Unsupervised hyperspectral image segmentation of films: a hierarchical clustering-based approach

Hyperspectral imaging (HSI) has been drastically applied in recent years to cultural heritage (CH) analysis, conservation, and also digital restoration. However, the efficient processing of the large datasets registered remains challenging and still in development. In this paper, we propose to use the hierarchical clustering algorithm (HCA) as an alternative machine learning approach to the most common practices, such as principal component analysis(PCA). HCA has shown its potential in the past decades for spectral data classification and segmentation in many other fields, maximizing the information to be extracted from the high-dimensional spectral dataset via the formation of the agglomerative hierarchical tree. However, to date, there has been very limited implementation of HCA in the field of cultural heritage. Data used in this experiment were acquired on real historic film samples with various degradation degrees, using a custom-made push-broom VNIR hyperspectral camera (380–780nm). With the proposed HCA workflow, multiple samples in the entire dataset were processed simultaneously and the degradation areas with distinctive characteristics were successfully segmented into clusters with various hierarchies. A range of algorithmic parameters was tested, including the grid sizes, metrics, and agglomeration methods, and the best combinations were proposed at the end. This novel application of the semi-automating and unsupervised HCA could provide a basis for future digital unfading, and show the potential to solve other CH problems such as pigment mapping

The new york city covid‐19 spread in the 2020 spring: A study on the potential role of particulate using time series analysis and machine learning

This study investigates the potential association between the daily distribution of the PM2,5 air pollutant and the initial spreading of COVID‐19 in New York City. We study the period from 4 March to 22 March 2020, and apply our analysis to all five counties, including the city, plus seven neighboring counties, including both urban and peripheral districts. Using the Granger causality methodology, and considering the maximum lag period (14 days) between infection and the correspondent diagnosis, we found that the time series of the new daily infections registered in those 12 counties appear to correlate to the time series of the concentrations of the PM2.5 particulate circulating in the air, with 33 over 36 statistical tests with a p‐value less than 0.005, thus confirming such a hypothesis. Moreover, looking for further confirmation of this association, we train four different machine learning algorithms on a portion of those time series. These are able to predict that the number of the new daily infections would have surpassed a given infections threshold for the remaining portion of the series, with an average accuracy ranging from 84% to 95%, depending on the algorithm and/or on the specific county under observation. This is similar to other results obtained from several polluted urban areas, e.g., Wuhan, Xiaogan, and Huanggang in China, and Northern Italy. Our study provides further evidence that ambient air pollutants can be associated with a daily COVID‐19 infection incidence

On supporting university communities in indoor wayfinding: An inclusive design approach

Mobility can be defined as the ability of people to move, live and interact with the space. In this context, indoor mobility, in terms of indoor localization and wayfinding, is a relevant topic due to the challenges it presents, in comparison with outdoor mobility, where GPS is hardly exploited. Knowing how to move in an indoor environment can be crucial for people with disabilities, and in particular for blind users, but it can provide several advantages also to any person who is moving in an unfamiliar place. Following this line of thought, we employed an inclusive by design approach to implement and deploy a system that comprises an Internet of Things infrastructure and an accessible mobile application to provide wayfinding functions, targeting the University community. As a real word case study, we considered the University of Bologna, designing a system able to be deployed in buildings with different configurations and settings, considering also historical buildings. The final system has been evaluated in three different scenarios, considering three different target audiences (18 users in total): i. students with disabilities (i.e., visual and mobility impairments); ii. campus students; and iii. visitors and tourists. Results reveal that all the participants enjoyed the provided functions and the indoor localization strategy was fine enough to provide a good wayfinding experience

On combining Big Data and machine learning to support eco-driving behaviours

A conscious use of the battery is one of the key elements to consider while driving an electric vehicle. Hence, supporting the drivers, with information about it, can be strategic in letting them drive in a better way, with the purpose of optimizing the energy consumption. In the context of electric vehicles, equipped with regenerative brakes, the driver\u2019s braking style can make a significant difference. In this paper, we propose an approach which is based on the combination of big data and machine learning techniques, with the aim of enhancing the driver\u2019s braking style through visual elements (displayed in the vehicle dashboard, as a Human\u2013Machine Interface), actuating eco-driving behaviours. We have designed and developed a system prototype, by exploiting big data coming from an electric vehicle and a machine learning algorithm. Then, we have conducted a set of tests, with simulated and real data, and here we discuss the results we have obtained that can open interesting discussions about the use of big data, together with machine learning, so as to improve drivers\u2019 awareness of eco-behaviours

