On Chromatic Polynomial and Ordinomial

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How chatbots are used in recruitment and selection practices?

In the modern era, Artificial Intelligence (AI) has affected different functions of businesses, including Human Resources, in recruitment processes. With Chatbots (conversational agents) systems in place, HR can perform tasks like identifying, selecting, and interviewing talented candidates with more speed and consequently focus on strategic goals more effectively. This study aims to assess the current state of chatbot usage in HR processes in organisations, particularly in Higher Education Institutions (HEIs). For Part Ⅰ, the chatbot’s role is evaluated in detail for each stage of recruitment (i.e., sourcing, screening, selection and onboarding of candidates). Moreover, we will discuss how chatbot providers develop this service in terms of needed technical technologies and communicational aspects. The findings will help identify the best practices in developing better chatbots that align with the demands of modern hiring. In addition, we investigate chatbots’ impact on higher education with the rise of online learning and the Covid-19 pandemic. In part two, we develop a chatbot using the Google DialogFlow platform to support the admission process for prospective PhD students in the Doctoral Management Program of the UPC. This FAQ bot will be implemented as a supplementary channel in the doctoral program website to understand students’ queries and provide predefined answers. A survey is conducted based on the TAM framework to assess the chatbot’s functionality, quality, and intention of use. Based on the responses and findings, we will discuss how chatbots are a viable option to create new innovative services that are helpful for both candidates and educators. In the end, based on lessons learned, we propose conclusions, discussion and several recommendations for these intelligent systems. It is hoped that this work will open up new research possibilities for future optimisations in the fields of chatbots and recruitment in the future.En la era moderna, la Inteligencia Artificial (IA) ha afectado a diferentes funciones de las empresas, incluida la de Recursos Humanos, en los procesos de contratación. Con los sistemas de Chatbots (agentes conversacionales) implementados, HR puede realiza r tareas como identificar, seleccionar y entrevistar a personas candidat as talentos a s con más velocidad y, en consecuencia, enfocarse en objetivos estratégicos de manera más efectiva. Este estudio tiene como objetivo evaluar el estado actual del uso de chatbots en los procesos de recursos humanos en las organizaciones, particularmente en las Instituciones de Educación Superior (IES). Para la Parte Ⅰ, el rol del chatbot se evalúa en detalle para cada etapa del reclutamiento (i.e., planificación, abastecimiento, selec ción, verificación de referencias, selección e incorporación de candidatos). Además, discutiremos cómo los proveedores de chatbots desarrollan este servicio en términos de tecnologías técnicas necesarias y aspectos de comunicación. Los hallazgos ayudarán a identificar las mejores prácticas para desarrollar mejores chatbots que se alineen con las demandas de la contratación moderna. Además, investigamos el impacto de los chatbots en la educación superior con el aumento del aprendizaje en línea y la pandemia de Covid19. En la segunda parte, desarrollamos un chatbot utilizando la plataforma Google DialogFlow para apoyar el proceso de admisión de futuros estudiantes de doctorado Doctorado de la UPC. Este bot de preguntas en el Programa de Gestión de frecuentes se implementará como un canal complementario en el sitio web del programa de doctorado para comprender las consultas de los estudiantes y proporcionar respuestas predefinidas. Se realiza una encuesta basada en el marco TAM para evaluar la funcio nalidad, la calidad y la intención de uso del chatbot. Según las respuestas y los hallazgos, analizaremos cómo los chatbots son una opción viable para crear nuevos servicios innovadores que sean útiles tanto para los personas como para los educadores. Al candidat as final, en base a las lecciones aprendidas, proponemos conclusiones, discusión y varias recomendaciones para estos sistemas inteligentes. Se espera que este trabajo abra nuevas posibilidades de investigación para futuras optimizaciones en los campos de los chatbots y el reclutamiento en el futur

Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering

Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibility of data analysis pipelines in large population studies. We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set. We propose 6 different strategies to build the training set, which we evaluate on 2 datasets, a synthetic one modeling a population with a growing number of subject types, and a real one obtained with neuroinformatics pipelines. Results show that one sampling method, "Random File Numbers (Uniform)" is able to predict computational reproducibility with a good accuracy. We also analyze the relevance of including file and subject biases in the collaborative filtering model. We conclude that the proposed method is able to speedup reproducibility evaluations substantially, with a reduced accuracy loss

Polymer Percolation Threshold in Multi-Component HPMC Matrices Tablets

Introduction: The percolation theory studies the critical points or percolation thresholds of the system, where onecomponent of the system undergoes a geometrical phase transition, starting to connect the whole system. The application of this theory to study the release rate of hydrophilic matrices allows toexplain the changes in release kinetics of swellable matrix type system and results in a clear improvement of the design of controlled release dosage forms. Methods: In this study, the percolation theory has been applied to multi-component hydroxypropylmethylcellulose (HPMC) hydrophilic matrices. Matrix tablets have been prepared using phenobarbital as drug,magnesium stearate as a lubricant employing different amount of lactose and HPMC K4M as a fillerandmatrix forming material, respectively. Ethylcelullose (EC) as a polymeric excipient was also examined. Dissolution studies were carried out using the paddle method. In order to estimate the percolation threshold, the behaviour of the kinetic parameters with respect to the volumetric fraction of HPMC at time zero, was studied. Results: In both HPMC/lactose and HPMC/EC/lactose matrices, from the point of view of the percolation theory, the optimum concentration for HPMC, to obtain a hydrophilic matrix system for the controlled release of phenobarbital is higher than 18.1% (v/v) HPMC. Above 18.1% (v/v) HPMC, an infinite cluster of HPMC would be formed maintaining integrity of the system and controlling the drug release from the matrices. According to results, EC had no significant influence on the HPMC percolation threshold. Conclusion: This may be related to broad functionality of the swelling hydrophilic matrices