A new multiparametric topological method for determining the primary cosmic ray mass composition in the knee energy region

The determination of the primary cosmic ray mass composition from the characteristics of extensive air showers (EAS), obtained at an observation level in the lower half of the atmosphere, is still an open problem. In this work we propose a new method of the Multiparametric Topological Analysis and show its applicability for the determination of the mass composition of the primary cosmic rays at the PeV energy region.Comment: 8 pages, 4 figures, talk given at Vulcano 2004 Workshop 'Frontier Objects in Physics and Astrophysics', Vulcano, Italy, 24-29.05.04, to be published in the Proceedings of the Worksho

Machine dependence and reproducibility for coupled climate simulations: the HadGEM3-GC3.1 CMIP Preindustrial simulation

When the same weather or climate simulation is run on different high-performance computing (HPC) platforms, model outputs may not be identical for a given initial condition. While the role of HPC platforms in delivering better climate projections is to some extent discussed in the literature, attention is mainly focused on scalability and performance rather than on the impact of machine-dependent processes on the numerical solution. Here we investigate the behaviour of the Preindustrial (PI) simulation prepared by the UK Met Office for the forthcoming CMIP6 (Coupled Model Intercomparison Project Phase 6) under different computing environments. Discrepancies between the means of key climate variables were analysed at different timescales, from decadal to centennial. We found that for the two simulations to be statistically indistinguishable, a 200-year averaging period must be used for the analysis of the results. Thus, constant-forcing climate simulations using the HadGEM3-GC3.1 model are reproducible on different HPC platforms provided that a sufficiently long duration of simulation is used. In regions where El Niño–Southern Oscillation (ENSO) teleconnection patterns were detected, we found large sea surface temperature and sea ice concentration differences on centennial timescales. This indicates that a 100-year constant-forcing climate simulation may not be long enough to adequately capture the internal variability of the HadGEM3-GC3.1 model, despite this being the minimum simulation length recommended by CMIP6 protocols for many MIP (Model Intercomparison Project) experiments. On the basis of our findings, we recommend a minimum simulation length of 200 years whenever possible

Special Issue: Advances in SARS-CoV-2 Infection

Since the beginning of the pandemic, and particularly during the first and second waves, the goal of this Special Issue has been to highlight the crucial aspects of SARS- CoV-2 and the disease in light of the new knowledge that was emerging. A total of 15 manuscripts have been published in this Special Issue. These papers provided insights into epidemiology, pathogenesis, epigenetics COVID-19 emergencies in hospital settings, advanced diagnosis, vaccination, and SARS-CoV-2 infection in the experimental setting. The high scientific rigor, originality, and, for some of them, the high number of citations obtained, are well evident

Rewiring strategies for changing environments

A typical pervasive application executes in a changing environment: people, computing resources, software services and network connections come and go continuously. A robust pervasive application needs adapt to this changing context as long as there is an appropriate rewiring strategy that guarantees correct behavior. We combine the MERODE modeling methodology with the ReWiRe framework for creating interactive pervasive applications that can cope with changing environments. The core of our approach is a consistent environment model, which is essential to create (re)configurable context-aware pervasive applications. We aggregate different ontologies that provide the required semantics to describe almost any target environment. We present a case study that shows a interactive pervasive application for media access that incorporates parental control on media content and can migrate between devices. The application builds upon models of the run-time environment represented as system states for dedicated rewiring strategies

mHealth Applications to Monitor Lifestyle Behaviors and Circadian Rhythm in Clinical Settings: Current Perspective and Future Directions

Metabolic diseases are a global rising health burden, mainly due to the deleterious interaction of current lifestyles with the underlying biology of these diseases. Daily habits and behaviors, such as diet, sleep, and physical exercise impact the whole-body circadian system through the synchronization of the peripheral body clocks that contribute to metabolic homeostasis. The disruption of this system may promote the development of metabolic diseases, including obesity and diabetes, emphasizing the importance of assessing and monitoring variables that affect circadian rhythms. Advances in technology are generating innovative resources and tools for health care management and patient monitoring, particularly important for chronic conditions. The use of mobile health technologies, known as mHealth, is increasing and these approaches are contributing to aiding both patients and healthcare professionals in disease management and education. The mHealth solutions allow continuous monitoring of patients, sharing relevant information and data with physicians and other healthcare professionals and accessing education resources to support informed decisions. Thus, if properly used, these tools empower patients and help them to adopt healthier lifestyles. This article aims to give an overview of the influence of circadian rhythms disruption and lifestyle habits in the progression of metabolic diseases while also reviewing some of the mobile applications available to monitor lifestyle behaviors and individual chronobiology. Herein is also described the design and development of the NutriClock system, an mHealth solution developed by our team to monitor these variables

Improving the Sustainability of Dairy Slurry by A Commercial Additive Treatment

Ammonia (NH3), methane (CH4), nitrous oxide (N2O), and carbon dioxide (CO2) emissions from livestock farms contribute to negative environmental impacts such as acidification and climate change. A significant part of these emissions is produced from the decomposition of slurry in livestock facilities, during storage and treatment phases. This research aimed at evaluating the eectiveness of the additive \u201cSOP LAGOON\u201d (made of agricultural gypsum processed with proprietary technology) on (i) NH3 and Greenhouse Gas (GHG) emissions, (ii) slurry properties and N loss. Moreover, the Life Cycle Assessment (LCA) method was applied to assess the potential environmental impact associated with stored slurry treated with the additive. Six barrels were filled with 65 L of cattle slurry, of which three were used as a control while the additive was used in the other three. The results indicated that the use of the additive led to a reduction of total nitrogen, nitrates, and GHG emissions. LCA confirmed the higher environmental sustainability of the scenario with the additive for some environmental impact categories among which climate change. In conclusion, the additive has beneficial eects on both emissions and the environment, and the nitrogen present in the treated slurry could partially displace a mineral fertilizer, which can be considered an environmental credit

Geographical information retrieval with ontologies of place

Geographical context is required of many information retrieval tasks in which the target of the search may be documents, images or records which are referenced to geographical space only by means of place names. Often there may be an imprecise match between the query name and the names associated with candidate sources of information. There is a need therefore for geographical information retrieval facilities that can rank the relevance of candidate information with respect to geographical closeness of place as well as semantic closeness with respect to the information of interest. Here we present an ontology of place that combines limited coordinate data with semantic and qualitative spatial relationships between places. This parsimonious model of geographical place supports maintenance of knowledge of place names that relate to extensive regions of the Earth at multiple levels of granularity. The ontology has been implemented with a semantic modelling system linking non-spatial conceptual hierarchies with the place ontology. An hierarchical spatial distance measure is combined with Euclidean distance between place centroids to create a hybrid spatial distance measure. This is integrated with thematic distance, based on classification semantics, to create an integrated semantic closeness measure that can be used for a relevance ranking of retrieved objects