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Climate change is increasing the occurrence of the so-called heatwaves with a trend that is expected to worsen in the next years due to global warming. The growing intensity and duration of these extreme weather events are leading to a significant number of power system failures, especially in urban areas. This is drastically affecting the reliability and normal operation of power distribution grids around the world, with high financial costs and huge negative impacts on people's life. Typically, the response to these failure events is approached by post-event analysis, aimed at identifying the grid areas that require resources to increase the resilience of the system and prevent future outages. Nevertheless, understanding the nature of heatwaves and forecasting their impact on power distribution systems can be useful to anticipate them and accelerate a reaction, possibly avoiding negative impacts on power systems and customers. In this study, a structured method to predict distribution grid disruptions caused by heatwaves is defined. The proposed method relies on machine learning to analyze previous failure data and forecast power grid outages using operational and meteorological information. The method is evaluated using real failure data from a large power distribution network located in southern Italy.

Listeriosis is an infective complication that primarily affects pregnant women, patients at extremes of age or those with weakened immune systems. Ingestion of food contaminated with Listeria monocytogenes is the most common source of infection, causing self-limiting illness in immunocompetent hosts but associated with invasive infection and high mortality in high-risk patient groups. Milder illness presents as gastroenteritis with fever, diarrhoea, nausea and vomiting common in the 7 days post exposure. Invasive infection, characterised by bacteraemia and encephalitis, can develop in high-risk patients. Fetal loss is a major complication of listeriosis during pregnancy. Penicillin-based therapy (high dose penicillin or amoxicillin) in combination with gentamicin is advised for invasive infection; co-trimoxazole may be considered for patients intolerant to penicillin. Vulnerable individuals, notably pregnant women, should be counseled on appropriate preventative strategies including avoiding foods commonly contaminated with L. monocytogenes, such as soft ripened cheeses, pate, cooked chilled meats, unpasteurised milk, and ready to eat poultry unless thoroughly cooked.

PURPOSE Prognostic tools to estimate the risk of relapse for patients with liver-limited metastatic colorectal cancer (LL-mCRC) undergoing resection with curative intent are needed. Circulating tumor DNA (ctDNA) as a surrogate of postsurgical minimal residual disease is a promising marker in localized CRC. We explored the role of postoperative ctDNA as a marker of minimal residual disease in patients with radically resected LL-mCRC. MATERIALS AND METHODS Seventy-six patients with LL-mCRC were retrospectively included. DNA from tumor tissue was sequenced, and one somatic mutation was then assessed by digital droplet polymerase chain reaction in plasma samples collected after surgery to identify the persistence of ctDNA. Relapse-free survival and postresection overall survival were compared between patients with positive vs negative postoperative ctDNA. RESULTS ctDNA was found in 39 (51%) of 76 patients with LL-mCRC. At a median follow-up of 77 months, 33 of 39 ctDNA-positive patients and 20 of 37 ctDNA-negative patients experienced disease relapse ( P = .008). ctDNA-positive patients reported significantly shorter RFS than ctDNA-negative ones (median RFS 12.7 v 27.4 months hazard ratio, 2.09, P = .008). In the multivariable model including other prognostic covariates, this association was still significant ( P = .046) and a trend toward shorter overall survival among ctDNA-positive patients was reported (hazard ratio, 1.65, P = .183). CONCLUSION The detection of postsurgical ctDNA is an independent negative prognostic marker and identifies patients at high risk of relapse after liver metastases resection.

In this paper, specific control logics defined for generators, controllable loads and electric energy storage systems are illustrated in order to improve the reliability of AC/DC hybrid microgrids in their grid-off operation following failure events such as short-circuits, line breaks, sudden disconnections of generating units or loads, etc. After defining specific control schemes, these were modelled in a digital environment and finally tested with dynamic simulations. Finally, the paper highlights the role of virtual synchronous machine control for the self-healing capacity of islanded hybrid microgrids.

