Una aproximación a las emociones en los equipos deportivos (An approach to emotions in sports teams)

Este trabajo se adentra en la convivencia de equipos deportivos pertenecientes a disciplinas que implican contacto con el equipo rival en su práctica, a partir del análisis empírico procedente de 30 entrevistas en profundidad con entrenadores/as y deportistas de equipos de la Comunidad de Madrid de las disciplinas de fútbol, baloncesto y rugby. En un acercamiento feminista desde la sociología del deporte, se señalan las formas de organización de los equipos deportivos, visibilizando las estrictas jerarquías y sistemas de disciplinamiento normalizados, incidiendo en ciertas prácticas exacerbadas que pueden llegar a justificarse en el contexto. A partir de aquí, se realiza un análisis a través de las especiales sensibilidades del deporte, incidiendo en el estudio de las emociones y las dinámicas afectivas en estos equipos deportivos, reflexionando acerca de su misión como sustento de sistemas que perpetúan fragilidades que darán pie a ciertas vulnerabilidades en el espacio, especialmente hacia las mujeres. Abstract. This work analyze the coexistence of sports teams of disciplines that involve contact with the rival team in their practice, based on the empirical analysis from 30 interviews with coaches and athletes of teams from the Community of Madrid of the soccer, basketball and rugby disciplines. In a feminist approach from the sociology of sport, the forms of organization of sports teams are pointed out, making visible the strict hierarchies and normalized disciplinary systems, influencing certain exacerbated practices that can be justified in the context. From here, an analysis is carried out through the special sensitivities of sport that affects the study of emotions and affective dynamics in these sports teams, reflecting on their mission as support for systems that perpetuate fragility that will give rise to certain vulnerabilities in space, especially against women.

A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.

Analyzing the Association between Pattern and Returns Using Goodman–Kruskal Prediction Error Reduction Index (λ)

For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.