The xSAP Safety Analysis Platform

This paper describes the xSAP safety analysis platform. xSAP provides several model-based safety analysis features for finite- and infinite-state synchronous transition systems. In particular, it supports library-based definition of fault modes, an automatic model extension facility, generation of safety analysis artifacts such as Dynamic Fault Trees (DFTs) and Failure Mode and Effects Analysis (FMEA) tables. Moreover, it supports probabilistic evaluation of Fault Trees, failure propagation analysis using Timed Failure Propagation Graphs (TFPGs), and Common Cause Analysis (CCA). xSAP has been used in several industrial projects as verification back-end, and is currently being evaluated in a joint R&D Project involving FBK and The Boeing Company

Analysis of the seismic site effects along the ancient Via Laurentina (Rome)

This paper presents an evaluation of the Local Seismic Response (LSR) along the route of the ancient Roman road Via Laurentina, which has been exposed in several areas of southwest Rome over the last decade during the construction of new buildings and infrastructures. It is an example of LSR analysis applied to ancient and archaeological sites located in alluvial valleys with some methodological inferences for the design of infrastructure and urban planning. Since the ancient road does not cross the alluvial valley (namely the Fosso di Vallerano Valley) normal to its sides, it was not possible to directly perform 2D numerical modelling to evaluate the LSR along the road route. Therefore, outputs of 2D numerical models, obtained along three cross sections that were normal oriented respect to the valley, were projected along the route of the Via Laurentina within a reliable buffer attributed according to an available high-resolution geological model of the local subsoil. The modelled amplification functions consider physical effects due to both the 2D shape of the valley and the heterogeneities of the alluvial deposits. The 1D and 2D amplification functions were compared to output that non-negligible effects are related to the narrow shape of the fluvial valley and the lateral contacts between the lithotecnical units composing the alluvial fill. The here experienced methodology is suitable for applications to the numerical modelling of seismic response in case of linear infrastructures (i.e., roads, bridges, railways) that do not cross the natural system along physically characteristic directions (i.e. longitudinally or transversally)

Introducing temporal correlation in rainfall and wind prediction from underwater noise

While in the past the prediction of wind and rainfall from underwater noise was performed using empirical equations fed with very few spectral bins and fitted to the data, it has recently been shown that regression performed using supervised machine learning techniques can benefit from the simultaneous use of all spectral bins, at the cost of increased complexity. However, both empirical equations and machine learning regressors perform the prediction using only the acoustic information collected at the time when one wants to know the wind speed or the rainfall intensity. At most, averages are made between spectra measured at subsequent times (spectral compounding) or between predictions obtained at subsequent times (prediction compounding). In this article, it is proposed to exploit the temporal correlation inherent in the phenomena being predicted, as has already been done in methods that forecast wind and rainfall from their values (and sometimes those of other meteorological quantities) in the recent past. A special architecture of recurrent neural networks, the long shortterm memory, is used along with a data set composed of about 16 months of underwater noise measurements (acquired every 10 min, simultaneously with wind and rain measurements above the sea surface) to demonstrate that the introduction of temporal correlation brings significant advantages, improving the accuracy and reducing the problems met in the widely adopted memoryless prediction performed by random forest regression. Working with samples acquired at 10-min intervals, the best performance is obtained by including three noise spectra for wind prediction and six spectra for rainfall prediction

Predicting FTS products through artificial neural network modelling

Fischer-Tropsch synthesis is essential for converting CO2 into hydrocarbons, creating sustainable fuels and olefins. However, challenges in production yield and reaction kinetics remain. This study introduces an artificial neural network (ANN) to predict FT synthesis products from specific inputs, including temperature, pressure, GHSV, H2/CO2 ratio, and catalyst composition (Fe weight and K as a promoter). The ANN's ability to predict outputs like CH4, C2-4, C5+, CO2 conversion, and CO selectivity, without detailed reaction mechanisms, is a key innovation. This approach circumvents complex kinetic models. The network architecture is optimized for minimal error, and results are validated against a comprehensive database

The anomalous warming of summer 2003 in the surface layer of the Central Ligurian Sea (Western Mediterranean)

"Meteorological and sea temperature data from the ODAS Italia 1 buoy (Ligurian Sea, Western Mediterranean) are used to study the anomalous warming of summer 2003 at sea. The event was related to the record heat wave that interested much of Europe from June to September of that year. The data show that the anomalous warming was prevalently confined to within a few meters below the sea surface. On the contrary, the temperatures in the underlying layers were lower than usual. The limited vertical propagation of heat is ascribed to the high temperature difference that arose between the surface and the deeper layers due to protracted calm weather conditions. The degree of penetration of heat deduced from the observations is consistent with that computed on the basis of an energetic argument, wherein the wind constitutes the sole supply of kinetic energy, while the heating is viewed as the source of potential energy that must be ""subtracted"" by mixing. The results support the hypothesis that the scanty energy from the wind is mainly responsible for the development of the temperature anomaly at the sea surface.

Monumento funerario

He aquí una bella muestra de arquitectura aplicada al tema del "Monumento Conmemorativo". Pero, además, al difícil monumento funerario, que tan desastrosos ejemplos de mal gusto ofrece a la vista de cualquier visitante de panteones en el mundo y, muy especialmente, en el mundo latino. La vanidad humana, llevada más allá de la muerte, se plasma en pequeños templos barrocos, neoclásicos y hasta aztecas, negación de todo sentido de proporción arquitectónica y un motivo más para amar la vida y temer la muerte

Self-sustainable bio-methanol & bio-char coproduction from 2nd generation biomass gasification

Methanol is an important intermediate in the synthesis of different chemicals. It is mainly produced by reforming of natural gas in centralized facilities with productive capacities on the order of 109 tons per day. Production of methanol from biomass suffers from the cost and logistics of the transportation of biomass and it has not yet maturated into commercial scale. The techno-economic feasibility of the co-production of bio-methanol and bio-char is assessed through detailed computer simulations using process simulator Aspen HYSYS® together with the gasification simulator GASDS. This work further elaborates the previous results on the bio-methanol production process, presenting particularities and updates on previously reported values. The production model is seen to be valid, with payback times that go from 3 to 6 years according to the capacity of the plant (100 to 1000 kt of biomass per year). Self-sustainability is possible but a 50/50 mix of producing and buying electricity yields the most economic choice. © Copyright 2017, AIDIC Servizi S.r.l

Confluence reduction for Markov automata

Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude

A-DInSAR performance for updating landslide inventory in mountain areas. An example from Lombardy region (Italy)

This work focuses on the capabilities and limitations of the Advanced Satellite SAR (Synthetic Aperture Radar) Interferometry (A-DInSAR) in wooded and mountainous regions, with the aim to get insights on the performances for studying slow-moving landslides. The considered critical issues are related to the SAR acquisition geometries (angle of incidence of the satellite line of sight, ascending and descending geometries) and to the physical and morphological features of the slopes (land use, aspect and slope angles), which influence the measuring points coverage. 26 areas in Lombardy Region (Italy), affected by known slope instability phenomena, have been analyzed through A-DInSAR technique, using COSMO-SkyMed images. The results allowed to outline general considerations about the effectiveness of A-DInSAR analysis of a single dataset (descending or ascending dataset), selected accordingly to the aspect of the slopes. Moreover, we aimed to quantitatively describe the capability to update the state of activity of several previously mapped landslides using satellite SAR Interferometry results. Although in a wooded and mountainous region, where the chances of retrieving radar targets for satellite SAR analysis are generally low, the A-DInSAR results have allowed to detect landslides’ reactivations or new landslides and to update the inventory for about 70% of the investigated areas