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.

Abstract
With excess energy use from non-renewable sources, new energy generation solutions must be adopted to make up for this excess. In this sense, the integration of renewable energy sources in high-rise buildings reduces the need for energy from the national power grid to maximize the self-sustainability of common services. Moreover, self-consumption in low-voltage and medium-voltage networks strongly facilitates a reduction in external energy dependence. For consumers, the benefits of installing small wind turbines and energy storage systems include tax benefits and reduced electricity bills as well as a profitable system after the payback period. This paper focuses on assessing the wind potential in a high-rise building through computational fluid dynamics (CFD) simulations, quantifying the potential for wind energy production by small wind turbines (WT) at the installation site. Furthermore, a mathematical model is proposed to optimize wind energy production for a self-consumption system to minimize the total cost of energy purchased from the grid, maximizing the return on investment. The potential of a CFD-based project practice that has wide application in developing the most varied processes and equipment results in a huge reduction in the time and costs spent compared to conventional practices. Furthermore, the optimization model guarantees a significant decrease in the energy purchased at peak hours through the energy stored in energy storage systems (ESS). The results show that the efficiency of the proposed model leads to an investment amortization period of 7 years for a lifetime of 20 years.

Abstract
Currently, there is increasing implementation of renewable energy communities, where consumers and producers come together to form energy cooperatives. This growing trend has been accompanied by several studies aiming to optimize energy exchanges and sharing inside the community, always taking into account the most favorable tariff regimes for community members. This paper presents an analysis that, based on applying a linear programming model, optimizes energy transactions in a renewable energy community with the integration of storage systems. The results show the developed model's effectiveness, presenting substantial profits for the community.

Abstract
The last two decades have witnessed a new paradigm in terms of electrical energy production. The production of electricity from renewable sources has come to play a leading role, thus allowing us not only to face the global increase in energy consumption, but also to achieve the objectives of decarbonising the economies of several countries. In this scenario, where onshore wind energy is practically exhausted, several countries are betting on constructing offshore wind farms. Since all the costs involved are higher when compared to onshore, optimising the efficiency of this type of infrastructure as much as possible is essential. The main aim of this paper was to develop an optimisation model to find the best wind turbine locations for offshore wind farms and to obtain the wind farm layout to maximise the profit, avoiding cable crossings, taking into account the wake effect and power losses. The ideal positioning of wind turbines is important for maximising the production of electrical energy. Furthermore, a techno-economic analysis was performed to calculate the main economic indicators, namely the net present value, the internal rate of return, and the payback period, to support the decision-making. The results showed that the developed model found the best solution that maximised the profits of the wind farm during its lifetime. It also showed that the location of the offshore substation played a key role in achieving these goals.

Abstract
This work addresses the wind farm (WF) optimization layout considering several substations. It is given a set of wind turbines jointly with a set of substations, and the goal is to obtain the optimal design to minimize the infrastructure cost and the cost of electrical energy losses during the wind farm lifetime. The turbine set is partitioned into subsets to assign to each substation. The cable type and the connections to collect wind turbine-produced energy, forwarding to the corresponding substation, are selected in each subset. The technique proposed uses a genetic algorithm (GA) and an integer linear programming (ILP) model simultaneously. The GA creates a partition in the turbine set and assigns each of the obtained subsets to a substation to optimize a fitness function that corresponds to the minimum total cost of the WF layout. The fitness function evaluation requires solving an ILP model for each substation to determine the optimal cable connection layout. This methodology is applied to four onshore WFs. The obtained results show that the solution performance of the proposed approach reaches up to 0.17% of economic savings when compared to the clustering with ILP approach (an exact approach).