Covid-19 versus H1N1: a comparative study of the impact of viruses on small and large economies on the Stock Market: The special study of Portugal

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Risk Analysis and Management.Esta dissertação descreve e detalha, o impacto que as crises pandémicas têm na volatilidade dos mercados da bolsa de pequenas e grandes economias. Relativamente aos países estudados, escolhemos como casos de estudo os Estados Unidos da América (grande economia), a Grécia (pequena economia), e Portugal, como comparação aos dois países mencionados anteriormente, mas apenas no que se refere à consequência da pandemia provocada pela Covid-19. Em termos de metodologia, a volatilidade financeira diária é tradicional para modelar um processo GARCH (1,1). Este modelo foi utilizado no programa SAS para provar se a volatilidade podia ser ou não correlacionada. Adicionalmente, os coeficientes de correlação linear de Pearson foram realizados e analisados em várias variáveis, tais como o valor no fecho, casos e mortes confirmados com a volatilidade diária entre cada país e a volatilidade histórica diária. Finalmente, o estudo mostra como as pandemias do século XXI tiveram impacto tanto na bolsa de valores (financeiramente), como no produto interno bruto (economicamente). Esta dissertação comprova que existe, de facto, uma enorme volatilidade no mercado de bolsa no início de um fenómeno atípico. Contudo, após um determinado período de tempo, o mercado de bolsa corrige-se. Saliento, que na pandemia de Covid-19, apesar dos Estados Unidos da América terem sofrido uma repercussão no seu Produto Interno Bruto, Portugal teve implicações ainda mais fortes na economia, tal como a Grécia (países de economia pequena). Referente à parte financeira, Portugal compara-se igualmente à Grécia aquando se realizou as correlações entre as volatilidades históricas diárias. No entanto aproxima se dos Estados Unidos da América nas correlações das várias variáveis, transcritas anteriormente, tais como o valor no fecho e os casos e mortes confirmados com a volatilidade diária entre cada país. Conclui-se que as pandemias causaram impacto nos mercados de bolsa nos países estudados e mencionados supra

Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Perez-Jorge, S., Tobena, M., Prieto, R., Vandeperre, F., Calmettes, B., Lehodey, P., & Silva, M. A. Environmental drivers of large-scale movements of baleen whales in the mid-North Atlantic Ocean. Diversity and Distributions, 00, (2020): 1-16, doi:10.1111/ddi.13038.Aim Understanding the environmental drivers of movement and habitat use of highly migratory marine species is crucial to implement appropriate management and conservation measures. However, this requires quantitative information on their spatial and temporal presence, which is limited in the high seas. Here, we aimed to gain insights of the essential habitats of three baleen whale species around the mid‐North Atlantic (NA) region, linking their large‐scale movements with information on oceanographic and biological processes. Location Mid‐NA Ocean. Methods We present the first study combining data from 31 satellite tracks of baleen whales (15, 10 and 6 from fin, blue and sei whales, respectively) from March to July (2008–2016) with data on remotely sensed oceanography and mid‐ and lower trophic level biomass derived from the spatial ecosystem and population dynamics model (SEAPODYM). A Bayesian switching state‐space model was applied to obtain regular tracks and correct for location errors, and pseudo‐absences were created through simulated positions using a correlated random walk model. Based on the tracks and pseudo‐absences, we applied generalized additive mixed models (GAMMs) to determine the probability of occurrence and predict monthly distributions. Results This study provides the most detailed research on the spatio‐temporal distribution of baleen whales in the mid‐NA, showing how dynamic biophysical processes determine their habitat preference. Movement patterns were mainly influenced by the interaction of temperature and the lower trophic level biomass; however, this relationship differed substantially among species. Best‐fit models suggest that movements of whales migrating towards more productive areas in northern latitudes were constrained by depth and eddy kinetic energy. Main conclusions These novel insights highlight the importance of integrating telemetry data with spatially explicit prey models to understand which factors shape the movement patterns of highly migratory species across large geographical scales. In addition, our outcomes could contribute to inform management of anthropogenic threats to baleen whales in sparsely surveyed region.We are very grateful to Cláudia Oliveira, Irma Cascão, Maria João Cruz, Miriam Romagosa and many volunteers, skilled skippers, crew and spotters that participated in the tagging fieldwork. This work was supported by Fundação para a Ciência e Tecnologia (FCT), Azores 2020 Operational Programme and Fundo Regional da Ciência e Tecnologia (FRCT) through research projects FCT‐Exploratory project (IF/00943/2013/CP1199/CT0001), TRACE (PTDC/MAR/74071/2006) and MAPCET (M2.1.2/F/012/2011) co‐funded by FEDER, COMPETE, QREN, POPH, ESF, ERDF, Portuguese Ministry for Science and Education, and Proconvergencia Açores/EU Program. We also acknowledge funds provided by FCT to MARE, through the strategic project UID/MAR/04292/2013. SPJ was supported by a postdoctoral grant (REF.GREENUP/001‐2016), MT by a DRCT doctoral grant (M3.1.a/F/028/2015), MAS by an FCT‐Investigator contract (IF/00943/2013), FV by an FCT Investigator contract (CEECIND/03469/2017) and RP by an FCT postdoctoral grant (SFRH/BPD/108007/2015). LMTL modelling work has been supported by the CMEMS Service Evolution GREENUP project, funded by Mercator Ocean. We are grateful to Elliott Hazen for offering guidance and advice, and to two anonymous referees whose comments greatly improved this work

Identifying significant factors and optimal sites for commercial salmon farming in northern Norway. An integrated GIS and machine learning approach using random forest.

This study presents a data-driven modelling approach to identify important factors influencing the growth- and mortality rate for farmed salmon in northern Norway. Furthermore, a model is trained to determine the best fish farming sites and identify optimal areas with the best geographical conditions. Aquaculture site production and location data from 323 salmon farming sites (all licensed aquaculture sites) in northern Norway were obtained from the Directory of Fisheries. Two dependent variables, growth- and mortality rate, were calculated based on the monthly increase in biomass and mortality. These variables were combined with state-of-the-art environmental- and exploratory socio-economic data obtained from the institute of marine research (IMR), the Norwegian Meteorological Institute, Delft University of Technology, Norwegian Coastal Administration, and Statistics Norway. Using random forest regression and recursive feature elimination, a data-driven ensemble approach identified significant variables. Prediction of optimal sites for salmon farming in northern Norway was done with a species distribution modelling approach using random forest classification. The important factors affecting salmon growth were specific feeding rate, temperature, and total biomass. The important factors influencing salmon mortality were temperature and total biomass. The predicted optimal areas were inside Vefsnfjorden, Ranfjorden, Sørfjorden and Glomfjorden, small areas near the coast and around the small islands stretching from Gladstad to Narvik. Areas near the coast of Lofoten, Værøy, Røst, Vesterålen, Sortland and Senja. Further north, some dispersed regions were predicted as optimal outside Tromsø and Sørøya. Also large areas around Varangerhalvøya, Olderdalen/Kåfjorden, Lille Altafjorden and near the shore on both sides of Stjernøysundet. The results clearly show that space is a scares resource and that there is an urge to evaluate the regulations and legislations concerning aquaculture in Norway. Especially the minimum distances between the fairways and aquaculture locations. The incorporation of machine learning approaches in GIS-based MCE analysis is suggested to help planners and decision-makers make informed and sustainable decisions about sea-area use

Temporal correlation of population composition and environmental variables in the marine invader Ciona robusta

The capacity for ascidians to inhabit coastal sea floor worldwide relies on their peculiar tolerance to environmental variables and pollution, which is considered, together with high levels of genetic diversity, among the main drivers of their invasive potential. In spite of the continued interest in the genetics of invasive species, little attention has been paid toward the microevolutionary processes that drive structure and fate of ascidian populations over time under chemically polluted conditions. Understanding the interplay between environmental and population dynamics is critical to predict the biodiversity of marine coastal ecosystems. In the present study, a local population of the ascidian Ciona robusta living in the Fusaro Lagoon has been monitored over a 13-month period of sampling. Physico-chemical parameters (temperature, salinity, turbidity, dissolved oxygen, heavy metals), genetic composition (microsatellites, ITS-2), abundance and biomass (wet and dry weight) were assessed with the aim to infer fine-scale temporal variation of population structure with respect to rapid environmental change. Analysis of biomass showed that C. robusta is highly sensitive to salinity and oxygen concentrations. Further, genetic analysis suggested a highly dynamic population structure, likely due to the strong clustering of temporal samples and distinct responses to environmental conditions, including bioaccumulation of heavy metals. Here, we hypothesize that rapid variation in allele frequencies of neutral markers in C. robusta populations may increase the ability of the species to colonize habitats that are subject to strong variation and are under heavy human pressure

