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In recent years, the significance of Environmental, Social, and Governance criteria in assessing financial investments has grown significantly. This paper presents an AI-driven analysis of ESG concepts and their evolution from 1980 to 2022, with a specific focus on media sources from the United States and the United Kingdom. The primary data source utilized is the Dow Jones News Article dataset, providing a comprehensive and high-quality collection of news articles. The study introduces a novel technique for information extraction from news articles, involving the structuring of extracted data into a knowledge graph. The findings identified key trends associated with ESG aspects emerging in recent years. In the environmental dimension, we identified a pronounced emphasis on climate change, renewable energy sources, and biodiversity conservation. Within the social aspect, the analysis pointed out the increasing significance of issues such as racism, gender identity, and human rights, as well as the increasing role of charities, and the ethical challenges of modern supply chains. Finally, in the governance domain, the findings emphasized issues related to corporate governance accountability, workplace ethics, and the conduct and remuneration of executives.

Electromagnetic induction (EMI) allows time-lapse profiling of electrical conductivity (EC). In recent years, progress has been made in the study of the intra-field variability and soil-plant correlations at the scale of a few meters. Yet, some methodological challenges still hinder the possibility to resolve the spatiotemporal complexity at the smaller scales typically associated with irrigation and evapotranspiration (ET) dynamics, and thus central to the agroecosystems and precision agriculture, particularly in orchard farming.This study characterizes the 3D EC variability in an orange orchard in eastern Sicily (Italy). To the best of our knowledge, this is the first 3D investigation capturing both irrigation and ET effects at the meter scale. The characterization successfully distinguishes plant rows and interrows dynamics. The EC in the plant rows increases upwards, from the drier root-water-uptake region to the drip irrigation region above. In the interrows, the EC increases downwards from the drier evaporation-dominated layer to the deeper soil where the irrigation water accumulates without significant ET. The intermediate zones, between the plant rows and interrows, show yet another conductivity profile, homogeneous and relatively conductive. Local effects, such as the plant size, further complicate this conceptual model and add both inter- and intra-row heterogeneity.While the results confirmed the EMI potential, some methodological challenges were equally important. First, a Geophex GEM-2 and a CMD Mini-Explorer were used, the latter in vertical and horizontal configuration. The choice of instruments and surveys appears now suitable for this field site but it is surely not a priori obvious and/or always possible. We highlight how the use of a single instrument would probably lead to misinterpreting the root water uptake or the evaporation contributions.Second, the quantitative use of the two instruments required alignment and joint inversion. However, a st

The aim of this cross-sectional study is to investigate the role of psychological inflexibility in the relationship between anxiety and depression and emotional eating in a sample of 123 inpatient Italian adult individuals with obesity. Participants completed the Anxiety and Depression subscales of the Psychological General Well-Being Inventory, the Acceptance and Action Questionnaire, and the Emotional Eating subscale of the Dutch Eating Behavior Questionnaire to assess anxiety and depression, psychological inflexibility, and emotional eating, respectively. Results showed that the indirect effect of PGWBI-A on DEBQ-EE through AAQ-II was significant [b = −0.0155; SE = 0.076; 95% BC-CI (−0.0320 to −0.023)]. Similarly, the indirect effect of PGWBI-D on DEBQ-EE through AAQ-II was significant [b = −0.0383; SE = 0.0207; 95% BC-CI (−0.0810 to −0.0002)]. These findings may help to plan and develop specific psychological interventions aimed at addressing emotional eating through targeting psychological inflexibility to be included in obesity treatment programs.

Obesity is a global health problem that affects both physical and psychological health and well-being. Psychological flexibility is one of the key components related to psychological health. This cross-sectional study aims to investigate the impact of psychological flexibility on psychological well-being in a sample of 220 individuals with obesity. Multivariate analysis was performed to investigate the role of psychological flexibility in explaining psychological well-being, controlling for confounding factors (sex, age, and Body Mass Index). According to the results, psychological flexibility significantly explained psychological well-being. Our study provides additional evidence of the impact of psychological flexibility on psychological well-being. It also provides further support for the importance of integrating psychological flexibility in the psychological interventions for obesity.

In this contribution we provide an overview of a currently on-going project related to the development of a methodology for building economic and financial indicators capturing investor’s emotions and topics popularity which are useful to analyse the sovereign bond markets of countries in the EU.These alternative indicators are obtained from the Global Data on Events, Location, and Tone (GDELT) database, which is a real-time, open-source, large-scale repository of global human society for open research which monitors worlds broadcast, print, and web news, creating a free open platform for computing on the entire world’s media. After providing an overview of the method under development, some preliminary findings related to the use case of Italy are also given. The use case reveals initial good performance of our methodology for the forecasting of the Italian sovereign bond market using the information extracted from GDELT and a deep Long Short-Term Memory Network opportunely trained and validated with a rolling window approach to best accounting for non-linearities in the data.