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Heatwaves (HWs) are extreme atmospheric events that produce significant societal and environmental impacts. Predicting these extreme events remains challenging, as their complex interactions with large-scale atmospheric and climatic variables are difficult to capture with traditional statistical and dynamical models. This work presents a general method for driver identification in extreme climate events. A novel framework (STCO-FS) is proposed to identify key immediate (short-term) HW drivers by combining clustering algorithms with an ensemble evolutionary algorithm. The framework analyzes spatio-temporal data, reduces dimensionality by grouping similar geographical nodes for each variable, and develops driver selection in spatial and temporal domains, identifying the best time lags between predictive variables and HW occurrences. The proposed method has been applied to analyze HWs in the Adda river basin in Italy. The approach effectively identifies significant variables influencing HWs in this region. This research can potentially enhance our understanding of HW drivers and predictability., Comment: 28 pages, 10 figures, 4 tables

Quantifying causal effects of exposures on outcomes, such as a treatment and a disease respectively, is a crucial issue in medical science for the administration of effective therapies. Importantly, any related causal analysis should account for all those variables, e.g. clinical features, that can act as risk factors involved in the occurrence of a disease. In addition, the selection of targeted strategies for therapy administration requires to quantify such treatment effects at personalized level rather than at population level. We address these issues by proposing a methodology based on categorical Directed Acyclic Graphs (DAGs) which provide an effective tool to infer causal relationships and causal effects between variables. In addition, we account for population heterogeneity by considering a Dirichlet Process mixture of categorical DAGs, which clusters individuals into homogeneous groups characterized by common causal structures, dependence parameters and causal effects. We develop computational strategies for Bayesian posterior inference, from which a battery of causal effects at subject-specific level is recovered. Our methodology is evaluated through simulations and applied to a dataset of breast cancer patients to investigate cardiotoxic side effects that can be induced by the administrated anticancer therapies.

In recent years, artificial intelligence (AI) has deeply impacted various fields, including Earth system sciences. Here, AI improved weather forecasting, model emulation, parameter estimation, and the prediction of extreme events. However, the latter comes with specific challenges, such as developing accurate predictors from noisy, heterogeneous and limited annotated data. This paper reviews how AI is being used to analyze extreme events (like floods, droughts, wildfires and heatwaves), highlighting the importance of creating accurate, transparent, and reliable AI models. We discuss the hurdles of dealing with limited data, integrating information in real-time, deploying models, and making them understandable, all crucial for gaining the trust of stakeholders and meeting regulatory needs. We provide an overview of how AI can help identify and explain extreme events more effectively, improving disaster response and communication. We emphasize the need for collaboration across different fields to create AI solutions that are practical, understandable, and trustworthy for analyzing and predicting extreme events. Such collaborative efforts aim to enhance disaster readiness and disaster risk reduction.

Estimating dependence relationships between variables is a crucial issue in many applied domains, such as medicine, social sciences and psychology. When several variables are entertained, these can be organized into a network which encodes their set of conditional dependence relations. Typically however, the underlying network structure is completely unknown or can be partially drawn only; accordingly it should be learned from the available data, a process known as structure learning. In addition, data arising from social and psychological studies are often of different types, as they can include categorical, discrete and continuous measurements. In this paper we develop a novel Bayesian methodology for structure learning of directed networks which applies to mixed data, i.e. possibly containing continuous, discrete, ordinal and binary variables simultaneously. Whenever available, our method can easily incorporate known dependence structures among variables represented by paths or edge directions that can be postulated in advance based on the specific problem under consideration. We evaluate the proposed method through extensive simulation studies, with appreciable performances in comparison with current state-of-the-art alternative methods. Finally, we apply our methodology to well-being data from a social survey promoted by the United Nations, and mental health data collected from a cohort of medical students.

