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Road networks are crucial for the movement of resources, passenger transportation, and supply chains. In seismically active areas like Italy, earthquakes can compromise road infrastructure, leading to structural failures and connectivity disruptions. Bridges, vital for travel and emergency response, are especially vulnerable to these extreme events, making their maintenance and recovery crucial for preserving transport efficiency. This study examines the resilience of the Apulian road network against bridge failures by assessing seismic hazards, the structural vulnerability of each bridge to seismic actions, and the systemic consequences of its disruption. A bridge criticality score is defined to support data-driven decision-making for bridge maintenance and recovery. This novel quantitative metric integrates seismic hazard data at each bridge site, fragility curves, and topological complex network analysis to provide a comprehensive evaluation of bridge criticality. Additionally, the risk of isolation for inner municipalities due to bridge disruptions is assessed using centrality metrics. By combining the bridge criticality score with an emphasis on inner municipalities, this approach offers valuable insights to improve road network resilience, mitigate isolation risks, and promote territorial sustainability in earthquake-prone zones.

The impacts and threats posed by wildfires are dramatically increasing due to climate change. In recent years, the wildfire community has attempted to estimate wildfire occurrence with machine learning models. However, to fully exploit the potential of these models, it is of paramount importance to make their predictions interpretable and intelligible. This study is a first attempt to provide an eXplainable artificial intelligence (XAI) framework for estimating wildfire occurrence using a Random Forest model with Shapley values for interpretation. Our findings accurately detected regions with a high presence of wildfires (area under the curve 81.3%) and outlined the drivers empowering occurrence, such as the Fire Weather Index and Normalized Difference Vegetation Index. Furthermore, our analysis suggests the presence of anomalous hotspots. In contexts where human and natural spheres constantly intermingle and interact, the XAI framework, suitably integrated into decision support systems, could support forest managers to prevent and mitigate future wildfire disasters and develop strategies for effective fire management, response, recovery, and resilience.

Autism is a genetically and clinically very heterogeneous group of disorders. Gene co-expression network analysis can help unravel its complex genetic architecture through the identification of communities of genes that are dysregulated. Using a publicly available brain microarray dataset (experiment GSE28475), we performed a gene co-expression analysis based on Leiden community detection to identify stable communities of genes and used them within a robust machine learning framework with feature selection. We reached an accuracy as high as ( 98 ± 1 ) % in discriminating between autism and control subjects and validated our results on an independent microarray experiment obtaining an accuracy of ( 88 ± 3 ) % . Furthermore, we found two communities of 43 and 44 genes that were enriched for genetically associated variants and reached an accuracy of ( 78 ± 5 ) % and ( 75 ± 4 ) % on the independent set, respectively. An eXplainable Artificial Intelligence analysis on these two causal communities confirmed the pivotal role of autism specific variants thus independently validating our analysis. Further analysis on the restricted number of genes in the identified communities may reveal essential mechanisms responsible for autism spectrum disorder.

In recent years, several studies have demonstrated that machine learning and deep learning systems can be very useful to accurately predict brain age. In this work, we propose a novel approach based on complex networks using 1016 T1-weighted MRI brain scans (in the age range 7−64years). We introduce a structural connectivity model of the human brain: MRI scans are divided in rectangular boxes and Pearson’s correlation is measured among them in order to obtain a complex network model. Brain connectivity is then characterized through few and easy-to-interpret centrality measures; finally, brain age is predicted by feeding a compact deep neural network. The proposed approach is accurate, robust and computationally efficient, despite the large and heterogeneous dataset used. Age prediction accuracy, in terms of correlation between predicted and actual age r=0.89and Mean Absolute Error MAE =2.19years, compares favorably with results from state-of-the-art approaches. On an independent test set including 262 subjects, whose scans were acquired with different scanners and protocols we found MAE =2.52. The only imaging analysis steps required in the proposed framework are brain extraction and linear registration, hence robust results are obtained with a low computational cost. In addition, the network model provides a novel insight on aging patterns within the brain and specific information about anatomical districts displaying relevant changes with aging.

The European Quality of Government Index (EQI) measures the perceived level of government quality by European Union citizens, combining surveys on corruption, impartiality and quality of provided services. It is, thus, an index based on individual subjective evaluations. Understanding the most relevant objective factors affecting the EQI outcomes is important for both evaluators and policy makers, especially in view of the fact that perception of government integrity contributes to determine the level of civic engagement. In our research, we employ methods of Artificial Intelligence and complex systems physics to measure the impact on the perceived government quality of multifaceted variables, describing territorial development and citizen well-being, from an economic, social and environmental viewpoint. Our study, focused on a set of regions in European Union at a subnational scale, leads to identifying the territorial and demographic drivers of citizens’ confidence in government institutions. In particular, we find that the 2021 EQI values are significantly related to two indicators: the first one is the difference between female and male labour participation rates, and the second one is a proxy of wealth and welfare such as the average number of rooms per inhabitant. This result corroborates the idea of a central role played by labour gender equity and housing policies in government confidence building. In particular, the relevance of the former indicator in EQI prediction results from a combination of positive conditions such as equal job opportunities, vital labour market, welfare and availability of income sources, while the role of the latter is possibly amplified by the lockdown policies related to the COVID-19 pandemics. The analysis is based on combining regression, to predict EQI from a set of publicly available indicators, with the eXplainable Artificial Intelligence approach, that quantifies the impact of each indicator on the prediction. Such a procedure does not require any ad-hoc hypotheses on the functional dependence of EQI on the indicators used to predict it. Finally, using network science methods concerning community detection, we investigate how the impact of relevant indicators on EQI prediction changes throughout European regions. Thus, the proposed approach enables to identify the objective factors at the basis of government quality perception by citizens in different territorial contexts, providing the methodological basis for the development of a quantitative tool for policy design.

