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Abstract The unprecedented success of image reconstruction approaches based on deep neural networks has revolutionised both the processing and the analysis paradigms in several applied disciplines. In the field of digital humanities, the task of digital reconstruction of ancient frescoes is particularly challenging due to the scarce amount of available training data caused by ageing, wear, tear and retouching over time. To overcome these difficulties, we consider the Deep Image Prior (DIP) inpainting approach which computes appropriate reconstructions by relying on the progressive updating of an untrained convolutional neural network so as to match the reliable piece of information in the image at hand while promoting regularisation elsewhere. In comparison with state-of-the-art approaches (based on variational/PDEs and patch-based methods), DIP-based inpainting reduces artefacts and better adapts to contextual/non-local information, thus providing a valuable and effective tool for art historians. As a case study, we apply such approach to reconstruct missing image contents in a dataset of highly damaged digital images of medieval paintings located into several chapels in the Mediterranean Alpine Arc and provide a detailed description on how visible and invisible (e.g., infrared) information can be integrated for identifying and reconstructing damaged image regions.

(1) Background: Food waste (FW) in Romania is 70 Kg/capita/year, while 70% of food waste comes from public catering, retail services, and households (over 50%–47 million tons). The present study investigates the socio-demographic factors, behaviors, motivations, and attitudes related to food waste management in Romanian households. (2) Methods: A cross-sectional observational study was conducted using an online questionnaire via the Google Forms platform from 15 April 2023 to 15 May 2023. The questionnaire was designed to assess various aspects, such as some socio-demographic information (age, sex, occupation, area of residence, study level, household members number, children

Elevated immunoglobulin E (IgE) levels are commonly associated with allergies. However, high IgE levels are also found in several other infectious and non-infectious disorders. Elevated IgE levels typically suggest allergies, eczema, or recurrent skin infections. Hyperimmunoglobulin E (hyper-IgE) levels typically reflect a monogenic atopic condition or inborn immune defects with an atopic phenotype. The aim of our research is to investigate and observe the clinical characteristics of children with increased IgE levels who have previously manifested infectious diseases. Furthermore, the retrospective study considers other factors, such as demographic characteristics (sex, area/environment, and age), and their effect on IgE serum levels. To answer this question, we conducted a one-year hospital-based retrospective study that included 200 hospitalized children who had at least two viral or bacterial infections in the six months preceding hospitalization. Measurements of IgE and allergen panels (respiratory and digestive) using blood samples revealed that individuals who tested positive for the body’s synthesis of hyper-IgE were not observably allergic to any potential allergens despite having higher total serum IgE. According to the results, there was a strong correlation between elevated IgE serum levels and a history of infectious diseases among the patients.