A novel internet-of-things infrastructure to support self-healing distribution systems

© 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 software infrastructure to foster new services in smart grids with particular emphasis on supporting self-healing distribution systems. This infrastructure exploits the rising Internet-of-Things paradigms to build and manage an interoperable peer-to-peer network of our prototype smart meters, also presented in this paper. The proposed three-phase smart meter, called 3-SMA, is a low cost and open-source Internet-connected device that provides features for self-configuration. In addition, it selectively run on-board-algorithms for smart grid management depending on its deployment on the distribution network. Finally, we present the experimental results of Hardware-In-the-Loop simulations we performed

Halo Clustering with Non-Local Non-Gaussianity

We show how the peak-background split can be generalized to predict the effect of non-local primordial non-Gaussianity on the clustering of halos. Our approach is applicable to arbitrary primordial bispectra. We show that the scale-dependence of halo clustering predicted in the peak-background split (PBS) agrees with that of the local-biasing model on large scales. On smaller scales, k >~ 0.01 h/Mpc, the predictions diverge, a consequence of the assumption of separation of scales in the peak-background split. Even on large scales, PBS and local biasing do not generally agree on the amplitude of the effect outside of the high-peak limit. The scale dependence of the biasing - the effect that provides strong constraints to the local-model bispectrum - is far weaker for the equilateral and self-ordering-scalar-field models of non-Gaussianity. The bias scale dependence for the orthogonal and folded models is weaker than in the local model (~ 1/k), but likely still strong enough to be constraining. We show that departures from scale-invariance of the primordial power spectrum may lead to order-unity corrections, relative to predictions made assuming scale-invariance - to the non-Gaussian bias in some of these non-local models for non-Gaussianity. An Appendix shows that a non-local model can produce the local-model bispectrum, a mathematical curiosity we uncovered in the course of this investigation.Comment: 12 pages, 4 figures; submitted to Phys. Rev. D; v2: references added; v3: some more comments on kernel-bispectrum relation in appendi

An online grey-box model based on unscented kalman filter to predict temperature profiles in smart buildings

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions

Adhesive restoration of endodontically treated premolars: influence of posts on cuspal deflection

To determine, by means of a non-destructive experimental procedure, the effectiveness of adhesive restorations in reducing the cuspal deflection of endodontically treated premolars, with or without root canal fiber posts.To determine, by means of a non-destructive experimental procedure, the effectiveness of adhesive restorations in reducing the cuspal deflection of endodontically treated premolars, with or without root canal fiber posts. MATERIALS AND METHODS: The cuspal deflection of ten sound, intact maxillary premolars was evaluated. A loading device induced deformation by axial force (ranging from 98 to 294 N) applied on the occlusal surface of teeth while laser sensors registered the amount of deflection. Once tested, teeth were endodontically treated and the marginal ridges were removed. The teeth were randomly divided into two groups and restored with: group 1) dual curing adhesive, flowable composite, and microhybrid composite; group 2) the same materials associated with root canal glass fiber post and composite cement. The cuspal deflection test was repeated with the same protocol after restorative procedures, allowing a direct comparison of the same samples. Statistical analysis was performed using ANOVA at a significance level of 0.05. RESULTS: Different average cuspal deflection was detected in the two groups: composite resin with post insertion resulted in lower deformation compared with composite alone. Mean deflection ranged from 3.43 to 12.17 μm in intact teeth, from 14.42 to 26.93 μm in group 1, and from 15.35 to 20.39 μm in group 2. ANOVA found significant differences (p = 0.02). CONCLUSION: Bonded composite restorations with fiber posts may be more effective than composite alone in reducing the cuspal deflection in endodontically treated premolars in which the marginal ridges have been lost

Observational signatures of Jordan-Brans-Dicke theories of gravity

We analyze the Jordan-Brans-Dicke model (JBD) of gravity, where deviations from General Relativity (GR) are described by a scalar field non-minimally coupled to gravity. The theory is characterized by a constant coupling parameter, $\omega_{\rm JBD}$; GR is recovered in the limit $\omega_{\rm JBD} \to \infty$. In such theories, gravity modifications manifest at early times, so that one cannot rely on the usual approach of looking for inconsistencies in the expansion history and perturbations growth in order to discriminate between JBD and GR. However, we show that a similar technique can be successfully applied to early and late times observables instead. Cosmological parameters inferred extrapolating early-time observations to the present will match those recovered from direct late-time observations only if the correct gravity theory is used. We use the primary CMB, as will be seen by the Planck satellite, as the early-time observable; and forthcoming and planned Supernov{\ae}, Baryonic Acoustic Oscillations and Weak Lensing experiments as late-time observables. We find that detection of values of $\omega_{\rm JBD}$ as large as 500 and 1000 is within reach of the upcoming (2010) and next-generation (2020) experiments, respectively.Comment: minor revision, references added, matching version published in JCA

A comparison analysis of ble-based algorithms for localization in industrial environments

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters

Role of microRNAs in the main molecular pathways of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common primary liver malignant neoplasia. HCC is characterized by a poor prognosis. The need to find new molecular markers for its diagnosis and prognosis has led to a progressive increase in the number of scientific studies on this topic. MicroRNAs (miRNAs) are small noncoding RNA that play a role in almost all main cellular pathways. miRNAs are involved in the regulation of expression of the major tumor-related genes in carcinogenesis, acting as oncogenes or tumor suppressor genes. The aim of this review was to identify papers published in 2017 investigating the role of miRNAs in HCC tumorigenesis. miRNAs were classified according to their role in the main molecular pathways involved in HCC tumorigenesis: (1) mTOR; (2) Wnt; (3) JAK/STAT; (4) apoptosis; and (5) MAPK. The role of miRNAs in prognosis/response prediction was taken into consideration. Bearing in mind that the analysis of miRNAs in serum and other body fluids would be crucial for clinical management, the role of circulating miRNAs in HCC patients was also investigated. The most represented miRNA-regulated pathway in HCC is mTOR, but apoptosis, Wnt, JAK/STAT or MAPK pathways are also influenced by miRNA expression levels. These miRNAs could thus be used in clinical practice as diagnostic, prognostic or therapeutic targets for HCC treatment

A confirmation of agreement of different approaches for scalar gauge-invariant metric perturbations during inflation

We revisit an extension of the well-known formalism for gauge-invariant scalar metric fluctuations, to study the spectrums for both, the inflaton and gauge invariant (scalar) metric fluctuations in the framework of a single field inflationary model where the quasi-exponential expansion is driven by an inflation which is minimally coupled to gravity. The proposal here examined is valid also for fluctuations with large amplitude, but for cosmological scales, where vector and tensor perturbations can be neglected and the fluid is irrotacional.Comment: Version accepted in EPJC with new title. 11 pages, no figure

A novel framework for chimeric transcript detection based on accurate gene fusion model

Next generation sequencing plays a key role in the detection of structural variations. Chimeric transcripts are relevant examples of such variations, as they are involved in several diseases. In this work, we propose an effective methodology for the detection of fused transcripts in RNA-Seq paired-end data. The proposed methodology is based on an accurate fusion model implemented by a set of filters reducing the impact of artifacts. Moreover, the methodology accounts for transcripts consistently expressing in the sample under study even if they are not annotated. The effectiveness of the proposed solution has been experimentally validated on of Chronic Myelogenous Leukemia (CML) samples, providing both the genes involved in the fusion and the exact chimeric sequence. \ua9 2011 IEEE