Les corps comme preuve. M\ue9decins et inquisiteurs dans les pratiques judiciaires du Saint-Office

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A cloud-based smart metering infrastructure for distribution grid services and automation

© 2017 The Authors The evolution of the power systems towards the smart grid paradigm is strictly dependent on the modernization of distribution grids. To achieve this target, new infrastructures, technologies and applications are increasingly required. This paper presents a smart metering infrastructure that unlocks a large set of possible services aimed at the automation and management of distribution grids. The proposed architecture is based on a cloud solution, which allows the communication with the smart meters from one side and provides the needed interfaces to the distribution grid services on the other one. While a large number of applications can be designed on top of the cloud, in this paper the focus will be on a real-time distributed state estimation algorithm that enables the automatic reconfiguration of the grid. The paper will present the key role of the cloud solution for obtaining scalability, interoperability and flexibility, and for enabling the integration of different services for the automation of the distribution system. The distributed state estimation algorithm and the automatic network reconfiguration will be presented as an example of coordinated operation of different distribution grid services through the cloud

A Distributed IoT Infrastructure to Test and Deploy Real-Time Demand Response in Smart Grids

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy

Design and Accuracy Analysis of Multilevel State Estimation Based on Smart Metering Infrastructure

© 1963-2012 IEEE. While the initial aim of smart meters is to provide energy readings for billing purposes, the availability of these measurements could open new opportunities for the management of future distribution grids. This paper presents a multilevel state estimator that exploits the smart meter measurements for monitoring both low and medium voltage grids. The goal of this paper is to present an architecture that is able to efficiently integrate smart meter measurements and to show the accuracy performance achievable if the use of real-Time smart meter measurements for state estimation purposes was enabled. The design of the state estimator applies the uncertainty propagation theory for the integration of the data at different hierarchical levels. The coordination of the estimation levels is realized through a cloud-based infrastructure, which also provides the interface to auxiliary functions and the access to the estimation results for other distribution grid management applications. A mathematical analysis is performed to characterize the estimation algorithm in terms of accuracy and to show the performance achievable at different levels of the distribution grid when using the smart meter data. Simulations are presented, which validate the analytical results and demonstrate the operation of the multilevel estimator in coordination with the cloud-based platform

Low voltage system state estimation based on smart metering infrastructure

© 2016 IEEE. The accurate monitoring of distribution grids is essential to enable the intelligent management and control of future Smart Grids. Several challenges prevent an easy development of the state estimation tools needed to assess the operating conditions of distribution networks. The lack of a suitable measurement infrastructure is one of the most challenging aspects to face. However, in last years, several utilities started a massive deployment of smart meters in their networks. The proper use of these measurements is key to enhance the performance of distribution system state estimators. This paper presents a two-level approach conceived to efficiently include smart meter measurements in low voltage grid state estimation. The proposed solution relies on a cloud-based smart metering architecture, which allows scalability and interoperability among different off-the-shelf meters. Moreover, a suitable design of the estimation algorithm, using the uncertainty propagation theory, is proposed in order to maximize the accuracy of the estimation results. Tests performed on a sample low voltage network show the performance and the main features of the proposed state estimation solution

Expression and Differential Responsiveness of Central Nervous System Glial Cell Populations to the Acute Phase Protein Serum Amyloid A

Acute-phase response is a systemic reaction to environmental/inflammatory insults and involves hepatic production of acute-phase proteins, including serum amyloid A (SAA). Extrahepatically, SAA immunoreactivity is found in axonal myelin sheaths of cortex in Alzheimer's disease and multiple sclerosis (MS), although its cellular origin is unclear. We examined the responses of cultured rat cortical astrocytes, microglia and oligodendrocyte precursor cells (OPCs) to master pro-inflammatory cytokine tumour necrosis factor (TNF)-\u3b1 and lipopolysaccaride (LPS). TNF-\u3b1 time-dependently increased Saa1 (but not Saa3) mRNA expression in purified microglia, enriched astrocytes, and OPCs (as did LPS for microglia and astrocytes). Astrocytes depleted of microglia were markedly less responsive to TNF-\u3b1 and LPS, even after re-addition of microglia. Microglia and enriched astrocytes showed complementary Saa1 expression profiles following TNF-\u3b1 or LPS challenge, being higher in microglia with TNF-\u3b1 and higher in astrocytes with LPS. Recombinant human apo-SAA stimulated production of both inflammatory mediators and its own mRNA in microglia and enriched, but not microglia-depleted astrocytes. Co-ultramicronized palmitoylethanolamide/luteolin, an established anti-inflammatory/neuroprotective agent, reduced Saa1 expression in OPCs subjected to TNF-\u3b1 treatment. These last data, together with past findings suggest that co-ultramicronized palmitoylethanolamide/luteolin may be a novel approach in the treatment of inflammatory demyelinating disorders like MS

