Search

Background: Nurses are increasingly demanded to achieve gold-standards of care with fewer resources. Dealing effectively with stress experienced in their daily-work-life is thus crucial. This study is based on the Demands-Resources-and-Individual-Effects (DRIVE) Nurses Model and applied the person-centred approach with a twofold objective: 1. to identify patterns of coping strategies (Problem-Focused; Seek-Advice; Self-Blame; Wishful-Thinking; Escape/Avoidance) adopted by nurses to deal with perceived stress; 2. to explore potential differences in perceived Demands (Effort), Resources (Rewards, Job-Control, Social-Support), and Psychopathological Symptoms (Anxiety, Phobic-Anxiety, Obsessive–Compulsive, Somatization, Depression, Interpersonal-Sensitivity, Hostility, Psychoticism, Paranoid-Ideation) according to the emerged patterns. Method: This cross-sectional study was reported by using the STROBE Checklist. Overall, 265 nursing professionals completed self-report measures. Non-hierarchical k-means-cluster-analysis was employed to derive patterns of coping. MANOVAs were used to test differences in Demands, Resources, and Psychopathological Symptoms according to the emerged patterns. Results: Three stable and meaningful patterns of coping were identified and labelled as Active/Solution-Oriented, Dysregulated/Emotion-focused, and Passive/Disengaged. Nurses belonging to Dysregulated/Emotion-focused group emerged to be at higher risk (higher effort/psychopathological suffering; lower resources) – followed by Passive/Disengaged group – in comparison with nurses belonging to Active/Solution-Oriented group. Conclusion: Fostering nurses' awareness of their latent coping patterns and supporting active approaches/emotional regulation strategies for stress management should represent a key goal when defining interventions promoting nurses' health within/beyond the healthcare settings. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. [ABSTRACT FROM AUTHOR]

The present study examined Italian adoptive mothers' perspective about Adoptee Searching for their Origins. 37 adoptive mothers were interviewed. Interviews were analyzed through an automated method for content analysis combining a combining Cluster Analysis and Correspondence Analysis. In their whole the results highlight the difficulty encountered by mothers who seem to chase a model of family normality rather than confront the specificity of their own family history. Beyond the results, the research work suggests professionals to support adoptive families in their life path, recognizing their specificity as a way to favor the integration of their children in a long-life perspective. [ABSTRACT FROM AUTHOR]

The transition to sustainable food systems and the achievement of SDGs 2 and 12 entails the adoption of sustainable consumption and production patterns, such as the Mediterranean diet. The Mediterranean diet and its impact on nutritional status and health have been the focus of recent research. The aim of this study was to investigate the associations between adherence to the Mediterranean diet, lifestyle, socioeconomic status, BMI (kg/m2), and age of children. The geographical area is the Metropolitan City of Bari (Apulia region, southern Italy), which is involved in the development of an effective food policy aimed at ensuring a sustainable food transition. An online survey was filled out by parents or guardians responsible for the dietary patterns of children between 2 and 15 years of age to collect data about their Mediterranean diet adherence through the Mediterranean Diet Quality Index (KIDMED) and sociodemographic features, food habits and lifestyles, and anthropometric variables. Descriptive statistics and k-means cluster analysis were applied for profiling children, contributing to identifying the main factors that might affect individual Mediterranean diet adherence. Four clusters were characterized, taking into account food habits and lifestyle index, socioeconomic index, children’s age, and body mass index (BMI). Results showed an association between low adherence to the Mediterranean diet and high prevalence of overweight and obesity in children. The factors positively associated with following an optimal Mediterranean diet adherence were the mother’s education level and level of income; skipping breakfast is linked to low adherence to the Mediterranean diet. Therefore, targeted strategies should be put in place to improve Mediterranean diet adherence among children, promoting healthy dietary habits. [ABSTRACT FROM AUTHOR]

One of the challenges of studying entrepreneurship is the lack of longitudinal data. One way to address this is through a promising new statistical approach called prosopography, where standardised biographies of prominent entrepreneurs are systematically compiled and analysed using quantitative methodology. This method combines the detailed information available in biographies at the micro-level, with a macro-level systematic approach – an approach that, notably, has been previously applied to Italy. Here, we compile a dataset of 267 Swedish entrepreneurial biographies that focus on individuals active from the early 19th century until the present day. We find five distinctive clusters of entrepreneurs. Innovation intensity appears to be important, and, as expected, inventors represent a distinct cluster in Sweden. Other results are more novel, and in part go against the conventional view of Sweden as an industrial economy. For instance, many Swedish entrepreneurs, of both sexes, have developed what are best described as advanced service innovations, and stand as sole founders of the ventures. The institutional context clearly affects the type of entrepreneurs that emerge, but there is also evidence of interaction and reverse causation where prominent entrepreneurs have influenced the institutional development of Sweden. As a result, there is a hybrid version of capitalism combining high redistributive taxes with free market capitalism and ample room for Schumpeterian entrepreneurs as well as dynastic entrepreneurs – thereby also combining private wealth creation with distribution. While one of the world's least family -oriented cultures, high -impact Swedish entrepreneurs are surprisingly dynastic in their entrepreneurial endeavours. [ABSTRACT FROM AUTHOR]

Background: Orthorexia nervosa (ON) is a relatively new potential eating disorder characterized by an intense fixation on one's eating habits and the imposition of rigid and inflexible rules on oneself. Psychological factors such as obsessive–compulsive tendencies, perfectionism and self-esteem may interact in complex ways and contribute to the development and maintenance of ON. Methods: This cross-sectional study included 977 participants from Italy, Lebanon, and Poland. Participants completed a questionnaire consisting of socio-demographic information, the Eating Habits Questionnaire, Obsessive–Compulsive Inventory, Obsessive Beliefs Questionnaire-44, Multidimensional Perfectionism Scale, and Rosenberg Self-Esteem Scale. Cluster analysis was used to identify subgroups of individuals with common psychological characteristics associated with ON. Results: Three distinct clusters were identified based on their levels of obsession-compulsive beliefs, perfectionism, and self-esteem. The first group, labeled "High Self-Mastery," consisted of 37.0% of participants and exhibited low levels of obsession-compulsion, obsessive beliefs, and perfectionism, but high self-esteem. The second group, "Moderate Self-Mastery," comprised 39.5% of participants and had moderate levels of these traits. The third group, "Low Self-Mastery," consisted of 23.6% of participants and exhibited the highest levels of obsession-compulsion, obsessive beliefs, and perfectionism, but the lowest self-esteem. Additionally, a multivariable analysis revealed that being Lebanese (Beta = 3.39) and belonging to the last cluster (Beta = 4.53) were significantly associated with higher ON tendencies. Conclusion: Our findings show that individuals with low self-mastery, characterized by low self-esteem and high levels of obsessive perfectionism, are more likely to exhibit ON tendencies. This study emphasizes the need to have a comprehensive understanding of how cultural and psychological factors interact in the development of eating disorders. Plain English summary: Orthorexia nervosa (ON) is a relatively new eating disorder that involves an intense focus on eating habits and strict rules about food. This study aimed to identify factors that may predict the development of ON. A questionnaire was given to 977 participants from Italy, Lebanon, and Poland to assess their psychological characteristics such as obsessive–compulsive tendencies, perfectionism, and self-esteem. Cluster analysis was performed to identify subgroups of individuals with common psychological characteristics associated with ON. The study found that participants from Lebanon, and individuals with low self-mastery, characterized by low self-esteem and high levels of obsessive perfectionism, are more likely to exhibit ON tendencies and were particularly vulnerable to the disorder. [ABSTRACT FROM AUTHOR]

