Behaviour of piles driven in chalk

Driving resistance is difficult to predict in chalk strata, with both pile free-fall self-weight ‘runs’ and refusals being reported. Axial capacity is also highly uncertain after driving. This paper reviews recent research that has explored these topics. Programmes of onshore tests and novel, high-value offshore, experiments involving static, dynamic and cyclic loading are described. The key findings form the basis of the Chalk ICP-18 approach for estimating the driving resistance and axial capacity of piles driven in low-to medium-density chalk. The new approach is presented and the highly significant impact of set-up after driving is emphasised. It is shown that Chalk ICP-18 overcomes the main limitations of the industry’s current design guidelines by addressing the underlying physical mechanisms. While further tests are required to enlarge the available test database, the new approach is able to provide better predictions for tests available from suitably characterised sites. A new Joint Industry Project is outlined that extends the research to cover further axial, lateral, static and cyclic loading cases

Performance and precision of double digestion RAD (ddRAD) genotyping in large multiplexed datasets of marine fish species

The development of Genotyping-By-Sequencing (GBS) technologies enables cost-effective analysis of large numbers of Single Nucleotide Polymorphisms (SNPs), especially in “non-model” species. Nevertheless, as such technologies enter a mature phase, biases and errors inherent to GBS are becoming evident. Here, we evaluated the performance of double digest Restriction enzyme Associated DNA (ddRAD) sequencing in SNP genotyping studies including high number of samples. Datasets of sequence data were generated from three marine teleost species (>5500 samples, >2.5 × 1012 bases in total), using a standardized protocol. A common bioinformatics pipeline based on STACKS was established, with and without the use of a reference genome. We performed analyses throughout the production and analysis of ddRAD data in order to explore (i) the loss of information due to heterogeneous raw read number across samples; (ii) the discrepancy between expected and observed tag length and coverage; (iii) the performances of reference based vs. de novo approaches; (iv) the sources of potential genotyping errors of the library preparation/bioinformatics protocol, by comparing technical replicates. Our results showed use of a reference genome and a posteriori genotype correction improved genotyping precision. Individual read coverage was a key variable for reproducibility; variance in sequencing depth between loci in the same individual was also identified as an important factor and found to correlate to tag length. A comparison of downstream analysis carried out with ddRAD vs single SNP allele specific assay genotypes provided information about the levels of genotyping imprecision that can have a significant impact on allele frequency estimations and population assignment. The results and insights presented here will help to select and improve approaches to the analysis of large datasets based on RAD-like methodologies

Self-Diagnosis technique for Virtual Private Networks combining Bayesian Networks and Case-Based Reasoning

International audienceFault diagnosis is a critical task for operators in the context of e-TOM (enhanced Telecom Operations Map) assurance process. Its purpose is to reduce network maintenance costs and to improve availability, reliability and performance of network services. Although necessary, this operation is complex and requires significant involvement of human expertise. The study of the fundamental properties of fault diagnosis shows that the diagnosis process complexity needs to be addressed using more intelligent and efficient approaches. In this paper, we present a hybrid approach that combines Bayesian networks and case-based reasoning in order to overcome the usual limits of fault diagnosis techniques and to reduce human intervention in this process. The proposed mechanism allows the identification of the root cause with a finer precision and a higher reliability. At the same time, it helps to reduce computation time while taking into account the network dynamicity. Furthermore, a study case is presented to show the feasibility and performance of the proposed approach based on a real-world use case: a virtual private network topology

Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning

International audienceFault diagnosis is one of the most important tasks in fault management. The main objective of the fault management system is to detect and localize failures as soon as they occur to minimize their effects on the network performance and therefore on the service quality perceived by users. In this paper, we present a new hybrid approach that combines Bayesian Networks and Case-Based Reasoning to overcome the usual limits of fault diagnosis techniques and reduce human intervention in this process. The proposed mechanism allows identifying the root cause failure with a finer precision and high reliability while reducing the process computation time and taking into account the network dynamicity

Optimization of fault diagnosis based on the combination of Bayesian Networks and case Based Reasoning",

International audienc

"Scalable and Fast Root Cause Analysis Using Inter Cluster Inference"

International audienc

Scalable and Fast Root Cause Analysis Using Inter Cluster Inference

International audienceThe capability to diagnose the root cause of an observed problem precisely and quickly is a desirable feature for large communication networks. However, the design of a technique that is at the same time fast, scalable and accurate is a challenging task. In this paper, we propose a novel method based on inter-cluster inference to overcome the usual limits of fault diagnosis techniques. The approach is based on two important concepts: a cluster decomposition of the dependency graph in order to ensure scalability, and the introduction of duplicated nodes aiming at preserving the end-to-end network view. The evaluation of the proposed approach has demonstrated a significant reduction in the complexity and the computation time of the root cause analysis, since it is based on a set of small-scale dependency graphs

SPECT analysis of a new implant surface: a human preliminary report

Aim: Several recent medical reports have focused attention on the possible application of skeletal scintigraphy imaging in odontostomatology. The aim of the present report was to assess the influence of a new implant surface on peri-implant osteoblastic activity through bone scintigraphy. Methods: Implants were placed in one healthy subject. A nuclear medicine investigation with single-photon emission-computed tomography (SPECT) was performed at 30 and 90 days after implant placement. The study was completed with acquisition of planar images of the skull in an anterior view and the use of regions of interest (ROIs) of the same size in the area corresponding to new surfaces implants and in the opposite maxilla (at the control sites). Count density ratios (counts/pixel) obtained from each ROI were used for a quantitative/relative assessment. Tomographic images were evaluated with a qualitative method. Results: Routine planar methodology provided a direct measure of cellular activity of the examined areas. The difference in count density ratio registered from the same ROI between the first and the second scintigraphy revealed the course of peri-implant osteoblastic activity, which was very high in the first month and then declined during subsequent months. Conclusion: In spite of the small number of involved patients, the results obtained from this pilot study suggest that nuclear medicine investigation held advantages in oral implantology to clarify those aspects still unknown dealing with osteoblastic activity

Coordination of self-organizing network mechanisms: Framework and enablers

Future wireless access networks, e.g. LTE and LTE-Advanced, will be empowered by Self-Organizing Network (SON) mechanisms with the objective to increase performance, reduce the cost of operations, and simplify the network management. This article describes a management framework which enables the automatic, policy-driven coordination of SON control functions, and introduces future necessary evolutions that will allow to fully benefiting from the SON paradigm in operational networks. © 2012 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering