Abstract

Abstract
Traditional historical data analytics is at risk in a world where volatility, uncertainty, complexity, and ambiguity are the new normal. While Streaming Machine Learning (SML) and Time-series Analytics (TSA) attack some aspects of the problem, we still need a comprehensive solution. SML trains models using fewer data and in a continuous/adaptive way relaxing the assumption that data points are identically distributed. TSA considers temporal dependence among data points, but it assumes identical distribution. Every Data Scientist fights this battle with ad-hoc solutions. In this paper, we claim that, due to the temporal dependence on the data, the existing solutions do not represent robust solutions to efficiently and automatically keep models relevant even when changes occur, and real-time processing is a must. We propose a novel and solid scientific foundation for Time-Evolving Analytics from this perspective. Such a framework aims to develop the logical, methodological, and algorithmic foundations for fast, scalable, and resilient analytics.

Abstract

Abstract
BackgroundThe COVID-19 pandemic reinforced the need to invest in nursing practice environments and health institutions were led to implement several changes. In this sense, this study aimed to analyze the impact of the changes that occurred in nursing practice environments between the first and fourth critical periods of the pandemic.MethodsQuantitative, observational study, conducted in a University Hospital, with the participation of 713 registered nurses. Data were collected through a questionnaire with sociodemographic and professional characterization and the Scale for the Environments Evaluation of Professional Nursing Practice, applied at two different points in time: from 1 to 30 June 2020 and from 15 August to 15 September 2021. Data were processed using descriptive and inferential statistics.ResultsOverall, the pandemic had a positive impact on nursing practice environments. However, the Process component remained favourable to quality of care, while the Structure and Outcome components only moderately favourable. Nurses working in Medicine Department services showed lower scores in several dimensions of the Structure, Process and Outcome components. On the other hand, nurses working in areas caring for patients with COVID-19 showed higher scores in several dimensions of the Structure, Process and Outcome components.ConclusionsThe pandemic had a positive impact on various dimensions of nursing practice environments, which denotes that regardless of the adversities and moments of crisis that may arise, investment in work environments will have positive repercussions.However, more investment is needed in Medicine Department services, which have historically been characterised by high workloads and structural conditions that make it difficult to promote positive and sustainable workplaces.

Abstract
It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect testing for heteroscedasticity. The results show the distribution of the test statistics is changed by systematic sampling, causing a serious power loss that increases with the sampling interval. Consequently, the tests often fail to reject the hypothesis of no conditional heteroscedasticity, leading to the wrong decision and missing the true nature of the data-generating process.

Abstract
Climate change will increase the frequency of drought, heat waves, and wildfires. We intended to analyse how fire recurrence and/or induced water stress can affect seed germination and root cell division in Pinus pinaster Aiton. Seeds from stands with no prior fire history and from post-fire regeneration (in areas burnt once, twice, and thrice) in northern Portugal were germinated in distilled water (control) and polyethylene glycol (PEG) to simulate water stress for four weeks, followed by a recovery period. Roots were analysed cytogenetically. The germination index of the Pinus pinaster seeds was not statistically influenced by the induction of osmotic stress, nor by the fire recurrence of the stands. The mean germination time (MGT) was 10-29 days and 1-36 days for the stress and recovery periods, respectively, and increased with PEG concentration. The 20% PEG treatment inhibited root growth after germination. The 10% PEG treatment induced a high frequency of cytogenetic anomalies, mostly in the sites which experienced fire exposure. While fire recurrence did not affect the germination rate, it seemed to reduce the water stress response, negatively impacting cell division and impair root growth.