Introduction: The COVID-19 epidemic and its lockdown dramatically impacted the general well-being of the population and affected sociosexual experiences, thus modifying sexual behavior, desire, and well-being. Clustering analysis has not yet been applied to research and data investigating sociosexuality. The cluster analysis method could be a valid support for clinicians in investigating the condition of a population with respect to problems related to sociosexuality. The aim of the present study was to analyze the different perceptions of the sociosexual experiences in southern population during the COVID-19 pandemic. Methods: We enrolled 734 (450 female) participants with a carried out anonymous web-based survey from the 16th of April 2020 to the 3rd June of 2020. The revised Sociosexual Orientation Inventory (SOI-R) is a self-report test assessing three theoretically meaningful facets of sociosexual orientation (behavior, attitude, and desire). Results: We found eleven clusters, and the findings showed, for the first time, an intra- and inter-diagnostic heterogeneity in the sexual profile of participants. Theoretically, we identified subtype clusters whose sexual attitude was to avoid sexual promiscuity with significant gender differences. Women show a greater propensity for attitude and desire facet than men. Conclusions: Our new method of unsupervised learning could represent a reliable tool to support socio-cultural analysis studies on issues influenced by cultural mechanisms in a quick and explanatory way, as in the case of sexual orientation and attitude differences between men and women. Social and Policy Implications: Understanding these gaps is fundamental for policy makers, managers of social networks, those who deal with engaged couples and families, and sexuality starting from the very youngest adolescents. We claim to devise a strategy to measure how much a sexist culture implicitly and explicitly limits the freedom of sexual expression and how this can affect psycho-sexual well-being in a society. [ABSTRACT FROM AUTHOR]

The present study investigated the effects of the first COVID-19 lockdown on the Cultural and Social Capitals in Italy in a large group of adults (n = 1125). The relationships between the COVID-19 spread and participants' Cultural Capital, Social Capital, educational level, occupational prestige, and age were studied using structural equation models. For women but not for men, pandemic spread was positively affected by occupational prestige and it had a positive relationship with their Social Capital (women: CFI = 0.949; RMSEA = 0.059 [CI = 0.045-0.075]; men: CFI = 0.959; RMSEA = 0.064 [CI = 0.039–0.087]). Moreover, the participants were divided into three validated clusters based on their Cultural and Social Capitals levels to investigate changes in the Capitals compared with the pre-lockdown period. It was found that the lockdown contributed to improving the gap among individuals increasing high levels and decreasing low levels of both the Capitals. People with high Cultural and Social Capitals seemed to have seized the opportunity given by COVID-19 restrictions to cultivate their cultural interests and become more involved within their networks. In contrast, individuals with low Cultural and Social Capitals paid the highest price for the social isolation. Given that the Capitals encourage healthy behavior and influence well-being and mental health, institutions should develop or improve their policies and practices to foster individual resources, and make fairer opportunities available during the pandemic. [ABSTRACT FROM AUTHOR]

In the context of green finance, minibonds play a crucial role. This financial instrument was introduced in 2012 as a valid alternative to bank credit for corporate financing, aimed mainly at small and medium‐sized enterprises. Minibonds also represent useful support for implementing the ecological transaction agreed upon in COP 21, held in 2015 in Paris. Indeed, as of 2017, this instrument has been expanded from an environmental perspective by allowing the issuance of green minibonds. This article contributes to the debate on minibonds and the companies issuing them. Specifically, it proposes a cluster analysis approach for comparing the issuance level of minibonds in 2016 and the subsequent performances of the issuing companies in the triennium 2017–2019. Performance is divided into three macrocategories: profitability, productivity, and growth opportunities. The results suggest a nonlinear connection between the variables of interest, showing heterogeneous effects of minibonds on performance. However, the statistical data analysis seems to establish a positive relationship between minibonds' issuance level and companies' performance. [ABSTRACT FROM AUTHOR]

Road crashes have been internationally recognized as one of the main causes of death. On one hand, in Europe, many governments are struggling with the ambitious target of zero road deaths by 2050. On the other hand, they are facing remediation of illegal waste dumps, subject to European infringement procedures and involving a lot of workers and heavy materials transportation. With the aim to further explore occupational health and safety (OHS) issues related to the remediation of such contaminated sites close to urban areas, we decided to focus our attention on road crashes involving people while working in the transport of materials and goods (i.e., occupational road safety). In the scientific literature, it is considered an emerging matter of concern, but no significant contribution nor specific procedures have been provided in this research field for workers in charge of contaminated sites. With the aim to fill such a gap, we decided to, first, investigate the impacts of a landfill remediation site (Malagrotta landfill, near Rome—Italy) on road safety in the surrounding context. Then, road safety management measures for workers driving heavy vehicles from and toward the reference site were suggested through the means of cluster analysis. The main road accident determinants (road safety signs and traffic conditions) for heavy vehicles in the Rome municipality, derived from a sample of 166 events, occurred in the period 2017–2021 on target road infrastructures for the case study. The events were finally grouped with a k-means three-centroid solution. Overall, despite the intrinsic limits related to the data's details, this paper provides a specific and data-driven methodology to address occupational road safety near a landfill remediation site and encourages further research in this field. [ABSTRACT FROM AUTHOR]

The 17 Sustainable Development Goals (SDGs) require the implementation of 167 targets aimed at eradicating poverty, protecting the planet and improving the quality of life of humankind. The United Nations calls for uniform sustainable development at the global, local and individual levels. This research pursues a twofold objective: first, to obtain evidence on the extent to which the achievement of the (SDGs) may be uniform across territories; second, to identify the socioeconomic characteristics that contribute to sustainable development. The empirical analysis has been carried out using clustering, cross efficiency and contingency tables applied to statistical information from 101 municipalities in Spain and Italy. The results provide evidence of inequalities between territories, revealing that only in the dimensions People (SDGs 1, 2, 3, 4, and 5) and Prosperity (SDGs 7, 8, 9, 10, and 11) has the desired homogeneity been attained. Notable differences are found in the degree of compliance with the other SDGs. Furthermore, it is shown that the socioeconomic characteristics associated with the geographical location contribute substantially to the gap between municipalities. In order to ensure countries' sustainable development, there is a need for environmental policies adapted to the specific features of each region. [ABSTRACT FROM AUTHOR]

Industrial production has brought increased wellbeing in the last years, but the amount of solid waste has undoubtedly increased. Thus, open dumpsites and landfills have been created throughout the world, with serious impacts on the environment and public health. In such a context, occupational health and safety (OHS) issues related to workers that have to deal with landfill characterization or management have not been considered sufficiently. To reduce such a research gap, in 2019 a research project started in Italy on OHS risk assessment in landfills. In fact, in such facilities, workers can be subjected to direct contact with the polluted environment and might not be completely aware of the entity and type of pollution (e.g., in open dumpsites). Starting with the analysis of INAIL data on accidents at work which occurred in Italy during the period 2008–2019, a specific risk analysis was carried out with the goal of defining risk determinants and profiles by means of K-means cluster analysis. Such an analysis allowed us to recognize the use of work equipment and the work environment as the main determinants of the accidents on the one hand, and the "driver of the excavator" as the most risky activity on the other. The achieved results take a step forward towards the characterization of occupational health and safety issues in landfills. Accordingly, the research outcomes represent a basis on which to address further research work in this field. [ABSTRACT FROM AUTHOR]

