Prevention of binge drinking in young adolescents through the promotion of a balanced level of psychological well-being: be-dry

This doctoral thesis presents a project carried out in secondary schools located in the city of Ferrara with the primary objective of demonstrating the effectiveness of an intervention based on Well-Being Therapy (Fava, 2016) in reducing alcohol use and improving lifestyles. In the first part (chapters 1-3), an introduction on risky behaviors and unhealthy lifestyle in adolescence is presented, followed by an examination of the phenomenon of binge drinking and of the concept of psychological well-being. In the second part (chapters 4-6), the experimental study is presented. A three-arm cluster randomized controlled trial including three test periods was implemented. The study involved eleven classes that were randomly assigned to receive well-being intervention (WBI), lifestyle intervention (LI) or not receive intervention (NI). Results were analyzed by linear mixed model and mixed-effects logistic regression with the aim to test the efficacy of WBI in comparison with LI and NI. AUDIT-C total score increased more in NI in comparison with WBI (p=0.008) and LI (p=0.003) at 6-month. The odds to be classified as at-risk drinker was lower in WBI (OR 0.01; 95%CI 0.01–0.14) and LI (OR 0.01; 95%CI 0.01–0.03) than NI at 6-month. The odds to use e-cigarettes at 6-month (OR 0.01; 95%CI 0.01–0.35) and cannabis at post-test (OR 0.01; 95%CI 0.01–0.18) were less in WBI than NI. Sleep hours at night decreased more in NI than in WBI (p = 0.029) and LI (p = 0.006) at 6-month. Internet addiction scores decreased more in WBI (p = 0.003) and LI (p = 0.004) at post-test in comparison with NI. Conclusions about the obtained results, limitations of the study, and future implications are discussed. In the seventh chapter, the data of the project collected during the pandemic are presented and compared with those from recent literature.

The assessment of change in rural landscape and the analysis of the driving forces. Proposal of a spatial model for the rural built environment

The presented study carried out an analysis on rural landscape changes. In particular the study focuses on the understanding of driving forces acting on the rural built environment using a statistical spatial model implemented through GIS techniques. It is well known that the study of landscape changes is essential for a conscious decision making in land planning. From a bibliography review results a general lack of studies dealing with the modeling of rural built environment and hence a theoretical modelling approach for such purpose is needed. The advancement in technology and modernity in building construction and agriculture have gradually changed the rural built environment. In addition, the phenomenon of urbanization of a determined the construction of new volumes that occurred beside abandoned or derelict rural buildings. Consequently there are two types of transformation dynamics affecting mainly the rural built environment that can be observed: the conversion of rural buildings and the increasing of building numbers. It is the specific aim of the presented study to propose a methodology for the development of a spatial model that allows the identification of driving forces that acted on the behaviours of the building allocation. In fact one of the most concerning dynamic nowadays is related to an irrational expansion of buildings sprawl across landscape. The proposed methodology is composed by some conceptual steps that cover different aspects related to the development of a spatial model: the selection of a response variable that better describe the phenomenon under study, the identification of possible driving forces, the sampling methodology concerning the collection of data, the most suitable algorithm to be adopted in relation to statistical theory and method used, the calibration process and evaluation of the model. A different combination of factors in various parts of the territory generated favourable or less favourable conditions for the building allocation and the existence of buildings represents the evidence of such optimum. Conversely the absence of buildings expresses a combination of agents which is not suitable for building allocation. Presence or absence of buildings can be adopted as indicators of such driving conditions, since they represent the expression of the action of driving forces in the land suitability sorting process. The existence of correlation between site selection and hypothetical driving forces, evaluated by means of modeling techniques, provides an evidence of which driving forces are involved in the allocation dynamic and an insight on their level of influence into the process. GIS software by means of spatial analysis tools allows to associate the concept of presence and absence with point futures generating a point process. Presence or absence of buildings at some site locations represent the expression of these driving factors interaction. In case of presences, points represent locations of real existing buildings, conversely absences represent locations were buildings are not existent and so they are generated by a stochastic mechanism. Possible driving forces are selected and the existence of a causal relationship with building allocations is assessed through a spatial model. The adoption of empirical statistical models provides a mechanism for the explanatory variable analysis and for the identification of key driving variables behind the site selection process for new building allocation. The model developed by following the methodology is applied to a case study to test the validity of the methodology. In particular the study area for the testing of the methodology is represented by the New District of Imola characterized by a prevailing agricultural production vocation and were transformation dynamic intensively occurred. The development of the model involved the identification of predictive variables (related to geomorphologic, socio-economic, structural and infrastructural systems of landscape) capable of representing the driving forces responsible for landscape changes.. The calibration of the model is carried out referring to spatial data regarding the periurban and rural area of the study area within the 1975-2005 time period by means of Generalised linear model. The resulting output from the model fit is continuous grid surface where cells assume values ranged from 0 to 1 of probability of building occurrences along the rural and periurban area of the study area. Hence the response variable assesses the changes in the rural built environment occurred in such time interval and is correlated to the selected explanatory variables by means of a generalized linear model using logistic regression. Comparing the probability map obtained from the model to the actual rural building distribution in 2005, the interpretation capability of the model can be evaluated. The proposed model can be also applied to the interpretation of trends which occurred in other study areas, and also referring to different time intervals, depending on the availability of data. The use of suitable data in terms of time, information, and spatial resolution and the costs related to data acquisition, pre-processing, and survey are among the most critical aspects of model implementation. Future in-depth studies can focus on using the proposed model to predict short/medium-range future scenarios for the rural built environment distribution in the study area. In order to predict future scenarios it is necessary to assume that the driving forces do not change and that their levels of influence within the model are not far from those assessed for the time interval used for the calibration.

