Conformal Dynamics of 0-Branes

We investigate the dynamics of dilatonic D-dimensional 0-branes in the near-horizon regime. The theory is given in a twofold form: two-dimensional dilaton gravity and nonlinear sigma model. Using asymptotic symmetries, duality relations, and sigma model techniques we find that the theory has three conformal points which correspond to (a) the asymptotic (Anti-de Sitter) region of the two-dimensional spacetime, (b) the horizon of the black hole, and (c) the infinite limit of the coupling parameter. We show that the conformal symmetry is perturbatively preserved at one-loop, identify the corresponding conformal field theories, and calculate the associated central charges. Finally, we use the conformal field theories to explain the thermodynamical properties of the two-dimensional black holes.Comment: 22 pages, LaTex fil

Heat Transfer Analysis of Damaged Shrouded High-Pressure Turbine Rotor Blades

Due to the increasingly high turbine inlet temperatures, heat transfer analysis is now, more than ever, a vital part of the design and optimization of high-pressure turbine rotor blades of a modern jet engine. The present study aimed to find out how shape deviation and in-service deterioration affect heat exchange patterns on the rotor blade. The rotor geometries used for this analysis are represented by a set of high-resolution 3D structured light scans of blades with the same number of in-service hours. An automatic meshing technique was employed to generate high-resolution meshes directly on the scanned rotor geometries, which captured all the surface features with high fidelity. Steady-state 3D RANS flow simulations with a k-ω SST turbulence model were conducted on a one-and-a-half stage computational domain of the scanned geometries. First, the distribution of the heat transfer coefficient was calculated for each blade; then, a correlation was sought between the heat transfer coefficient and parametrized shape deviation, to assess the impact of each parameter on HTC levels

THE VERNACULAR HERITAGE OF GJIROKASTRA (ALBANIA): ANALYSIS OF URBAN AND CONSTRUCTIVE FEATURES, THREATS AND CONSERVATION STRATEGIES

Abstract. The old town of Gjirokastra (Albania), was included in the World Heritage List in 2005 thanks to the valuable presence of several remarkable examples of Ottoman-styled houses and in the integrity of the vernacular urban landscape. The urban structure is strongly influenced by the orography of the Drino valley and its slopes where the city was founded. Stone is the building material that characterizes the paving of the streets, the walls of the buildings and the roof coverings. The wood, mostly local, was used to build the frame structure of the upper floors and the roofs, in order to provide large windows and bright interior spaces. In December 2018, as part of the activities of the 3D Past project, founded by Eu Creative Europe Programme, Italian and Albanian students took part in a workshop in Gjirokastra. Such an initiative was designed to understand the tangible and intangible components of the vernacular heritage of Gjrokastra. In a multidisciplinary approach, students, professors, researchers and local experts analysed the morphological features of the historic center, the public spaces, and the traditional building systems. Traditional instruments such as the direct survey, the on-site observation and the interviews were adopted in combination with more innovative tools such as the laser scanner and the photogrammetry. This contribution not only illustrates the results of a multi-scale analysis, but it also highlights the transformations and threats that endanger the transmission of the unique characteristics of the city to the future generations. Moreover, it deals with the conservation strategies currently in use and some possible future measures that can contribute to the sustainable safeguard and development of the site

Statistical arbitrage powered by Explainable Artificial Intelligence

Machine learning techniques have recently become the norm for detecting patterns in financial markets. However, relying solely on machine learning algorithms for decision-making can have negative consequences, especially in a critical domain such as the financial one. On the other hand, it is well-known that transforming data into actionable insights can pose a challenge even for seasoned practitioners, particularly in the financial world. Given these compelling reasons, this work proposes a machine learning approach powered by eXplainable Artificial Intelligence techniques integrated into a statistical arbitrage trading pipeline. Specifically, we propose three methods to discard irrelevant features for the prediction task. We evaluate the approaches on historical data of component stocks of the S&P500 index and aim at improving not only the prediction performance at the stock level but also overall at the stock set level. Our analysis shows that our trading strategies that include such feature selection methods improve the portfolio performances by providing predictive signals whose information content suffices and is less noisy than the one embedded in the whole feature set. By performing an in-depth risk-return analysis, we show that the proposed trading strategies powered by explainable AI outperform highly competitive trading strategies considered as baselines