Typically, integrated radio frequency continuum spectra of supernova remnants (SNRs) exhibit a power-law form due to their synchrotron emission. In numerous cases, these spectra show an exponential turnover, long assumed due to thermal free-free absorption in the interstellar medium. We use a compilation of Galactic radio continuum SNR spectra, with and without turnovers, to constrain the distribution of the absorbing ionized gas. We introduce a novel parameterization of SNR spectra in terms of a characteristic frequency v_*, which depends both on the absorption turnover frequency and the power-law slope. Normalizing to v_* and to the corresponding flux density, S_*, we demonstrate that the stacked spectra of our sample reveal a similarity in behavior with low scatter (r.m.s. ~15%), and a unique exponential drop-off fully consistent with the predictions of a free-free absorption process. Observed SNRs, whether exhibiting spectral turnovers or not, appear to be spatially well mixed in the Galaxy without any evident segregation between them. Moreover, their Galactic distribution does not show a correlation with general properties such as heliocentric distance or Galactic longitude, as might have been expected if the absorption were due to a continuous distribution of ionized gas. However, it naturally arises if the absorbers are discretely distributed, as suggested by early low-frequency observations. Modeling based on HII regions tracking Galactic spiral arms successfully reproduces the patchy absorption observed to date. While more extensive statistical datasets should yield more precise spatial models of the absorbing gas distribution, our present conclusion regarding its inhomogeneity will remain robust., Comment: Accepted for A&A. Comments welcome. 11 pages, 11 figures, 1 table

We consider the problem of learning causal Directed Acyclic Graphs (DAGs) using combinations of observational and interventional experimental data. Current methods tailored to this setting assume that interventions either destroy parent-child relations of the intervened (target) nodes or only alter such relations without modifying the parent sets, even when the intervention targets are unknown. We relax this assumption by proposing a Bayesian method for causal discovery from general interventions, which allow for modifications of the parent sets of the unknown targets. Even in this framework, DAGs and general interventions may be identifiable only up to some equivalence classes. We provide graphical characterizations of such interventional Markov equivalence and devise compatible priors for Bayesian inference that guarantee score equivalence of indistinguishable structures. We then develop a Markov Chain Monte Carlo (MCMC) scheme to approximate the posterior distribution over DAGs, intervention targets and induced parent sets. Finally, we evaluate the proposed methodology on both simulated and real protein expression data.

The optimal operation of water reservoir systems is a challenging task involving multiple conflicting objectives. The main source of complexity is the presence of the water inflow, which acts as an exogenous, highly uncertain disturbance on the system. When model predictive control (MPC) is employed, the optimal water release is usually computed based on the (predicted) trajectory of the inflow. This choice may jeopardize the closed-loop performance when the actual inflow differs from its forecast. In this work, we consider - for the first time - a stochastic MPC approach for water reservoirs, in which the control is optimized based on a set of plausible future inflows directly generated from past data. Such a scenario-based MPC strategy allows the controller to be more cautious, counteracting droughty periods (e.g., the lake level going below the dry limit) while at the same time guaranteeing that the agricultural water demand is satisfied. The method's effectiveness is validated through extensive Monte Carlo tests using actual inflow data from Lake Como, Italy., Comment: Modeling, Estimation and Control Conference, Lake Tahoe, Nevada, USA, October 2-5 2023

