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The construction of better alternatives to the present lifestyles and the solution of the environmental problems generated by modern economies and intensive exploitation of natural resources certainly require better science and technology. However, it is very unlikely that the needed global and complex solutions may be provided by one disciplinary field only, although very important, as science is. Solutions require broad minded approaches and comprehensive understanding of the multi-dimensionality of life on earth. A variety of tools and ways to approach the transition towards a cleaner and healthier environment is needed, not only to capture the complexity of life expressions, but also to value and appreciate the complexity of languages, narratives and points of view (scientific, artistic, economic, social, among others) that may lead to the “Celestial City”, a metaphor for better ways of living. This work explores the meaning and the potential contribution of a musical piece, representative of art and social languages, to inspire a transition process, with specific focus on Jing-Jin-Ji (JJJ) region, China. The piece is the 'Hope for a Celestial City – A Triptych' is a three-sections work for solo violin, composed by Carlotta Ferrari. It is inspired by the designed future 'urban' agglomerations such as the urbanised region of Jing-Jin-Ji, China. The piece refers to Bunyan's 'Celestial City' (this expression refers to 'The Pilgrim's Progress', written by John Bunyan in 1678) as a way to reflect on the past, present and future of JJJ. The work is inspired by three sets of quotes. Each of them is divided in two sub-sections: the first one is referred to as 'literature quote', which serves also as non-scientific reference for each section of the musical composition; the second one, on the other side, contains a set of scientific and technical references to guide and base the inspiration of the music. The first part of the piece represents the arrogant industrial civilization, “humans subdue nature”, with a particular attention to JJJ. The second part is focused on puzzling and soul-searching energy and work. In particular, the concept is that human activities can be either harmful or beneficial both for the environment and for elevating the society. The third part represents the 'pilgrimage' of human life and activities towards a different city: the Celestial City, where efforts achieve the result of harmonious and sustainable life. In other words, a more sustainable city can be built by transforming the present urban systems from competition to cooperation within and beyond Jing-Jin-Ji region borders. Implementing control targets, optimizing industrial structures and infrastructures, and achieving both coal consumption and air pollution reduction are seen as important technical steps towards more equitable and fulfilling life, where new patterns of connection, interaction, and exchange among residents are achieved within a healthier urban environment. The use of aesthetic inquiry, multiple languages and new creative approaches represents a support for accelerating the transition toward a more sustainable and equitable post fossil-carbon society, in particular in the case of expanding urban agglomerations. This work represents a methodological approach to artistic engagement trough music creation and performance. The purpose of such a process is to generate a collective reflection and to produce a transformative power. The collective reflection is stimulated by music performance. Furthermore, this is a way to include the contribution of artists into an important sustainability issue. Finally, this work demonstrates that music can support the communication of scientific messages, which could be difficult to be delivered in their original science form.

Hyperspectral imaging allows to easily acquire tens of thousands of spectra for a single sample in a few seconds; though valuable, this data-richness poses many problems due to the difficulty of handling a representative amount of samples altogether. For this reason, we recently proposed an approach based on the idea of reducing each image into a one-dimensional signal, named hyperspectrogram , which accounts both for spatial and for spectral information. In this manner, a dataset of hyperspectral images can be easily and quickly converted into a set of signals (2D data matrix), which in turn can be analyzed using classical chemometric techniques. In this work, the hyperspectrograms obtained from a dataset of 800 NIR-hyperspectral images of two different apple varieties were used to discriminate bruised from sound apples using iPLS-DA as variable selection algorithm, which allowed to efficiently detect the presence of bruises. Moreover, the reconstruction as images of the selected variables confirmed that the automated procedure led to the exact identification of the spatial features related to the onset and to the subsequent evolution with time of the bruise defect.

(1) Background: Police forces and security administrations are nowadays considering Improvised explosives (IEs) as a major threat. The chemical substances used to prepare IEs are called precursors, and their presence could allow police forces to locate a bomb factory where the on-going manufacturing of IEs is carried out. (2) Methods: An expert system was developed and tested in handling signals from a network of sensors, allowing an early warning. The expert system allows the detection of one precursor based on the signal provided by a single sensor, the detection of one precursor based on the signal provided by more than one sensor, and the production of a global alarm level based on data fusion from all the sensors of the network. (3) Results: The expert system was tested in the Italian Air Force base of Pratica di Mare (Italy) and in the Swedish Defence Research Agency (FOI) in Grindsjön (Sweden). (4) Conclusion: The performance of the expert system was successfully evaluated under relevant environmental conditions. The approach used in the development of the expert system allows maximum flexibility in terms of integration of the response provided by any sensor, allowing to easily include in the network all possible new sensors.

