Human Factor and Energy Efficiency in Buildings: Motivating End-Users Behavioural Change

Energy efficiency in buildings does not only rely on efficient technical solutions and design of the building features, but is also highly dependent on how occupants decide to set their comfort criteria, as well as on their energy-related and environmental lifestyles. In this perspective, raising user awareness among occupants by training them to adopt a more “green” and energy-friendly behaviour has become a crucial aspect for reaching energy efficiency goals in buildings. Motivating occupants to change their behaviour can become a challenging task, especially if they are expected to internalize and adopt the new behaviour on a long term. This means that information and feedback provided to the occupants must be stimulating, easy to understand, and easy to adopt in the daily routine. In this context, first methodological progresses are here presented within an European project, designed to raise user awareness, reduce energy consumptions and improve health and IEQ conditions in different typologies of demonstration case studies by providing combined feedback on energy, indoor environmental quality, and health. In particular, this paper presents one out of five MOBISTYLE demonstration testbeds – a residence hotel - located in central Turin (IT). In detail, this paper describes the setup of a tailored engagement campaign for hotel apartments and the reception area. Based on selected monitored variables, user-friendly feedback was defined to provide the users with real-time information on energy use and environmental quality, as well as guidance on how to save energy and optimize consumption profiles while creating an acceptably comfortable and healthy indoor environment

Application of 1H HR-MAS NMR-Based Metabolite Fingerprinting of Marine Microalgae

Natural products from the marine environment as well as microalgae, have been known for the complexity of the metabolites they produce due to their adaptability to different environmental conditions, which has been an inexhaustible source of several bioactive properties, such as antioxidant, anti-tumor, and antimicrobial. This study aims to characterize the main metabolites of three species of microalgae (Nannochloropsis oceanica, Chaetoceros muelleri, and Conticribra weissflogii), which have important applications in the biofuel and nutrition industries, by 1H High-resolution magic angle spinning nuclear magnetic resonance (1H HR-MAS NMR), a method which is non-destructive, is highly reproducible, and requires minimal sample preparation. Even though the three species were found in the same ecosystem and a superior production of lipid compounds was observed, important differences were identified in relation to the production of specialized metabolites. These distinct properties favor the use of these compounds as leaders in the development of new bioactive compounds, especially against environmental, human, and animal pathogens (One Health), and demonstrate their potential in the development of alternatives for aquaculture

A methodological framework to motivate and assess behavioral change: Insights into an interdisciplinary user awareness campaign

Raising awareness among building occupants on how their behavior, comfort crite-ria settings, and lifestyles affect building energy use has become a central topic of inno-vative energy efficiency strategies. Indeed, reaching European energy efficiency goals does not only require the optimization of building design and features, but also necessi-tates the real energy consumers to be more aware of their energy-related interactions with the building. However, motivating occupants to change their behavior can become a challenging task. It is essential to provide novel, stimulating, and easily understandable information that help triggering a more energy-friendly behavior on a daily basis. Another challenge is to assess and evaluate the effectiveness of human-centered interventions. In this con-text, this paper presents an interdisciplinary methodological framework developed within an European project (H2020-MOtivating end-users Behavioral change by combined ICT based modular Information on energy use, indoor environment, health and lifestyle), de-signed to raise user awareness, reduce energy consumptions and improve health and IEQ conditions by providing combined feedback on energy, indoor environmental quality, and health. This paper identifies methodological steps designed to evaluate the outcomes, pro-cess and impacts of an interdisciplinary user-centered engagement campaign, including the application of Cost-Benefit Analysis in assessing co-benefits related to behavioral changes

Dating of Brazilian shells through electron paramagnetic resonance

In the present work we report dating procedure of these shells by using the electron paramagnetic resonance. The first step of the analysis was the definition of the optimal procedure for sample preparation. At this aim the analysis of the sample composition was carried out by X-ray fluorescence (XRF) measurements and an accurate analysis was study on the effect of the chemical etching with varying typology and concentration of acid to be used for removing the external layer of shells which are affected by alpha particles

Machine learning classification for COVID19 patients performed on small datasets of CT scans.

In this work we evaluated the possibility of carrying out classifications of the outcome of patients with COVID19 disease through machine learning (ML) techniques working on small datasets of computed tomography (CT) images. In fact, one of the most common problems for medical artificial intelligence (AI) applications is the limited availability of annotated clinical data for model training. In the framework of the artificial intelligence in medicine (AIM) project funded by INFN, we analyzed datasets of CT scans of 79 subjects combined with clinical data containing information relating to positive outcome (no need for intensive care) or poor prognosis (admission into intensive care unit and/or death). After segmentation of ground glass opacities related to this pathology, the radiomic features were subsequently extracted from the CTs, selected through various algorithms of dimension reduction or fea ture selection and used for the training various classifiers. Values of the area under the ROC curve (AUC) of 0.84 were obtained with Gradient Boosting after BORUTA feature selection. Features selected are related to disease characteristics of poor prognosis patients