Resistenza a fatica di strutture in leghe di alluminio: normative a confronto e verifica sperimentale

Le problematiche relative all’utilizzo delle normative nell’ambito della progettazione a fatica di strutture in leghe di alluminio sono state di recente affrontate in numerosi lavori che hanno messo in luce sia le difficoltà legate al passaggio da normative oramai obsolete ad altre di nuova concezione, ma di caratteristiche e struttura completamente diverse, che la sostanziale carenza anche all’interno delle più recenti normative europee, quale l’Eurocodice 9, di molti risultati e metodi sviluppati in anni di ricerca scientifica e ormai indiscutibilmente consolidati. Il presente lavoro si propone di approfondire entrambe le tematiche tramite il confronto tra risultati sperimentali tratti da letteratura e le corrispondenti curve di resistenza proposte rispettivamente dalla normativa italiana UNI 8634, di recente ritirata, e dall’Eurocodice 9. In questo modo verranno messe in luce le differenze tra i valori di resistenza proposti dalle due norme e verrà illustrata sia la corrispondenza a volte poco soddisfacente con i risultati sperimentali che le conseguenze dovute alla mancata applicazione di assodati risultati teorici

Analysis of the fatigue strength under two load levels of a stainless steel based on energy dissipation

In this paper the fatigue behaviour of a stainless steel AISI 304L is analysed. In the first part of the work the results obtained under constant amplitude fatigue are presented and synthesised in terms of both stress amplitude and energy released to the surroundings as heat by a unit volume of material per cycle, Q. Then some specimens have been fatigued in variable amplitude, two different load level tests: the first level was set higher while the second was lower than the constant amplitude fatigue limit. The Q values, evaluated during the second part of the fatigue test, have been compared with those calculated under constant amplitude fatigue at the same load level. The comparison allowed us to notice that the Q parameter is sensitive to the fatigue damage accumulated by the material during the first part of the fatigue test

Enabling High-Level Application Development in the Internet of Things

International audienceThe sensor networking field is evolving into the Internet of Things~(IoT), owing in large part to the increased availability of consumer sensing devices, including modern smart phones. However, application development in the IoT still remains challenging, since it involves dealing with several related issues, such as lack of proper identification of roles of various stakeholders, as well as lack of suitable (high-level) abstractions to address the large scale and heterogeneity in IoT systems. Although the software engineering community has proposed several approaches to address the above in the general case, existing approaches for IoT application development only cover limited subsets of above mentioned challenges. In this paper, we propose a multi-stage model-driven approach for IoT application development based on a precise definition of the role to be played by each stakeholder involved in the process -- domain expert, application designer, application developer, device developer, and network manager. The abstractions provided to each stakeholder are further customized using the inputs provided in the earlier stages by other stakeholders. We have also implemented code-generation and task-mapping techniques to support our approach. Our initial evaluation based on two realistic scenarios shows that the use of our techniques/framework succeeds in improving productivity in the IoT application development process

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c

Neuroinflammation and structural injury of the fetal ovine brain following intra-amniotic Candida albicans exposure.