Social Support and Self-Efficacy on Turnover Intentions: The Mediating Role of Conflict and Commitment

Turnover intentions are a phenomenon that affects the life of organizations and causes highly negative consequences. Based on previous studies, it is possible to consider antecedents to turnover in terms of both individual and social perceived resources, which previous research does not usually examine simultaneously. The aim of this study was to explore the role of both resources (individual and social) on turnover intentions. Thus, we hypothesized that perceived social support and self-efficacy have an impact on turnover intentions and that this relationship is mediated by interpersonal conflict and Affective Commitment. A total of 392 Italian employees completed a self-report questionnaire. A structural equation model was tested. The results showed that interpersonal conflict and Affective Commitment fully mediated the relationship between social support, self-efficacy and turnover intentions. Practical implications are discussed

Ensembling and Dynamic Asset Selection for Risk-Controlled Statistical Arbitrage

In recent years, machine learning algorithms have been successfully employed to leverage the potential of identifying hidden patterns of financial market behavior and, consequently, have become a land of opportunities for financial applications such as algorithmic trading. In this paper, we propose a statistical arbitrage trading strategy with two key elements: an ensemble of regression algorithms for asset return prediction, followed by a dynamic asset selection. More specifically, we construct an extremely heterogeneous ensemble ensuring model diversity by using state-of-the-art machine learning algorithms, data diversity by using a feature selection process, and method diversity by using individual models for each asset, as well models that learn cross-sectional across multiple assets. Then, their predictive results are fed into a quality assurance mechanism that prunes assets with poor forecasting performance in the previous periods. We evaluate the approach on historical data of component stocks of the SP500 index. By performing an in-depth risk-return analysis, we show that this setup outperforms highly competitive trading strategies considered as baselines. Experimentally, we show that the dynamic asset selection enhances overall trading performance both in terms of return and risk. Moreover, the proposed approach proved to yield superior results during both financial turmoil and massive market growth periods, and it showed to have general application for any risk-balanced trading strategy aiming to exploit different asset classes

Tachyons in de Sitter space and analytical continuation from dS/CFT to AdS/CFT

We discuss analytic continuation from d-dimensional Lorentzian de Sitter (dS$_d$) to d-dimensional Lorentzian anti-de Sitter (AdS$_{d}$) spacetime. We show that AdS$_{d}$, with opposite signature of the metric, can be obtained as analytic continuation of a portion of dS$_d$. This implies that the dynamics of (positive square-mass) scalar particles in AdS$_{d}$ can be obtained from the dynamics of tachyons in dS$_d$. We discuss this correspondence both at the level of the solution of the field equations and of the Green functions. The AdS/CFT duality is obtained as analytic continuation of the dS/CFT duality.Comment: 17 pages, 1 figure, JHEP styl

The current crisis of academia-led research: A threat to the common good? Preliminary data from Europe and the United States

Objective: This research note aimed to analyze the scientific productivity trends 2015-2019, focusing on the top 30 universities in Europe and United States and on the top 30 private companies - as classified in the SCImago Institutions Ranking. Our hypothesis is that private companies are gaining an increasingly prominent role in the research field, while academia is losing its predominance. Results: From 2015 to 2019, all universities in Europe and the United States lost positions in the scientific production ranking, while private companies gained positions. These trends seem to be driven mainly by the scientific productivity sub-indicator "Innovation". These data suggest that the role private companies will play in the future will not be limited to support research economically or influence it from "outside". Private companies have taken a path that may lead them to directly control all stages of production/communication of knowledge, including research - a role once bestowed on universities. Our data, although preliminary, seem to suggest that, at present, academia risks losing its predominance in the research field. This scenario deserves attention because of the threats it may pose to the independence of research and its role in supporting human equity and sustainable health for all