The new Common Agricultural Policy (CAP) for the period 2023–2027 commits farmers towards achieving ambitious environmental objectives through farm organization and management. This European agricultural policy has adapted to the contemporary challenges faced by the new model of agricultural development. It aims to enhance the contribution of agriculture to the EU's environmental and climate objectives while providing better targeted support to small-scale farmers to promote farms' competitiveness. The main objective of this paper is to describe Italian farms and classify them into groups based on their main characteristics, as well as to analyze their performance and behavior in terms of sustainability and competitiveness. The novelty and innovativeness of this study are found in the data used; a 2020 dataset from the Italian Farm Accounting Data Network (FADN) was used. The quality of FADN data in farm sustainability assessment is widely acknowledge in the literature. To achieve the purpose of this study, a multivariate analysis, in particular, the Principal Components Analysis (PCA), and a Cluster Analysis (CA) were applied. These analyses helped us to obtain the factorial axes which then enabled us to identify economic information on farms, and a better interpretation of farmers' aptitude to undertake environmentally friendly actions. As a result, eight groups of farms were identified, and their characteristics and performance were described at Italian district level. The results of the study reflect the influence of European interventions towards encouraging farmers to use more environmentally friendly agricultural practices. Under this perspective, the findings contribute to the current debate on green architecture pursued by the future European agricultural policy. [ABSTRACT FROM AUTHOR]

This study analyses the collective management of irrigation water in 42 WUAs operating in agricultural regions of Italy, Spain and Turkey. The Principal Component Analysis (PCA) and Agglomerative Hierarchical Cluster Analysis (AHCA) are used for benchmarking the technical and financial performance of the WUAs. The analysis shows that the Spanish WUAs, which have less staff (–90%) and higher revenue collection (-53%), perform better than the WUAs of the other two countries. PCA and AHCA indicate that the technical and financial performance of the Turkish and some Spanish WUAs are similar. The Italian and Turkish associations have very large irrigation areas, and WUAs, which may be one of the reasons for the poor management in some organization areas of these countries. Specifically, Italy has excessive Management, Operation and Maintenance costs (up to 1500 €/ha-yr) and large staff numbers (5 persons per 100 ha of irrigated area), while Turkey has high water waste (up to 15000 m3/ha-yr) and insufficient revenue collection (about 60%). Based on the benchmarking results, recommendations are made, to improve the water use and financial performance of the Italian and Turkish WUAs. [ABSTRACT FROM AUTHOR]

Economists and other social scientists are increasingly leveraging web data to tackle socio-economic issues and integrate existing sources of information. The data generated by online platforms and websites offer a wealth of multidimensional information with diverse potential applications in economic analysis. Online job advertisement data, for instance, allows us to enhance our understanding of the labour market by providing timely insights into business demand and the skills required for various job positions. This paper examines skill similarities at the regional level in Italy and the variations in skills sought by businesses over time using Lightcast data for 2019 and 2020. In light of this, we use two measures related to the regional skill similarities, an index for the regional skill changes, jointly with some features of the local economy, in order to provide a picture of Italian regions. • We analysed online job ads data to study similarities and changes (between 2019 and 2020) in the skill sets across Italian regions. • We used the ESCO taxonomy to apply an index of by-occupation skill importance. Moreover, we made use of special statistical methods to face sparse data in the computation of regional skill similarity. • We found a different pattern of skill similarity and skill change across regions that partially reflects the North-South divide. • We interpreted these findings by also considering variables expressing the dynamism and innovation of the regions in terms of business turnover and youth entrepreneurship. [ABSTRACT FROM AUTHOR]

Despite findings from previous studies, there is still little consistent knowledge regarding the co-occurrence patterns of somatic, depressive and anxiety symptoms in childhood and adolescence. Moreover, functional disability due to somatic symptoms at different concomitant levels of depression and anxiety is understudied. The present study examined the co-occurrence patterns of somatic symptoms and symptoms of depression and anxiety, in children and adolescents using two-step cluster analysis. Differences in functional disability due to somatic symptoms were tested with ANCOVA controlling for gender and age. The sample comprised 1127 Italian children and adolescents (48.7% males, n = 549) aged 8–16 years (Mage = 11.7, SD = 2.37). Data were collected using the Children Somatization Inventory-24, the Children Depression Inventory, the Screen for Child Anxiety Related Emotional Disorders, and the Functional Disability Inventory. A four-cluster solution based on the co-occurrence of internalizing symptoms best fit the data. The four clusters were labelled as follows: cluster 1: "High somatic symptoms and average depression/anxiety"; cluster 2: "High somatic symptoms and high depression/anxiety"; cluster 3: "Average somatic symptoms and above average depression/anxiety"; and cluster 4: "Low somatic symptoms and low depression/anxiety". Significant differences between the four groups according to gender and age were shown. Participants with high levels of somatic, depressive, and anxiety symptoms reported greater functional disability due to somatic symptoms than the other three groups. Our findings indicate that children and adolescents who demonstrate high symptoms of depression and anxiety also reported higher levels of disability in daily life due to somatic symptoms. [ABSTRACT FROM AUTHOR]

Over the last decade, gambling opportunities worldwide have increased significantly, providing continual access to more people. Thus, standardized measures to assess various facets of Gambling Disorder and holistic approaches to identifying sub-groups of disordered gamblers are needed. The Gambling Pathways Questionnaire (GPQ), based on the Pathways Model of problem gambling, is designed to assign gamblers to subtypes at intake and identify a variety of salient etiological factors. This study sought to validate the Italian version of the GPQ. After the translation and adaptation of the scale to the Italian language and culture, the GPQ was administered to 490 treatment-seeking disordered gamblers (84% males, Mage = 43.77, SD = 13.25) who attended 54 treatment centers distributed across all of Italy. Confirmatory factor analysis (CFA) supported the six-factor structure and internal coherence coefficients showed good to very good reliability. Moreover, cluster analysis revealed that the three-cluster solution produced the best model fit, which generally corresponded with the Pathways Model. The Italian version of the GPQ evidenced good psychometric properties, and the Italian-adapted scoring corresponds to the cluster analysis. [ABSTRACT FROM AUTHOR]

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions., (© 2024. The Author(s).)

Background and Aim: COVID-19 is a highly contagious infectious disease, and it has affected people's daily life and has raised great concern for governments and public health officials. Forecasting its future behavior may be useful for allocating medical resources and defining effective strategies for disease control, etc. Materials and Methods: The collected data was the cumulative and the absolute number of confirmed, death, and recovered cases of COVID-19 from February 20 to July 03, 2021. We used hierarchical cluster analysis. To forecast the future behavior of COVID-19, the Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), Automatic Forecasting Procedure (Prophet), Naive, Seasonal Naive (s-Naive), boosted ARIMA, and boosted Prophet models were used. Results: The results of clustering showed a similar behavior of coronavirus in Iran and other countries such as France, Russia, Turkey, United Kingdom (UK), Argentina, Colombia, Italy, Spain, Germany, Poland, Mexico, and Indonesia. It also revealed similar patterns of SARS-CoV-2 for the same countries in six groups. Results showed that XGBoost models' family had higher accuracy than other models. Conclusion: In Iran, COVID-19 showed similar behavior patterns compared to the studied developed countries. The family of XGBoost models showed practical results and high precision in forecasting behavior patterns of the virus. Concerning the rapid spread of the virus worldwide, these models can be used to forecast the behavior patterns of SARS-CoV-2. Preventing the spread of the coronavirus, controlling the disease, and breaking down its chain necessitates community assistance, and in this mission, the role of statisticians cannot be neglected. [ABSTRACT FROM AUTHOR]

