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Nochmals zu ὔβρις φυτεύει τύραννον (Soph. OT 873-882 und Plat. Resp. 565-566)
No full textCette notice defende le texte ὕβρις φυτεύει τύραννον dans le deuxieme stasimon de l ́ Oedipe-Roi de Sophocle (v. 873) sur la base d ́ un rapprochement avec un passage de la Republique de Platon sur la tyrannie et envisage l ́ hypothese que le passage contient une allusion aux discussions poliques du Veme siecle avant J.-C
Il diritto delle famiglie nel pluralismo delle formazioni familiari e delle relazioni affettive
No full textThe volume offers a systematic reinterpretation of the law of persons and families from a plural and constitutionally oriented legal perspective, guiding the reader through an analytical and reconstructive path that traverses the main stages of family legislation, the most significant judicial openings and the insights of the most attentive doctrinal scholarship, in constant dialogue with the cultural and social transformations of our time. Overcoming the rigidities of the 1942 codified framework and the semantic ambiguities inherent in the singular expression "family law", it critically questions the legitimacy of a legal model exclusively centered on the matrimonial family, proposing a reformulation of traditional categories in light of cultural pluralism, supranational sources and the centrality of the person. From this emerges the transition from “family law” (singular) to “law of families” (plural), which thus becomes the hallmark of a new phase in contemporary legal experience, marked by the progressive recognition of multiple affective models and relational structures, premised on the recognition of the “right to family” as a general, ordering and foundational principle
An Integrated Hybrid-Stochastic Framework for Agro-Meteorological Prediction Under Environmental Uncertainty
Get PDFThis study presents a comprehensive framework for agro-meteorological prediction, combining stochastic modeling, machine learning techniques, and environmental feature engineering to address challenges in yield prediction and wind behavior modeling. Focused on mango cultivation in the Mediterranean region, the workflow integrates diverse datasets, including satellite-derived variables such as NDVI, soil moisture, and land surface temperature (LST), along with meteorological features like wind speed and direction. Stochastic modeling was employed to capture environmental variability, while a proxy yield was defined using key environmental factors in the absence of direct field yield measurements. Machine learning models, including random forest and multi-layer perceptron (MLP), were hybridized to improve the prediction accuracy for both proxy yield and wind components (U and V that represent the east–west and north–south wind movement). The hybrid model achieved mean squared error (MSE) values of 0.333 for U and 0.181 for V, with corresponding R2 values of 0.8939 and 0.9339, respectively, outperforming the individual models and demonstrating reliable generalization in the 2022 test set. Additionally, although NDVI is traditionally important in crop monitoring, its low temporal variability across the observation period resulted in minimal contribution to the final prediction, as confirmed by feature importance analysis. Furthermore, the analysis revealed the significant influence of environmental factors such as LST, precipitable water, and soil moisture on yield dynamics, while wind visualization over digital elevation models (DEMs) highlighted the impact of terrain features on the wind patterns. The results demonstrate the effectiveness of combining stochastic and machine learning approaches in agricultural modeling, offering valuable insights for crop management and climate adaptation strategies
Dove si fanno le cose, il silenzio? Le conseguenze della critica dell’ilomorfismo sul rapporto tra tecnologia e ambiente
No full textEndocervical Curettage and Extended HPV Genotyping as Predictors of Residual Disease After Hysterectomy in Postmenopausal Women Previously Treated with LEEP for CIN3: A Multivariate Analysis
Get PDFIn postmenopausal women with high-grade cervical intraepithelial neoplasia (CIN3), hysterectomy is frequently performed after loop electrosurgical excision procedure (LEEP) due to the concern for residual disease or occult carcinoma. However, the decision to proceed with hysterectomy is often made without validated predictive criteria, increasing the risk of overtreatment or underdiagnosis. The aim of this study is to identify independent predictors of residual CIN2+ (CIN2, CIN3, adenocarcinoma in situ, invasive carcinoma) or invasive disease in hysterectomy specimens following LEEP in this high-risk population. Methods: We conducted a multicenter retrospective study including 154 postmenopausal women (aged 50–75) who underwent total hysterectomy within 12 months after LEEP for histologically confirmed CIN3. Data collected included human papillomavirus (HPV) genotyping (pre- and post-LEEP), endocervical curettage (ECC), cone margin status, transformation zone type, and histopathological outcomes of the hysterectomy specimen. Logistic regression and ROC curve analysis were used to assess predictive factors. Results: Residual disease (CIN2+, AIS, or carcinoma) was found in 38 patients (24.7%), including 7 cases (4.5%) of occult carcinoma. Persistent high-risk HPV post-LEEP was the strongest independent predictor (adjusted OR for HPV 16/18: 74.0; p < 0.001), followed by positive ECC (OR: 3.64; p = 0.028). Cone margin status was not independently associated. The multivariate model showed good discriminative performance (AUC = 0.860; sensitivity 67.2%, specificity 72.8%). Conclusions: Our findings suggest that persistent high-risk HPV infection and positive ECC are reliable predictors of residual or occult disease. These markers should be integrated into post-LEEP follow-up protocols to better identify candidates for hysterectomy and minimize unnecessary surgeries
Who takes care of the burden of emotions in palliative care workers? A study with the job demands-resources perspective
