The principle of equity to the test of the exercise of jurisdiction [A "different" certainty]

Il testo considera il rapporto tra misericordia e giustizia attraverso una breve introduzione teologica in conformità ai parametri dell'ontologia trinitaria. L'ambito delle seguenti considerazioni è quello della giurisdizione dalla prospettiva della filosofia del diritto per proporre, sotto un approccio ermeneutico, il rapporto tra verità e giustizia, nella dinamica della misericordia. In particolare, si fa riferimento, al diritto canonico e al sistema di equità inglese.The text considers the relationship between mercy and justice through a short theological introduction in accordance with to the parameters of the trinitary ontology. The aim of the following considerations is the jurisdiction from the perspective of the philosophy of law in order to propose, under an hermeneutical approach, the relationship between truth and justice, within the dynamics of mercy. In particular, reference is made to Roman law, Canon law and to the English equity system.Ciencias ReligiosasDerech

Earthquake Early Warning and Preparatory Phase Detection through the use of Machine Learning Techniques

In this thesis I present 3 different works developed during the PhD. These three works are already published. My research has been focused on onsite EEW techniques oriented to the seismic risk reduction for buildings. As matter of fact, in the first work (Iaccarino et al., 2020; Chapter 2), "Onsite earthquake early warning: Predictive models for acceleration response spectra considering site effects" , we presented an EEW method that predict Response Spectra of Acceleration (RSA) at nine different periods from P-wave parameters (i. e., Pd and IV2) on 3s window. RSA is a ground motion parameter of particular interest for structural engineers since it better correlates with structural damage than peak parameters such as PGA and PGV (Elenas and Meskouris, 2001). To account for site-effects, we retrieved a partially non-ergodic model using a mixed-effect regression analysis. This procedure helped us to reduce the prediction uncertainty. Finally, we analyzed the correction terms by station, and we found that the stations with the more positive ones (grater RSA) were the same stations to have amplification effects highlighted by H/V analysis. Furthermore, our models improve the EEW performances both in terms of true negatives and false positives. The second work I present, "Earthquake Early Warning System for Structural Drift Prediction using Machine Learning and Linear Regressors" (Iaccarino et al., 2021; Chapter 3), uses data recorded from in-building sensors from Japanese and Californian structures. Here, we developed a method to predict Structural Drift using P-wave features (i. e., Pd, IV2, and ID2) from 1s, 2s, and 3s windows. We studied the effects of the complexity of the dataset on the predictions subdividing the Japanese dataset in three subsets: data from one building; data from buildings with the same material of construction; entire dataset. From this study, we found that the variability of the dataset plays a key role in the predictions increasing the uncertainties of the predictions for the complete dataset. Moreover, we compared the performances of linear least square models and non-linear machine learning regressors finding that the last ones perform always better. In the end, we tried to export the model retrieved on Japanese buildings to the Californian buildings, finding that the drift predictions are underestimated by a bias. We proposed to correct this bias using magnitude dependent correction terms, finding that the linear models are more able to adapt in these conditions. In the end, I present "Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network" (Chapter 4; Picozzi and Iaccarino, 2021). Here, we used catalogue information from a very complete dataset of the Californian geothermal area, The Geysers. From the catalogue, we chose 8 events with M>=3.9, and we selected the first 5 as training set and rest as testing set. Then, we extracted 9 features as time-series: the b-value and completeness magnitude, Mc, of the Gutenberg-Richter law; the fractal dimension of hypocenters, Dc; the generalized distance between pairs of earthquakes, η; the Shannon's information entropy, h; the moment magnitude, Mw, and moment rate, M ̇_0; the total duration of event groups, ΔT, and the inter-event time, Δt. We wanted to assess the possibility to detect changes in time of these features that can be related to deviations from the background seismicity. We built two Recurrent Neural Networks, one to detect preparatory phase the other to detect the aftershocks phase. The method is able to discriminate both the preparatory phase and the aftershock phase on the testing set. In the end, merging the predictions of two methods, we found that all the three events in testing set present a preparatory phase that lasts from 4 hours to 2 days before the main event

Forecasting the Preparatory Phase of Induced Earthquakes by Recurrent Neural Network

Earthquakes prediction is considered the holy grail of seismology. After almost a century of efforts without convincing results, the recent raise of machine learning (ML) methods in conjunction with the deployment of dense seismic networks has boosted new hope in this field. Even if large earthquakes still occur unanticipated, recent laboratory, field, and theoretical studies support the existence of a preparatory phase preceding earthquakes, where small and stable ruptures progres- sively develop into an unstable and confined zone around the future hypocenter. The problem of recognizing the preparatory phase of earthquakes is of critical importance for mitigating seismic risk for both natural and induced events. Here, we focus on the induced seismicity at The Geysers geothermal field in California. We address the preparatory phase of M~4 earthquakes identification problem by developing a ML approach based on features computed from catalogues, which are used to train a recurrent neural network (RNN). We show that RNN successfully reveal the preparation of M~4 earthquakes. These results confirm the potential of monitoring induced microseismicity and should encourage new research also in predictability of natural earthquakes

