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

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

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

Aspectos ambientais do cultivo de arroz orgânico, biodinâmico e convencional.

Este trabalho tem o objetivo de comparar aspectos ambientais dos diferentes sistemas manejo de arroz irrigado orgânico, biodinâmico e convencional.SIAPEX

Ultrafast emission from colloidal nanocrystals under pulsed X-ray excitation

Fast timing has emerged as a critical requirement for radiation detection in medical and high energy physics, motivating the search for scintillator materials with high light yield and fast time response. However, light emission rates from conventional scintillation mechanisms fundamentally limit the achievable time resolution, which is presently at least one order of magnitude slower than required for next-generation detectors. One solution to this challenge is to generate an intense prompt signal in response to ionizing radiation. In this paper, we present colloidal semiconductor nanocrystals (NCs) as promising prompt photon sources. We investigate two classes of NCs: two-dimensional CdSe nanoplatelets (NPLs) and spherical CdSe/CdS core/giant shell quantum dots (GS QDs). We demonstrate that the emission rates of these NCs under pulsed X-ray excitation are much faster than traditional mechanisms in bulk scintillators, i.e. 5d-4f transitions. CdSe NPLs have a sub-100 ps effective decay time of 77 ps and CdSe/CdS GS QDs exhibit a sub-ns value of 849 ps. Further, the respective CdSe NPL and CdSe/CdS GS QD X-ray excited photoluminescence have the emission characteristics of excitons (X) and multiexcitons (MX), with the MXs providing additional prospects for fast timing with substantially shorter lifetimes

Alcohol-abuse drug disulfiram targets pediatric glioma via MLL degradation

Pediatric gliomas comprise a broad range of brain tumors derived from glial cells. While high-grade gliomas are often resistant to therapy and associated with a poor outcome, children with low-grade gliomas face a better prognosis. However, the treatment of low-grade gliomas is often associated with severe long-term adverse effects. This shows that there is a strong need for improved treatment approaches. Here, we highlight the potential for repurposing disulfiram to treat pediatric gliomas. Disulfiram is a drug used to support the treatment of chronic alcoholism and was found to be effective against diverse cancer types in preclinical studies. Our results show that disulfiram efficiently kills pediatric glioma cell lines as well as patient-derived glioma stem cells. We propose a novel mechanism of action to explain disulfiram’s anti-oncogenic activities by providing evidence that disulfiram induces the degradation of the oncoprotein MLL. Our results further reveal that disulfiram treatment and MLL downregulation induce similar responses at the level of histone modifications and gene expression, further strengthening that MLL is a key target of the drug and explaining its anti-oncogenic properties

Sistema e custo de produção de gado de corte no Estado do Rio Grande do Sul - região da Campanha.

Programa da pecuária de corte no Rio Grande so Sul. Descrição do sistema de produção de gado de corte da região da campanha - Rio Grande do Sul. Benfeitoria, máquinas e equipamentos. Composição do rebanho e desempenho zootécnico. Controle sanitário. Mão de obra. Sistema gerancial e contábil. Resultados econômicos do sistema modal de cria, recria e engorda. Estrutua de custos. Receita e sua composição. Custo de produção e margens econômicas. Custos de produção variando a capacidade de suporte dos pastos e a taxa de natalidade. Considerações finais.bitstream/item/79815/1/COT95.pdfCNPGC