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

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

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)

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

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

Infrared characterization of silicon carbide nanowires

Silicon carbide nanowires have been obtained via combustion synthesis route. X-ray diffraction analysis confirmed that the synthesized material is the 3C polytype of silicon carbide with zincblende unit cell. Detailed investigations of such SiC 1D nanostructures were carried out exploiting Fourier transform infrared spectroscopy. IR measurements we performed using BRUKER HYPERION FT-IR microscope. For the purpose of comparison, a series of powder samples were examined, including raw synthesis product, purified SiC nanowires and several commercially available microand nanopowders (from Alpha Aesar and PlasmaChem). Comprehensive comparative analysis of the MIR spectra has been performed. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2063

Seroprevalence and incidence of Toxoplasma gondii infection in the Legnano area of Italy

ABSTRACTThe decreasing prevalence of anti-Toxoplasma antibodies in Europe has re-opened the question of the appropriateness of serological screening during pregnancy. A study of 3426 pregnant women, resident in the Legnano area of Italy, revealed that the IgG seroprevalence according to ELISA was 21.5%, and that of IgM according to ELISA and enzyme-linked fluorescent assay was 1.2% and 0.9%, respectively. The incidence of infection, estimated on the basis of IgG avidity, was 0.9%. These results confirm a decrease in the prevalence of IgG, but indicate a high incidence of infection, thus suggesting that screening for anti-Toxoplasma antibodies during pregnancy should be maintained

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

Infrared and transport properties of LuFe2O4 under electric fields

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