Il processo quale locus dialogico per la ricerca della verità

Il contributo intende approfondire lariflessione sul modello dialogico-relazionale del diritto, sviluppando la narrazione dialogica dei fatti offerta dalle parti nel corso del contraddittorio nel processo, con particolare riguardo all’ambito del diritto canonico. Il processo si configura, infatti, come uno “spazio tridimensionale” entro il quale si struttura tra le parti una particolare relazione, tale da favorire la ricerca del “medium” ermeneutico della ricerca della verità. Gli strumenti propri del giusto processo permettono un ulteriore sviluppo di questa prospettiva, garantendo il confronto e l’apertura al dialogo.The text intends to deepen the reflection on the dialogico-relational model of law, developing the dialogic narration of the facts offered by the parties during the course of the contradictory process, with particular regard to the field of canon law. The process is configured, in fact, as a “three-dimensional space”within which a particular relationship is structured between the parties, such as to favor the search for the hermeneutical “medium”of the search for truth. The proper tools of due process allow a further development of this perspective, guaranteeing comparison and openness to dialogue.Ciencias ReligiosasDerech

Il processo quale locus dialogico per la ricerca della verità

The text intends to deepen the reflection on the dialogico-relational model of law, developing the dialogic narration of the facts offered by the parties during the course of the contradictory process, with particular regard to the field of canon law. The process is configured, in fact, as a “three-dimensional space” within which a particular relationship is structured between the parties, such as to favor the search for the hermeneutical “medium” of the search for truth. The proper tools of due process allow a further development of this perspective, guaranteeing comparison and openness to dialogue

Left atrial anomalous muscular band as incidental finding during video-assisted mitral surgery

Congenital fibromuscular bands have been described inleft ventricle or right atrium and have been diagnosed by echocardiography and CT scan. The first report of anomalous band in the left atrium was described in 1897 by Rollestone (1). We hereby present a case of a patient with an incidental finding of left atrial band during a minimally invasive mitral surgery procedure

Surgical embolectomy for acute massive pulmonary embolism: state of the art

Massive pulmonary embolism (PE) is a severe condition that can potentially lead to death caused by right ventricular (RV) failure and the consequent cardiogenic shock. Despite the fact thrombolysis is often administrated to critical patients to increase pulmonary perfusion and to reduce RV afterload, surgical treatment represents another valid option in case of failure or contraindications to thrombolytic therapy. Correct risk stratification and multidisciplinary proactive teams are critical factors to dramatically decrease the mortality of this global health burden. In fact, the worldwide incidence of PE is 60–70 per 100,000, with a mortality ranging from 1% for small PE to 65% for massive PE. This review provides an overview of the diagnosis and management of this highly lethal pathology, with a focus on the surgical approaches at the state of the art

Cerebrovascular complications and infective endocarditis. impact of available evidence on clinical outcome

Infective endocarditis (IE) is a life-threatening disease. Its epidemiological profile has substantially changed in recent years although 1-year mortality is still high. Despite advances in medical therapy and surgical technique, there is still uncertainty on the best management and on the timing of surgical intervention. The objective of this review is to produce further insight intothe short- and long-term outcomes of patients with IE, with a focus on those presenting cerebrovascular complications

Detection of Spatial and Temporal Stress Changes During the 2016 Central Italy Seismic Sequence by Monitoring the Evolution of the Energy Index

We consider approximately 23,000 microearthquakes that occurred between 2005 and 2016 in central Italy to investigate the crustal strength before and after the three largest earthquakes of the 2016 seismic sequence (i.e., the Mw 6.2, 24 August 2016 Amatrice, the Mw 6.1, 26 October 2016 Visso, and the Mw 6.5, 30 October 2016 Norcia earthquakes). We monitor the spatiotemporal deviations of observed radiated energy, ES, with respect to theoretical values, ESt, derived from a scaling model between ES and M0 calibrated for background seismicity in central Italy. These deviations, defined here as Energy Index (EI), allow us to identify in the years following the Mw 6.1, 2009 L’Aquila earthquake a progressive evolution of the dynamic properties of microearthquakes and the existence of high EI patches close to the Amatrice earthquake hypocenter. We show the existence of a crustal volume with high EI even before the Mw 6.5 Norcia earthquake. Our results agree with the previously suggested hypothesis that the Norcia earthquake nucleated at the boundary of a large patch, highly stressed by the two previous mainshocks of the sequence. We highlight the mainshocks interaction both in terms of EI and of the mean loading shear stress associated to microearthquakes occurring within the crustal volumes comprising the mainshock hypocenters. Our study shows that the dynamic characteristics of microearthquakes can be exploited as beacons of stress change in the crust and thus be exploited to monitor the seismic hazard of a region and help to intercept the preparation phase of large earthquakes

Estimation of glomerular filtration rate from skeletal muscle mass. A new equation independent from age, weight, gender, and ethnicity

The most used indicator for the renal function is the glomerular filtration rate (GFR). Current used predictive GFR equations were calibrated on patients with chronic kidney disease. Thus, they are not very precise in healthy individuals. The estimation of skeletal muscle mass (SMM) allows the prediction of the daily urinary creatinine excretion (24hUCrE). This study proposes an equation for the estimation of GFR based on SMM (eGFRMuscle) and serum creatinine (SCr)