The spread of the COVID-19 pandemic induced many countries, including Italy, to implement social distancing measures and to suspend the majority of educational and working activities, which significantly impacted peoples' lifestyles. To support water utilities in understanding the impacts of the COVID-19 pandemic on water consumption and improving water distribution system resilience, the effects of the lockdown were investigated with reference to a residential district metered area (DMA) in the city of Rovigo (northern Italy), in which smart monitoring of water consumption at the level of individual users started in 2019. The water consumption recorded during the lockdown period was analyzed at different levels of temporal and spatial aggregation and compared with the consumption recorded in the same period of the previous year. The results show that, during the lockdown period, the overall water consumption in this mainly residential area increased by 18%. Moreover, water consumption was observed to be more spread out over the day, with a decrease (and a delay) in peak morning consumption, which was particularly evident on weekdays. [ABSTRACT FROM AUTHOR]

Background: Multimorbidity is associated with negative results and poses difficulties in clinical management. New methodological approaches are emerging based on the hypothesis that chronic conditions are non-randomly associated forming multimorbidity patterns. However, there are few longitudinal studies of these patterns, which could allow for better preventive strategies and healthcare planning. The objective of the MTOP (Multimorbidity Trajectories in Older Patients) study is to identify patterns of chronic multimorbidity in a cohort of older patients and their progression and trajectories in the previous 10 years., Methods: A retrospective, observational study with a cohort of 3988 patients aged > 65 was conducted, including suspected and confirmed COVID-19 patients in the reference area of Parc Taulí University Hospital. Real-world data on socio-demographic and diagnostic variables were retrieved. Multimorbidity patterns of chronic conditions were identified with fuzzy c-means cluster analysis. Trajectories of each patient were established along three time points (baseline, 5 years before, 10 years before). Descriptive statistics were performed together with a stratification by sex and age group., Results: 3988 patients aged over 65 were included (58.9% females). Patients with ≥ 2 chronic conditions changed from 73.6 to 98.3% in the 10-year range of the study. Six clusters of chronic multimorbidity were identified 10 years before baseline, whereas five clusters were identified at both 5 years before and at baseline. Three clusters were consistently identified in all time points (Metabolic and vascular disease, Musculoskeletal and chronic pain syndrome, Unspecific); three clusters were only present at the earliest time point (Male-predominant diseases, Minor conditions and sensory impairment, Lipid metabolism disorders) and two clusters emerged 5 years before baseline and remained (Heart diseases and Neurocognitive). Sex and age stratification showed different distribution in cluster prevalence and trajectories., Conclusions: In a cohort of older patients, we were able to identify multimorbidity patterns of chronic conditions and describe their individual trajectories in the previous 10 years. Our results suggest that taking these trajectories into consideration might improve decisions in clinical management and healthcare planning., Trial Registration Number: NCT05717309., (© 2024. The Author(s).)

Background: the study of the possible determinants of the rise and fall of infections can be of great relevance, as was experienced during the COVID-19 pandemic. One of the methods to understand whether determinants are simultaneous or develop through contiguity between different areas is the study of the diagnostic replication index RDt among regions., Objectives: to introduce the analysis of RDt variability and the subsequent application of a recently introduced functional clustering method as highly useful procedures for recognizing the presence of clusters with similar trends in epidemic curves., Design: within the considered period, trends in regional RDt are analyzed in detail over four different time intervals., Setting and Participants: to exemplify this methodology, the study of variability in the period from the end of 2021 to the beginning of 2022 may be of interest., Main Outcomes Measures: the variability in the regional RDt indices is assessed by means of the correlation coefficient weighted with respect to the populations of the individual regions. The clustering procedure is applied to the time series of absolute RDt values., Results: it emerges that the periods of increasing variability in the RDt correspond to the initial growth or decrease in the number of infections, while functional clustering identifies macro-areas in which the epidemic curves have had similar trends. What caused contagions to increase seems to relate to a factor that is not specific to certain areas, with the contribution in some cases of a contagion dynamic between adjacent areas., Conclusions: the variability in the trend of regional diagnostic replication indices, which are calculated with only a few days delay, is a further indicator for the early detection of major changes in the trend of epidemic curves. The clustering of epidemic index curves may be useful to determine whether determinants act simultaneously or by contiguity between adjacent areas.

Background: Multimorbidity is typically associated with deficient health-related quality of life in mid-life, and the likelihood of developing multimorbidity in women is elevated. We address the issue of data sparsity in non-prevalent features by clustering the binary data of various rare medical conditions in a cohort of middle-aged women. This study aims to enhance understanding of how multimorbidity affects COVID-19 severity by clustering rare medical conditions and combining them with prevalent features for predictive modeling. The insights gained can guide the development of targeted interventions and improved management strategies for individuals with multiple health conditions., Methods: The study focuses on a cohort of 4477 female patients, (aged 45-60) in Piedmont, Italy, and utilizes their multimorbidity data prior to the COVID-19 pandemic from their medical history from 2015 to 2019. The COVID-19 severity is determined by the hospitalization status of the patients from February to May 2020. Each patient profile in the dataset is depicted as a binary vector, where each feature denotes the presence or absence of a specific multimorbidity condition. By clustering the sparse medical data, newly engineered features are generated as a bin of features, and they are combined with the prevalent features for COVID-19 severity predictive modeling., Results: From sparse data consisting of 174 input features, we have created a low-dimensional feature matrix of 17 features. Machine Learning algorithms are applied to the reduced sparsity-free data to predict the Covid-19 hospital admission outcome. The performance obtained for the corresponding models are as follows: Logistic Regression (accuracy 0.72, AUC 0.77, F1-score 0.69), Linear Discriminant Analysis (accuracy 0.7, AUC 0.77, F1-score 0.67), and Ada Boost (accuracy 0.7, AUC 0.77, F1-score 0.68)., Conclusion: Mapping higher-dimensional data to a low-dimensional space can result in information loss, but reducing sparsity can be beneficial for Machine Learning modeling due to improved predictive ability. In this study, we addressed the issue of data sparsity in electronic health records and created a model that incorporates both prevalent and rare medical conditions, leading to more accurate and effective predictive modeling. The identification of complex associations between multimorbidity and the severity of COVID-19 highlights potential areas of focus for future research, including long COVID and intervention efforts., (© 2024. The Author(s).)

Nutrition has been acknowledged as crucial in IBD and is relevant to patients' motives behind food choices, which are affected by health engagement (HE) and food involvement (FI). This study aimed to profile IBD patients according to their levels of health engagement and food involvement to identify patterns of different motives behind food choices, particularly regarding the use of food to regulate mood. A cross-sectional study was conducted with 890 Italian IBD patients who completed an online survey in April 2021. We measured health engagement, food involvement, motives behind food choices, emotional states, and food-related quality of life (Fr-QoL). K-means cluster analysis was performed to identify participants with similar levels of health engagement and food involvement. Four clusters were identified: "Health-conscious (high HE, low FI)", "Balanced (high HE, high FI)", "Hedonist (high FI, low HE)", and "Careless (low FI, low HE)". Clusters with high FI are inclined toward seeking pleasurable food, but when supported with high health engagement, individuals were less prone to use food to manage mood. Groups with higher health engagement demonstrated lower hospitalization rates and relapses and better Fr-QoL. Profiling IBD patients regarding FI and HE could aid clinicians in identifying individuals at greater risk of maladaptive food-related behaviors.