The Effect of Communication and the Ability of Employee Performance through Motivation in PT CGGS Indonesia

Entering the 21st century is a necessity for the business world that the wave of globalization is happening; every country must participate or forced to participate in the global trade arena. On the other hand, human resources in the company's business sector have an important role in the achievement of the objectives of the company to participate in this global trade competition. The human resources within a company cover all employees within the company, from the highest level of the hierarchy to the lowest. Every individual who is in the company has their respective roles that need to be addressed continuously so that creativity and innovation do not stop and die suspended in its development. This study aims to analyze the influence of communication, workability to employee performance through motivation. Population and sample of research are employees of PT. CGGS Indonesia are 40 employees with simple random sampling technique. The results showed that motivation variable as an intervening variable can be proved perfectly that motivation variable gives indirect influence on communication variable to employee performance and variable ability to employee performance. This statement can be proven by the amount of indirect influence communication on employee performance and ability to employee performance through motivation identified as an intervening variable

Disruption to control network function correlates with altered dynamic connectivity in the wider autism spectrum.

Autism is a common developmental condition with a wide, variable range of co-occurring neuropsychiatric symptoms. Contrasting with most extant studies, we explored whole-brain functional organization at multiple levels simultaneously in a large subject group reflecting autism's clinical diversity, and present the first network-based analysis of transient brain states, or dynamic connectivity, in autism. Disruption to inter-network and inter-system connectivity, rather than within individual networks, predominated. We identified coupling disruption in the anterior-posterior default mode axis, and among specific control networks specialized for task start cues and the maintenance of domain-independent task positive status, specifically between the right fronto-parietal and cingulo-opercular networks and default mode network subsystems. These appear to propagate downstream in autism, with significantly dampened subject oscillations between brain states, and dynamic connectivity configuration differences. Our account proposes specific motifs that may provide candidates for neuroimaging biomarkers within heterogeneous clinical populations in this diverse condition

Enhancing Prediction and Analysis of UK Road Traffic Accident Severity Using AI: Integration of Machine Learning, Econometric Techniques, and Time Series Forecasting in Public Health Research

This research investigates road traffic accident severity in the UK, using a combination of machine learning, econometric, and statistical methods on historical data. We employed various techniques, including correlation analysis, regression models, GMM for error term issues, and time-series forecasting with VAR and ARIMA models. Our approach outperforms naive forecasting with an MASE of 0.800 and ME of -73.80. We also built a random forest classifier with 73% precision, 78% recall, and a 73% F1-score. Optimizing with H2O AutoML led to an XGBoost model with an RMSE of 0.176 and MAE of 0.087. Factor Analysis identified key variables, and we used SHAP for Explainable AI, highlighting influential factors like Driver_Home_Area_Type and Road_Type. Our study enhances understanding of accident severity and offers insights for evidence-based road safety policies.Comment: 3

Italian Equity Funds: Efficiency and Performance Persistence

Have Italian mutual funds been able to generate “extra-return”? Were some of them able to persistently beat the competitors? In this paper we address these questions and provide a detailed and systematic performance and return persistence analysis of the Italian equity mutual funds. We show that, in general, fund managers have not been able to score extra-performances and only few managers had stock picking ability or market timing ability. This evidence is consistent with the market efficiency hypothesis. Moreover, concerning performance persistence, first, we cannot trace out the hot-hand phenomenon on raw returns. The no persistence effect is fairly robust to: the performance measure, the temporal lag and the different methodology employed for testing persistence. Second, there has not been long-run persistence on risk-adjusted returns (we find a weak evidence of the reversal effect). Finally, the past performance displays weak evidence of the hot-hand effect on risk-adjusted returns on four-month using cross-section tests. However, as soon as we analyse yearly intervals any evidence of persistence disappears.Mutual funds, Performance evaluation