AC power systems and their devices can be subjected to different failure events due to thermal, environmental, and aging phenomena. In this context, DC grids intervention could mitigate customers black-out by adequately controlling their internal resources (renewable generators and storage systems). In this paper, failure events concerning HV/MV transformer are considered. Possible actions to support the AC grid to avoid interruption in load areas are analyzed and experimentally validated. Tests campaigns are carried out taking advantage of a research nanogrid infrastructure available at ENEA Centre of Portici. It consists of indoor equipment able to emulate actual generation, load and storage systems and outdoor facilities. In addition, the availability of Hardware In the Loop system allows to reproduce TSO and DSO grids connection. The emulation of fault conditions is carried out to verify DC grids strategies correctness. Finally, Key Performance Indices (Interruption Time and Not Supplied Energy amount) are obtained for an urban case study.

The growing interest towards the renewable energy sources remarks the relevance of smart grid exploiting in the electrical scenario. If in one hand smart grids introduces new perspective in the global energy management, on the other hand such innovative approach highlights the necessity to face with several issues related to the hybrid networks. Among them, the reliability of AC/DC hybrid grids is a crucial aspect for the effective development of hybrid smart grid. Studies on reliability of hybrid grids are present in the literature at component level, while the reliability related to the integration of smart grids in a traditional AC network is not exhaustively discussed. The present work proposes a Reliability Assessment Tool (RAT) aimed to the evaluation of specific hybrid grid configurations, as well as to manage contingency issues aimed to improve the safety and sustainability performances.

Power systems reliability assessment represents a complex task due to large number of systems and subsystems and for the significant number of different stresses affecting on individual components. Different models, methods and metrics can be used for electric and electronic systems in specific applications. The aim of this paper consists in the development of a reliability prediction models for power transformers in hybrid AC/DC grids. Future energy scenario with bidirectional power flows could stress transformers components. The propose approach intends to evaluate thermo-mechanical, environment and quality stress factors for each transformer subsystem with particular attention to ageing phenomena. The overall transformer reliability performances are calculated in real operating conditions characterizing 2030 scenarios.

Interest in microgrids is advancing as they contribute to local energy management while preserving the main grid operation. However, their introduction poses problems of reliability, particularly when renewable sources are present. Therefore, devices and management systems need preliminary tests in lab facilities using emulators before their deployment on the field. This paper presents an architecture designed to help researchers test the envisioned management algorithms and control techniques on a lab-scale microgrid facility. On that base, a prototype control system was implemented to assess the operation in close to real conditions of an advanced control logic aiming at avoiding congestion issues. The physical architecture of the microgrid was easily adapted to the purpose. Results show that the microgrid effectively acted as expected, following the commands dispatched by the control system.

Using data from a regional census survey of high school students, we compared substance use patterns between transgender and cisgender students, and students who did not know if they are transgende...

Background Although several researches investigated Default Mode Network (DMN) alterations in individuals with anxiety disorders, up to now no studies have investigated DMN functional connectivity in non-clinical individuals with high-trait-anxiety using quantitative electroencephalography (EEG). Here, the main aim was to extend previous findings investigating the association between trait anxiety and DMN EEG functional connectivity. Methods Twenty-three individuals with high-trait-anxiety and twenty-four controls were enrolled. EEG was recorded during 5 min of resting state (RS). EEG analyses were conducted by means of the exact Low-Resolution Electromagnetic Tomography software (eLORETA). Results Compared to controls, individuals with high-trait-anxiety showed a decrease of theta connectivity between right medial prefrontal cortex (mPFC) and right posterior cingulate/retrosplenial cortex. A decrease of beta connectivity was also observed between right mPFC and right anterior cingulate cortex. Furthermore, DMN functional connectivity strength was negatively related with STAI-T total score (i.e., lower connectivity was associated with higher trait anxiety), even when controlling for potential confounding variables (i.e., sex, age, and general psychopathology). Limitations Small sample size makes it difficult to draw definitive conclusions. Furthermore, we did not assess state variation of anxiety, which make our interpretation specific to trait anxiety. Conclusions Taken together, our results suggest that high-trait-anxiety individuals fail to synchronize DMN during RS, reflecting a possible top-down cognitive control deficit. These results may help in the understanding of the individual differences in functional brain networks associated with trait anxiety, a crucial aim in the prevention and in the early etiology understanding of clinical anxiety and related sequelae.