In the meat industry the fat portions coming from two different subcutaneous layers, i.e., inner and outer, are destined to the manufacturing of different products, hence the availability of cheap, rapid and affordable methods for the characterization of the overall fat quality is desirable. In this work the potential usefulness of three techniques, i.e. tristimulus colorimetry, FT-NIR spectroscopy and NIR hyperspectral imaging, were tested to rapidly discriminate fat samples coming from the two different layers. To this aim, various multivariate classification methods were used, also including signal processing and feature selection techniques. The classification efficiency in prediction obtained using colorimetric data did not reach excellent results (78.1%); conversely, the NIR-based spectroscopic methods gave much more satisfactory models, since they allowed to reach a prediction efficiency higher than 95%. In general, the samples of the outer layer showed a high degree of variability with respect to the samples of the inner layer. This is probably due to a greater variability of the outer samples in terms of fatty acid composition and water amount.

The analytical capability to detect hydrogen peroxide vapour can play a key role in localizing a site where a H2O2 based Improvised Explosive (IE) is manufactured. In security activities it is very important to obtain information in a short time. For this reason, an analytical method to be used in security activity needs portable devices. The authors have developed the first analytical method based on a portable luminometer, specifically designed and validated to locate IE manufacturing sites using quantitative on-site vapour analysis for H2O2. The method was tested both indoor and outdoor. The results demonstrate that the detection of H2O2 vapours could allow police forces to locate the site, while terrorists are preparing an attack. The collected data are also very important in developing new sensors, able to give an early alarm if located at a proper distance from a site where an H2O2 based IE is prepared.

Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest. In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham. Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470-5880 cm1 ) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500-5800 cm1 region, and a FT-MIR microscopy operating in the 4000-675 cm1 region. Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.

In this work, FT-NIR spectroscopy was employed to determine iodine value (IV) and fatty acids (FA) content of pig fat samples, through the combined use of signal preprocessing, multivariate calibration, and variable selection methods. In particular, the main focus was on the use of variable selection methods, both in order to improve the predictive performance of the calibration models, and to identify relevant wavelengths that could be subsequently used for the development of simple, fast, and cheap hand-held devices, able to measure IV and FA content directly on the fat without the need of any sample pretreatment. Firstly, for each property of interest, partial least squares (PLS) multivariate calibration models were calculated considering the whole spectral range and testing different signal preprocessing methods. Then, once chosen the optimal signal preprocessing method, a two-step variable selection procedure was applied. In the first step, the interval-PLS variable selection algorithm was used to calculate a set of calibration models, whose outcomes were considered altogether in the second step, in order to select the optimal calibration model. The variable selection procedure allowed to lower the number of spectral variables retained by the model, and often led to an increase of the performance in prediction of the external test set samples.

Official methods for the detection of bacteria are based on culture techniques. These methods have limitations such as time consumption, cost, detection limits and the impossibility to analyse a large number of samples. For these reasons, the development of rapid, low-cost and non-destructive analytical methods is a task of growing interest. In the present study, the capability of spectral and hyperspectral techniques to detect bacterial surface contamination was investigated preliminarily on gel cultures, and subsequently on sliced cooked ham. In more detail, two species of lactic acid bacteria (LAB) were considered, namely Lactobacillus curvatus and Lactobacillus sakei, both of which are responsible for common alterations in sliced cooked ham. Three techniques were investigated, with different equipment, respectively: a macroscopic hyperspectral scanner operating in the NIR (10,470-5880cm(-1)) region, a FT-NIR spectrophotometer equipped with a transmission arm as the sampling tool, working in the 12,500-5800cm(-1) region, and a FT-MIR microscopy operating in the 4000-675cm(-1) region. Multivariate exploratory data analysis, in particular principal component analysis (PCA), was applied in order to extract useful information from original data and from hyperspectrograms. The results obtained demonstrate that the spectroscopic and imaging techniques investigated can represent an effective and sensitive tool to detect surface bacterial contamination in samples and, in particular, to recognise species to which bacteria belong.

