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Endomyocardial biopsy (EMB) is an invasive procedure originally developed for the monitoring of heart transplant rejection. Over the year, this procedure has gained a fundamental complementary role in the diagnostic work-up of several cardiac disorders, including cardiomyopathies, myocarditis, drug-related cardiotoxicity, amyloidosis, other infiltrative and storage disorders, and cardiac tumours. Major advances in EMB equipment and techniques for histological analysis have significantly improved diagnostic accuracy of EMB. In recent years, advanced imaging modalities such as echocardiography with three-dimensional and myocardial strain analysis, cardiac magnetic resonance and bone scintigraphy have transformed the non-invasive approach to diagnosis and prognostic stratification of several cardiac diseases. Therefore, it emerges the need to re-define the current role of EMB for diagnostic work-up and management of cardiovascular diseases. The aim of this review is to summarize current knowledge on EMB in light of the most recent evidences and to discuss current indications, including challenging scenarios encountered in clinical practice.

With the Clean Energy Package, the European Union introduced the concept of Renewable Energy Communities: groups of citizens, small and medium enterprises and local authorities that decide to join forces to equip themselves with systems to produce and share energy from renewable energy sources. The Italian legislation recently started an experimental phase in which renewable energy communities receive an incentivising tariff for the energy produced and shared within the community. This paper faces the problem of creating a new renewable energy community in two steps. First, a mathematical model of the energy flows among the members of the community is characterised according to the Italian schema. This model is used to find the optimal portfolio for the energy community, given energy requests and local source availability. Secondly, the Shapley value, a particular solution of cooperative games known to be the most fair method to allocate costs and profits of shared infrastructures, is proposed to distribute benefits among community members. The methodology has been applied to a case study based on a real low voltage network, and the economics for consumers and producers in participating to the project have been evaluated. The proposed solution, simulated adopting real economic parameters defined in the Italian regulatory framework, results to be economically viable from the point of view of the investors with a profitability index of 1.36 and, at the same time, aligned with the social purposes of the energy communities.

The interest in modeling the operation of large-scale battery energy storage systems (BESS) for analyzing power grid applications is rising. This is due to the increasing storage capacity installed in power systems for providing ancillary services and supporting nonprogrammable renewable energy sources (RES). BESS numerical models suitable for grid-connected applications must offer a trade-off, keeping a high accuracy even with limited computational effort. Moreover, they are asked to be viable in modeling for real-life equipment, and not just accurate in the simulation of the electrochemical section. The aim of this study is to develop a numerical model for the analysis of the grid-connected BESS operation; the main goal of the proposal is to have a test protocol based on standard equipment and just based on charge/discharge tests, i.e., a procedure viable for a BESS owner without theoretical skills in electrochemistry or lab procedures, and not requiring the ability to disassemble the BESS in order to test each individual component. The BESS model developed is characterized by an experimental campaign. The test procedure itself is framed in the context of this study and adopted for the experimental campaign on a commercial large-scale BESS. Once the model is characterized by the experimental parameters, it undergoes the verification and validation process by testing its accuracy in simulating the provision of frequency regulation. A case study is presented for the sake of presenting a potential application of the model. The procedure developed and validated is replicable in any other facility, due to the low complexity of the proposed experimental set. This could help stakeholders to accurately simulate several layouts of network services.

Myocarditis is a polymorphic disease characterized by great variability in clinical presentation and evolution. Patients presenting with severe left ventricular dysfunction and life-threatening arrhythmias represent a demanding challenge for the clinician. Modern techniques of cardiovascular imaging and the exhaustive molecular evaluation of the myocardium with endomyocardial biopsy have provided valuable insight into the pathophysiology of this disease, and several clinical registries have unraveled the disease's long-term evolution and prognosis. However, uncertainties persist in crucial practical issues in the management of patients. This article critically reviews current information for evidence-based management, offering a rational and practical approach to patients with myocarditis. For this review, we searched the PubMed and MEDLINE databases for articles published from January 1, 1980, through December 31, 2015, using the following terms: myocarditis, inflammatory cardiomyopathy, and endomyocardial biopsy. Articles were selected for inclusion if they represented primary data or were review articles published in high-impact journals. In particular, a risk-oriented approach is proposed. The different patterns of presentation of myocarditis are classified as low-, intermediate-, and high-risk syndromes according to the most recent evidence on prognosis, clinical findings, and both invasive and noninvasive testing, and appropriate management strategies are proposed for each risk class.

Off-grid power systems based on photovoltaic and battery energy storage systems are becoming a solution of great interest for rural electrification. The storage system is one of the most crucial components since inappropriate design can affect reliability and final costs. Therefore, it is necessary to adopt reliable models able to realistically reproduce the working condition of the application. In this paper, different models of lithium-ion battery are considered in the design process of a microgrid. Two modeling approaches (analytical and electrical) are developed based on experimental measurements. The derived models have been integrated in a methodology for the robust design of off-grid electric power systems which has been implemented in a MATLAB-based computational tool named Poli.NRG (POLItecnico di Milano—Network Robust desiGn). The procedure has been applied to a real-life case study to compare the different battery energy storage system models and to show how they impact on the microgrid design.