Neural Networks for Hyperspectral Imaging of Historical Paintings: A Practical Review

Hyperspectral imaging (HSI) has become widely used in cultural heritage (CH). This very efficient method for artwork analysis is connected with the generation of large amounts of spectral data. The effective processing of such heavy spectral datasets remains an active research area. Along with the firmly established statistical and multivariate analysis methods, neural networks (NNs) represent a promising alternative in the field of CH. Over the last five years, the application of NNs for pigment identification and classification based on HSI datasets has drastically expanded due to the flexibility of the types of data they can process, and their superior ability to extract structures contained in the raw spectral data. This review provides an exhaustive analysis of the literature related to NNs applied for HSI data in the CH field. We outline the existing data processing workflows and propose a comprehensive comparison of the applications and limitations of the various input dataset preparation methods and NN architectures. By leveraging NN strategies in CH, the paper contributes to a wider and more systematic application of this novel data analysis method

One-step bioengineering of magnetic nanoparticles via a surface diazo transfer/azide\u2013alkyne click reaction sequence

We have developed an efficient conversion of amino iron oxides to carbohydrate and protein derived nanoparticles with highly conserved bioactivity through a combination of diazo transfer and azide\u2013alkyne click technolog

Novel bioprinted 3D model to human fibrosis investigation

Fibrosis is shared in multiple diseases with progressive tissue stiffening, organ failure and limited therapeutic options. This unmet need is also due to the lack of adequate pre-clinical models to mimic fibrosis and to be challenged novel by anti-fibrotic therapeutic venues. Here using bioprinting, we designed a novel 3D model where normal human healthy fibroblasts have been encapsulated in type I collagen. After stimulation by Transforming Growth factor beta (TGFβ), embedded cells differentiated into myofibroblasts and enhanced the contractile activity, as confirmed by the high level of α − smooth muscle actin (αSMA) and F-actin expression. As functional assays, SEM analysis revealed that after TGFβ stimulus the 3D microarchitecture of the scaffold was dramatically remolded with an increased fibronectin deposition with an abnormal collagen fibrillar pattern. Picrius Sirius Red staining additionally revealed that TGFβ stimulation enhanced of two logarithm the collagen fibrils neoformation in comparison with control. These data indicate that by bioprinting technology, it is possible to generate a reproducible and functional 3D platform to mimic fibrosis as key tool for drug discovery and impacting on animal experimentation and reducing costs and time in addressing fibrosis

Factors influencing the participation of gastroenterologists and hepatologists in clinical research

<p>Abstract</p> <p>Background</p> <p>Although clinical research is integral to the advancement of medical knowledge, physicians face a variety of obstacles to their participation as investigators in clinical trials. We examined factors that influence the participation of gastroenterologists and hepatologists in clinical research.</p> <p>Methods</p> <p>We surveyed 1050 members of the American Association for the Study of Liver Diseases regarding their participation in clinical research. We compared the survey responses by specialty and level of clinical trial experience.</p> <p>Results</p> <p>A majority of the respondents (71.6%) reported involvement in research activities. Factors most influential in clinical trial participation included funding and compensation (88.3%) and intellectual pursuit (87.8%). Barriers to participation were similar between gastroenterologists (n = 160) and hepatologists (n = 189) and between highly experienced (n = 62) and less experienced (n = 159) clinical researchers. These barriers included uncompensated research costs and lack of specialized support. Industry marketing was a greater influence among respondents with less trial experience, compared to those with extensive experience (15.7% vs 1.6%; <it>P </it>< .01). Hepatologists and respondents with extensive clinical trial experience tended to be more interested in phase 1 and 2 studies, whereas gastroenterologists and less experienced investigators were more interested in phase 4 studies.</p> <p>Conclusion</p> <p>This study suggests that the greatest barrier to participation in clinical research is lack of adequate resources. Respondents also favored industry-sponsored research with less complex trial protocols and studies of relatively short duration.</p