Smart farming technologies, such as information and communication technologies, can provide many advantages for farm management. Nevertheless, their diffusion is still limited, especially in mountainous areas. For this reason and considering the crucial role of mountain farming, public policies have begun to increasingly support innovation practices in these areas. The purpose of this study was to investigate how attitudes and the characteristics of farmers and farms influence the use of technological devices (i.e., smartphones, tablets and computers). The case study was set in the rural area of Valtellina in the Italian Alps, where 63 dairy farmers were interviewed. Cluster analysis was used to classify farmers, considering their levels of technophobia and technophilia as well as their perceived obstacles and motivations to use new technologies. Three different classes of farmers were thus identified: technophobes , insecure technophiles and technophiles. The results show that attitudes towards new technologies are affected by a farmer's age, education level, farm size, actual smartphone usage for professional duties, and optimistic behaviour towards the future of the farm. This study supports policy makers in developing tailored policies in support of mountain farmers and service providers, indicating future directions for the design of their products. [Display omitted] • Smart farming technologies, such as ICTs, can provide many advantages for farm management. • Despite the advantages provided by ICTs, their diffusion is still limited, especially in mountainous areas. • Cluster analysis was used to classify farmers considering attitudinal determinants. • Three different segments of farmers were identified: technophobes, insecure technophiles and technophiles. • The poor success of public policies of smart farming may be due to a top-down undifferentiated intervention. [ABSTRACT FROM AUTHOR]

Past research has provided mixed evidence on the relationship between High-Performance Work Practices (HPWPs) and wages. Drawing from more than 1,800 interviews with the HR managers in a stratified sample of Italian manufacturing plants collected in 2008, the authors argue that the results are inconclusive unless the configurations of the HPWPs and the mechanisms underpinning employee involvement are taken into account. Their results show that HPWPs are associated with higher wages for core employees only when they align with an ideal type, and they result in higher wage equality only when they emphasize training, competence enhancement, and power delegation. [ABSTRACT FROM PUBLISHER]

To evaluate the value added by information reported in narratives (extracted through text mining techniques) in enhancing the characterization of falls patterns. Data on falls notified to the Risk Management Service of a Local Health Authority in Italy were considered in the analysis. Each record reported detailed pre-coded information about patient and fall's characteristics, together with a narrative description of the fall. At first, multiple correspondence analysis (MCA) was performed on pre-coded information only. Then, it was re-run on the pre-coded data augmented with a variable representing the output analysis of the narrative records. This second analysis required a pre-processing of the narratives followed by text mining. Finally, a Hierarchical Clustering on the two MCA was carried out to identify distinct fall patterns. The dataset included 202 falls' records. Three clusters corresponding to three distinct profiles of falls were identified through the Hierarchical Clustering performed using only pre-coded information. Hierarchical Clustering with the topic variable provided overlapping results. The present findings showed that the cluster analysis is effective in characterizing fall patterns; however, they do not sustain the hypothesis that the analysis of free-text information improves our understanding of such phenomenon. [ABSTRACT FROM AUTHOR]

Background: Clinical heterogeneity in bronchiectasis remains a challenge for improving the appropriate targeting of therapies and patient management. Antimicrobial peptides (AMPs) have been linked to disease severity and phenotype.Research Question: Can we identify clusters of patients based on the levels of AMPs, airway inflammation, tissue remodeling, and tissue damage to establish their relationship with disease severity and clinical outcomes?Study Design and Methods: A prospective cohort of 128 stable patients with bronchiectasis were recruited across three centers in three different countries (Spain, Scotland, and Italy). A two-step cluster strategy was used to stratify patients according to levels of lactoferrin, lysozyme, LL-37, and secretory leukocyte protease inhibitor in sputum. Measurements of inflammation (IL-8, tumor growth factor β, and IL-6), tissue remodeling and damage (glycosaminoglycan, matrix metallopeptidase 9, neutrophil elastase, and total and bacterial DNA), and neutrophil chemotaxis were assessed.Results: Three clusters of patients were defined according to distinct airway profiles of AMPs. They represented groups of patients with gradually distinct airway infection and disease severity. Each cluster was associated with an airway profile of inflammation, tissue remodeling, and tissue damage. The relationships between soluble mediators also were distinct between clusters. This analysis allowed the identification of the cluster with the most deregulated local innate immune response. During follow-up, each cluster showed different risk of three or more exacerbations occurring (P = .03) and different times to first exacerbations (P = .03).Interpretation: Bronchiectasis patients can be stratified in different clusters according to profiles of airway AMPs, inflammation, tissue remodeling, and tissue damage. The combination of these immunologic variables shows a relationship with disease severity and future risk of exacerbations. [ABSTRACT FROM AUTHOR]

The definition of a fossil ecological community from fossil associations has always been problematic. Determining coevality of the members of a fossil association has posed one of the most ravelling problems in this matter. However, in case of relicts of fossil reefs, this problem seems, to some extent, solvable due to the frequency of fossil findings in life position. In addition, the interaction between these fossils poses the possibility of performing statistical analyses in order to effectively quantify the relation that existed among the members of a fossil association as well as between fossil associations. In this paper, we present a method to obtain fossil communities by means of a combined algorithm (WPGMA‐Jaccard Index) performed onto 14 fossil associations from the Cipit boulders from the Middle‐to‐Late Triassic St Cassian Formation (Dolomites, Northeastern Italy), which have been previously reported in the literature. Presence/absence data of environmentally diagnostic micro‐encrusters and hexactinellid sponges were input into a matrix, which underwent cluster analysis. This analysis rendered a phenogram, where five different communities (phena) could be recognized. Each community has its particular palaeoenvironmental constraints, defined in turn by the particular array of micro‐encrusters/hexactinellid sponges. The method used in this work could be successfully applied in other Triassic reef deposits, taking into account the extensive amount of reports performed in the last decade. [ABSTRACT FROM AUTHOR]

The study examined the relationship between perception of COVID-19, travel risk perception and travel behaviour among travellers in the DACH region (Germany, Austria, Switzerland) – an important tourism market and, after Italy, the second region in Europe that was impacted by COVID-19. Data were collected at two points of time: the sample of the study in Period 1 (n = 1158) was collected at a critical point in time in the beginning of March 2020, when Italy was already massively affected by COVID-19; the sample of the study in Period 2 (n = 212) was collected two weeks later, when Europe has seen immense impacts and COVID-19 was declared a pandemic. Cluster analysis was performed and defined three unique clusters in both periods with distinctive characteristics. In addition, results revealed a significant increase in risk perception of COVID-19, travel risk perception and travel behaviour over a short period of time. [ABSTRACT FROM AUTHOR]

Italy and Spain are two representative examples on strict lockdown last March 2020, also suffering a high rate of mortality in Europe. The aim of this study is to examine their attitudes confronting death awareness during the Covid-19 outbreak. Moreover, Personality was also considered. Different sociodemographic, in situ questions related to attitudes and the brief Big Five of Personality were employed in a cross-sectional design. The main results suggested that Personality traits were stable across countries. A relationship was found between Fear to contagious diseases and Neuroticism and other attitudes during the Covid-19 outbreak, and two different clusters were identified with regards to attitudes, however these did not differ on Personality. Finally, a Cluster group, Neuroticism, Age and Sense of belonging to the Country did predict Fear to contagious diseases. Of note, no differences were found across countries during grief. [ABSTRACT FROM AUTHOR]

In the last two decades, scientific research has explored the problematic use of internet, videogames and mobile phones. However, there is still little consistent knowledge regarding the co‐occurrence of problematic technology use and the role of maladaptive personality characteristics in adolescence. The present study aimed to investigate adolescents' styles of technology use with a cluster analysis approach focusing on personality functioning. The sample comprised 408 Italian adolescents (46.3% males) aged 11 to 18 years (M age = 13.80; SD = 2.08). Data were collected using the Internet Addiction Test, the Videogame Dependency Scale, the Test of Mobile‐Phone Dependence Brief Form and the Personality Inventory for DSM5 Brief Form. Results provided a four‐cluster solution based on the co‐occurrence of problematic technology use. The four clusters were labeled as follows: cluster 1: "Above average internet and mobile‐phone use"; cluster 2: "Below average technology use"; cluster 3: "Above average videogame use"; and cluster 4: "Problematic technology use." Analyses on demographic variables (e.g., gender and age) demonstrated significant differences between the four groups. Adolescents with high levels of problematic technology use reported greater overall personality dysfunction than the other three groups. This finding supported our hypothesis on maladaptive personality functioning in adolescents at risk for addiction. Finally, the Antagonism domain played a specific role in differentiating the severity of adolescents' involvement in technology use. Further studies are needed to confirm our findings and to plan preventive interventions as well as therapeutic treatments. [ABSTRACT FROM AUTHOR]

