A Project Portfolio Management model adapted to non-profit organizations

As they strive towards greater professionalism in carrying out their activities, non-profit organizations (NPOs) have begun paying attention to project management. The non-profit sector (NPS) has also begun to adopt strategic planning techniques, thus making the acceptance of project portfolio management (PPM) methodology a natural consequence. This article aims to propose a project portfolio management model adapted to the context of NPOs

MODELING AND SIMULATION OF THERMOELECTRIC PLANT OF COMBINED CYCLES AND ITS ENVIRONMENTAL IMPACT

The impact any power plant has upon the environment must be minimized as much as possible. Due to its high efficiency, low emission levels and low cooling requirements, combined cycle plants are considered to be environmentally friendly. This study evaluates the effect of operational conditions on pollutants (CO, CO2, SOx, NOx) emissions levels, waste-heat and wastewater of a combined-cycle natural gas and steam power plant. The HYSYS process simulation was used for modelling and simulation. The study clearly shows that the absolute quantity of pollutants emitted is high. Also, it was possible to verify that the unit operate in the condition of minimal emissions regarding the maximum possible, and thus a reduction or elimination of such pollutants is not possible

Tuberculous Spondylitis: A Report of Different Clinical Scenarios and Literature Update

Tuberculosis is still one of the most important health problems in the world. In developed countries, the proportion of extrapulmonary tuberculosis cases is increasing. Nowadays tuberculous spondylitis, also known as Pott disease, is a rare clinical condition but can cause severe vertebral and neurological sequelae that can be prevented with an early correct diagnosis. The aim of this paper is to increase awareness of tuberculous spondylitis in modern times, describing three different cases and discussing its best diagnostic and therapeutic approach based on the current literature.info:eu-repo/semantics/publishedVersio

Sputtering of Oxygen Ice by Low Energy Ions

Naturally occurring ices lie on both interstellar dust grains and on celestial objects, such as those in the outer solar system. These ices are continu- ously subjected to irradiation by ions from the solar wind and/or cosmic rays, which modify their surfaces. As a result, new molecular species may form which can be sputtered off into space or planetary atmospheres. We determined the experimental values of sputtering yields for irradiation of oxygen ice at 10 K by singly (He+, C+, N+, O+ and Ar+) and doubly (C2+, N2+ and O2+) charged ions with 4 keV kinetic energy. In these laboratory experiments, oxygen ice was deposited and irradiated by ions in an ultra high vacuum chamber at low temperature to simulate the environment of space. The number of molecules removed by sputtering was observed by measurement of the ice thickness using laser interferometry. Preliminary mass spectra were taken of sputtered species and of molecules formed in the ice by temperature programmed desorption (TPD). We find that the experimental sputtering yields increase approximately linearly with the projectile ion mass (or momentum squared) for all ions studied. No difference was found between the sputtering yield for singly and doubly charged ions of the same atom within the experimental uncertainty, as expected for a process dominated by momentum transfer. The experimental sputter yields are in good agreement with values calculated using a theoretical model except in the case of oxygen ions. Preliminary studies have shown molecular oxygen as the dominant species sputtered and TPD measurements indicate ozone formation.Comment: to be published in Surface Science (2015

Modelação e Previsão do fluxo de Turismo em Portugal: Perspetivas para uma gestão estratégica

Purpose: The increase of Tourism in Portugal, as well as the companies related to it, it is necessary to analyze and forecast the flow of tourists so that the management of the business is endowed with a competitive strategy. Given the changes in the 'recent' dynamics of tourism data, this article discusses the contributions and limitations of using classical forecasting methodologies, when applied to this sector, namely to the number of overnight stays in tourist accommodation establishments in Portugal. Methodology: The study focuses on the modeling and forecasting of time series related to the number of monthly overnight stays, in tourist accommodation establishments in Portugal, between January 2002 and March 2022. As a result of some suggestions contained in the scientific literature, it was resorted the Exponential Smoothing (ETS) methodologies. In computational terms, we used the Jupyter computational environment, with the Python programming language (version 3.7.3). Findings: The results were presented and discussed through the analysis of two time series: (1) Total number of overnight stays in tourist accommodation establishments in Portugal – Total series; (2) Number of overnight stays spent by residents in Portugal in tourist accommodation establishments in Portugal – Residents series. Overall, from the analysis of the time series, there was a growth of Tourism in Portugal since 2002, with a visible drop in 2020, due to the pandemic situation. Regarding the ETS methodologies used in the modeling and forecasting, although they corresponded positively in the forecast of the Total series (with some error), the same did not happen in the Residents series. In this series, due to the recent dynamics that are completely atypical, it appears that the ETS methodologies, potentially more adequate, do not converge, in general. However, it is important to mention that it was the overnight stays of residents that, in the pandemic period, dictated the dynamics present in the Total series. Research limitations: The literature points to a good performance of ETS methodologies in time series with characteristics present in the series under study (with the presence of a trend cycle and clear seasonality), a fact that motivated its choice. However, the difficulty of these methodologies in dealing with abrupt breaks in the data history was evident in this study. Despite how adjusted the forecasts are, the highlight is the non-convergence of some models that could be better adjusted to the historical data. In this sense, it is necessary to search for alternative forecasting methodologies, where Machine Learning methodologies, namely Deep Learning (Deep Neural Networks) have been pointed out in the scientific literature as quite promising. This will be the next step of the investigation. Originality: Given the importance that Tourism has both in the economic and social dimension of Portugal, and being a very volatile and constantly changing sector, it is imperative to define a strategy for future action to understand how, internally, the sector can define policies to avoid situations of external dependence. In addition to a current analysis of the data history, resulting from an atypical period of pandemic, we need to critically evaluate the predictive capacity of (classical) econometric models, which can be used by the industry related to tourism. This not only contributes to a better understanding of the phenomenon under study, but also constitutes a tool to support decision-making.info:eu-repo/semantics/publishedVersio