ABSTRACT
Predictive analytics of financial markets in developed and emerging economies during the COVID-19 regime is undeniably challenging due to unavoidable uncertainty and the profound proliferation of negative news on different platforms. Tracking the media echo is crucial to explaining and anticipating the abrupt fluctuations in financial markets. The present research attempts to propound a robust framework capable of channeling macroeconomic reflectors and essential media chatter-linked variables to draw precise forecasts of future figures for Spanish and Indian stock markets. The predictive structure combines Isometric Mapping (ISOMAP), which is a non-linear feature transformation tool, and Gradient Boosting Regression (GBR), which is an ensemble machine learning technique to perform predictive modelling. The Explainable Artificial Intelligence (XAI) is used to interpret the black-box type predictive model to infer meaningful insights. The overall results duly justify the incorporation of local and global media chatter indices in explaining the dynamics of respective financial markets. The findings imply marginally better predictability of Indian stock markets than their Spanish counterparts. The current work strives to compare and contrast the reaction of developed and developing financial markets during the COVID-19 pandemic, which has been argued to share a close resemblance to the Black Swan event when applying a robust research framework. The insights linked to the dependence of stock markets on macroeconomic indicators can be leveraged for policy formulations for augmenting household finance. © 2023 by the authors.
ABSTRACT
Non Fungible Tokens (NFT) and Decentralized Finance (DeFi) assets have seen a growing media coverage and garnered considerable investor traction despite being classified as a niche in the digital financial sector. The lack of substantial research to demystify the dynamics of NFT and DeFi coins motivates the scrupulous analysis of the said sector. This work aims to critically delve into the evolutionary pattern of the NFTs and DeFis for performing predictive analytics of the same during the COVID-19 regime. The multivariate framework comprises the systematic inclusion of explanatory features embodying technical indicators, key macroeconomic indicators, and constructs linked to media hype and sentiment pertinent to the pandemic, nonlinear feature engineering, and ensemble machine learning. Isometric Mapping (ISOMAP) and Uniform Manifold Approximation and Projection (UMAP) techniques are conjugated with Gradient Boosting Regression (GBR) and Random Forest (RF) for enabling the predictive analysis. The predictive performance rationalizes the frameworks' capacity to accurately predict the prices of the majority of the NFT and DeFi coins during the ongoing financial distress period. Additionally, Explainable Artificial Intelligence (XAI) methodologies are used to comprehend the nature of the impact of the explanatory variables. Findings suggest that the daily movement of the NFTs and DeFi highly depends on their past historical movement. © 2023 The Authors
ABSTRACT
This study presents a dynamic model for the management of a cruise port, simulating the behavior of the system under different scenarios for the period 2015–2025. The proposed management system considers the factors that determine the demand and supply of cruise passengers and the existing relationships between them, which has allowed simulations to be carried out using System Dynamics (SD) modelling. With information referring to the case Malaga's cruise port (Spain), the results show future scenarios in which the flow of cruise passengers is conditioned both by the effects of COVID-19 and by the tourism and macroeconomic environment. The results also suggest that SD modelling is a tool that can be used to support decision-making in cruise port management by helping to identify strategies that guarantee the sustainability of the system.
ABSTRACT
Background: Human cardiac pericytes (PC) were proposed as the main cellular target for SARS-CoV-2 in the heart due to high transcriptional levels of the angiotensin-converting enzyme 2 (ACE2) receptor. Emerging reports indicate CD147/Basigin (BSG), highly expressed in endothelial cells (EC), is an alternative SARS-CoV-2 receptor. To date, the mechanism by which the virus infects and disrupts the heart vascular cells was not identified yet. Moreover, cleaved Spike (S) protein molecules could be released into the bloodstream from the leaking pulmonary epithelial-endothelial barrier in patients with severe COVID-19, opening to the possibility of non-infective diseases in organs distant from the primary site of infection. Purposes: (1) to confirm that human primary cardiac PC express ACE2 and CD147;(2) to verify if PC are permissible to SARS-CoV-2 infection;(3) to investigate if the recombinant SARS-CoV-2 S protein alone, without the other viral elements, can trigger molecular signalling and induce functional alterations in PC;(4) to explore which viral receptor is responsible for the observed events. Methods and results: Cardiac PC express both the ACE2 and CD147 receptors at mRNA and protein level. Incubation of PC for up to 5 days with SARS-CoV-2 expressing the green fluorescent protein (GFP) did not show any evidence of cell infection or viral replication. Next, we exposed the PC to the recombinant S protein (5.8 nM) and confirmed that the protein engaged with cellular receptors (western blot analysis of S protein in treated and control PC). Incubation with the S protein increased PC migration (wound closure assay, P<0.01 vs ctrl) and reduced the formation of tubular structures between PC and EC in a Matrigel assay (P<0.01 vs ctrl). Moreover, the S protein promoted the production of pro-inflammatory factors typical of the cytokine storm in PC (ELISA measurement of MCP1, IL-6, IL-1β, TNFα, P<0.05 vs ctrl), and induced the secretion of proapoptotic factors responsible for EC death (Caspase 3/7 assay, P<0.05 vs ctrl). Signalling studies revealed that the S protein triggers the phosphorylation/ activation of the extracellular signal-regulated kinase 1/2 (ERK1/2) through the CD147 receptor, but not ACE2, in cardiac PC. The neutralization of CD147, using a blocking antibody, prevented ERK1/2 activation in PC, and was reflected into a partial rescue of the cell functional behaviour (migration and pro-angiogenic capacity). In contrast, blockage of CD147 failed to prevent the pro-inflammatory response in PC. Conclusions: We propose the novel hypothesis that COVID-19 associated heart's microvascular dysfunction is prompted by circulating S protein molecules rather than by the direct coronavirus infection of PC. Besides, we propose CD147, and not ACE2, as the leading receptor mediating S protein signalling in cardiac PC.