This paper presents a comprehensive analysis of the Galactic SNR Kes 17 (G304.6+0.1) with focus on its radio synchrotron emission, environs, and the factors contributing to the observed gamma rays. The firstly-obtained integrated radio continuum spectrum from 88 to 8800 MHz yields an index alpha = -0.488 +/- 0.023 (S_nu $\propto$ nu^alpha), indicative of a linear particle acceleration process at the shock front. Accounting for the SNR radio shell size, the distribution of atomic hydrogen (n_H ~ 10 cm^-3), and assuming the SNR is in the Sedov-Taylor stage of its evolution, we estimate Kes 17 to be roughly 11 kyr. From 12CO and 13CO (J=1-0) emission-line data as a proxy for molecular hydrogen we provided the first evidence that the eastern shell of Kes 17 is engulfing a molecular enhancement, with 4.2 x 10^4 M_sun and n ~ 300 cm^-3. Towards the western boundary of Kes 17 there are not CO signatures above 3 sigma, despite previously reported infrared observations have revealed shocked molecular gas at that location. This suggests the existence of a CO-dark interacting molecular gas, a phenomenon also recorded in other Galactic SNRs (e.g. CTB 37A and RX J1713.7-3946). Additionally, by analysing ~14.5 yr of data from Fermi-LAT, we determined a power-law photon index in the 0.3-300 GeV range of Gamma = 2.39 +/- 0.04^+0.063_-0.114 (+/-stat +/-syst) in agreement with prior studies. The energy flux turns out to be (2.98 +/- 0.14) x 10^-11 erg cm-2 s-1 implying a luminosity (2.22 +/- 0.45) x 10^35 erg s-1 at ~8 kpc. Finally, we successfully modelled the multiwavelength SED by incorporating the improved radio synchrotron spectrum and the new gamma-ray measurements. Our analysis indicates that the observed GeV flux most likely originates from the interaction of Kes 17 with western ''dark'' CO zone with a proton density n_p ~ 400 cm-3., Comment: 9 pages, 6 figures, 2 tables. Accepted for publication in the Astronomy & Astrophysics Journal. The quality of the figures has been degraded

We consider a a collection of categorical random variables. Of special interest is the causal effect on an outcome variable following an intervention on another variable. Conditionally on a Directed Acyclic Graph (DAG), we assume that the joint law of the random variables can be factorized according to the DAG, where each term is a categorical distribution for the node-variable given a configuration of its parents. The graph is equipped with a causal interpretation through the notion of interventional distribution and the allied "do-calculus". From a modeling perspective, the likelihood is decomposed into a product over nodes and parents of DAG-parameters, on which a suitably specified collection of Dirichlet priors is assigned. The overall joint distribution on the ensemble of DAG-parameters is then constructed using global and local independence. We account for DAG-model uncertainty and propose a reversible jump Markov Chain Monte Carlo (MCMC) algorithm which targets the joint posterior over DAGs and DAG-parameters; from the output we are able to recover a full posterior distribution of any causal effect coefficient of interest, possibly summarized by a Bayesian Model Averaging (BMA) point estimate. We validate our method through extensive simulation studies, wherein comparisons with alternative state-of-the-art procedures reveal an outperformance in terms of estimation accuracy. Finally, we analyze a dataset relative to a study on depression and anxiety in undergraduate students.

The optimal operation of regulated lakes is a challenging task involving conflicting objectives, ranging from controlling lake levels to avoid floods and low levels to water supply downstream. The traditional approach to operation policy design is based on an offline optimization, where a feedback control rule mapping lake storage into daily release decisions is identified over a set of observational data. In this paper, we propose a receding-horizon policy for a more frequent, online regulation of the lake level, and we discuss its tuning as compared to benchmark approaches. As side contributions, we provide a daily alternative based on the same rationale, and we show that this is still valid under some assumptions on the water inflow. Numerical simulations are used to show the effectiveness of the proposed approach. We demonstrate the approach on the regulated lake Como, Italy., Comment: 6 pages, 5 figures, IFAC 2nd-CMWRS 2022, Milan

Atmospheric Extreme Events (EEs) cause severe damages to human societies and ecosystems. The frequency and intensity of EEs and other associated events are increasing in the current climate change and global warming risk. The accurate prediction, characterization, and attribution of atmospheric EEs is therefore a key research field, in which many groups are currently working by applying different methodologies and computational tools. Machine Learning (ML) methods have arisen in the last years as powerful techniques to tackle many of the problems related to atmospheric EEs. This paper reviews the ML algorithms applied to the analysis, characterization, prediction, and attribution of the most important atmospheric EEs. A summary of the most used ML techniques in this area, and a comprehensive critical review of literature related to ML in EEs, are provided. A number of examples is discussed and perspectives and outlooks on the field are drawn., Comment: 93 pages, 18 figures, under review

