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Energy poverty is a significant problem in Greece, affecting many people and exacerbating social inequality. Lack of access to affordable and reliable energy negatively impacts health, comfort, and economic opportunities, with long-term consequences for communities. While various initiatives have been implemented to address energy poverty, energy communities are emerging as a promising approach to tackle this issue. Energy communities refer to groups of people who come together to produce, consume, and manage their energy resources collectively. They enable households and communities to reduce their energy bills, improve living conditions, and enhance social and economic resilience. Energy communities can also stimulate local development and promote renewable energy technologies, contributing to a more sustainable and equitable energy system. This paper reviews energy poverty alleviation actions by community energy initiatives across Europe, identifies best practices and challenges, and proposes relevant energy poverty alleviation actions for energy communities in Greece. The paper seeks to contribute to the ongoing debate on how energy communities can contribute in energy poverty alleviation.

The evaluation and treatment of the posterior malleolus fracture in unstable ankle injuries remain a topic of controversy. The main objective of this systematic review was to examine the available literature and identify the variables that affect the management of posterior malleolar fractures and how these are related to the outcomes. To that end, a systematic review was performed based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A comprehensive search of MEDLINE, Embase, and Cochrane Library was conducted. The search terms used were as follows: "posterior malleolar", "ankle fractures", "trimalleolar fractures", "ORIF", "surgery", "operative", and "conservative". The available studies were screened against the inclusion and exclusion criteria. Based on the review of the available literature, we have concluded that the size of the posterior malleolar fragment is not an accurate indicator, and clinicians should consider other factors such as fracture configuration and articular surface congruity. Also, the risk for the development of post-traumatic arthritis increases when the joint surface is not restored regardless of the surgical intervention and fragment size. The complications of posterior malleolus fractures necessitate evidence-based management. The assessment and the final treatment of these injuries in unstable ankle fractures should not be based on the traditional fragment-size parameters. Clinicians should assess the fracture configuration through imaging modalities and try to preserve the articular surface congruity so as to achieve optimal outcomes. Finally, more studies with high-level evidence are required in order to determine the most appropriate management pathway for these patients.

Background The incidence of total elbow arthroplasty (TEA) is increasing, and an improved understanding of elbow kinematics and biomaterials has driven advances in implant design. In modern practice, cemented, semiconstrained devices are most frequently used. The Discovery TEA has demonstrated promising early results, although there are a paucity of follow-up studies and no dedicated mid- to long-term series. We therefore present the longest, most complete such study to date. Methods A prospectively maintained local joint registry was interrogated to yield a consecutive series of Discovery TEAs performed at a single non-design center. The minimum follow-up period was set at 5 years. Revision procedures and TEAs performed for acute trauma were excluded. The primary outcome was survivorship of the implant. The secondary outcomes included clinical, radiographic, and patient-reported outcomes. Results We identified 67 TEAs in 58 patients for inclusion at a mean of 98.5 ± 20.4 months from surgery. Four cases (6%) were lost to follow-up, and implant survival was censored accordingly. The implant was revised in 14 cases (20.9%). The Kaplan-Meier method showed an implant survivorship rate of 76.8% at 119 months. A significant difference in survival was found between dominant and nondominant elbows (P = .012, Breslow test), with elbow dominance conferring a 4.5-fold increased risk of revision (relative risk, 4.5; 95% confidence interval, 1.1-18.5). Pooled clinical outcomes (70.9% follow-up at minimum of 60 months and median of 77.8 months) were also determined. Conclusions We present the longest-term and most complete single-center follow-up study of the Discovery TEA to date. Further long-term survival studies are required to elucidate the performance of this implant compared with more established designs. We have also demonstrated differences in implant survivorship owing to hand dominance for the first time.

Energy poverty is a multifaceted phenomenon that affects many Europeans. Alleviating energy poverty is high in the EU, national, and local policy agendas. Despite the attention the phenomenon has been gaining from a policy perspective, especially after the current energy crisis, there are still some gaps due to the complexity of the issue and its vastly different manifestations across Europe. This manuscript presents the policy implications stemming from the implementation of the POWEPROOR approach in alleviating energy poverty in eight European countries, as co-created with relevant stakeholders in each country. The knowledge gained from empowering energy-poor citizens by promoting behavioural changes and small-scale energy efficiency interventions, as well as by encouraging the uptake of renewable energy sources in the form of collective energy initiatives while leveraging innovative financing schemes, resulted in policy recommendations for national and sub-national governments and lessons for civil society and the private sector.