Creep and damage models for the service behaviour of structural members

Deformability is often a crucial to the conception of many civil-engineering structural elements. Also, design is all the more burdensome if both long- and short-term deformability has to be considered. In this thesis, long- and short-term deformability has been studied from the material and the structural modelling point of view. Moreover, two materials have been handled: pultruded composites and concrete. A new finite element model for thin-walled beams has been introduced. As a main assumption, cross-sections rigid are considered rigid in their plane; this hypothesis replaces that of the classical beam theory of plane cross-sections in the deformed state. That also allows reducing the total number of degrees of freedom, and therefore making analysis faster compared with twodimensional finite elements. Longitudinal direction warping is left free, allowing describing phenomena such as the shear lag. The new finite-element model has been first applied to concrete thin-walled beams (such as roof high span girders or bridge girders) subject to instantaneous service loadings. Concrete in his cracked state has been considered through a smeared crack model for beams under bending. At a second stage, the FE-model has been extended to the viscoelastic field and applied to pultruded composite beams under sustained loadings. The generalized Maxwell model has been adopted. As far as materials are concerned, long-term creep tests have been carried out on pultruded specimens. Both tension and shear tests have been executed. Some specimen has been strengthened with carbon fibre plies to reduce short- and long- term deformability. Tests have been done in a climate room and specimens kept 2 years under constant load in time. As for concrete, a model for tertiary creep has been proposed. The basic idea is to couple the UMLV linear creep model with a damage model in order to describe nonlinearity. An effective strain tensor, weighting the total and the elasto-damaged strain tensors, controls damage evolution through the damage loading function. Creep strains are related to the effective stresses (defined by damage models) and so associated to the intact material.

Multilevel Analysis in Household Survey: An Application to Health Condition Data

The aim of this thesis is to apply multilevel regression model in context of household surveys. Hierarchical structure in this type of data is characterized by many small groups. In last years comparative and multilevel analysis in the field of perceived health have grown in size. The purpose of this thesis is to develop a multilevel analysis with three level of hierarchy for Physical Component Summary outcome to: evaluate magnitude of within and between variance at each level (individual, household and municipality); explore which covariates affect on perceived physical health at each level; compare model-based and design-based approach in order to establish informativeness of sampling design; estimate a quantile regression for hierarchical data. The target population are the Italian residents aged 18 years and older. Our study shows a high degree of homogeneity within level 1 units belonging from the same group, with an intraclass correlation of 27% in a level-2 null model. Almost all variance is explained by level 1 covariates. In fact, in our model the explanatory variables having more impact on the outcome are disability, unable to work, age and chronic diseases (18 pathologies). An additional analysis are performed by using novel procedure of analysis :"Linear Quantile Mixed Model", named "Multilevel Linear Quantile Regression", estimate. This give us the possibility to describe more generally the conditional distribution of the response through the estimation of its quantiles, while accounting for the dependence among the observations. This has represented a great advantage of our models with respect to classic multilevel regression. The median regression with random effects reveals to be more efficient than the mean regression in representation of the outcome central tendency. A more detailed analysis of the conditional distribution of the response on other quantiles highlighted a differential effect of some covariate along the distribution.

Evaluation and management of demersal stocks in the Adriatic: the case of common sole (Solea solea, L.) in the northern and central Adriatic Sea

This PhD thesis aimed to assess the status of common sole, one of the main commercial stocks in the Adriatic Sea, using a mix of conventional and innovative techniques to provide more reliable estimates of stock status compared to past advice. First, a meta-analysis was carried out using data-poor assessment model to analyze the whole catch assemblage of rapido fishery. The outcomes were used to estimate rebuilding time and forecast catches under different harvest control rule scenarios, with a reduction of 20% of fishing effort being suggested as a way to allow most of the species to recover to sustainable levels. Secondly, an ensemble of data-rich assessment models was developed to better incorporate uncertainty by using alternative hypotheses of main parameters. This was the first time an ensemble of models has been used in the Mediterranean to provide management advice. Consistent with data-poor analysis results, the ensemble outcomes indicated that the common sole stock was showing a recovering trend probably due to the effective management actions underway in the area rather than the moderate effort reduction according to the actual management plan. Moreover, back-calculation measurements were used to fit and compare monophasic and biphasic growth curves through the use of non-linear mixed effects models. The analyses revealed that the fitting of the biphasic curve was superior, confirming the theory that growth in size would decrease as a consequence of reproductive effort. A stock assessment simulation showed how the use of the monophasic pattern would result in a critical overestimation of biomass that could lead to a greater risk of overfishing. As a final step, a simulation-testing procedure was applied to determine the best performing reference points using stock-specific characteristic. The procedure could be routinely adopted to increase transparency in reference points calculation enhancing the credibility of scientific advice.