities and future recommendations have been provided to the relevant authorities to conduct and deliver smooth online education in the country during pandemic.El principal objetivo de este estudio es de explorar varias barreras que previenen a los estudiantes de educación superior (universidades) de Paquistán de aprender en línea en esta pandemia de Covid-19. Al aplicar un diseño de investigación cualitativa, doce (12) entrevistas en profundidad se llevaron a cabo con participantes de la escuela de negocios, seleccionados en niveles de pregrado y posgrado. Los datos se recolectaron de seis universidades en Islamabad, Paquistán. Luego de recolectar los datos usando un cuestionario de desarrollo propio, se aplicó un método de análisis temático dentro de la investigación cualitativa para descubrir varias barreras relacionadas con el objetivo de este estudio. Ocho temas emergieron de los datos de las entrevistas, tales como: acceso a internet, el contenido de diapositivas digitales, la percepción de los estudiantes hacia el aprendizaje en línea, cortes de energía durante la pandemia, miedo de los estudiantes a perder notas y su impacto en el empleo, habilidades del profesorado en el uso de la tecnología para enseñar en línea, el estrés de los estudiantes y la salud, y las habilidades para usar herramientas de aprendizaje a distancia. Basado en los hallazgos, los estudiantes que viven en zonas rurales son los más afectados debido a las barreras identificadas y lo más importante a la falta de infraestructura tecnológica. Se han proporcionado oportunidades y recomendación a las autoridades pertinentes para desarrollar y entregar educación en línea fluida en el país durante la pandemia

Evaluation of the root and canal morphology of mandibular first permanent molars in a sample of Pakistani population by cone-beam computed tomography

Objective: To evaluate the root canal morphology of permanent mandibular first molars using Cone Beam Computed Tomography. Methods: The retrospective study was done at Aga Khan University Hospital, Karachi, and comprised medical records of patients who visited the dental clinics from December 2016 to March 2017. Mandibular first permanent molars were evaluated on Cone Beam Computed Tomography images. Frequency distribution of Vertucci\u27s classification was determined, and so was the association between Vertucci\u27s classifications and gender. SPSS 20 was used for data analysis. Results: A total of 142 mandibular first permanent molars were evaluated on 78 Cone Beam Computed Tomography images. The most common Vertucci\u27s classification was Type IV for mesial root 86(60.56%) and Type I for distal root 63(44%). There was no difference in the two genders for root canal morphology (p\u3e0.05). Conclusions: Type IV Vertucci\u27s were prevalent in the mesial root and Type I were common in the distal root of permanent mandibular first molars

Leaking pseudoaneurysm of hepatic artery: A potentially life-threatening complication of a common procedure

We report a case of leaking pseudoaneurysm of right hepatic artery in a 54-year old female after laparoscopic cholecystectomy who presented with massive gastrointestinal haemorrhage and was successfully managed with angiography and coil embolization