DE EUROPA A LATINOAMÉRICA A TRAVÉS DE LA GENEALOGÍA GENÉTICA: ESTUDIO DE CASOS DE MIGRACIÓN DESDE IRLANDA Y ESPAÑA

This article introduces the basic concepts of the human genome and the genetic genealogy Y- and autosomal test to complement the genealogic research. These tests are beneficial for studying the European migration to America when the documentation is minimal, especially before and during the XIX century. This article explores the case of John Denton Creamer. He migrated from London or Ireland to New York and then to Ecuador during the presidency of García Moreno to work on constructing the Trans-Andean railways. This research discovers Creamer’s Irish roots and his relationship with the Cremin family of the McCarthy clan. After introducing the genetic evolution in Latin America and Spain, this research studies the genetic group of a representative of the Guillén family established in the Aragonese Pyrenees for more than seven hundred years and its regional impact.Este artículo introduce brevemente el genoma humano, el ADN, y los tests del cromosoma Y y el test autosomal usado por la genealogía genética para ampliar las investigaciones genealógicas. Estos tests son especialmente útiles para estudiar los casos de las migraciones europeas a América donde la documentación de origen y destino puede ser muy limitada especialmente durante y antes del siglo XIX. El artículo muestra la aplicación de este enfoque al estudio de caso de John Denton Creamer (JDC) quien migró de Inglaterra o Irlanda a Nueva York y de allí a Ecuador durante el periodo de la presidencia de Gabriel García Moreno para participar en la construcción del ferrocarril Trasandino. La investigación genética ayuda a descubrir sus raíces irlandesas y su relación con los Cremin del clan McCarthy. Adicionalmente, este artículo, luego de introducir brevemente la evolución genética en Latinoamérica y España, explora la proyección de los resultados genéticos de una persona a una familia (Guillén) establecida en los Pirineos aragoneses por más de 700 años y su impacto regional

Overallocation and Correction of Carbon Emissions in the Evaluation of Carbon Footprint

This paper points to several carbon footprint data distortions that overallocate carbon footprints to individual companies, and to several carbon data intricacies that lead to improved data integrity. Data distortion due to the same company being listed in multiple geographical jurisdictions or through different share classes overstates Emissions Scope 1 by 4.6%, Emissions Scope 2 by 5.5%, Emissions Scope 3 by 10.6% and Reserves by 6.0%. Data distortion due to index construction by having different market capitalization in representative indices overallocates Emissions Scope 1 by 33.9%, Emissions Scope 2 by 27.6%, Emissions Scope 3 by 21.3% and Reserves by 57.2%. A significant amount of carbon data is not precise but is estimated by third-party providers through proprietary techniques. The estimated data for Scope 1 Emissions is 46.4% for the companies in the index. In addition, carbon data is stale, resulting in 94.5% of data being two years old or more. Usage of carbon data in a present format may incorrectly remove some companies from portfolios (negative screen, complete removal) or incorrectly reduce some companies’ weight in a portfolio (partial screen, fractional removal)

Discovering Organizational Hierarchy through a Corporate Ranking Algorithm: The Enron Case

This paper proposes the CorpRank algorithm to extract social hierarchies from electronic communication data. The algorithm computes a ranking score for each user as a weighted combination of the number of emails, the number of responses, average response time, clique scores, and several degree and centrality measures. The algorithm uses principal component analysis to calculate the weights of the features. This score ranks users according to their importance, and its output is used to reconstruct an organization chart. We illustrate the algorithm over real-world data using the Enron corporation’s e-mail archive. Compared to the actual corporate work chart, compensation lists, judicial proceedings, and analyzing the major players involved, the results show promise

Hybrid Human and Machine Learning Algorithms to Forecast the European Stock Market

This paper explores the power of news sentiment to predict financial returns, particularly the returns of a set of European stocks. Building on past decision support work going back to the Delphi method, this paper describes a text analysis expert weighting algorithm that aggregates the responses of both humans and algorithms by dynamically selecting the best answer according to previous performance. The proposed system is tested through an experiment in which ensembles of experts, crowds, and machines analyzed Thomson Reuters news stories and predicted the returns of the relevant stocks mentioned right after the stories appeared. In most cases, the expert weighting algorithm was better than or as good as the best algorithm or human. The algorithm’s capacity to dynamically select the best answers from humans and machines results in an evolving collective intelligence: the final decision is an aggregation of the best automated individual answers, some of which come from machines and some from humans. Additionally, this paper shows that the groups of humans, algorithms, and expert weighting algorithms have associated with them, particularly, news topics that these groups are good at making predictions from

Healthcare Sustainability: Hospitalization Rate Forecasting with Transfer Learning and Location-Aware News Analysis

Monitoring and forecasting hospitalization rates are of essential significance to public health systems in understanding and managing overall healthcare deliveries and strategizing long-term sustainability. Early-stage prediction of hospitalization rates is crucial to meet the medical needs of numerous patients during emerging epidemic diseases such as COVID-19. Nevertheless, this is a challenging task due to insufficient data and experience. In addition, relevant existing work neglects or fails to exploit the extensive contribution of external factors such as news, policies, and geolocations. In this paper, we demonstrate the significant relationship between hospitalization rates and COVID-19 infection cases. We then adapt a transfer learning architecture with dynamic location-aware sentiment and semantic analysis (TLSS) to a new application scenario: hospitalization rate prediction during COVID-19. This architecture learns and transfers general transmission patterns of existing epidemic diseases to predict hospitalization rates during COVID-19. We combine the learned knowledge with time series features and news sentiment and semantic features in a dynamic propagation process. We conduct extensive experiments to compare the proposed approach with several state-of-the-art machine learning methods with different lead times of ground truth. Our results show that TLSS exhibits outstanding predictive performance for hospitalization rates. Thus, it provides advanced artificial intelligence (AI) techniques for supporting decision-making in healthcare sustainability

Using Boosting for Financial Analysis and Performance Prediction: Application to S&P 500 Companies, Latin American ADRs and Banks

Financial analysis, Machine learning, Adaboost, Data mining,