Hybrid Systems in microgrid applications have gained relevance in power flow management in the context of the worldwide power grids transformation. Successfully integrating several technologies of micro resources and storage systems is a key component of microgrid applications. To address this issue, dc-bus signaling (DBS) is proposed here and used as a distributed decentralized control strategy in which the control nodes, as the generation sources/storage interface converters, induce DC bus voltage-level changes to communicate with the other control nodes. The DC bus voltage thresholds are identified and assigned to each converter to trigger the point at which it begins discharging or charging for six different DC Nano Grid (DCNG) configurations, thereby integrating both conventional and unconventional storage systems. Several test cases have been analyzed to verify the effectiveness of the proposed control logic.

Nowadays, a power system failure can drastically affect the reliability and normal operation of power distribution grids. The preparation for these failure events is currently approached with post-event analysis to identify the area of the system that requires the most resources in order to prevent future failures. Nevertheless, the forecasting of such events can be useful to anticipate the failure and possibly avoid it. In this work, we employ several machine learning approaches to analyze historical failure data and predict power grid outages based on operational and meteorological data. The approach is tested with real failure data of a power distribution network in the South of Italy, demonstrating advantageous results also to determine areas requiring particular attention.

The present paper deals with the integration of Renewable Energy Sources (RES) in the present power systems, in particular in reference to the transmission grids. Starting from a focus on RES in terms of technologies and impacts on the transmission grids, an overview on last generation solutions for RES integration, is reported. The main issues and perspectives of the integration process are investigated and summarized, in reference to a wide international literature on this topic. The investigated integration solutions are classified in: innovative materials, new electrical components, advanced electronic devices, automated control systems, smart technologies and new management mechanics (business and market models, incentives, etc.). Some considerations on economics and costs for the implementation of the RES integration solutions are included.

Due to their intrinsic intermittent and random nature, renewable energy sources can dramatically influence the short-term scheduling of distributed energy resources (DER) and if their uncertainties are not properly handled, the operation strategies of DER may deviate from the optimal ones. In order to overcome this issue, this paper presents a comprehensive method for modeling uncertainties of solar power generation through a scenario-generation approach and historical data for a specific area of interest. The proposed approach is particularly suitable to be used in smart grid environment for stochastic operation optimization of DER. The Roulette Wheel mechanism has been used for generating an initial set of scenarios, and a reduction process based on Fast Forward method has been then applied in order to preserve the most representative ones, while reducing the computational efforts of next potential stochastic optimization phase. A sensitivity analysis has been carried out to evaluate the effects of key parameters on the solar irradiance generated scenarios. The results could be useful as a guide to tune scenarios' generation process aimed at obtaining scenarios with specific characteristics. Moreover, case study results show that the proposed method allows generating plausible scenarios for solar power generation to be used as input for DER stochastic optimization purposes. Moreover, its high flexibility allows to estimate the behavior of stochastic operation optimization of DER in the presence of more fluctuating, but ever plausible, solar irradiance patterns.

In this work, we investigate an Information Fusion architecture based on a Factor Graph in Reduced Normal Form. This paradigm permits to describe the fusion in a completely probabilistic framework and the information related to the different features are represented as messages that flow in a probabilistic network. In this way we build a sort of context for observed features conferring to the solution a great flexibility for managing different type of features with wrong and missing values as required by many real applications. Moreover, modifying opportunely the messages that flow into the network, we obtain an effective way to condition the inference based on the different reliability of each information source or in presence of single unreliable signal. The proposed architecture has been used to fuse different detectors for an identity document classification task but its flexibility, extendibility and robustness make it suitable to many real scenarios where the signal can be wrongly received or completely missing.

Electricity load forecasting plays an important role in planning and a vital role in the operational management of an electric power system based on smart grids. In this work, several data-driven approaches are used to forecast the individual electricity demand for the subsequent hour of three Italian households in a nanogrid context. For each user, the tested prediction models exploit only the historic series of total electricity consumption. The results show similar performances in all models implemented. Despite a widespread delay, the predictions follow the measurement trend well, while also highlighting the particular difficulty of predicting peak values.