The aim of the paper is to analyze the specific features of tourism in Italy through tourists' behaviour and satisfaction level and to individuate future perspectives. A representative sample was analyzed through factor and cluster analysis to interpret the factors that affect the touristic performance according to specific indicators, which affect tourists' features and their tourism expectation. The findings show three segmented customer's groups with different levels of desire, satisfaction and perceived happiness and identify also synergistically strategic actions to revitalize tourism in the considered region. The research is the first of this kind in the area and a base for further studies. [ABSTRACT FROM AUTHOR]

The restoration of the Roman building of the third-fourth century CE called Minerva Medica, in Rome, highlighted a problem concerning the choice of the color of the bricks for integration. Current conservation practice in Italy favours a restoration that as much as possible is consistent with the original but leaves some visible differences to a careful observer. Color measurement is usually performed using portable spectrocolorimeters that provide the reflectance spectrum and also colorimetric parameters such as L* a* b*, h* L* C*, RGB, etc. In other sectors, such as for olive oils, for wines, and also for the colors of commercial products, sensory panels are used to provide results of difference/similarity and are known to be similar to observations made by consumers. In this study, data from a top quality spectrocolorimeter and from a sensory panel are compared. Twenty non-professional people were involved in the comparison between seven restoration bricks and three ancient Roman bricks from the studied site. Observations took place in a room with controlled lighting, and each observer filled out a sensory card using about 2000 known comparison colors. Comparison of the results was carried out with graphic methods and by means of cluster analysis. The results show that the sensory panel could be a useful alternative to colorimetry, while cluster analysis allowed a significant response to questions posed by conservation professionals, and was confirmed by other graphic-statistical methods. [ABSTRACT FROM AUTHOR]

Two science teaching workshops for students of the elementary teacher education degree course at the University of Palermo, Italy are discussed, one based on inquiry-based methods and the other on "traditional" teaching methods. A questionnaire aimed to understand the teaching styles preferred by students, their reasons for learning/teaching science, and their beliefs about the difficulties a teacher faces when planning and trying out science teaching activities in the class were completed by the students before the first workshop, at its end, and the end of the second workshop. The answers given by the students were studied using cluster analysis methods. The results of the analysis of answers given to initial and intermediate questionnaires indicate that the students recognize the importance of teaching scientific subjects in elementary school. However, the self-perception of their abilities to effectively teach science is negative, both before and after attending the "traditional" type teaching workshop. After doing the inquiry-based teaching workshop and holding several days of a science fair, most of the students' negative beliefs have changed into positive ones. A better general understanding of the fundamental role played by the teacher in encouraging the natural curiosity of children and offering scientific activities based on everyday life experiences was observed. Two science teaching workshops for students of the elementary teacher education degree course at the University of Palermo, Italy are discussed, one based on inquiry-based methods and the other on "traditional" teaching methods. A questionnaire aimed to understand the teaching styles preferred by students, their reasons for learning/teaching science, and their beliefs about the difficulties a teacher faces when planning and trying out science teaching activities in the class were completed by the students before the first workshop, at its end, and the end of the second workshop. The answers given by the students were studied using cluster analysis methods. The results of the analysis of answers given to initial and intermediate questionnaires indicate that the students recognize the importance of teaching scientific subjects in elementary school. However, the self-perception of their abilities to effectively teach science is negative, both before and after attending the "traditional" type teaching workshop. After doing the inquiry-based teaching workshop and holding several days of a science fair, most of the students' negative beliefs have changed into positive ones. A better general understanding of the fundamental role played by the teacher in encouraging the natural curiosity of children and offering scientific activities based on everyday life experiences was observed. [ABSTRACT FROM AUTHOR]

This article provides a first investigation on the attitude of South Tyrolean residents (northern Italy) toward the return of the wolf in their province. Data were collected through an online questionnaire, which was available for 54,527 residents (≥ 18 years old) of South Tyrol with internet access and a Facebook® account and was shared on the institute's homepage. In total, 1818 valid responses were collected in 2 months in 2018. Cluster analysis evidenced four homogenous groups of respondents characterized by the components of attitude, their district community, the wolf distribution in their area, belief factors, and the personal fear of wolves. The data analysis revealed differences and relations between the four clusters in the level of knowledge, experience with wolves, education level, and the expected impact on the tourist sector. The obtained results (1) evidence some key factors influencing the attitude of residents, (2) underline the importance of considering small spatial scale attitudinal differences, and (3) urge for an extended human dimension of wolf coexistence research to support local management strategies. [ABSTRACT FROM AUTHOR]

In this study, the genetic relationships of 29 grape genotypes were defined with six SSR (simple sequence repeat) markers, and 65 polymorphic bands were identified, with matrix correlation (r) of 0.79. The expected heterozygosity (He) ranged from 0.783 (VrZAG62) to 0.869 (VVMD7) and the observed heterozygosity (Ho) from 0.483 (VrZAG62) to 0.897 (VVS2). According to the results, the smaller main cluster included only one ancient cultivar, 'Buca Razakısı'. Foça Karası', an ancient cultivar in the large main group, was separated from other grapes varieties. The first sub-cluster (S1) formed by mainly introduced cultivars included three cultivars, 'Cabernet Sauvignon', 'Cabarnet Franc' and 'Merlot'. The second sub-cluster (S2) was the largest group, and included 19 cultivars, 'Semillion', 'Alicante Boushet', 'Delbele', 'Çeşme Pembesi', 'Grenache', 'Öküzgözü', 'Petit Syrah', 'Papaz Karası', 'Colombard', 'Harsleleh', 'Moiseylative', 'Şika', 'Müşküle', 'Ohannes', 'Cinsaut', 'Kırmızı Şam', 'Kozak Gemresi', 'Siyah Gemre' and 'Yuvarlak Razakı'. The third sub-cluster (S3) included four cultivars, 'Cardinal', 'Italia', 'Hafızali' and 'Malbee'. 'Hafizali is an ancient cultivar and is included in this group. The fourth sub-cluster included only one ancient cultivar, 'Pek Üzümü'. Based on the rates of similarities of the cultivars included in this study, the highest rate was recorded for 'Öküzgözü-Petit Syrah' (93%) in the second sub-cluster. The results reported here are important as the first steps towards better characterization of these grape genotypes and will aid future germplasm management and breeding efforts. [ABSTRACT FROM AUTHOR]

A choice experiment was conducted in Piedmont, Italy, to define purchasing preferences and behaviours of Grappa consumers. A total of 667 individuals were interviewed at different points of Grappa purchase/consumption. The most important attributes considered during Grappa purchase by consumer were defined using the Best-Worst Scaling methodology. The Latent Class Analysis was employed to identify consumer clusters characterized by different Grappa preferences and consumption styles. For Piedmont consumers, Grappa choice was related to previous experience, product knowledge and origin. Conversely, consumers considered "alcohol content" and "packaging" the two least important factors to be considered during purchase. The lClass analysis allowed the identification of four clusters of Grappa consumers that were described in function of socio-demographic variables. [ABSTRACT FROM AUTHOR]

