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BackgroundIn Italy, rigorous studies obtained with specific and validated questionnaires that explore the impact of exclusion diets on health-related quality of life (HRQoL) in children with food allergies are lacking. In this cross-sectional study, we wished to validate the Italian version of a disease-specific quality of life questionnaire, and assess the impact of exclusion diets on the HRQoL in a cohort of Italian children with IgE-mediated food allergies.MethodsChildren on an exclusion diet for ≥1 food were enrolled consecutively, and their parents completed the validated Italian version of the Food Allergy Quality of Life Questionnaire–Parent Form (FAQLQ-PF) and Food Allergy Independent Measure (FAIM).ResultsNinety-six parents of children aged 0–12 years answered the FAQLQ–PF. The validity of the construct of the questionnaire was assessed by correlation between the FAQLQ–PF and FAIM–PF (r = 0.85). The Italian version of the FAQLQ had good internal consistency (Cronbach's α >0.70). Factors that mainly influenced the HRQoL were older age, severity of food allergy, and the duration of the cow milk-exclusion diet.ConclusionsThe FAQLQ–PF, validated in Italian, is a reliable instrument. Worse QoL scores were observed among older children, those with severe systemic reactions, and those with a prolonged cow milk-free diet. It is very important to consider the QoL assessment as an integral part of food-allergy management. These results emphasize the need to administer exclusion diets only for the necessary time and the importance of assessment of the HRQoL in these patients.

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The measure of Quality of Life (QoL) has become one of the most important criteria used to assess the impact of chronic illness, such as asthma, on the patient's daily life, in adults and children alike. The objective of our open observational study was to measure the QoL and analyze several factors that potentially affect QoL, such as symptoms and functional respiratory parameters, in a cohort of children with asthma. One hundred and twenty-seven children with asthma, 6 to 14 years of age, living in the city of Rome, were enrolled as outpatients. They were subjected to Skin Prick Tests (SPT), underwent spirometry and filled out the Pediatric Asthma Quality of Life Questionnaire (PAQLQ). One hundred and eleven children were diagnosed with intermittent asthma, 12 (10%) with mild asthma, and four with moderate persistent asthma. Ninety-six children had a positive SPT. The mean total score of QoL, obtained from the questionnaire, was 5.4 (±1.2 SD). Two QoL groups were created. Children with total QoL score <5.5 were included in the “Lower QoL” score group while children with total QoL score ≥ 5.5 were included in the “Higher QoL” score group. Children in the Higher group and their mothers had a higher mean age, suffered from fewer asthma exacerbations during the year preceding the study, and showed a higher mean value of forced expiratory volume (FEV1) compared to the children in the Lower category. Using Logistic regression we identified the main factors that may affect QoL as FEV1, symptoms in the previous year and mother's age. QoL is correlated with the frequency of asthma exacerbations and FEV1 values. Furthermore, our research shows that a significant impairment of QoL may also occur in patients with normal lung function, pointing out the importance of evaluating QoL in all children with asthma.

Battery Management System (BMS) design for Lithium-ion batteries State of Charge (SoC) prediction has a crucial role in Electric Vehicles (EVs) and smart grids development. The need to design compact, light and fast devices requires finding a suitable trade-off between effectiveness and efficiency. In the literature, it is well emphasized that the application of electrochemical-based methods such as the Pseudo-Two-Dimensional (P2D) model is computationally prohibitive and requires significant simplifications. Conversely, plain Equivalent Circuit Models (ECM) are too simple and unable to represent the cell dynamics. The application of an Ensemble Neural Network (ENN) as Equivalent Neural Network Circuit (ENNC) emerged as a promising solution able to synthesize expressive and computationally efficient models. Indeed, with the support of a suitable dataset, an ENN can be configured to represent a given ECM, modeling each lumped parameter through an assigned Neural Network (NN). Accordingly, the ENNC system is able to keep a physical description of the battery cell while approximating the non-linear dynamic of each component. The paper proposes a novel ENNC battery named Physical Inspired-Equivalent Neural Network Circuit (PI-ENNC) whose ensemble architecture relies on a fractional-order Extended Single Particle (ESP) Lithium-ion cell formulation. The PI-ENNC is designed to approximate the ESP transfer functions referred to the ohmic effects, the electrolyte diffusion and the non-uniform charge distribution in the cell. The proposed model has been tested with three publicly available datasets, investigating the model behavior according to two different training strategies and with different input configurations. In order to prove its effectiveness, results have been compared with a simpler version proposed in a previous work. Results highlight the effectiveness of PI-ENNC in SoC prediction, underlining the importance of designing an ENN architecture that leverages on equations and constraints that reflect the physical phenomena of the cell.

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With the breakthrough of pervasive advanced networking infrastructures and paradigms such as 5G and IoT, cybersecurity became an active and crucial field in the last years. Furthermore, machine learning techniques are gaining more and more attention as prospective tools for mining of (possibly malicious) packet traces and automatic synthesis of network intrusion detection systems. In this work, we propose a modular ensemble of classifiers for spotting malicious attacks on Wi-Fi networks. Each classifier in the ensemble is tailored to characterize a given attack class and is individually optimized by means of a genetic algorithm wrapper with the dual goal of hyper-parameters tuning and retaining only relevant features for a specific attack class. Our approach also considers a novel false alarm management procedure thanks to a proper reliability measure formulation. The proposed system has been tested on the well-known AWID dataset, showing performances comparable with other state of the art works both in terms of accuracy and knowledge discovery capabilities. Our system is also characterized by a modular design of the classification model, allowing to include new possible attack classes in an efficient way.