Graphical models based on Directed Acyclic Graphs (DAGs) are widely used to answer causal questions across a variety of scientific and social disciplines. However, observational data alone cannot distinguish in general between DAGs representing the same conditional independence assertions (Markov equivalent DAGs); as a consequence the orientation of some edges in the graph remains indeterminate. Interventional data, produced by exogenous manipulations of variables in the network, enhance the process of structure learning because they allow to distinguish among equivalent DAGs, thus sharpening causal inference. Starting from an equivalence class of DAGs, a few procedures have been devised to produce a collection of variables to be manipulated in order to identify a causal DAG. Yet, these algorithmic approaches do not determine the sample size of the interventional data required to obtain a desired level of statistical accuracy. We tackle this problem from a Bayesian experimental design perspective, taking as input a sequence of target variables to be manipulated to identify edge orientation. We then propose a method to determine, at each intervention, the optimal sample size capable of producing a successful experiment based on a pre-experimental evaluation of the overall probability of substantial correct evidence.

We present the first detailed study on the Galactic supernova remnant (SNR) G46.8-0.3 and its environment since its discovery more than 50 years ago. With new flux measurements from radio surveys plus a compilation from the literature we created an improved integrated radio spectrum of G46.8-0.3, which is well fitted by a simple power-law with an index alpha = -0.535 +/- 0.012, then excluding the presence of absorption by abundant ionised gas in the line of sight, or in the SNR's proximity. The analysis of local changes in the radio spectral index across G46.8-0.3 suggests a slight steepening at about 1 GHz, which does not impact on the integrated spectrum of the source. From atomic hydrogen (HI) spectra we placed the remnant at 8.7 +/- 1.0 kpc, and revisited the distance to the nearby HII region G046.495-00.241 to 7.3 +/- 1.2 kpc. From evolutionary models and our distance estimate, we conclude G46.8-0.3 is a middle-aged ($\sim$1 x 10^4 yr) SNR. We recognised several 12CO and 13CO molecular structures in the proximity of the remnant and used combined CO-HI profiles to derive the kinematic distances to these features and characterise their physical properties. We also provided compelling evidences for environmental molecular clouds physically linked to G46.8-0.3 at its centre, on its eastern edge, and towards the northern and southwestern rims on the far side of the SNR shell. Our study does not confirm that the remnant is embedded in a molecular cavity as previously suggested. G46.8-0.3 shows a line-of-sight coincidence with the gamma-ray source 4FGL J1918.1+1215c, detected at GeV energies by the space telescope Fermi. A rough analysis based on the properties of the interstellar matter close G46.8-0.3 indicates that the GeV gamma-rays photons detected in direction to the SNR can be plausibly attributed to hadronic collisions and/or bremsstrahlung radiation., Comment: Accepted for publication in Astronomy and Astrophysics (on 08 March 2022), 14 pages, 9 figures (the quality of the figures has been degrated), 2 tables

Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estimation of causal effects between variables also from pure observational data. In this setting, the process of inferring the DAG structure from the data is referred to as causal structure learning or causal discovery. We introduce BCDAG, an R package for Bayesian causal discovery and causal effect estimation from Gaussian observational data, implementing the Markov chain Monte Carlo (MCMC) scheme proposed by Castelletti & Mascaro (2021). Our implementation scales efficiently with the number of observations and, whenever the DAGs are sufficiently sparse, with the number of variables in the dataset. The package also provides functions for convergence diagnostics and for visualizing and summarizing posterior inference. In this paper, we present the key features of the underlying methodology along with its implementation in BCDAG. We then illustrate the main functions and algorithms on both real and simulated datasets.