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

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

Designing Interfaces to Display Sensor Data: A Case Study in the Human-Building Interaction Field Targeting a University Community

The increase of smart buildings with Building Information Modeling (BIM) and Building Management Systems (BMS) has created a large amount of data, including those coming from sensors. These data are intended for monitoring the building conditions by authorized personnel, not being available to all building occupants. In this paper, we evaluate, from a qualitative point of view, if a user interface designed for a specific community can increase occupants’ context-awareness about environmental issues within a building, supporting them to make more informed decisions that best suit their needs. We designed a user interface addressed to the student community of a smart campus, adopting an Iterative Design Cycle methodology, and engaged 48 students by means of structured interviews with the aim of collecting their feedback and conducting a qualitative analysis. The results obtained show the interest of this community in having access to information about the environmental data within smart campus buildings. For example, students were more interested in data about temperature and brightness, rather than humidity. As a further result of this study, we have extrapolated a series of design recommendations to support the creation of map-based user interfaces that we found to be effective in such contexts

A LoRa-mesh based system for marine Social IoT

Recently in the world of boats we hear about "smart boats", or 3.0 connected boats. For many years, technology has entered the world of boats, especially as regards safety, some systems have become mandatory. With the spread of IoT systems, boats could be considered as sources of information for other boats in the same sea area or for the construction of coastal IoT services. In the marine context, measurements of environmental values such as sea water temperature, wave period and height or direction of currents, are carried out using buoys of considerable size and at a great distance from the coast. In this paper we want to present a Social Internet of Things system that allows the distribution of information between boaters and the integration with fixed IoT networks consisting of buoys and coastal stations. A preliminary LoRa mesh communication test is presented showing a stable mesh network in which each node is able to communicate in the sea with a radius of 5 Km

Chiral spin currents and spectroscopically accessible single merons in quantum dots

We provide unambiguous theoretical evidence for the formation of correlation-induced isolated merons in rotationally-symmetric quantum dots. Our calculations rely on neither the lowest-Landau-level approximation, nor on the maximum-density-droplet approximation, nor on the existence of a spin-polarized state. For experimentally accessible system parameters, unbound merons condense in the ground state at magnetic fields as low as $B^* = 0.2$ T and for as few as N = 3 confined fermions. The four-fold degenerate ground-state at $B^*$ corresponds to four orthogonal merons $\ket{QC}$ characterized by their topological chirality $C$ and charge $Q$. This degeneracy is lifted by the Rashba and Dresselhaus spin-orbit interaction, which we include perturbatively, yielding spectroscopic accessibility to individual merons. We further derive a closed-form expression for the topological chirality in the form of a chiral spin current and use it to both characterize our states and predict the existence of other topological textures in other regions of phase space, for example, at N=5. Finally, we compare the spin textures of our numerically exact meron states to ansatz wave-functions of merons in quantum Hall droplets and find that the ansatz qualitatively describes the meron states.Comment: 4 pages, 5 figures; minor title change, typos fixe

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 exploiting Data Visualization and IoT for Increasing Sustainability and Safety in a Smart Campus

In a world that is getting increasingly digital and interconnected, and where more and more physical objects are integrated into the information network (Internet of Things, IoT), Data Visualization can facilitate the understanding of huge volumes of data. In this paper, we present the design and implementation of a testbed where IoT and Data Visualization have been exploited to increase the sustainability and safety of the Cesena (Smart) Campus. In particular, we detail the overall system architecture and the interactive dashboard that facilitates the management of the campus premises and the timetabling. Exploiting our system, we show how we can improve the campus sustainability (in terms of energy saving) and safety (considering the COVID-19 restrictions and regulations)

Designing human-centric software artifacts with future users: a case study

The quality and quantity of participation supplied by human beings during the different phases of the design and development of a software artifact are central to studies in human-centered computing. With this paper, we have investigated on what kind of experienced people should be engaged to design a new computational artifact, when a participatory approach is adopted. We compared two approaches: the former including only future users (i.e., novices) in the design process, and the latter enlarging the community to expert users. We experimented with the design of a large software artifact, in use at the University of Bologna, engaging almost 1500 users. Statistical methodologies were employed to validate our findings. Our analysis has provided mounting evidence that expert users have contributed to the design of the artifact only by a small amount. Instead, most of the innovative initiatives have come from future users, thus surpassing some traditional limitations that tend to exclude future users from this kind of processes. We here challenge the traditional opinion that expert users provide typically a more reliable contribution in a participatory software design process, demonstrating instead that future users would be often better suited. Along this line of sense, this is the first paper, in the field of human-centric computing, that discusses the relevant question to offer to future users a larger design space, intended as a higher level of freedom given in a software design situation, demarcated by precise design constraints. In this sense, the outcome has been positiv

Arousal effects on Fitness-to-Drive assessment: algorithms and experiments

Several elements can affect the drivers' behaviour while they are performing driving activities. Ranging from visual to cognitive distractions, emotions and other drivers' conditions (that could emerge from biometric data, such as temperature, heartbeat, pressure, etc.) can play a significant role, performing as a factor that can increase drivers' response time. This could be crucial in avoiding dangerous situations and in deciding and performing actions that could influence the happening of car accidents. This paper introduces the concept of the "Fitness-to-Drive" index and aims to evaluate how the arousal effects can influence the drivers' status. The paper presents some experimental evaluations we have conducted on a driver simulator, discussing the obtained results

Raveguard: A noise monitoring platform using low-end microphones and machine learning

Urban noise is one of the most serious and underestimated environmental problems. According to the World Health Organization, noise pollution from traffic and other human activities, negatively impact the population health and life quality. Monitoring noise usually requires the use of professional and expensive instruments, called phonometers, able to accurately measure sound pressure levels. In many cases, phonometers are human-operated; therefore, periodic fine-granularity city-wide measurements are expensive. Recent advances in the Internet of Things (IoT) offer a window of opportunities for low-cost autonomous sound pressure meters. Such devices and platforms could enable fine time\u2013space noise measurements throughout a city. Unfortunately, low-cost sound pressure sensors are inaccurate when compared with phonometers, experiencing a high variability in the measurements. In this paper, we present RaveGuard, an unmanned noise monitoring platform that exploits artificial intelligence strategies to improve the accuracy of low-cost devices. RaveGuard was initially deployed together with a professional phonometer for over two months in downtown Bologna, Italy, with the aim of collecting a large amount of precise noise pollution samples. The resulting datasets have been instrumental in designing InspectNoise, a library that can be exploited by IoT platforms, without the need of expensive phonometers, but obtaining a similar precision. In particular, we have applied supervised learning algorithms (adequately trained with our datasets) to reduce the accuracy gap between the professional phonometer and an IoT platform equipped with low-end devices and sensors. Results show that RaveGuard, combined with the InspectNoise library, achieves a 2.24% relative error compared to professional instruments, thus enabling low-cost unmanned city-wide noise monitoring