Currently, distribution system operators (DSOs) are asked to operate distribution grids, managing the rise of the distributed generators (DGs), the rise of the load correlated to heat pump and e-mobility, etc. Nevertheless, they are asked to minimize investments in new sensors and telecommunication links and, consequently, several nodes of the grid are still not monitored and tele-controlled. At the same time, DSOs are asked to improve the network’s resilience, looking for a reduction in the frequency and impact of power outages caused by extreme weather events. The paper presents a machine learning GIS-based approach to estimate a secondary substation’s load profiles, even in those cases where monitoring sensors are not deployed. For this purpose, a large amount of data from different sources has been collected and integrated to describe secondary substation load profiles adequately. Based on real measurements of some secondary substations (medium-voltage to low-voltage interface) given by Unareti, the DSO of Milan, and georeferenced data gathered from open-source databases, unknown secondary substations load profiles are estimated. Three types of machine learning algorithms, regression tree, boosting, and random forest, as well as geographic information system (GIS) information, such as secondary substation locations, building area, types of occupants, etc., are considered to find the most effective approach.

The diffusion of nonprogrammable power plants, together with the decommissioning of conventional, rotating generators, is increasing the need for flexible resources to always ensure the safe and secure operation of the European electric-power system. Beyond technological advances, policy aspects also play a fundamental role in the opening of electricity markets to new players; in this regard, System Operations Guideline EU 2017/1485 and Italian Regulatory Authority documents require the Italian transmission-system operator (TSO; Terna) to publish all exploited algorithms and methodologies for the management of market balancing. In this context, the present paper develops and presents a data-driven methodology to estimate secondary and tertiary reserve needs; a numerical real-life case study, focused on the North Italy geographical zone, is presented. Data for 2017, 2018, and 2019 on electricity consumption and production (forecasted and actual) were gathered. Following the European TSOs Organization (ENTSO-E) and the Italian TSO (Terna) prescriptions, methodology for the calculation of reserve needs was developed. Results are presented under graphical form and refer, among others, to spinning and nonspinning reserve duration curves, forecast error contribution to reserve calculation, and samples considered for analysis. While a comparison with available market observations is not very helpful, results suggest that the developed methodology could be useful for the evaluation of reserve needs in different control areas.

Energy communities (EC) are expected to have a pivotal role to reach European decarbonization targets. One of the key aspects is the regulatory framework adopted by each Member State to properly manage such new customers’ aggregation. The paper firstly provides an updated overview of the EC regulation, focusing on the current Italian legislation. Next, a novel methodology for the design and management of energy community initiatives is proposed. The procedure firstly solves a design and operation optimization problem to calculate the best size of energy assets (boiler, heat pump, photovoltaic, thermal storage) to be installed. Second, a Shapley value-based approach is exploited to distribute a part of the community’s incomes to members, based on their contribution to the overall welfare. Results demonstrate that the adopted methodology is effective in ensuring a proper cash flow for the community, while pushing its members towards energy efficient behaviors.

This systematic review meticulously explores the transformative impact of digital technologies on the grid planning, grid operations, and energy market dynamics of power distribution grids. Utilizing a robust methodological framework, over 54,000 scholarly articles were analyzed to investigate the integration and effects of artificial intelligence, machine learning, optimization, the Internet of Things, and advanced metering infrastructure within these key subsections. The literature was categorized to show how these technologies contribute specifically to grid planning, operation, and market mechanisms. It was found that digitalization significantly enhances grid planning through improved forecasting accuracy and robust infrastructure design. In operations, these technologies enable real-time management and advanced fault detection, thereby enhancing reliability and operational efficiency. Moreover, in the market domain, they support more efficient energy trading and help in achieving regulatory compliance, thus fostering transparent and competitive markets. However, challenges such as data complexity and system integration are identified as critical hurdles that must be overcome to fully harness the potential of smart grid technologies. This review not only highlights the comprehensive benefits but also maps out the interdependencies among the planning, operation, and market strategies, underlining the critical role of digital technologies in advancing sustainable and resilient energy systems.

Energy Communities (EC) are becoming a major driver to foster the energy transition in Europe and the regulatory framework adopted by each Member State (MS) plays a key role for a prosperous deployment of ECs. This paper is thus divided into two layers. The first layer of this paper addresses the current regulations introduced by MSs regarding ECs, providing a critical comparison of each solution used. The second layer of research concerns the introduction of a Mixed Integer Linear Programming (MILP) optimization algorithm early studied by some of the authors furtherly developed to assess the conditions that favour prosumers’ participation to ECs. Both these models have been tested on a case study located in the city of Magliano Alpi, in the north of Italy. The results demonstrate that the proposed methodology correctly evaluates the key parameters influencing participation of citizens in ECs and indicate that for the Italian EC under study, there is the possibility to further expand the capacity installed without undermining the profitability of investment.