One day at The Sands: Exploring Las Vegas’ intangible heritage through virtual reality

The construction, negotiation and dissemination of cultural heritage in the digital era can take advantage of several approaches, including Virtual Reality (VR). Leveraging on the provision of contextualized multimedia content, VR can foster awareness about the histories of specific times and places, encouraging personal interpretative processes.This paper presents the design, implementation and evaluation of “One Day at the Sands”, a VR application aimed at conveying the atmosphere of one of the most famous casinos in Las Vegas from the 1950s onwards. First, we collected a digital database, including a wide range of historical material; then, we developed a presentation medium that allows viewers to navigate the virtual reconstructions and access the archival material following their curiosity and interests. User tests underlined the capability of the application to facilitate a deep and meaningful exploration of the contents reinforcing the argument that virtual reality solutions represent valuable tools to engender awareness and explorative attitudes towards heritage

A Novel Matrix Transformation for Decoupled Control of Modular Multiphase PMSM Drives

When multiphase drives are used for specific applications, the modular solutions are preferred as they use consolidated power electronics technologies. The literature reports two modeling approaches for multiphase machines having a modular configuration of the stator winding. The first approach is the vector space decomposition (VSD) that models the energy conversion as for an equivalent three-phase machine. The main alternative to the VSD is the multistator (MS) modeling that emphasizes machine modularity in terms of torque production. Both approaches have advantages and disadvantages for multiphase machines with a modular structure. Therefore, this article aims to combine the VSD and MS approaches, defining a new matrix transformation and, hence, developing a new modeling approach for multiphase machines with a modular structure. The proposed transformation allows a decoupled and independent torque control of the sets composing the machine, preserving the torque regulation's modularity. Together with a new vector control scheme, it has been applied to a modular permanent magnet synchronous machine (PMSM) with a nonstandard spatial shift between windings. Experimental results are presented for a nine-phase PMSM prototype with a triple-three-phase stator winding configuration

Robust decadal hydroclimate predictions for northern Italy based on a twofold statistical approach

The Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions

Robust decadal hydroclimate predictions for northern Italy based on a twofold statistical approach

The Mediterranean area belongs to the regions most exposed to hydroclimatic changes, with a likely increase in frequency and duration of droughts in the last decades. However, many climate records like, e.g., North Italian precipitation and river discharge records, indicate that significant decadal variability is often superposed or even dominates long-term hydrological trends. The capability to accurately predict such decadal changes is, therefore, of utmost environmental and social importance. Here, we present a twofold decadal forecast of Po River (Northern Italy) discharge obtained with a statistical approach consisting of the separate application and cross-validation of autoregressive models and neural networks. Both methods are applied to each significant variability component extracted from the raw discharge time series using Singular Spectrum Analysis, and the final forecast is obtained by merging the predictions of the individual components. The obtained 25-year forecasts robustly indicate a prominent dry period in the late 2020s/early 2030s. Our prediction provides information of great value for hydrological management, and a target for current and future near-term numerical hydrological predictions

Water Mass Transport Changes through the Venice Lagoon Inlets from Projected Sea-Level Changes under a Climate Warming Scenario

In this study, an ensemble of numerical simulations with a state-of-the-art hydrodynamic model for coastal applications is used to characterize, for the first time, the expected mid-21st-century changes in circulation and associated sea-level height inside the Venice lagoon induced by projected Mediterranean sea level rise and atmospheric circulation changes over the Adriatic Sea under the RCP8.5 emission scenario. Our results show that water transports through the three inlets connecting the Venice lagoon to the open sea are expected to change significantly, with consequent significant persistent alterations of the circulation and sea-level height inside the lagoon. The projected water mass redistributions motivate further studies on the implications of climate change for the lagoon environment

LC-MS/MS-Based Profiling of Tryptophan-Related Metabolites in Healthy Plant Foods

Food plants contain hundreds of bioactive phytochemicals arising from different secondary metabolic pathways. Among these, the metabolic route of the amino acid Tryptophan yields a large number of plant natural products with chemically and pharmacologically diverse properties. We propose the identifier "indolome" to collect all metabolites in the Tryptophan pathway. In addition, Tryptophan-rich plant sources can be used as substrates for the fermentation by yeast strains to produce pharmacologically active metabolites, such as Melatonin. To pursue this technological development, we have developed a UHPLC-MS/MS method to monitor 14 Tryptophan, Tryptamine, amino-benzoic, and pyridine metabolites. In addition, different extraction procedures to improve the recovery of Tryptophan and its derivatives from the vegetal matrix were tested. We investigated soybeans, pumpkin seeds, sesame seeds, and spirulina because of their botanical diversity and documented healthy effects. Four different extractions with different solvents and temperatures were tested, and water extraction at room temperature was chosen as the most suitable procedure to extract the whole Tryptophan metabolites pattern (called by us "indolome") in terms of ease, high efficiency, short time, low cost, and sustainability. In all plant matrices, Tryptophan was the most abundant indole compound, while the pattern of its metabolites was different in the diverse plants extracts. Overall, 5-OH Tryptamine and Kynurenine were the most abundant compounds, despite being 100-1000-fold lower than Tryptophan. Melatonin was undetected in all extracts, but sesame showed the presence of a Melatonin isomer. The results of this study highlight the variability in the occurrence of indole compounds among diverse food plants. The knowledge of Tryptophan metabolism in plants represents a relevant issue for human health and nutrition