A way to measure the opinion of Europeans about Utilities

EnThis paper concerns the measure the opinion of European citizens about utilities. The Eurobarometer Survey data are considered and two non-standard techniques - Rasch Model and Nonlinear Principal Component Analysis - originally proposed in other fields are applied and discussed. The potential of both methods is highlighted; in particular the methods allow the questionnaire to be calibrated and the consumer satisfaction to be assessed and compared among European countries and different years

Social networks, happiness and health: from sentiment analysis to a multidimensional indicator of subjective well-being

This paper applies a novel technique of opinion analysis over social media data with the aim of proposing a new indicator of perceived and subjective well-being. This new index, namely SWBI, examines several dimension of individual and social life. The indicator has been compared to some other existing indexes of well-being and health conditions in Italy: the BES (Benessere Equo Sostenibile), the incidence rate of influenza and the abundance of PM10 in urban environments. SWBI is a daily measure available at province level. BES data, currently available only for 2013 and 2014, are annual and available at regional level. Flu data are weekly and distributed as regional data and PM10 are collected daily for different cities. Due to the fact that the time scale and space granularity of the different indexes varies, we apply a novel statistical technique to discover nowcasting features and the classical latent analysis to study the relationships among them. A preliminary analysis suggest that the environmental and health conditions anticipate several dimensions of the perception of well-being as measured by SWBI. Moreover, the set of indicators included in the BES represent a latent dimension of well-being which shares similarities with the latent dimension represented by SWBI.Comment: 26 pages, 5 figur

Bayesian Network Applications to Customer Surveys and InfoQ

AbstractModelling relationships between variables has been a major challenge for statisticians in a wide range of application areas. In conducting customer satisfaction surveys, one main objective, is to identify the drivers to overall satisfaction (or dissatisfaction) in order to initiate proactive actions for containing problems and/or improving customer satisfaction. Bayesian Networks (BN) combine graphical analysis with Bayesian analysis to represent relations linking measured and target variables. Such graphical maps are used for diagnostic and predictive analytics. This paper is about the use of BN in the analysis of customer survey data. We propose an approach to sensitivity analysis for identifying the drivers of overall satisfaction. We also address the problem of selection of robust networks. Moreover, we show how such an analysis generates high information quality (InfoQ) and can be effectively combined with an integrated analysis considering various models

Smart visualization of mixed data

In this work, we propose a new protocol that integrates robust classification and visualization techniques to analyze mixed data. This protocol is based on the combination of the Forward Search Distance-Based (FS-DB) algorithm (Grané, Salini, and Verdolini 2020) and robust clustering. The resulting groups are visualized via MDS maps and characterized through an analysis of several graphical outputs. The methodology is illustrated on a real dataset related to European COVID-19 numerical health data, as well as the policy and restriction measurements of the 2020-2021 COVID-19 pandemic across the EU Member States. The results show similarities among countries in terms of incidence and the management of the emergency across several waves of the disease. With the proposed methodology, new smart visualization tools for analyzing mixed data are provided

A proposal to deal with sampling bias in social network big data

[EN] Selection bias is the bias introduced by the non random selection of data, it leads to question whether the sample obtained is representative of the target population. Generally there are different types of selection bias, but when one manages web-surveys or data from social network as Twitter or Facebook, one mostly need to focus with sampling and self-selection bias. In this work we propose to use offcial statistics to anchor and remove the sampling bias and unreliability of the estimations, due to the use of social network big data, following a weighting method combined with a small area estimations (SAE) approach.Iacus, SM.; Porro, G.; Salini, S.; Siletti, E. (2018). A proposal to deal with sampling bias in social network big data. En 2nd International Conference on Advanced Reserach Methods and Analytics (CARMA 2018). Editorial Universitat Politècnica de València. 29-37. https://doi.org/10.4995/CARMA2018.2018.8302OCS293

Lignocellulolytic Potential of Microbial Consortia Isolated from a Local Biogas Plant: The Case of Thermostable Xylanases Secreted by Mesophilic Bacteria

Lignocellulose biomasses (LCB), including spent mushroom substrate (SMS), pose environmental challenges if not properly managed. At the same time, these renewable resources hold immense potential for biofuel and chemicals production. With the mushroom market growth expected to amplify SMS quantities, repurposing or disposal strategies are critical. This study explores the use of SMS for cultivating microbial communities to produce carbohydrate-active enzymes (CAZymes). Addressing a research gap in using anaerobic digesters for enriching microbiomes feeding on SMS, this study investigates microbial diversity and secreted CAZymes under varied temperatures (37 °C, 50 °C, and 70 °C) and substrates (SMS as well as pure carboxymethylcellulose, and xylan). Enriched microbiomes demonstrated temperature-dependent preferences for cellulose, hemicellulose, and lignin degradation, supported by thermal and elemental analyses. Enzyme assays confirmed lignocellulolytic enzyme secretion correlating with substrate degradation trends. Notably, thermogravimetric analysis (TGA), coupled with differential scanning calorimetry (TGA-DSC), emerged as a rapid approach for saccharification potential determination of LCB. Microbiomes isolated at mesophilic temperature secreted thermophilic hemicellulases exhibiting robust stability and superior enzymatic activity compared to commercial enzymes, aligning with biorefinery conditions. PCR-DGGE and metagenomic analyses showcased dynamic shifts in microbiome composition and functional potential based on environmental conditions, impacting CAZyme abundance and diversity. The meta-functional analysis emphasised the role of CAZymes in biomass transformation, indicating microbial strategies for lignocellulose degradation. Temperature and substrate specificity influenced the degradative potential, highlighting the complexity of environmental-microbial interactions. This study demonstrates a temperature-driven microbial selection for lignocellulose degradation, unveiling thermophilic xylanases with industrial promise. Insights gained contribute to optimizing enzyme production and formulating efficient biomass conversion strategies. Understanding microbial consortia responses to temperature and substrate variations elucidates bioconversion dynamics, emphasizing tailored strategies for harnessing their biotechnological potential

Support to SMEs - Increasing research and innovation in SMEs and SME development. Final report. Work package 2

Ex post evaluation of Cohesion Policy programmes 2007-2013, focusing on the European Regional Development Fund (ERDF) and the Cohesion Fund (CF)

Frailty Network in an Acute Care Setting: The New Perspective for Frail Older People

The incidence of elderly patients who come to the emergency room is progressively increasing. The specialization of the physician units might not be adequate for the evaluation of this complexity. The present study aimed to present a standard procedure, called ‘The Geriatric Frailty Network’, operating at the Policlinico Gemelli IRCCS Foundation, which is configured specifically for the level II assessment of frail elderly patients. This was a retrospective study in 1191 patients aged over 65, who were evaluated by the Geriatric Frailty Unit directly after emergency department admission for one year. All patients underwent multidimensional geriatric evaluation. Data were collected on demographics, co-morbidity, disease severity, and Clinical Frailty Scale. Among all patients, 723 were discharged directly from the emergency room with early identification of continuity of care path. Globally, 468 patients were hospitalized with an early assessment of frailty that facilitated the discharge process. The geriatric frailty network model aims to assist the emergency room and ward doctor in the prevention of the most common geriatric syndromes and reduce the number of incongruous hospitalization