A DC nanogrid is a hybrid power supply system integrating several small-scale resources, generation, conversion and storage technologies on a common DC bus. To ensure a reliable, safe and efficient operating condition, all resources included in a DC nanogrid must be properly integrated and a suitable control strategy has to be implemented. A distributed control strategy based on DBS is usually applied to control a DC nanogrid. The DBS is used as a distributed decentralized control strategy in which the control nodes, the source/storage interface converters, induce DC bus voltage-level changes to communicate with the other control nodes. It is an extension of the concept of using charge/discharge thresholds to schedule individual sources in a distributed fashion, which induces DC bus voltage level changes to activate communications between difference source/storage interface converters. A DC bus voltage threshold is assigned to each converter to trigger the point at which it begins discharging or charging. In this paper, an advanced DBS is proposed. More in detail, the DC bus voltage thresholds are dynamically changed depending on the real DC nanogrid configuration. Two case studies are analyzed to show the ability of the proposed advanced DBS to make the DC nanogrid stable in all normal and critical operating conditions.

This paper presents the results of an exploratory survey based on a multi-purpose questionnaire collecting data from 250 residential end-users who live in the southern regions of Italy. The survey aims to evaluate the level of knowledge about storage technologies and assess the social acceptability of energy communities and distributed storage systems for creating distributed energy storage communities. The survey is, conceptually, structured in three parts. The first part collects socio-demographic data and investigates preferences about the media that people know and use and propensity to participate in collective actions. The second part of the questionnaire focuses on examining the propensity toward storage technologies and people's perceptions, expectations and concerning to adopt storage systems. In the final part, the questionnaire examines people's knowledge and propensity to participate in energy communities. The data obtained from the survey were processed using the Statistical Package for Social Science software. Researchers and operators may use the survey's results as suggestions for improving the diffusion of distributed storage systems and increasing their potential market.

Introduction: Historically, substance use prevention relied heavily on scare tactics and fear-based messages. However, these messages were discouraged from use due to research finding them ineffect...

Aims: We investigated default mode network (DMN) electroencephalography (EEG) functional connectivity differences between individuals with self-reported high mentalization capability and low psychopathological symptoms, versus participants with mentalization impairments and high psychopathological symptoms. Methods: Forty-nine students (35 women) with a mean age of 22.92 ± 2.53 years were administered the Mentalization Questionnaire (MZQ) and the Symptom Checklist-90-Revised. Five minutes of EEG during resting state were also recorded for each participant. DMN functional connectivity analyses were conducted by means of the exact Low Resolution Electric Tomography software (eLORETA). Results: Compared to the individuals with high mentalization capability and lower self-reported psychopathological symptoms, participants with mentalization impairments and high psychopathological symptoms showed a decrease of EEG beta connectivity between: (i) the right and left medial frontal lobe, and (ii) the left medial frontal lobe and the right anterior cingulate cortex. Furthermore, while MZQ total score was positively associated with DMN network connections (i.e., right and left medial frontal lobes), several psychopathological symptoms (i.e., interpersonal sensitivity, depression, and psychoticism) were negatively associated with DMN connectivity. Conclusion: Our results may reflect a top-down emotion regulation deficit which is associated with both internalizing and externalizing behavior problems.

Several studies showed the effectiveness of alpha/theta (A/T) neurofeedback training in treating some psychiatric conditions. Despite the evidence of A/T effectiveness, the psychological and neurobiological bases of its effects is still unclear. The aim of the present study was to explore the usefulness of the A/T training in increasing mentalization in a non-clinical sample. The modifications of electroencephalographic (EEG) functional connectivity in Default Mode Network (DMN) associated with A/T training were also investigated. Forty-four subjects were enrolled in the study and randomly assigned to receive ten sessions of A/T training [neurofeedback group (NFG) = 22], or to act as controls [waiting list group (WLG) = 22]. All participants were administered the mentalization questionnaire (MZQ) and the Symptom Checklist-90-Revised (SCL-90-R). In the post training assessment, compared to WLG, NFG showed a significant increase of MZQ total scores (3.94 ± 0.73 vs. 3.53 ± 0.77; F1;43 = 8.19; p = 0.007; d = 0.863). Furthermore, A/T training was also associated with a significant increase of EEG functional connectivity in several DMN brain areas (e.g. Posterior Cingulate Cortex). Taken together our results support the usefulness of the A/T training in enhancing mentalization and DMN connectivity.