Network connectivity has been thoroughly investigated in several domains, including physics, neuroscience, and social sciences. This work tackles the possibility of characterizing the topological properties of real-world networks from a quantum-inspired perspective. Starting from the normalized Laplacian of a network, we use a well-defined procedure, based on the dressing transformations, to derive a 1-dimensional Schrödinger-like equation characterized by the same eigenvalues. We investigate the shape and properties of the potential appearing in this equation in simulated small-world and scale-free network ensembles, using measures of fractality. Besides, we employ the proposed framework to compare real-world networks with the Erdős-Rényi, Watts-Strogatz and Barabási-Albert benchmark models. Reconstructed potentials allow to assess to which extent real-world networks approach these models, providing further insight on their formation mechanisms and connectivity properties.

Predicting brain age has become one of the most attractive challenges in computational neuroscience due to the role of the predicted age as an effective biomarker for different brain diseases and conditions. A great variety of machine learning (ML) approaches and deep learning (DL) techniques have been proposed to predict age from brain magnetic resonance imaging scans. If on one hand, DL models could improve performance and reduce model bias compared to other less complex ML methods, on the other hand, they are typically black boxes as do not provide an in-depth understanding of the underlying mechanisms. Explainable Artificial Intelligence (XAI) methods have been recently introduced to provide interpretable decisions of ML and DL algorithms both at local and global level. In this work, we present an explainable DL framework to predict the age of a healthy cohort of subjects from ABIDE I database by using the morphological features extracted from their MRI scans. We embed the two local XAI methods SHAP and LIME to explain the outcomes of the DL models, determine the contribution of each brain morphological descriptor to the final predicted age of each subject and investigate the reliability of the two methods. Our findings indicate that the SHAP method can provide more reliable explanations for the morphological aging mechanisms and be exploited to identify personalized age-related imaging biomarker.

The advent of eXplainable Artificial Intelligence (XAI) has revolutionized the way human experts, especially from non-computational domains, approach artificial intelligence; this is particularly true for clinical applications where the transparency of the results is often compromised by the algorithmic complexity. Here, we investigate how Alzheimer's disease (AD) affects brain connectivity within a cohort of 432 subjects whose T1 brain Magnetic Resonance Imaging data (MRI) were acquired within the Alzheimer's Disease Neuroimaging Initiative (ADNI). In particular, the cohort included 92 patients with AD, 126 normal controls (NC) and 214 subjects with mild cognitive impairment (MCI). We show how graph theory-based models can accurately distinguish these clinical conditions and how Shapley values, borrowed from game theory, can be adopted to make these models intelligible and easy to interpret. Explainability analyses outline the role played by regions like putamen, middle and superior temporal gyrus; from a class-related perspective, it is possible to outline specific regions, such as hippocampus and amygdala for AD and posterior cingulate and precuneus for MCI. The approach is general and could be adopted to outline how brain connectivity affects specific brain regions.

Global warming is one of the most pressing and critical problems facing the world today. It is mainly caused by the increase in greenhouse gases in the atmosphere, such as carbon dioxide (CO 2 ). Understanding how soils respond to rising temperatures is critical for predicting carbon release and informing climate mitigation strategies. Q 10 , a measure of soil microbial respiration, quantifies the increase in CO 2 release caused by a Celsius rise in temperature, serving as a key indicator of this sensitivity. However, predicting Q 10 across diverse soil types remains a challenge, especially when considering the complex interactions between biochemical, microbiome, and environmental factors. In this study, we applied explainable artificial intelligence (XAI) to machine learning models to predict soil respiration sensitivity (Q 10 ) and uncover the key factors driving this process. Using SHAP (SHapley Additive exPlanations) values, we identified glucose-induced soil respiration and the proportion of bacteria positively associated with Q 10 as the most influential predictors. Our machine learning models achieved an accuracy of , precision of , an AUC-ROC of , and an AUC-PRC of , ensuring robust and reliable predictions. By leveraging t-SNE (t-distributed Stochastic Neighbor Embedding) and clustering techniques, we further segmented low Q 10 soils into distinct subgroups, identifying soils with a higher probability of transitioning to high Q 10 states. Our findings not only highlight the potential of XAI in making model predictions transparent and interpretable, but also provide actionable insights into managing soil carbon release in response to climate change. This research bridges the gap between AI-driven environmental modeling and practical applications in agriculture, offering new directions for targeted soil management and climate resilience strategies.

University rankings are published on a regular basis and taken as a reference by a widespread audience of students, researchers, and companies. Nonetheless, rankings can be affected by socio-economic dragging effects, since they often fail to incorporate information on the variegated conditions in which scores are reached. This inability to capture structural inequalities can generate self-reinforcing awarding mechanisms, e.g. in performance-based funding distribution, that amplify existing gaps and prevent from recognizing achievements of universities in difficult or emerging contexts. In a previous study, we demonstrated the existence of a socio-economic territorial bias in general rankings, which rate the global performance of institutions. However, the interplay of the variety of territorial contexts and the different features of specific disciplines can give rise to more complex effects. In this work, we investigate the influence of the local socio-economic condition on the performance of universities in rankings, considering a multidimensional representation of the phenomenon, involving the dependence on subject, time, and type of ranking. Our findings show that bibliometric rankings are significantly more affected than reputational ones by socio-economic dragging, which strikingly emerges especially in the natural and life science areas. We conclude the analysis by decoupling territorial dragging effects from the achieved ranked scores. Universities that benefit the most from the mitigation of the socio-economic territorial bias are typically located in territories, mostly outside Western Europe and North America, hosting either a capital or other important cities.