BackgroundIntra-amniotic Candida albicans (C. Albicans) infection is associated with preterm birth and high morbidity and mortality rates. Survivors are prone to adverse neurodevelopmental outcomes. The mechanisms leading to these adverse neonatal brain outcomes remain largely unknown. To better understand the mechanisms underlying C. albicans-induced fetal brain injury, we studied immunological responses and structural changes of the fetal brain in a well-established translational ovine model of intra-amniotic C. albicans infection. In addition, we tested whether these potential adverse outcomes of the fetal brain were improved in utero by antifungal treatment with fluconazole.MethodsPregnant ewes received an intra-amniotic injection of 10(7) colony-forming units C. albicans or saline (controls) at 3 or 5 days before preterm delivery at 0.8 of gestation (term ~ 150 days). Fetal intra-amniotic/intra-peritoneal injections of fluconazole or saline (controls) were administered 2 days after C. albicans exposure. Post mortem analyses for fungal burden, peripheral immune activation, neuroinflammation, and white matter/neuronal injury were performed to determine the effects of intra-amniotic C. albicans and fluconazole treatment.ResultsIntra-amniotic exposure to C. albicans caused a severe systemic inflammatory response, illustrated by a robust increase of plasma interleukin-6 concentrations. Cerebrospinal fluid cultures were positive for C. albicans in the majority of the 3-day C. albicans-exposed animals whereas no positive cultures were present in the 5-day C. albicans-exposed and fluconazole-treated animals. Although C. albicans was not detected in the brain parenchyma, a neuroinflammatory response in the hippocampus and white matter was seen which was characterized by increased microglial and astrocyte activation. These neuroinflammatory changes were accompanied by structural white matter injury. Intra-amniotic fluconazole reduced fetal mortality but did not attenuate neuroinflammation and white matter injury.ConclusionsIntra-amniotic C. albicans exposure provoked acute systemic and neuroinflammatory responses with concomitant white matter injury. Fluconazole treatment prevented systemic inflammation without attenuating cerebral inflammation and injury

Internet of things

Manual of Digital Earth / Editors: Huadong Guo, Michael F. Goodchild, Alessandro Annoni .- Springer, 2020 .- ISBN: 978-981-32-9915-3Digital Earth was born with the aim of replicating the real world within the digital world. Many efforts have been made to observe and sense the Earth, both from space (remote sensing) and by using in situ sensors. Focusing on the latter, advances in Digital Earth have established vital bridges to exploit these sensors and their networks by taking location as a key element. The current era of connectivity envisions that everything is connected to everything. The concept of the Internet of Things(IoT)emergedasaholisticproposaltoenableanecosystemofvaried,heterogeneous networked objects and devices to speak to and interact with each other. To make the IoT ecosystem a reality, it is necessary to understand the electronic components, communication protocols, real-time analysis techniques, and the location of the objects and devices. The IoT ecosystem and the Digital Earth (DE) jointly form interrelated infrastructures for addressing today’s pressing issues and complex challenges. In this chapter, we explore the synergies and frictions in establishing an efficient and permanent collaboration between the two infrastructures, in order to adequately address multidisciplinary and increasingly complex real-world problems. Although there are still some pending issues, the identified synergies generate optimism for a true collaboration between the Internet of Things and the Digital Earth

Towards trajectory anonymization: A generalization-based approach

Trajectory datasets are becoming,popular,due,to the massive,usage,of GPS and,location- based services. In this paper, we address privacy issues regarding the identification of individuals in static trajectory datasets. We first adopt the notion of k-anonymity,to trajectories and propose,a novel generalization-based approach,for anonymization,of trajectories. We further show,that releasing anonymized,trajectories may,still have,some,privacy,leaks. Therefore we propose,a randomization based,reconstruction,algorithm,for releasing anonymized,trajectory data and,also present how,the underlying,techniques,can be adapted,to other anonymity,standards. The experimental,results on real and,synthetic trajectory datasets show,the effectiveness of the proposed,techniques

The potential of metabolomics in the diagnosis of thyroid cancer

Thyroid cancer is the most common endocrine system malignancy. However, there is still a lack of reliable and specific markers for the detection and staging of this disease. Fine needle aspiration biopsy is the current gold standard for diagnosis of thyroid cancer, but drawbacks to this technique include indeterminate results or an inability to discriminate different carcinomas, thereby requiring additional surgical procedures to obtain a final diagnosis. It is, therefore, necessary to seek more reliable markers to complement and improve current methods. “Omics” approaches have gained much attention in the last decade in the field of biomarker discovery for diagnostic and prognostic characterisation of various pathophysiological conditions. Metabolomics, in particular, has the potential to identify molecular markers of thyroid cancer and identify novel metabolic profiles of the disease, which can, in turn, help in the classification of pathological conditions and lead to a more personalised therapy, assisting in the diagnosis and in the prediction of cancer behaviour. This review considers the current results in thyroid cancer biomarker research with a focus on metabolomics