•Few evidence-based substance use prevention programs exist for LGBQ populations.•LGB organizations benefit from evaluation capacity building initiatives.•Evaluators working with LGBQ programs face unique challenges.•Evaluators should possess basic evaluation skills and LGBQ cultural competence.

Microgrids represent a promising energetic scenario applicable in different contexts, especially in residential clusters. In this paper, authors propose a novel control logic to implement a coordinated management of generators, loads, and hybrid energy storage systems (HESS) in a microgrid by means of a hierarchical smart converter architecture. The innovative algorithm is embedded in a master converter. It allows the online management of energetic fluxes in cooperation with slave converters distributed among the microgrid resources. They carry out a smart coordination of microgrid generation, absorption, and battery-supercapacitor storage systems with the aim to improve the availability of the storage systems for providing ancillary services to the power grid. The effectiveness of the control is tested applying the smart converter master-slave architecture, including the combined management BESS-supercapacitor algorithm, to a grid-connected residential microgrid.

In last years, Modular Multilevel Converters have been applied in various contexts, especially as distributed renewable interfaces toward the electric grid. In these applications a critical task is represented by converters reliability assessment since preliminary design steps. In detail, the attention is focused on the Cascaded H-Bridge topology and its multilevel configurations. A military (Military Handbook 217F) and an industrial (FIDES 2009) prediction models are employed to CHB quantitative reliability evaluation. In this paper both Part Count and Part Stress methodologies are applied to operative case studies. The relative procedures are carried out by a commercial software tool (RAM Commander®) and by the Converter Design software developed at the ENEA Research Centre of Portici. The innovative aspect of the proposed software consists in a reliability-oriented design process dedicated to Modular Multilevel Converters.

Multilevel converters will be one of the most important conversion systems in future power networks. These technologies can facilitate the integration of flexible DER in the grid also getting a significant role in microgrids. Designing high performances converters is not a trivial task due to the numerous different variables involved in the design process. This paper proposes a novel design approach and an easy-to-use executable program, employing a graphical user interface, dedicated to the design of multilevel converters. This software tool aims to obtain multilevel trade-off solutions in terms of efficiency, Mean Time Between Failure and price. The core of this tool consists in a multi-criterion optimization technique, based on the use of an evolutionary genetic algorithm, matched to accurate circuital models (i.e. electro-thermal reliable routine - TER and multilevel converter price routine). The solutions, plotted in two-dimensional and three-dimensional graphics, allow to easily choose the optimal solution for the specific context and visualize the corresponding bill of materials (BOM). The software also permits to consider innovative converter systems employing novel switching devices as the silicon carbide and the gallium nitride types.

With the increasing penetration levels of distributed energy resources (DER), grid congestion events may occur by causing severe damages and potential black outs in power systems. Developing innovative operation planning strategies and control tools for the grid congestion management is thus a primary goal for ensuring network reliability and security. In this paper, we propose a Grid Congestion Management Controller (GCMC) to solve grid congestion issues by controlling the active power of flexible DER (e.g. storage, flexible loads, electric vehicles, distributed generators) present in the grid, thus increasing availability of active resources to maintain the network stability. The GCMC is developed here as a self-contained control acting on distribution level, and will be further developed to be integrated in the INTEgrated opeRation PLANning tool, which will be the main outcome of the INTERPLAN H2020 project. INTERPLAN (Nov.2016Nov.2020) aims to provide an integrated tool to support transmission system operators and distribution system operators in the operation planning of the pan-European network. The GCMC effectiveness is tested in the presence of specific congestion events by using the Cigrè benchmark grid model under different scenarios. Simulation results show that the proposed control logic is efficient in mitigating grid congestion through optimally re-dispatching active power variation to the flexible DER connected to each busbar in the network.