Composite materials, specifically carbon fiber reinforced plastics (CFRPs), are used in various applications such as the automotive, aerospace, and renewable energy industries, thus increasing their global production and volume consumption and creating a subsequent increase in CFRP waste. Especially in space applications and Vega launcher construction, the use of CFRP components to replace metal envisages significant benefits in the use phase by reducing weight and fuel consumption requirements. The current and future waste management and environmental legislation, considering the actual and impending EU framework on waste management, requires all engineering materials to be properly recovered and recycled from EoL products. In this study, the potential of recycling and the subsequent environmental benefits have been assessed by investigating the EoL of CFRPs through a life cycle assessment (LCA). LCA is a valuable tool for evaluating a composite material’s environmental ecological burdens over its lifetime. Therefore, it is important to the composites industry as a material selection tool when determining the applicability of recycled composites in the design phase. Particularly, the benefits from recycling methods were systematically studied in order to assess the environmental impacts of EoL scenarios, to underline the importance and necessity for the maturity increase in recycling technologies for CFRPs.

This co-creation workshop event was organised by the H2020 project NanoMECommonswith the support of the European Materials Modelling Council (EMMC ASBL)and the European Materials Characterisation Council (EMCC).

The present study focuses on the effect of two novel carbon fibre surface treatments, electropolymerisation of methacrylic acid and air pressure plasma, on the mechanical properties and structural integrity of carbon-fibre-reinforced composites under operational conditions. Extensive mechanical testing was applied, both in nano- and macro-scale, to assess the performance of the composites and the interphase properties after ultraviolet/humidity weathering. The results of the mechanical assessment are supported by structure, surface, and chemistry examination in order to reveal the failure mechanism of the composites. Composites with the electropolymerisation treatment exhibited an increase of 11.8% in interlaminar shear strength, while APP treatment improved the property of 23.9%, rendering both surface treatments effective in increasing the fibre-matrix adhesion. Finally, it was proven that the developed composites can withstand operational conditions in the long term, rendering them suitable for a wide variety of structural and engineering applications.

Thermoplastic nanocomposites were fabricated in order to deliver a suitable material for thermal management devices, commonly processed by injection molding or 3D printing. Scalable manufacturing of Carbon Nanotubes (CNTs) and multi-functional polymers with good thermal conductivity and ease of handling was demonstrated in the present study. The standard grade polyethylene reinforced with CNTs, was formed by melt mixing. Polyethylene glycol was used for concentrating CNTs in a masterbatch for its plasticizing properties, ensuring safe handling and even dispersion of CNTs. Through characterization of morphology and thermal properties, an interconnected CNTs network was established, which enhanced the heat transfer at high concentration by changing the main conduction path from polymer crystal lattice to CNTs conductive network. Moreover, it was shown that modification in the matrix crystallinity is key to maximize the phonon and electron conduction interfaces and achieve advances in thermal conductivity beyond 68% and 100% achieved for 15 wt% content of CNTs compared to neat PE and PE@PEG matrices, respectively, within this study.

The exposure of carbon-fiber-reinforced polymers (CFRPs) to open-field conditions was investigated. Establishment of structure–property relations with nanoindentation enabled the observation of modification effects on carbon-fiber interfaces, and impact resistance. Mapping of nanomechanical properties was performed using expectation-maximization optimization of Gaussian fitting for each CFRPs microstructure (matrix, interface, carbon fiber), while Weibull analysis connected the weathering effect to the statistically representative behavior of the produced composites. Plasma modification demonstrated reduced defect density and improved nanomechanical properties after weathering. Artificial intelligence for anomaly detection provided insights on condition monitoring of CFRPs. Deep-learning neural networks with three hidden layers were used to model the resistance to plastic deformation based on nanoindentation parameters. This study provides new assessment insights in composite engineering and quality assurance, especially during exposure under service conditions.

The aim of this work is to propose a methodology that will utilize the characteristic polyacrylonitrile (PAN) fiber length change as a process control parameter of oxidative stabilization. To this end, a multicomponent model is proposed that takes into account the contribution of every phenomenon (entropic shrinkage, creep, and chemical shrinkage) in fiber length change. Each component of the proposed model is connected with different structural features of the PAN fibers, which permits monitoring structural transformations during the process. Also, it is feasible to track cyclization progress.