No full textBackground: This study aims to investigate the risks and challenges that palliative care workers face in their daily work and their relationships with a source of stress typically associated with working closely with end-of-life issues: compassion fatigue, a condition of emotional exhaustion, cynicism, and impaired ability to feel empathy. The Job Demands-Resources (JD-R) model was used as a theoretical framework to understand and interpret the relationships between individual and work demands and resources of palliative care professionals with their perceptions of well-being and their levels of compassion fatigue. Specifically, we focused on surface acting (the need to manipulate the expression of one's real emotions), emotional self-efficacy, and the perceived meaningfulness attributed to work. We hypothesized that compassion fatigue was positively related to job demand and negatively related to job resources. Conversely, we hypothesized that perceived well-being was positively associated with job resources and negatively associated with job demand. Methods: The sample consisted of 236 palliative care workers (physicians, nurses, social and health workers, psychologists) from facilities distributed throughout Italy. The structural equation modeling (SEM) technique was used. Results: The results confirmed the hypothesized protective role of the meaningfulness of work and emotional self-efficacy, and the detrimental role of surface acting. Conclusions: With the present study, we aim to contribute to a better understanding of how to support the psychological and physical well-being of those who devote their professional lives to providing care and comfort to terminally ill individuals
Corpus-Assisted Discourse Studies for Digital Brand Analysis: Applications in Hospitality and Tourism
Get PDFFraming digital branding as a discursive process, this research examines digital brand discourse in the hospitality and tourism industry through Corpus-Assisted Discourse Studies (CADS), a methodological approach that combines corpus linguistics and discourse analysis. The dissertation includes three chapters. Chapter 1 reviews the literature on digital discourse, foregrounds the discursive dimension of digital branding, and outlines the foundations of CADS. Chapters 2 and 3 present case studies set in Florida, USA, where part of this research was conducted. Specifically, Chapter 2 analyzes digital discourse in restaurant branding, investigating how Italianness functions both as a cultural-ideological system and a brand positioning strategy on the official websites of Italian restaurants in Miami-Dade County. Chapter 3 shifts the focus to destination branding in Miami Beach, examining the role of digital prosumers on social media in responding to rebranding efforts and contributing to brand co-creation. The study advances a discourse-oriented understanding of digital branding and offers insights relevant to both academic research and industry practice
Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects
Get PDFAlthough artificial intelligence (AI) methods hold promise for medical imaging-based prediction tasks, their integration into medical practice may present a double-edged sword due to bias (i.e., systematic errors). AI algorithms have the potential to mitigate cognitive biases in human interpretation, but extensive research has highlighted the tendency of AI systems to internalize biases within their model. This fact, whether intentional or not, may ultimately lead to unintentional consequences in the clinical setting, potentially compromising patient outcomes. This concern is particularly important in medical imaging, where AI has been more progressively and widely embraced than any other medical field. A comprehensive understanding of bias at each stage of the AI pipeline is therefore essential to contribute to developing AI solutions that are not only less biased but also widely applicable. This international collaborative review effort aims to increase awareness within the medical imaging community about the importance of proactively identifying and addressing AI bias to prevent its negative consequences from being realized later. The authors began with the fundamentals of bias by explaining its different definitions and delineating various potential sources. Strategies for detecting and identifying bias were then outlined, followed by a review of techniques for its avoidance and mitigation. Moreover, ethical dimensions, challenges encountered, and prospects were discussed
Low-carbon heating solutions using road thermal collectors and seasonal energy storage in mediterranean climates
Get PDFThe building sector accounts for 40% of final energy consumption and 36% of energy-related greenhouse gas emissions in Europe, positioning it as a critical target for decarbonisation under the European Green Deal. Road Thermal Collectors (RTCs), a type of heat-harvesting system, utilize urban surfaces like roads to capture solar energy for Thermal applications. When combined with Borehole Thermal Energy Storage (BTES) and water-towater heat pumps, RTCs provide a multifunctional, low-carbon heating solution while also mitigating urban heat island effects. This study investigates an RTC-BTES hybrid heating system in a school building in Southern Italy, where t––he Mediterranean climate poses unique challenges and opportunities for seasonal thermal energy storage. The system’s performance is assessed through dynamic simulations using TRNSYS software, with RTC models validated against experimental data from the University of Palermo. A typical school with an annual heating demand of 166 MWh was analysed, comparing the performance of the proposed integrated heating system with one using conventional gas boilers. The results demonstrate that the integrated system significantly reduces primary energy consumption and CO2 emissions, offering a scalable and sustainable alternative to fossil fuel-based heating, advancing low-carbon solutions for non-residential buildings