To deal with the rising challenges associated with the increasing share of renewable energy resources (RES) in generation and installed capacity, as well as penetration of emerging technologies, such as storage and Demand Response (DR), the H2020 project INTERPLAN (Nov.2017-Nov.2020) has the aim to develop an INTEgrated network opeRation PLANning tool to support TSOs and DSOs in the operation planning of the pan-European network, by addressing a significant number of system operation planning challenges of the current and the future 2030+ EU power grid, from the perspective of the transmission system, the distribution system, and with a particular focus on the transmission-distribution interface. In order to design the integrated network operation planning considering a variety of emerging technologies, the future EU grid scenarios, as well as the potential barriers in interconnecting these emerging technologies to the grid need to be identified. This paper presents the INTERPLAN scenarios defined to depict the future challenges of the Pan-European network and rising technologies in order to meet the EU targets. Based on these scenarios, a critical assessment on the practical and regulatory barriers at EU level in connecting the growing share of emerging technologies to the grid with the aim to exploit effectively their flexibility potential is also carried out.

In recent years, power systems have been characterized by significant changes. The shift from centralized generation to non-dispatchable distributed generation needs crucial actions to maintain the network security and stability. In such a scenario, battery energy storage systems (BESS) are expected to have a key role for frequency restoration processes, thanks to their rapid response to frequency deviations. In this paper, an innovative control logic based on BESS management is proposed for dynamic frequency restoration in microgrids (μGs) with renewables. In addition to the BESS management, a new function, which is the State of Charge (SOC) Management Control (SMC), is developed. The SMC logic allows keeping the BESS SOC within the 'energy restoration band' as the SOC interval where the BESS is certainly able to participate to the frequency restoration process after system imbalances. This allows ensuring the needed amount of energy restoration reserves during the μG operation for next frequency deviation events. In simulation testing, carried out in DIgSILENT Powerfactory environment, two μGs in grid-connected and islanded modes are investigated. Results show that the SMC logic increases the possibility for the BESS to participate to frequency restoration processes as compared with the case without the control logic, by ensuring a higher availability of energy reserves, thus improving the μGs reliability for both the analyzed configurations.

Background Frontal alpha asymmetry (FAA) has been widely investigated in depressive disorders (DDs) with contradictory and not conclusive results. The main aim of the current study was to explore the association between a new neurophysiological index, the so-called frontal alpha connectivity asymmetry index (FACA-I), and depressive symptoms. Methods One hundred and thirteen participants (45 men and 68 women, mean age: 22.83 ± 2.26 years) were enrolled. Electroencephalographic (EEG) recordings were performed during 5 min of resting state (RS). FACA-I was computed by subtracting connectivity at left frontal regions from right frontal regions and dividing by their sum. RS FAA were also computed and compared to the FACA-I in all analyses. Results After controlling for the presence of potential confounding variables (i.e., sex, age and anxiety symptoms), only FACA-I scores between medial prefrontal cortex and subgenual anterior cingulate cortex were negatively associated with both somatic and cognitive/affective depressive symptoms and were lower in individuals with significant level of depressive symptoms. Limitations We focused on a sample of university students with no formal diagnosis of depression and we did not assess FAA and FACA-I during cognitive and/or emotional tasks, which make our interpretation specific to the RS condition. Conclusions Taken together our data suggest that alpha connectivity asymmetry between medial prefrontal cortex and subgenual anterior cingulate cortex may be a useful neurophysiological index in the assessment of depressive symptoms.

Integration of DC grids into AC networks will realize hybrid AC/DC grids, a new energetic paradigm which will become widespread in the future due to the increasing availability of DC-based generators, loads and storage systems. Furthermore, the huge connection of intermittent renewable sources to distribution grids could cause security and congestion issues affecting line behaviour and reliability performance. This paper aims to propose a planning tool for congestion forecasting and reliability assessment of overhead distribution lines. The tool inputs consist of a single line diagram of a real or synthetic grid and a set of 24-h forecasting time series concerning climatic conditions and grid resource operative profiles. The developed approach aims to avoid congestions criticalities, taking advantage of optimal active power dispatching among “congestion-nearby resources”. A case study is analysed to validate the implemented control strategy considering a modified IEEE 14-Bus System with introduction of renewables. The tool also implements reliability prediction formulas to calculate an overhead line reliability function in congested and congestions-avoided conditions. A quantitative evaluation underlines the reliability performance achievable after the congestion strategy action.