Financial Investment and Economic Policy Uncertainty in the UK

UK based financial firms following Brexit reported net disinvestment of 15 billion pounds. This was the fifth time financial disinvestment occurred since the production of this data: 1987. Parallel to this event, Economic Policy Uncertainty (EPU) in the UK experienced its biggest rise during Brexit June 2016. This note studies the relationship between EPU and its particular components and financial investment. I find that overall EPU and specifically fiscal policy, monetary policy, geopolitical, regulation and liquidity uncertainty have the highest negative sensitivity to financial investment

North Jameson Land 2019 Expedition Report

To explore the North Jameson Land area from a drop-off point at the head of Permdal

When Moneyball Meets the Beautiful Game: A Predictive Analytics Approach to Exploring Key Drivers for Soccer Player Valuation

To measure the market value of a professional soccer (i.e., association football) player is of great interest to soccer clubs. Several gaps emerge from the existing soccer transfer market research. Economics literature only tests the underlying hypotheses between a player’s market value or wage and a few economic factors. Finance literature provides very theoretical pricing frameworks. Sports science literature uncovers numerous pertinent attributes and skills but gives limited insights into valuation practice. The overarching research question of this work is: what are the key drivers of player valuation in the soccer transfer market? To lay the theoretical foundations of player valuation, this work synthesizes the literature in market efficiency and equilibrium conditions, pricing theories and risk premium, and sports science. Predictive analytics is the primary methodology in conjunction with open-source data and exploratory analysis. Several machine learning algorithms are evaluated based on the trade-offs between predictive accuracy and model interpretability. XGBoost, the best model for player valuation, yields the lowest RMSE and the highest adjusted R2. SHAP values identify the most important features in the best model both at a collective level and at an individual level. This work shows a handful of fundamental economic and risk factors have more substantial effect on player valuation than a large number of sports science factors. Within sports science factors, general physiological and psychological attributes appear to be more important than soccer-specific skills. Theoretically, this work proposes a conceptual framework for soccer player valuation that unifies sports business research and sports science research. Empirically, the predictive analytics methodology deepens our understanding of the value drivers of soccer players. Practically, this work enhances transparency and interpretability in the valuation process and could be extended into a player recommender framework for talent scouting. In summary, this work has demonstrated that the application of analytics can improve decision-making efficiency in player acquisition and profitability of soccer clubs

A Machine Learning Approach for Micro-Credit Scoring

© 2021 by the authors. In micro-lending markets, lack of recorded credit history is a significant impediment to assessing individual borrowers’ creditworthiness and therefore deciding fair interest rates. This research compares various machine learning algorithms on real micro-lending data to test their efficacy at classifying borrowers into various credit categories. We demonstrate that off-the-shelf multi-class classifiers such as random forest algorithms can perform this task very well, using readily available data about customers (such as age, occupation, and location). This presents inexpensive and reliable means to micro-lending institutions around the developing world with which to assess creditworthiness in the absence of credit history or central credit databases

A local feature engineering strategy to improve network anomaly detection

The dramatic increase in devices and services that has characterized modern societies in recent decades, boosted by the exponential growth of ever faster network connections and the predominant use of wireless connection technologies, has materialized a very crucial challenge in terms of security. The anomaly-based intrusion detection systems, which for a long time have represented some of the most efficient solutions to detect intrusion attempts on a network, have to face this new and more complicated scenario. Well-known problems, such as the difficulty of distinguishing legitimate activities from illegitimate ones due to their similar characteristics and their high degree of heterogeneity, today have become even more complex, considering the increase in the network activity. After providing an extensive overview of the scenario under consideration, this work proposes a Local Feature Engineering (LFE) strategy aimed to face such problems through the adoption of a data preprocessing strategy that reduces the number of possible network event patterns, increasing at the same time their characterization. Unlike the canonical feature engineering approaches, which take into account the entire dataset, it operates locally in the feature space of each single event. The experiments conducted on real-world data showed that this strategy, which is based on the introduction of new features and the discretization of their values, improves the performance of the canonical state-of-the-art solutions

A critical review on the vulnerability assessment of natural gas pipelines subjected to seismic wave propagation. Part 1:Fragility relations and implemented seismic intensity measures

© 2019 Elsevier Ltd Natural gas (NG) pipeline networks constitute a critical means of energy transportation, playing a vital role in the economic development of modern societies. The associated socio-economic and environmental impact, in case of seismically-induced severe damage, highlights the importance of a rational assessment of the structural integrity of this infrastructure against seismic hazards. Up to date, this assessment is mainly performed by implementing empirical fragility relations, which associate the repair rate, i.e. the number of repairs/damages per unit length of the pipeline, with a seismic intensity measure. A limited number of analytical fragility curves that compute probabilities of failure for various levels of predefined damage states have also been proposed, recently. In the first part of this paper, a thorough critical review of available fragility relations for the vulnerability assessment of buried NG pipelines is presented. The paper focuses on the assessment against seismically-induced transient ground deformations, which, under certain circumstances, may induce non-negligible deformations and strains on buried NG pipelines, especially in cases of pipelines crossing heterogeneous soil sites. Particular emphasis is placed on the efficiency of implemented seismic intensity measures to be evaluated or measured in the field and, more importantly, to correlate with observed structural damage on buried NG pipelines. In the second part of this paper, alternative methods for the analytical evaluation of the fragility of steel NG pipelines under seismically-induced transient ground deformations are presented. Through the discussion, recent advancements in the field are highlighted, whilst acknowledged gaps are identified, providing recommendations for future research

Healthcare Cyber Security Challenges and Solutions Under the Climate of COVID19: A Scoping Review

Background: COVID-19 has challenged the resilience of the health care information system, which has affected our ability to achieve the global goal of health and well-being. The pandemic has resulted in a number of recent cyberattacks on hospitals, pharmaceutical companies, the US Department of Health and Human Services, the World Health Organization and its partners, and others. Objective: The aim of this review was to identify key cybersecurity challenges, solutions adapted by the health sector, and areas of improvement needed to counteract the recent increases in cyberattacks (eg, phishing campaigns and ransomware attacks), which have been used by attackers to exploit vulnerabilities in technology and people introduced through changes to working practices in response to the COVID-19 pandemic. Methods: A scoping review was conducted by searching two major scientific databases (PubMed and Scopus) using the search formula “(covid OR healthcare) AND cybersecurity.” Reports, news articles, and industry white papers were also included if they were related directly to previously published works, or if they were the only available sources at the time of writing. Only articles in English published in the last decade were included (ie, 2011-2020) in order to focus on current issues, challenges, and solutions. Results: We identified 9 main challenges in cybersecurity, 11 key solutions that health care organizations adapted to address these challenges, and 4 key areas that need to be strengthened in terms of cybersecurity capacity in the health sector. We also found that the most prominent and significant methods of cyberattacks that occurred during the pandemic were related to phishing, ransomware, distributed denial-of-service attacks, and malware. Conclusions: This scoping review identified the most impactful methods of cyberattacks that targeted the health sector during the COVID-19 pandemic, as well as the challenges in cybersecurity, solutions, and areas in need of improvement. We provided useful insights to the health sector on cybersecurity issues during the COVID-19 pandemic as well as other epidemics or pandemics that may materialize in the future

Designing and implementing online assessment in the clinical workplace

Peer reviewe