Abstract
The exponential growth in energy demand is leading to massive energy consumption from fossil resources causing a negative effects for the environment. It is essential to promote sustainable solutions based on renewable energies infrastructures such as microgrids integrated to the existing network or as stand alone solution. Moreover, the major focus of today is being able to integrate a higher percentages of renewable electricity into the energy mix. The variability of wind and solar energy requires knowing the relevant long-term patterns for developing better procedures and capabilities to facilitate integration to the network. Precise prediction is essential for an adequate use of these renewable sources. This article proposes machine learning approaches compared to an hybrid method, based on the combination of machine learning with optimisation approaches. The results show the improvement in the accuracy of the machine learning models results once the optimisation approach is used. © 2023 - IOS Press. All rights reserved.

Abstract
Background Breast symmetry is an essential component of breast cosmesis. The Harvard Cosmesis scale is the most widely adopted method of breast symmetry assessment. However, this scale lacks reproducibility and reliability, limiting its application in clinical practice. The VECTRA (R) XT 3D (VECTRA (R)) is a novel breast surface imaging system that, when combined with breast contour measuring software (Mirror (R)), aims to produce a more accurate and reproducible measurement of breast contour to aid operative planning in breast surgery. Objectives This study aims to compare the reliability and reproducibility of subjective (Harvard Cosmesis scale) with objective (VECTRA (R)) symmetry assessment on the same cohort of patients. Methods Patients at a tertiary institution had 2D and 3D photographs of their breasts. Seven assessors scored the 2D photographs using the Harvard Cosmesis scale. Two independent assessors used Mirror (R) software to objectively calculate breast symmetry by analysing 3D images of the breasts. Results Intra-observer agreement ranged from none to moderate (kappa - 0.005-0.7) amongst the assessors using the Harvard Cosmesis scale. Inter-observer agreement was weak (kappa 0.078-0.454) amongst Harvard scores compared to VECTRA (R) measurements. Kappa values ranged 0.537-0.674 for intra-observer agreement (p < 0.001) with Root Mean Square (RMS) scores. RMS had a moderate correlation with the Harvard Cosmesis scale (r(s) = 0.613). Furthermore, absolute volume difference between breasts had poor correlation with RMS (R-2 = 0.133). Conclusion VECTRA (R) and Mirror (R) software have potential in clinical practice as objectifying breast symmetry, but in the current form, it is not an ideal test.

Abstract
In Mediterranean Europe, one of the expected consequences of climate change is the intensification of wildfire events. Given the importance of forests in helping regulate climate and the many ecosystem services they provide, it is crucial to identify how wildfires might impact them. In this context, the present work aims to identify the wildfire impacts caused to the ecosystem services in Portugal. This is done through a survey directed to Portuguese fire experts. Using The Economics of Ecosystems and Biodiversity (TEEB) definitions, experts were asked to share their perceptions on the fire impacts to forest ecosystem services in the short and long-term and indicate which services they feel require more policies to mitigate the impacts. The results showed that all ecosystem services are impacted to various degrees and different lengths of time. Regulating services were overall the most affected group and the most in need of specific policies. This study helped identify fire impacts, policy needs, and priorities in the perception of the experts in Portugal, which is valuable for guiding future research in various knowledge fields, especially related to raising awareness about behavioral adaptation to prevent and mitigate wildfire impacts in a changing climate. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

Abstract

Abstract
Machine learning has been facing significant challenges over the last years, much of which stem from the new characteristics of machine learning problems, such as learning from streaming data or incorporating human feedback into existing datasets and models. In these dynamic scenarios, data change over time and models must adapt. However, new data do not necessarily mean new patterns. The main goal of this paper is to devise a method to predict a model's performance metrics before it is trained, in order to decide whether it is worth it to train it or not. That is, will the model hold significantly better results than the current one? To address this issue, we propose the use of meta-learning. Specifically, we evaluate two different meta-models, one built for a specific machine learning problem, and another built based on many different problems, meant to be a generic meta-model, applicable to virtually any problem. In this paper, we focus only on the prediction of the root mean square error (RMSE). Results show that it is possible to accurately predict the RMSE of future models, event in streaming scenarios. Moreover, results also show that it is possible to reduce the need for re-training models between 60% and 98%, depending on the problem and on the threshold used.

Abstract
Lock-free data structures are an important tool for the development of concurrent programs as they provide scalability, low latency and avoid deadlocks, livelocks and priority inversion. However, they require some sort of additional support to guarantee memory reclamation. The Optimistic Access (OA) method has most of the desired properties for memory reclamation, but since it allows memory to be accessed after being reclaimed, it is incompatible with the traditional memory management model. This renders it unable to release memory to the memory allocator/operating system, and, as such, it requires a complex memory recycling mechanism. In this paper, we extend the lock-free general purpose memory allocator LRMalloc to support the OA method. By doing so, we are able to simplify the memory reclamation method implementation and also allow memory to be reused by other parts of the same process. We further exploit the virtual memory system provided by the operating system and hardware in order to make it possible to release reclaimed memory to the operating system.