Two 20-h modelling-based workshops focused on the explanation of thermally activated phenomena were held at the University of Palermo, Italy, during the Academic Year 2014–2015. One of them was conducted by applying an inquiry-based approach, while the other, still based on laboratory and modelling activities, was not focused on inquiry. Seventy-two students belonging to the Undergraduate Program for Chemical Engineering attended the two workshops. The related content was focused on an à la Feynman unifying approach to thermally activated phenomena. Questionnaires were administered to the students of both groups, before and post instruction. Responses were analysed using k-means cluster analysis and students' inferred lines of reasoning about the description and explanation of phenomena were studied in both groups. We find that both workshops can be considered effective in improving student's reasoning skills. However, the inquiry-based approach revealed to be more effective than the traditional one in helping students to build mechanisms of functioning and explicative models and to identify common aspects in apparently different phenomena. [ABSTRACT FROM AUTHOR]

Climate change has significant impacts on marine ecosystems, resulting in disruptions in biological interactions, shifts in community composition, and changes in the physiology of fish and other marine organisms. In this study conducted in the central Mediterranean Sea, the mean temperature of the catch (MTC) was employed as an indicator to investigate the climatological factors influencing the fish community. The MTC, which utilizes species-preferred temperatures, was calculated using bottom temperature (BT) data weighted against scientific catches. The estimated MTC increasing rates were 0.01 °C year−1 for the entire community, 0.017 °C year−1 for the shelf break, and 0.004 °C year−1 for the continental slope assemblage. We found that MTC is increasing at a lower rate compared to BT, suggesting a progressive under-adaptation of the fish community that seems not fully able to keep up with the ongoing pace of warming. The study identified sea surface temperature and bottom temperature as key drivers of changes in fish community composition. Notably, the fish community composition exhibited drastic changes over the studied period, and we suggest that the MTC can be a useful index to monitor such changes within the context of the EU's climate change adaptation strategy. • The mean temperature of the fish community has increased over the past 30 years but at a slower rate than sea temperature. • Fish community composition has changed due to the increased contribution of warm-affinity species. • The main changes were observed for the shelf break and continental slope species assemblages. • A main shift in species composition was observed after 2016. [ABSTRACT FROM AUTHOR]

The increasing demand for reliable traceability tools in the meat supply chain has prompted the exploration of innovative approaches that meet stringent quality standards. In this work, 57 elements were quantified by inductively coupled plasma mass spectrometry and direct mercury analysis in 80 muscle and 80 liver samples of Italian heavy pigs to investigate the potential of new tools based on multi-elemental profiles in supporting value-added meat supply chains. Samples from three groups of animals belonging to the protected designation of origin (PDO) Parma Ham circuit (conventionally raised; raised with genetically modified organism (GMO)-free feeds; raised with GMO-free feeds plus the supplementation of omega-3 polyunsaturated fatty acids (n-3 PUFA)) and a fourth group of samples from animals not compliant with the PDO Parma Ham production process were analyzed. Hierarchical cluster analysis allowed for the identification of three macro-clusters of liver or muscle samples, highlighting some inhomogeneities among the target groups. Following SIMCA analysis, better classification models were obtained by using liver elemental profiles (95% correct classification rate), with the highest classification accuracy observed for GMO-free livers (100%). The elements contributing the most to the separation of livers by class membership were La, Ce, and Pb for conventional, Li, Cr, Fe, As, and Sr for GMO-free + n-3 PUFA, and Lu for non-PDO samples. Given these findings, the analysis of the elemental profiles of pig tissues can be regarded as a promising method to confirm the declared pig meat label attributes, deter potential complex fraud, and support meat traceability systems., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

• Forest diversity is hypothesized to enhance vitality of trees. • Defoliation is currently assessed in European forests as proxy of tree vitality. • Relationships between tree diversity and defoliation vary per site and per species. • In Italy, there is a general trend of more defoliation in high diversity stands. • Tree diversity is more beneficial in stressful conditions and after stress events. Tree diversity is found to enhance ecosystem functions in forests and increase the resistance and resilience of trees subjected to environmental stress, including climate change. This effect, however, can be different depending on tree species assemblages and ecological contexts. The pan-European programme for monitoring forest health (ICP Forests) considers crown defoliation as an indicator of tree vitality. Only a few studies have analysed the role of tree diversity in crown defoliation, with contrasting results. This paper analyses the relationships between defoliation and forest diversity in a country (Italy) characterised by heterogeneous bioclimatic and edaphic characteristics (from Mediterranean to Alpine) and with a wide range of forest tree species and functional groups. National-scale results show that defoliation increased in more diverse forest stands. This result can be explained by the fact that the tree mixtures reflect varying ecological conditions in heterogeneous environments. The most relevant factors that explain tree defoliation at the national level, investigated through random forest analysis, were the geographic position, tree species identity and forest stand age and structure. Cluster analysis was applied to identify homogeneous groups of plots based on their ecological and vegetation features. Four clusters were selected. Within each homogeneous cluster, the crown defoliation of the tree species showed different and contrasting patterns, changing their relationships with diversity over time. A beneficial role of diversity on defoliation (i.e. decrease of defoliation) has been observed in Quercus ilex , growing in Mediterranean conditions, and Picea abies in Alpine areas after dry periods. In the latter species, drought led to a greater increase in defoliation in monospecific than in mixed stands, suggesting the stabilising role of tree diversity. [ABSTRACT FROM AUTHOR]

The aims of the present investigation are to evaluate the chemical controlling factors of the water resources and identify the source, as well as assess its quality with respect to domestic and irrigation criteria, to establish long-term environmental measures and a sustainable water resources management plan in the mountain regions. For these purposes, 28 water resources (14 drainage; 3 piezometric; 7 Arminaz Stream; 2 spring and 2 Dora River) samples were collected from different locations of the Mont de la Saxe region and analyzed for pH, electrical conductivity, total dissolved solids (TDS), total hardness, and major cations and anions. The pH of the water samples ranged from 7.6 to 11.6, indicating the alkaline nature of the water in the study area. The TDS ranged from 142.1 to 995.3 mg L−1 and exceeded the desirable limit of 500 mg L−1 at some sites. Ca–Mg–HCO3 and Ca–Mg–SO4 were the dominant hydrogeochemical facies; Ca2+ and Mg2+ were the dominant cations and HCO3− and SO42− were the dominant anions in the Mont de la Saxe region. High concentrations of SO42− were attributed to the dissolution of gypsum in the study area. The hydrogeochemical data show the mixing process from the springs pass through higher altitudes drains samples to lower drains samples as well as piezometer. The chemistry of water resources of the study area is mainly controlled by the rock-weathering phenomenon and ion exchange processes as well as by water flow direction of the area. High concentrations of TDS, Ca2+, SO42−, and TH and high salinity in many samples of the drainage and Arminaz Stream water make it unsuitable for domestic and irrigation uses in the study area. This study could be useful for future water resources management in the mountain regions. [ABSTRACT FROM AUTHOR]

Self-injurious behaviors and suicide attempts are more frequent in prison settings than in the general population and represent a crucial problem. The aims of this work are to assess the prevalence of self-injurious behaviors and suicide attempts in an Italian prison setting, to determine whether inmates could be differentiated based on profiles of psychological distress and impulsiveness, and to assess the predictive power of the proposed profiles. A sample of 1422 male inmates of a north Italian penitentiary was assessed upon admission with a clinical interview and completed a set of self-report questionnaires to assess psychological distress and impulsiveness; the number of self-injurious behaviors and suicide attempts occurring in the first year of detention was recorded. A cluster analysis approach was used. Prevalence of self-Injurious behaviors and suicide attempts is similar to what has been observed in previous work. Cluster analysis revealed four clusters: dysregulated (high impulsivity and distress), impulsive (high impulsivity and mean distress), mildly distressed (mean impulsivity and moderate distress) and well-balanced (low impulsivity and distress). The four clusters help to discriminate subjects more at risk of self-injurious behaviors and suicide attempts and are confirmed by the inclusion of risk factors such as marital status and relatives'/social support. Clinical implications are discussed. [ABSTRACT FROM AUTHOR]

