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Problem: The COVID‐19 pandemic has triggered or exacerbated eating disorders (EDs), especially in adolescents. This study examined the prevalence of admissions of patients with EDs at the Child and Adolescent Psychiatry Unit from the pre‐COVID‐19 pandemic to March 2023 and explored the differences in dimensions of ED's symptomatology according to the year of access. Methods: We included 174 children and adolescents, 94.3% females and 5.7% males, with a diagnosis of ED (Mage = 14.87; SD = 1.72). The Eating Disorder Inventory‐3 (EDI‐3), the Body Uneasiness Test (BUT) and Youth Self Report ASEBA (YSR) were assessed. A one‐way analysis of variance test was performed. Findings: EDs' hospitalization prevalence was higher in the years 2020 and 2021 compared to pre‐COVID‐19 and the year 2022. Considering the ED psychopathology (EDI‐3), findings showed a higher score in the dimension of the push to thinness, body dissatisfaction, asceticism, and fear of maturity in the year 2021 compared to pre‐pandemic. Regarding the discomfort related to the image of one's own body (BUT), results showed an increase in the global severity index in the year 2022 compared to pre‐pandemic and in weight phobia in the year 2021 compared to the year 2020. Concerning the internalizing symptoms (YSR), a tendency was found for withdrawal/depression, with higher levels in the year 2022 compared to the year 2020. Conclusions: Our study highlighted the increase of different types of EDs symptomatology related to concerns about weight, especially 2 and 3 years after the outbreak of the pandemic, on which the literature is still scarce, especially in the Italian context. [ABSTRACT FROM AUTHOR]

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant's productivity by not exceeding the process constraints. [ABSTRACT FROM AUTHOR]