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The correct estimation of building energy consumptions is assuming an always increasing importance, and a detailed reproduction of building structures, with all the single components involved, is necessary to achieve this aim. In addition, the current ecological development tries to limit the use of natural raw materials as building components, in favor of alternative (waste) materials, which ensure significant advantages from the economic, energetic and environmental point of views. In this work, dynamic heat and vapor transport in a typical three-dimensional (3D) building structure, involving different types of environmental-friendly concrete mixtures, have been simulated by using finite elements. In particular, the authors propose to substitute part of the aggregates with plastic waste and to use a fly ash based geopolymeric binder for the production of low conductivity concrete, to be employed in eco-efficient buildings. Concrete produced with natural limestone aggregates has been considered as the reference benchmark. The whole characterization of the different types of concrete tested in the present work has been obtained through laboratory experiments. The structure taken into account in the simulations is a 3D thermal bridge, typical of building envelopes. The thermal and hygrometric transient behavior of this structure, employing plastic waste in different percentages and geopolymer concrete, has been analyzed by the authors. [ABSTRACT FROM AUTHOR]

Forecasting user flows on transportation networks is a fundamental task for Intelligent Transport Systems (ITSs). Indeed, most control and management strategies on transportation systems are based on the knowledge of user flows. For implementing ITS strategies, the forecast of user flows on some network links obtained as a function of user flows on other links (for instance, where data are available in real time with sensors) may provide a significant contribution. In this paper, we propose the use of Artificial Neural Networks (ANNs) for forecasting metro onboard passenger flows as a function of passenger counts at station turnstiles. We assume that metro station turnstiles record the number of passengers entering by means of an automatic counting system and that these data are available every few minutes (temporal aggregation); the objective is to estimate onboard passengers on each track section of the line (i.e., between two successive stations) as a function of turnstile data collected in the previous periods. The choice of the period length may depend on service schedules. Artificial Neural Networks are trained by using simulation data obtained with a dynamic loading procedure of the rail line. The proposed approach is tested on a real-scale case: Line 1 of the Naples metro system (Italy). Numerical results show that the proposed approach is able to forecast the flows on metro sections with satisfactory precision. [ABSTRACT FROM AUTHOR]

This paper proposes a method for estimating traffic flows on some links of a road network knowing the data on other links that are monitored with sensors. In this way, it is possible to obtain more information on traffic conditions without increasing the number of monitored links. The proposed method is based on artificial neural networks (ANNs), wherein the input data are the traffic flows on some monitored road links and the output data are the traffic flows on some unmonitored links. We have implemented and tested several single-layer feed-forward ANNs that differ in the number of neurons and the method of generating datasets for training. The proposed ANNs were trained with a supervised learning approach where input and output example datasets were generated through traffic simulation techniques. The proposed method was tested on a real-scale network and gave very good results if the travel demand patterns were known and used for generating example datasets, and promising results if the demand patterns were not considered in the procedure. Numerical results have underlined that the ANNs with few neurons were more effective than the ones with many neurons in this specific problem. [ABSTRACT FROM AUTHOR]

In recent years, the food industry has become increasingly involved in researching vegetable fats and oils with appropriate mechanical properties (ease of transport, processing, and storage) and a specific lipidic composition to ensure healthy products for consumers. The chemical–physical behavior of these matrices depends on their composition in terms of single fatty acids (FA). However, as we demonstrate in this work, these properties, as well as the absorption, digestion and uptake in humans of specific FAs, are also largely determined by their regiosomerism within the TriAcylGlycerols (TAG) moieties (sn-1,2,3 positions). The goal of this work is to study for the first time vegetable fats obtained directly from a sample of natural cocoa butter (CB) through a process that manipulates the distribution of FAs but not their nature. Even if the initial percentage of each FA in the mixture remains the same, CB derivatives seem to show improved chemical–physical features. In order to understand which factors account for their physical and chemical characteristics, and to check whether or not the obtained new matrices could be considered as valid alternatives to other vegetable fats (e.g., palm oil (PO)), we carried out an experimental investigation at both the macroscopic and molecular level including: (i) Differential Scanning Calorimetry (DSC) analyses to examine thermal features; (ii) rheological testing to explore mechanical properties; (iii) powder X-ray diffraction (PXRD) to evaluate the solid-state phases of the obtained fats; and (iv) 1H and 13C Nuclear Magnetic Resonance (NMR, 1D and 2D) spectroscopy to rapidly analyze fatty acid composition including regioisomeric distribution on the glycerol backbone. These last results open up the possibility of using NMR spectroscopy as an alternative to the chromatographic techniques routinely employed for the investigation of similar matrices. [ABSTRACT FROM AUTHOR]

Cannabis sativa is a herbaceous multiple-use species commonly employed to produce fiber, oil, and medicine. It is now becoming popular for the high nutritional properties of its seed oil and for the pharmacological activity of its cannabinoid fraction in inflorescences. The present study aims to apply nuclear magnetic resonance (NMR) spectroscopy to provide useful qualitative and quantitative information on the chemical composition of seed and flower Cannabis extracts obtained by ultra-sound-assisted extraction, and to evaluate NMR as an alternative to the official procedure for the quantification of cannabinoids. The estimation of the optimal ω-6/ω-3 ratio from the 1H NMR spectrum for the seed extracts of the Futura 75 variety and the quantitative results from the 1H and 13C NMR spectra for the inflorescence extracts of the Tiborszallasi and Kompolti varieties demonstrate that NMR technology represents a good alternative to classical chromatography, supplying sufficiently precise, sensitive, rapid, and informative data without any sample pre-treatment. In addition, different extraction procedures were tested and evaluated to compare the elaboration of spectral data with the principal component analysis (PCA) statistical method and the quantitative NMR results: the extracts obtained with higher polarity solvents (acetone or ethanol) were poor in psychotropic agents (THC < LOD) but had an appreciable percentage of both cannabinoids and triacylgliceroles (TAGs). These bioactive-rich extracts could be used in the food and pharmaceutical industries, opening new pathways for the production of functional foods and supplements. [ABSTRACT FROM AUTHOR]

Heterogeneous bifuctional catalysts are multifunctional synthetic catalysts enabling efficient organic transformations by exploiting two opposite functionalities without mutual destruction. In this paper we report the first Er(III)-based metallorganic heterogeneous catalyst, synthesized by post-calcination MW-assisted grafting and modification of the natural aminoacid L-cysteine. The natural acid-base distance between sites was maintained to assure the cooperation. The applicability of this new bifunctional heterogeneous catalyst to C-C bond formation and the supposed mechanisms of action are discussed as well. [ABSTRACT FROM AUTHOR]