323,325 research outputs found
Classification of P,N-binucleating ligands for hetero- and homobimetallic complexes
No full textThis review focuses on P,N-ligands capable of establishing binucleating interactions and on the resulting bimetallic complexes.
The work is grouped by the type of P,N-binucleating ligand backbones which are closely responsible for the chemistry of their correspondent bimetallic complexes. The P,N-binucleating ligands are classified according to their length (the number of atoms between the N- and P-donor), their backbone structure (the relative disposition of the N- and P-donor), and the presence or not of additional donor atoms.
Similarities between the structures of the bimetallic complexes obtained with P,N-ligands inside each group are highlighted. Based on the analysis of the different groups, the structure of new metallo/P,N-ligand complexes not yet synthesized can be hypothesized. A summary of the few examples of P,N-binucleating ligand based bimetallic complexes that have catalytic activity is given
Between Numbers and Political Drivers: What Matters in Policy-Making
No full textThis chapter aims to investigate whether (restrictive) policy measures on migration across seven European countries (the Czech Republic, Denmark, Finland, Greece, Italy, Switzerland and the UK) are better explained by political factors, rather than the actual number of migrants/refugees/asylum seekers, their integration process or the effective European societies’ demographic and economic needs, within each national context. The analysis shows, indeed, that restrictive legislative and policy measures on immigration and integration issues seem to be not justified by the reality of immigration in the selected European countries. Conversely, these restrictive measures can be explained by some relevant political factors: prevalence of negative attitudes towards immigration among European citizens and salience of the immigration issue; electoral relevance of populist radical-right parties who mostly mobilized on immigration issues and significant diffusion of their authoritarian/traditionalist/nationalist positions within each country’s party system. These data confirm that citizens’ perceptions and party systems’ features are closely related phenomena, which influence one another and are all key factors that need to be considered to explain the law and policy-making of recent years on immigration issues
Solidarity with disabled people in times of crisis: A comparative analysis of Italy and the UK
No full textAgainst the background of crisis and cuts, citizens can express solidarity with groups in various ways. Using novel survey data this article explores the attitudes and behaviours of citizens in their expressions of solidarity with disabled people and in doing so illuminates the differences and similarities across two European contexts: Italy and the UK. The findings reveal pools of solidarity with disabled people across both countries that have on the one hand similar foundations such as the social embeddedness and social trust of citizens, while on the other hand contain some differences, such as the more direct and active nature of solidarity in Italy compared to the UK and the role of religiosity as an important determinant, particularly in Italy. Across both countries the role of ‘deservingness’ was key to understanding solidarity, and the study’s conclusions raise questions about a solidarity embedded by a degree of paternalism and even religious piety
Addressing Migrant Inequality in Youth Political Engagement: The Role of Parental Influences
No full textWhile citizenship acquisition varies across the EU, children of immigrants are expected to comprise a growing share of the voting‐age population in the coming years. Consequently, understanding the factors influencing their political integration has garnered increasing attention from researchers and policymakers. Existing studies highlight the complex and context‐dependent interplay of structural, cultural, and policy‐related factors that shape immigrant political engagement. Additionally, some scholars have noted
that the standard model of youth political socialisation—where political learning is transmitted from parent to child—may be “disrupted” in immigrant families. Against this backdrop, this article investigates the critical
role of family political discussions and parent–child political alignment in (re)producing ethnic inequalities in political engagement among late adolescents, using Lombardy (Italy) as a case study. The project MAYBE—Moving into Adulthood in uncertain times: Youth Beliefs, future Expectations, and life choices (University of Milan) collected survey data from 2,756 final‐year high school students (aged 18–19) between February 2023 and March 2024, including 620 students with migrant backgrounds. The study applied a
multilevel regression model—spanning 81 schools, 165 classes—to investigate individual and contextual factors, such as the classroom political climate and municipal electoral competitiveness. Migrant parents
navigate the host country’s political environment with varying levels of familiarity, shaped by their connections to the political culture of their country of origin. Findings suggest that these dynamics create unique pathways for the political socialisation of their children, in which the influence of socioeconomic status and intergenerational social learning on political engagement differs significantly from the patterns observed among native‐born youth
Neural poetry: learning to generate poems using syllables
No full textMotivated by the recent progresses on machine learning-based models that learn artistic styles, in this paper we focus on the problem of poem generation. This is a challenging task in which the machine has to capture the linguistic features that strongly characterize a certain poet, as well as the semantics of the poet’s production, that are influenced by his personal experiences and by his literary background. Since poetry is constructed using syllables, that regulate the form and structure of poems, we propose a syllable-based neural language model, and we describe a poem generation mechanism that is designed around the poet style, automatically selecting the most representative generations. The poetic work of a target author is usually not enough to successfully train modern deep neural networks, so we propose a multi-stage procedure that exploits non-poetic works of the same author, and also other publicly available huge corpora to learn syntax and grammar of the target language. We focus on the Italian poet Dante Alighieri, widely famous for his Divine Comedy. A quantitative and qualitative experimental analysis of the generated tercets is reported, where we included expert judges with strong background in humanistic studies. The generated tercets are frequently considered to be real by a generic population of judges, with relative difference of 56.25% with respect to the ones really authored by Dante, and expert judges perceived Dante’s style and rhymes in the generated text
Major and minor depression in pregnancy
No full textOBJECTIVE: To evaluate the time to onset, duration, and risk factors for major and minor depression in pregnant women attending the Centers for Prenatal Care.