Spectral resolution (R) and number of repeated scans (S) have a significant effect on the S/N ratio of Fourier transform-near infrared (FT-NIR) spectra, but the optimal values of these two parameters have to be determined empirically for a specific problem, considering separately both the nature of the analysed matrix and the specific instrumental setup. To achieve this aim, the instrumental noise of replicated FT-NIR spectra of wheat samples was modelled as a function of R and S by means of the Doehlert design. The noise amounts in correspondence to different experimental conditions were estimated by analysing the variance signals derived from replicate measurements with two different signal processing tools, Savitzky-Golay (SG) filtering and fast wavelet transform (FWT), in order to separate the "pure" instrumental noise from other variability sources, which are essentially connected to sample inhomogeneity. Results confirmed that R and S values leading to minimum instrumental noise can vary considerably depending on the type of analysed food matrix and on the different instrumental setups, and helped in the selection of the optimal measuring conditions for the subsequent acquisition of a wide spectral dataset.

Hyperspectral Imaging (HSI) is gaining increasing interest in the field of analytical chemistry, since this fast and non-destructive technique allows one to easily acquire a large amount of spectral and spatial information on a wide number of samples in very short times. However, the large size of hyperspectral image data often limits the possible uses of this technique, due to the difficulty of evaluating many samples altogether, for example when one needs to consider a representative number of samples for the implementation of on-line applications. In order to solve this problem, we propose a novel chemometric strategy aimed to significantly reduce the dataset size, which allows to analyze in a completely automated way from tens up to hundreds of hyperspectral images altogether, without losing neither spectral nor spatial information. The approach essentially consists in compressing each hyperspectral image into a signal, named hyperspectrogram, which is created by combining several quantities obtained by applying PCA to each single hyperspectral image. Hyperspectrograms can then be used as a compact set of descriptors and subjected to blind analysis techniques. Moreover, a further improvement of both data compression and calibration/classification performances can be achieved by applying proper variable selection methods to the hyperspectrograms. A visual evaluation of the correctness of the choices made by the algorithm can be obtained by representing the selected features back into the original image domain. Likewise, the interpretation of the chemical information underlying the selected regions of the hyperspectrograms related to the loadings is enabled by projecting them in the original spectral domain. Examples of applications of the hyperspectrogram-based approach to hyperspectral images of food samples in the NIR range (1000–1700 nm) and in the vis–NIR range (400–1000 nm), facing a calibration and a defect detection issue respectively, demonstrate the effectiveness of the proposed approach.

The effectiveness of Hyperspectral imaging (HSI) in the near infrared (NIR) range (1000–1700 nm) was evaluated to discriminate PET (polyethylene terephthalate) from PLA (poly(lactic acid)), two polymers commonly utilized as packaging for foodstuff, in order to improve their further recycling process. An internal calibration based on five reference materials was initially used to eliminate the variability existing among images, then Partial Least Squares-Discriminant Analysis (PLS-DA) was used to distinguish and classify the three classes, i.e., background, PET and PLA. Considering the high amount of data conveyed by the training image, the PLS-DA models were also calculated using as training set a reduced version of the original matrix, with the twofold aim to reduce the computational time and to deal with an equal number of spectra for each class, independently from the initial selected areas. A variable selection procedure by means of iPLS-DA was also applied on both the whole and the reduced matrix. The results obtained on the reduced matrix using only six variables provided a prediction efficiency higher than 98%. Moreover, the possibility to recognize PET and PLA polymers by HSI in the NIR range was further confirmed by using Multivariate Curve Resolution (MCR) as an alternative approach, which also allowed to evaluate the effect of thickness of the transparent plastic samples.

A significant step towards commercialization of novel CNT electronics involves the development of large-area and high-throughput processes for the fabrication of high quality CNT films. In this article, we demonstrate a reliable and reproducible spray deposition process for the fabrication of highly uniform CNT films exhibiting state-of-the-art performance. CNT films with average surface roughness as low as 5.8 nm, and conductivities reaching 4000 S/cm for films of 45 nm thickness, are presented. Incorporating this kind of high quality films as resistive networks for gas detection in ammonia (NH 3 ) gas sensors, yields device sensitivities reaching 5% at a concentration of 100 ppm for the short exposure interval of only 60 sec. This high NH 3 sensitivity is achieved without any further functionalization of the CNT film, thereby maintaining a simple fabrication process.