Validation of ESDS Using Epidemic-Based Data Dissemination Algorithms

The study of Distributed Systems (DS) is important as novel solutions in this area impact many sub-fields of Computer Science. Although, studying DS is not an easy task. A common approach is to deploy a test-bed to perform a precise evaluation of the system. This can be costly and time consuming for large scale platforms. Another solution is to perform network simulations, allowing for more flexibility and simplicity. Simulators implement various models such as wired/wireless network models and power consumption models. Extensible Simulator for Distributed Systems (ESDS) is a simulator designed for simulation of systems that include edge platforms, namely Internet of Things (IoT), Wireless Sensor Networks (WSN) and Cyber-Physical Systems (CPS). ESDS uses coarse-grained (flow-level) models for wired and wireless networks, and provides nodes power consumption models. However, to ensure accurate predictions, these models must be validated. In this paper, we propose to validate the flow-level wire-less model and the power consumption model of ESDS using epidemic-based data dissemination simulations. We show that ESDS has similar predictions than another validated flow-level network simulator, in terms of network performance and energy consumption

Experimental investigation into the influence of roughness on friction and film thickness in EHD contacts

The roughness of machined surfaces such as those in bearings and gears is characterized by asperities which cover a wide spectrum of wavelengths and heights. When the height of the asperities becomes comparable to the lubricant film thickness, the roughness is known to influence friction and wear behaviour. This thesis reports an experimental investigation into the effect of roughness on the film thickness and friction in EHD contact. This work focuses on the particular roughness case of ridges oriented parallel to the rolling-sliding direction, such as the ones produced on raceways during the manufacturing process of rolling element bearings. To carry-out this research a ball-on disk test rig was used to model the contact between the ball and the ring of a ball bearing. The disks were made of glass to enable the lubricant film thickness to be measured through an optical technique based on optical interferometry. The ball specimens were made of AISI 52100 steel and they were roughened with a cutting tool, resulting in longitudinally oriented roughness ridges showing a dominant wavelength and amplitude. The friction was measured through a torque meter attached to the ball shaft. A duo-chromatic system using two LEDs was developed in order to be able to measure the film thickness over a wider range of film thickness. A novel procedure was also introduced to enable the film thickness in rough EHD contacts to be measured accurately. The film thickness and the friction produced by specimens with various amplitude and wavelength were measured under pure rolling and rolling-sliding conditions. The roughness was found to have a big influence on both film build-up and friction. In particular, under the same operating condition, a rough specimen will generate a thinner minimum film and a higher friction compared to a smooth specimen. At the top of the asperities, a micro-EHD film was found to form. As suggested by friction and pressure measurements, the conditions in this micro film are severe enough to reach the limiting shear stress of the lubricant.Open Acces

Towards Data Dissemination Policy Prediction for Constrained Environments Using Analytics

In Cyber-Physical Systems (CPS) such as Wireless Sensors Networks (WSN), disseminating data is crucial. Under energy constraints with limited communications capabilities, performing data dissemination is challenging. In such contexts, common data dissemination methods cannot be used. Nodes must rely on device-to-device communications policies to mitigate the impact of communications on the nodes energy consumption. However, depending on nodes configuration (up-times duration, wireless technology capabilities and energy consumption), choosing a suitable communication policy is challenging. This work exposes the problem statement for using analytic algorithms to predict the most suitable device-to-device communication policy, for a given node configuration, to match a given coverage and energy consumption target in a constrained environment

Integrated molecular portrait of non-small cell lung cancers.

BACKGROUND: Non-small cell lung cancer (NSCLC), a leading cause of cancer deaths, represents a heterogeneous group of neoplasms, mostly comprising squamous cell carcinoma (SCC), adenocarcinoma (AC) and large-cell carcinoma (LCC). The objectives of this study were to utilize integrated genomic data including copy-number alteration, mRNA, microRNA expression and candidate-gene full sequencing data to characterize the molecular distinctions between AC and SCC. METHODS: Comparative genomic hybridization followed by mutational analysis, gene expression and miRNA microarray profiling were performed on 123 paired tumor and non-tumor tissue samples from patients with NSCLC. RESULTS: At DNA, mRNA and miRNA levels we could identify molecular markers that discriminated significantly between the various histopathological entities of NSCLC. We identified 34 genomic clusters using aCGH data; several genes exhibited a different profile of aberrations between AC and SCC, including PIK3CA, SOX2, THPO, TP63, PDGFB genes. Gene expression profiling analysis identified SPP1, CTHRC1and GREM1 as potential biomarkers for early diagnosis of the cancer, and SPINK1 and BMP7 to distinguish between AC and SCC in small biopsies or in blood samples. Using integrated genomics approach we found in recurrently altered regions a list of three potential driver genes, MRPS22, NDRG1 and RNF7, which were consistently over-expressed in amplified regions, had wide-spread correlation with an average of ~800 genes throughout the genome and highly associated with histological types. Using a network enrichment analysis, the targets of these potential drivers were seen to be involved in DNA replication, cell cycle, mismatch repair, p53 signalling pathway and other lung cancer related signalling pathways, and many immunological pathways. Furthermore, we also identified one potential driver miRNA hsa-miR-944. CONCLUSIONS: Integrated molecular characterization of AC and SCC helped identify clinically relevant markers and potential drivers, which are recurrent and stable changes at DNA level that have functional implications at RNA level and have strong association with histological subtypes