Abstract
The Brazilian Power System is mainly composed of renewable generation from hydroelectric and wind. Hence, spot and forward electricity prices tend to represent the inherently stochastic nature of these resources, while risk management is a measure taken by agents, especially hydro power plants (HPPs) to hedge against deep financial losses. A HPP goal is to maximize its profit considering uncertainties in forward electricity prices, spot prices, and generation scaling factor (GSF) for years ahead. Therefore, the objective of this work is to simulate the real decision-making process of a HPP, where they need to have a perspective of the forward market and future spot price assessment to negotiate forward electricity contracts. To do so, the present work models the uncertainty in electricity forward prices via two-stage stochastic programming, assessing the benefits of the stochastic solution in comparison to the deterministic one. In addition, different risk aversion levels are assessed using conditional value at risk (CVaR). An important conclusion is that the results show that the greater the HPP risk aversion is, the greater the energy selling via electricity forward contracts. Moreover, the proposed model has benefits in comparison to a deterministic approach.

Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Abstract
Nowadays global population and energy consumption and resources use are increasing, which means that the current consumption levels are unsustainable and a new approach for businesses of energy companies is required. This paper aims to conduct a systematic review of the literature about sustainable business models (SBM) in the energy sector. The main object of this study is to identify the current challenges and changes in business models in the energy sector to account for sustainability concerns. In this paper, 23 articles published in the last 5 years (Jan 2018 to Jun 2022) were selected and reviewed based on a meta-analysis. The articles were obtained from Scopus and Web of Science (WoS) databases departing from an appropriate set of keywords. The result of this study illustrates the importance of SBM in the energy sector and the important factors considered in their development.

Abstract
Team automata describe networks of automata with input and output actions, extended with synchronisation policies guiding how many interacting components can synchronise on a shared input/output action. Given such a team automaton, we can reason over communication properties such as receptiveness (sent messages must be received) and responsiveness (pending receivesmust be satisfied). Previouswork focused on how to identify these communication properties. However, automatically verifying these properties is non-trivial, as it may involve traversing networks of interacting automata with large state spaces. This paper investigates (1) how to characterise communication properties for team automata (and subsumed models) using test-free propositional dynamic logic, and (2) how to use this characterisation to verify communication properties by model checking. A prototype tool supports the theory, using a transformation to interact with the mCRL2 tool for model checking.

Abstract
A benchmarking evaluation instrument was designed with a European Union country regulatory authority for water supply and wastewater treatment services to determine the efficient operating cost of its service providers that operated in the wholesale market segment in the 2017-2021 period. To this end, the non-parametric Data Envelopment Analysis technique was adapted to a robust and conditional approach. The results point to similar mean efficiency scores between water supply and wastewater services in the five-year period, despite the greater heterogeneity in the latter. Furthermore, the estimated potential cost savings for both services ranged from about 2% to 3%.

Abstract
Electric vehicle (EV) sales and shared mobility are increasing worldwide. Despite its challenges, e-carsharing has an opportunity to still profit in periods of low rental demand compared to traditional carsharing. The purpose of this paper is to assess the profitability of an e-carsharing company based on distribution local marginal price (DLMP) and vehicle-to-grid (V2G) that cooperates with the distribution system operator (DSO) through a two -stage stochastic model. The AC optimal power flow (ACOPF) is modeled using second-order cone program-ming (SOCP) linearized by the global polyhedral approximation. The IEEE 33 bus test system and a real Kernel distribution for the EV rental demands are used in four planning cases in the GAMS environment. The results indicate that the proposed methodology does not affect EV user satisfaction. Moreover, the planning disregarding the power grid perspective is the most profitable, but the operation may not be possible in real applications due to the high-power flows via V2G. Finally, the e-carsharing planning considering the DSO perspective increased the charging cost by 1.66 % but also reduced the DLMP peak, losses, and peak demand by 2.5 %, 1.5 %, and 5.1 %, respectively. One important conclusion is that the technical benefits brought to the DSO by the e-carsharing company could be turned into services and advantages for both agents, increasing profit and mitigating negative impacts, such as higher operational costs.