METHODS: The presence of depressive symptoms and their severity were evaluated at monthly intervals in 154 pregnant women, using the Primary Care Evaluation of Mental Disorders and the Hospital Anxiety and Depression Scale. Comparisons between women with major and minor depression and nondepressed women were performed using the one-way analysis of variance with Bonferroni post-hoc analysis for continuous variables and with Fisher exact test for categorical variables.
RESULTS: Major depression was diagnosed in 19 women (12.3%) and minor depression in 28 (18.1%), whereas the remaining 107 did not show any depressive symptoms. Depression was later in onset and had a longer duration in women with major depression (mean +/- standard deviation 5.6 +/- 2.8 months and 2.3 +/- 1.7 months, respectively) than in women with minor depression (3.5 +/- 2.2 months and 1.6 +/- 0.7, respectively; P=.007 and P=.04). The risk of developing major depression was predicted at the beginning of pregnancy by the presence of previous depressive episodes (odds ratio [OR] 9.5, 95% confidence interval [CI] 2.5-29.2) and conflicts with husband/partner (OR 7.8, 95% Cl 1.02-62.7), whereas the risk of developing minor depression was predicted by being a housewife (OR 7.2, 95% Cl 2.3-22.1), presence of previous depressive episodes (OR 4.7, 95% Cl 1.4-15.3) and whether the pregnancy was unwanted (OR 2.4, 95% Cl 1.0-5.7).
CONCLUSION: Our study confirms that major and minor depression frequently affect pregnant women, particularly those with a history of depression, and they have different risk factors and onset and duration times. In most women, these disorders are present in a mild form (short duration and mild severity). (Obstet Gynecol 2009;113:1292-8
Generate and Revise: Reinforcement Learning in Neural Poetry
No full textWriters, poets, singers usually do not create their compositions in just one breath. Text is revisited, adjusted, modified, rephrased, even multiple times, in order to better convey meanings, emotions and feelings that the author wants to express. Amongst the noble written arts, Poetry is probably the one that needs to be elaborated the most, since the composition has to formally respect predefined meter and rhyming schemes. In this paper, we propose a framework to generate poems that are repeatedly revisited and corrected, as humans do, in order to improve their overall quality. We frame the problem of revising poems in the context of Reinforcement Learning and, in particular, using Proximal Policy Optimization. Our model generates poems from scratch and it learns to progressively adjust the generated text in order to match a target criterion. We evaluate this approach in the case of matching a rhyming scheme, without having any information on which words are responsible of creating rhymes and on how to coherently alter the poem words. The proposed framework is general and, with an appropriate reward shaping, it can be applied to other text generation problems
Information-Based Learning of Deep Architectures for Feature Extraction
No full textFeature extraction is a crucial phase in complex computer vision systems. Mainly two different approaches have been proposed so far. A quite common solution is the design of appropriate filters and features based on image processing techniques, such as the SIFT descriptors. On the other hand, machine learning techniques can be applied, relying on their capabilities to automatically develop optimal processing schemes from a significant set of training examples. Recently, deep neural networks and convolutional neural networks have been shown to yield promising results in many computer vision tasks, such as object detection and recognition. This paper introduces a new computer vision deep architecture model for the hierarchical extraction of pixel-based features, that naturally embed scale and rotation invariances. Hence, the proposed feature extraction process combines the two mentioned approaches, by merging design criteria derived from image processing tools with a learning algorithm able to extract structured feature representations from data. In particular, the learning algorithm is based on information-theoretic principles and it is able to develop invariant features from unsupervised examples. Preliminary experimental results on image classification support this new challenging research direction, when compared with other deep architectures models
Transnational Activism for Global Crises: Resources Matter! Transnational Solidarity Organisations in Comparative Perspective
No full textIn recent years, the global financial crisis and the ensuing austerity measures in European countries have resulted in dire cuts to public services, massive job losses, and diminished incomes. At the same time, and parallel to the economic crisis, a refugee crisis has arisen. In this context, ordinary citizens and new or re-energised networks of cooperation among civil society actors (e.g. non-governmental organisations (NGOs), churches, trade unions, cooperatives, grassroots initiatives) foster (transnational) solidarity practices. These practices grow in importance as they try to address people’s needs, often unmet by national governments given their lack of financial resources. This article investigates whether and to what extent civic initiatives and organisations are involved in transnational solidarity activities. Moreover, it seeks to identify those factors that seem to promote or inhibit the scope of transnational activities. The article critically analyses the initiatives and practices of Transnational Solidarity Organisations (TSOs) in eight European countries on the basis of data on transnationally oriented civic groups and organisations committed to organising solidarity activities in three fields of work (disabilities, unemployment, and assistance to refugees). The analysis aims to contribute, through fresh empirical data, to the scholarly discussion in the field of transnational solidarity mobilisation and organisations by pointing out that most solidarity organisations remain active primarily at the local and/or national level(s) and that only a minority of solidarity organisations are engaged in cross-national activities. Transnational activities are associated with formalisation and professionalisation. Moreover, maintaining a web of transnational partners, being able to communicate with such partners, and conventional action repertoires seem to be conducive to transnational activism. Organisational values linked to cosmopolitanism are also important, but their impact on transnational solidarity actions is mediated and conditioned by the TSOs’ level of formalisation
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