The management of rotator cuff tears continues to prove challenging for orthopaedic surgeons. Such tears affect most age groups and can lead to significant morbidity in patients. The aetiology of these tears is likely to be multifactorial; however, an understanding of the mechanisms involved is still under review. Despite advancements in surgical operative techniques and the materials used, post-operative recurrence rates after surgical repair remain high. A growing area of research surrounds biological adjuncts used to improve the healing potential of the repaired tissues. This review of recent publications focuses on the strengths and limitations of using stem cells and growth factors in rotator cuff repair.

Background Spondylodesis is a procedure aiming at providing stability in one or more spinal segments. The aim of our study was to compare sevoflurane and propofol as induction and maintenance agents, focusing on hemodynamic stability, recovery characteristics, postoperative nausea and vomiting, and pain intensity. Materials and methods Seventy patients, with a physical status according to American Society of Anesthesiologists (ASA) I–II, 50–72 y old, undergoing selective lumbar spondylodesis were enrolled. Results There was no statistically significant difference between groups in overall mean hemodynamic parameters. No differences in fluid administration and vasoactive substances used were noted. Postoperatively, there was a significant difference in overall mean visual analog score at rest and at cough, with the sevoflurane group showing lower values. No differences in the incidence of nausea, vomiting, shivering, postoperative sedation scores, and orientation to place were revealed. Orientation to time exhibited a statistically significant difference at the time just after transfer to the post-anesthesia care unit, where more patients of the sevoflurane group seemed to be well oriented. Conclusions Sevoflurane and propofol anesthesia for lumbar spondylodesis surgery provide safe and comparable results.

A systematic study on the effect of the electrochemical exfoliation of graphite was conducted to provide new insights on the structure and material properties. Graphene-like structures are produced at scale currently using the wet chemical approaches, which is usually followed by an additional reduction step. The electrochemical exfoliation constitutes a promising alternative method for green, fast, and safe industrial exploitation, due to its low cost, mild conditions, basic equipment requirement and eco-friendlier electrolytes. Additionally, crystal structure of graphene related materials demonstrates less defective sights due to the high speed of exfoliation. A thorough investigation was conducted on critical parameters of the electrochemical process. The parameters of concentration, voltage, temperature and sonication assistance were studied using the randomized block design approach for various electrolytes. After the evaluation and screening of the materials with several characterization techniques, three optimization routes were selected, which included a modified Hummers' method, exfoliation based on the surface energy of pure DMF, and violent pH neutralization. The production of graphene oxide nanoplatelets was achieved, with high oxidation rates, high crystallinity and limited imperfections. The materials were consisted of less than 10 layers and up to 5 layers as demonstrated by the low crystallite size, which was also demonstrated for GO that underwent post-treatment optimization. Higher yields were achieved with electrolytes known for their strong oxidative effect, while the hydrogen peroxide electrolyte and the ionic liquid demonstrated lower exfoliation rates.

Carbon fiber reinforced polymer manufacturing is emerging, with multiple studies to focus on the design of interfacial reinforcement to ensure the maximum of composite properties, but also respectively to be able to align with zero defect manufacturing. The controversy on the engineering approach is a data-driven task that can be efficiently tackled by involving Artificial Intelligence in order to establish unbiased structure-property relations. In the present study, nanoindentation mapping data were processed with Machine Learning classification models to identify the interfacial reinforcement. The data preparation included normalization and sorting out of highly similar data with k-means clustering, since nanoindentation on epoxy matrix does not enhance insight on the mechanism of reinforcement. The trained models included neural networks, classification trees, and support vector machines. Realization of models' performance was evaluated on the test dataset as screening to obtain best fitted models for each algorithm. Transfer learning potential was demonstrated by extrapolating the prediction of best trained models to a validation dataset at different indentation depth with support vector machines outperforming the other models. Overall accuracy was 67% on the test dataset, F1 Score was 65% in the prediction of reinforcement mechanism classes and 72% in case of pristine specimen, while accuracy on validation dataset was 72.7%. Prediction metrics were comparable to other case studies of real-world classification problems. Computational time-cost for tuning and training was sustainable and equal to 2.3 min.