In Vitro and In Vivo Human Herpesvirus 8 Infection of Placenta

Herpesvirus infection of placenta may be harmful in pregnancy leading to disorders in fetal growth, premature delivery, miscarriage, or major congenital abnormalities. Although a correlation between human herpesvirus 8 (HHV-8) infection and abortion or low birth weight in children has been suggested, and rare cases of in utero or perinatal HHV-8 transmission have been documented, no direct evidence of HHV-8 infection of placenta has yet been reported. The aim of this study was to evaluate the in vitro and in vivo susceptibility of placental cells to HHV-8 infection. Short-term infection assays were performed on placental chorionic villi isolated from term placentae. Qualitative and quantitative HHV-8 detection were performed by PCR and real-time PCR, and HHV-8 proteins were analyzed by immunohistochemistry. Term placenta samples from HHV-8-seropositive women were analyzed for the presence of HHV-8 DNA and antigens. In vitro infected histocultures showed increasing amounts of HHV-8 DNA in tissues and supernatants; cyto- and syncitiotrophoblasts, as well as endothelial cells, expressed latent and lytic viral antigens. Increased apoptotic phenomena were visualized by the terminal deoxynucleotidyl transferase-mediated deoxyuridine nick end-labeling method in infected histocultures. Ex vivo, HHV-8 DNA and a latent viral antigen were detected in placenta samples from HHV-8-seropositive women. These findings demonstrate that HHV-8, like other human herpesviruses, may infect placental cells in vitro and in vivo, thus providing evidence that this phenomenon might influence vertical transmission and pregnancy outcome in HHV-8-infected women

Applying Reversibility Theory for the Performance Evaluation of Reversible Computations

Reversible computations have been widely studied from the functional point of view and energy consumption. In the literature, several authors have proposed various formalisms (mainly based on process algebras) for assessing the correctness or the equivalence among reversible computations. In this paper we propose the adoption of Markovian stochastic models to assess the quantitative properties of reversible computations. Under some conditions, we show that the notion of time-reversibility for Markov chains can be used to efficiently derive some performance measures of reversible computations. The importance of time-reversibly relies on the fact that, in general, the process’s stationary distribution can be derived efficiently by using numerically stable algorithms. This paper reviews the main results about time-reversible Markov processes and discusses how to apply them to tackle the problem of the quantitative evaluation of reversible computationsReversible computations have been widely studied from the functional point of view and energy consumption. In the literature, several authors have proposed various formalisms (mainly based on process algebras) for assessing the correctness or the equivalence among reversible computations. In this paper we propose the adoption of Markovian stochastic models to assess the quantitative properties of reversible computations. Under some conditions, we show that the notion of time-reversibility for Markov chains can be used to efficiently derive some performance measures of reversible computations. The importance of time-reversibly relies on the fact that, in general, the process's stationary distribution can be derived efficiently by using numerically stable algorithms. This paper reviews the main results about time-reversible Markov processes and discusses how to apply them to tackle the problem of the quantitative evaluation of reversible computations

Ligand engagement of Toll-like receptors regulates their expression in cortical microglia and astrocytes

BACKGROUND: Toll-like receptor (TLR) activation on microglia and astrocytes are key elements in neuroinflammation which accompanies a number of neurological disorders. While TLR activation on glia is well-established to up-regulate pro-inflammatory mediator expression, much less is known about how ligand engagement of one TLR may affect expression of other TLRs on microglia and astrocytes. METHODS: In the present study, we evaluated the effects of agonists for TLR2 (zymosan), TLR3 (polyinosinic-polycytidylic acid (poly(I:C)), a synthetic analogue of double-stranded RNA) and TLR4 (lipopolysaccaride (LPS)) in influencing expression of their cognate receptor as well as that of the other TLRs in cultures of rat cortical purified microglia (>99.5 %) and nominally microglia-free astrocytes. Elimination of residual microglia (a common contaminant of astrocyte cultures) was achieved by incubation with the lysosomotropic agent L-leucyl-L-leucine methyl ester (L-LME). RESULTS: Flow cytometric analysis confirmed the purity (essentially 100 %) of the obtained microglia, and up to 5 % microglia contamination of astrocytes. L-LME treatment effectively removed microglia from the latter (real-time polymerase chain reaction). The three TLR ligands robustly up-regulated gene expression for pro-inflammatory markers (interleukin-1 and interleukin-6, tumor necrosis factor) in microglia and enriched, but not purified, astrocytes, confirming cellular functionality. LPS, zymosan and poly(I:C) all down-regulated TLR4 messenger RNA (mRNA) and up-regulated TLR2 mRNA at 6 and 24 h. In spite of their inability to elaborate pro-inflammatory mediator output, the nominally microglia-free astrocytes (>99 % purity) also showed similar behaviours to those of microglia, as well as changes in TLR3 gene expression. LPS interaction with TLR4 activates downstream mitogen-activated protein kinase and nuclear factor-κB signalling pathways and subsequently causes inflammatory mediator production. The effects of LPS on TLR2 mRNA in both cell populations were antagonized by a nuclear factor-κB inhibitor. CONCLUSIONS: TLR2 and TLR4 activation in particular, in concert with microglia and astrocytes, comprise key elements in the initiation and maintenance of neuropathic pain. The finding that both homologous (zymosan) and heterologous (LPS, poly(I:C)) TLR ligands are capable of regulating TLR2 gene expression, in particular, may have important implications in understanding the relative contributions of different TLRs in neurological disorders associated with neuroinflammation