Abstract
Body-positive content on social media offers a promising approach to promote positive body image (PBI). However, we need further research in order to better characterize and understand its nature. This study provides a content analysis of adolescents' image-based posts on Facebook. We aimed to determine whether the theoretical six -facet conceptualization of PBI was reflected in adolescents' posts, and whether there were different trends according to gender and time, over a 3-year period. A set of 6,503 images posted by 66 adolescents (51.5% male), were coded for PBI attributes. The results indicate that inner positivity and appreciation of body functionality through involvement in sports and fun activities were the most represented PBI facets. Conversely, imagery representing taking care of the body via healthy food/beverage choices, embracing body diversity, and filtering information in a body-preserving manner, was rarely used to project self-image on Facebook. Gender differences were only found in the appreciation of body functionality via sports activities (more prevalent in boys) and investment in appearance using benign methods, such as the use of cosmetics (more prevalent in girls). Posts addressing appearance and health -promoting self-care behaviors tended to increase in mid-adolescence. We conclude that the adolescents' posts on Facebook reflected several PBI characteristics, with a particular focus on those that enhance a functional view of the body. Future social media and school-level initiatives should prioritize the development of attuned self-care as well as mechanisms to filter messages that could endanger PBI, while also increasing the visibility of the diverse bodies that exist in the world.

Abstract
Premature ventricular contractions (PVC), or extrasystoles, represent a type of cardiac arrhythmia that is common among the general population and, notably, among athletes or individuals who exercise frequently. PVC may be asymptomatic and not clinically relevant when their rate is low, up to around 0.5%, or may be symptomatic and clinically relevant when it is high, in the order of or above 10%. ECG analysis in association with a cardiac stress test is important to detect and characterize PVC and to diagnose the heart condition and operation. In this paper, we describe and test a simple signal processing approach that can be used to effectively pinpoint subtle PVC occurrences in various physical effort conditions. In this regard, we discuss i) three important conditions to be met such that PVC are categorized as benign, ii) the design and implementation of a cardiac stress test and ECG data collection, iii) the algorithm analyzing and extracting information from the detected PVC occurrences, and iv) we present and discuss the obtained results, and conclude on their significance. © 2022 IEEE.

Abstract
Emerging technologies are helping to accelerate the ongoing energy transition. At the forefront of these new technologies is blockchain, which has the potential to disrupt energy trading markets. This article explores this potential by presenting an innovative multilevel transactive energy (TE) optimization model for the scheduling of distributed energy resources (DERs) within connected virtual power plants (VPPs). The model allows for energy transactions within a given VPP as well as between connected VPPs. A blockchain-based smart contract layer is applied on top of the TE optimization model to automate and record energy transactions. The model is formulated to adhere to the new regulations for the self-generation and self-consumption of energy in Portugal. This new set of regulations can ease barriers to entry for consumers and increase their active participation in energy markets. Results show a decrease in energy costs for consumers and increased generation of locally produced electricity. This model shows that blockchain-based smart contracts can be successfully integrated into a hierarchical energy trading model, which respects the novel energy regulation. This combination of technologies can be used to increase consumer participation, lower energy bills, and increase the penetration of locally generated electricity from renewable energy sources.

Abstract
This paper presents an alternative way of making predictions on the effectiveness and efficacy of Renewable Energy (RE) policies using Decision Trees (DT). As a data-driven process for decision-making, the analysis uses the Renewable Energy (RE) target achievement, predicting whether or not a RE target will likely be achieved (efficacy) and to what degree (effectiveness), depending on the different criteria, including geographical context, characterizing concerns, and policy characteristics. The results suggest different criteria that could help policymakers in designing policies with a higher propensity to achieve the desired goal. Using this tool, the policy decision-makers can better test/predict whether the target will be achieved and to what degree. The novelty in the present paper is the application of Machine Learning methods (through the Decision Trees) for energy policy analysis. Machine learning methodologies present an alternative way to pilot RE policies before spending lots of time, money, and other resources. We also find that using Machine Learning techniques underscores the importance of data availability. A general summary for policymakers has been included.

Abstract
The time required to restore distribution systems following an extreme event is highly dependent on damage assessment. Waiting for field assessors patrolling the feeders to identify fault locations is a bottleneck in improving restoration efficiency. This paper proposes an optimal service restoration model for resilient distribution systems considering the coordination with damage assessment, as a contribution to earlier studies. The restoration actions such as fault isolation, network reconfiguration, crew mobilization and fault repair are brought forward to the damage assessment stage and the restoration schedules are dynamically updated with the reveal of the damage status. The relationship between fault location, switch status and node status is established to optimize the network topology and guarantee crew operation safety under the condition that the network has multiple faults or unchecked potential faulted areas. Moreover, the crew routing formulations are modified to enable fault isolation and load island reconnection by manual switches during the restoration process. Case studies validate the effectiveness of the proposed model in reducing load shedding and restoration duration.

Abstract
The concept of smart cities has emerged as an ongoing research in recent years. In this case, there is a proven association between the smart cities and the smart devices, which have caused the power systems to become more flexible, controllable and detectable. Along with these promising results, many disputes have been generated over the cyber-attacks as unpredictable destructive threats, if not properly repelled, which could seriously endanger the power system. With this in mind, this paper explores a novel stochastic virtual assignment (SVA) method based on a directed acyclic graph (DAG) approach, where the essential data of the system sections are broadcasted decentralized through the data blocks, as a worthwhile step to deal with the cyber attacks' risk. To do so, an additional security layer is added to the data blocks aiming to enhance the security of the data against the long lasting data sampling by virtually assigning the hash addresses (HAs) to the data blocks, which are randomly changed based on a stochastic process. The basic network architecture is based on a Provchain structure as a new framework to constantly monitor data operation. Two pivotal strategies also represented to deal with the energy and time needed for the HAs generation process, which have improved the proposed method. In this paper, the proposed security framework is implemented in a smart city environment to provide a secure energy transaction platform. Results show the authenticity of this model and demonstrate the effectiveness of the SVA method in decreasing the successful probability of cyber threat, increasing the time needed for the cyber attacker to decrypt and manipulate the data block.