This paper analysed the evolution of landslide research and research foci in different countries. The data comprise 3105 landslide SCI articles published between January 1977 and June 2015 from the Web of Science. The data are extracted under interaction constraints of the journal title, category, and keywords. The complex network method is used for the analysis. First, from the perspective of topics and methods, the evolution is systematically assessed by generating a co-citation network of the articles and a semantic cluster analysis. Second, from the perspective of topics and landslide-related disasters, the focus in different countries is discussed by generating co-occurrence networks. These networks are the co-occurrence of the countries and keywords, and the co-occurrence of countries and landslide-related disaster phrases. The main conclusions are as follows: (1) landslide susceptibility analysis and methods of machine learning are popular research topics and methods, respectively. The topics change through time, and the article output is influenced by increasing landslide-related disasters, increasing economic losses and casualties, a desire for a more complete and accurate landslide inventory, and the use of effective methods, such as geographical information Science (GIS) and machine learning. (2) The research focus in each country is related with the country-specific disasters or economic costs caused by landslides to some degree. In addition to Italy and the USA, China is the country most commonly affected by landslides, and it should develop its own landslide database and complete in-depth studies of disaster mitigation. [ABSTRACT FROM AUTHOR]

In this paper we describe four new fossil associations of "reef" and "reef"-like environments of the St. Cassian Formation (Ladinian-Carnian, Dolomites, NE Italy), based on thirty thin sections from 10 "Cipit boulders" olistoliths, which slided from the Cassian platform into coeval basin sediments. The fossil associations were determined by means of microfacies analysis using point-counting and visual estimation, as well as with aid of statistical methods, based on all fractions with a biotic significance (biomorpha and microbialites). Cluster Analyses in Q-Mode were performed, coupling three algorithms and two indices. In all samples, the main components of the framework are microbialite (average of 75%), and macrofossils (average of 20%), whereas cements and allochtonous components, such as allomicrite, do not represent a significant fraction. Based on both microbialite and fossil content, Chaetetid--microencruster Association, Microbialite-microencruster Association, Dual-type Microbialite Association and Microbialite-Terebella Association, were differentiated. The palaeoenvironmental settings where the associations come from are separately discussed. Microencrusters helped determine energy and luminosity settings. Microencruster abundance and diversity, in addition to the conspicuousness of microbialite, indicate that all associations come either from a deep, or from a cryptic setting. [ABSTRACT FROM AUTHOR]

[Display omitted] • One thousand primary and lower secondary schools in Apulia (Italy) are considered. • Five homogeneous clusters were defined based on construction year and geometry. • Energy consumptions of 46 representative schools were collected over 5 years. • Factors affecting energy performance were defined based on a regression analysis. The refurbishment opportunities provided by climate policies require an adequate knowledge of the school building stock, characterised by an urgent need of maintenance. Nevertheless, empirical evidence on energy performance of school samples appears limited due to the difficulty in retrieving data, although field data analysis is crucial in the built environment management. This study aims to explore existing energy conditions of an educational building sample hosting pre-schools, primary and lower secondary schools, located in southern Italy (Apulia Region). Firstly, an overview of the schools based on data retrieved from the regional dataset was performed. Then, more than 1000 buildings were clustered based on two predictors (construction year and surface-to-volume ratio), identifying five clusters representing the majority Apulian schools. In addition, billed gas and electricity data collected for 47 schools over a five-year period (2017–2021) were analysed, identifying annual and monthly trends, benchmarks, and mean values, which account for 46.5 (gas consumption), 15.59 kWh/m2 (electricity consumption). On average, source total consumption in 2020 experienced a reduction of 20%, partly due to Covid-19 restrictive measures. Finally, factors affecting heating consumptions were explored, and a regression analysis was performed, identifying heating degree days, construction year and boiler power to be the most significant. [ABSTRACT FROM AUTHOR]

Using a 20-year (2002−2021) dataset of daily precipitation collected by 107 rain gauges in the period from October to May, this study introduces a classification of the main heavy precipitation spatial patterns for the Campania Region (southern Italy). To pursue this aim, we apply a cluster analysis on the most relevant principal modes extracted from a principal component analysis of the between-day correlation matrix. The characteristics of the identified patterns, as well as their interannual and monthly distribution, are presented and discussed. Moreover, using global and regional reanalysis products, we have determined the large-scale and mesoscale atmospheric circulation types associated with heavy precipitation patterns. The heavy precipitation episodes are generally triggered by an upper level trough approaching the southern Italy from west and promoting a very moist southwesterly flow. They have been clustered into six different patterns. The first four exhibit a rainfall amount distribution strongly connected with the orography of the investigated region. In such scenarios, the orographic lifting, the low-level wind convergence induced by the orography and the transport of moisture from local sources (the western and the southern Mediterranean) and from distant regions (the Atlantic and the Africa tropical areas) can be regarded as the primary forcing of heavy rainfall. In the other two patterns, the highest precipitation is generally observed in the coastal areas (Gulfs of Naples and Salerno) and in the northwestern side of the region (Caserta district), respectively. In such circumstances, the abundant precipitation is closely linked to convective activity over the Tyrrhenian Sea, which is sustained by a low-level convergence and, in the sixth pattern, by a moisture plume coming from the tropics. The results of this study provide new insights about the links between torrential precipitation spatial distribution and atmospheric circulation schemes in the southern Italy and promise to add a useful contribution for civil protection activities related to the management of environmental risks. • The major heavy precipitation patterns in Campania region have been retrieved. • New synoptic climatology elements have been introduced for southern Italy. • Orographic lifting, low-level convergence and transport of moisture are the primary forcing of heavy rainfall. • Convection over sea and vertical wind shear lead to heavy rainfall in coastal areas. • New predictive elements are available for civil protection activities. [ABSTRACT FROM AUTHOR]

Introduction/aims: Several microgeographic clusters of higher/lower incidence of amyotrophic lateral sclerosis (ALS) have been identified worldwide. Differences in the distribution of local factors were proposed to explain the excess ALS risk, whereas the contribution of known genetic/epigenetic factors remains unclear. The aim is to identify restricted areas of higher risk in Sardinia and to assess whether age, sex, and the most common causative genetic mutations in Sardinia (C9orf72 and TARDBP mutations) contributed to the variation in the ALS risk., Methods: We performed an ad hoc analysis of the 10-y population-based incident cohort of ALS cases from a recent study of a large Sardinian area. Cluster analysis was performed by age- and sex-adjusted Kulldorff's spatial scan statistic., Results: We identified a statistically significant cluster of higher ALS incidence in a relatively large area including 34 municipalities and >100,000 individuals. The investigated genetic mutations were more frequent in the cluster area than outside. Regardless of the genetic mutations, the excess of ALS risk was significantly associated with either sex or with age ≥ 65 y. Finally, an additive interaction between older age and male sex contributed to the excess of ALS risk in the cluster area but not outside., Discussion: Our analysis demonstrated that known genetic factors, age, and sex may contribute to microgeographic variation in ALS incidence. The significant additive interaction between older age and male sex we found in the high-incidence cluster could suggest the presence of a third factor connecting the analyzed risk factors., (© 2023 Wiley Periodicals LLC.)