Thermal Behaviour and Fatigue Estimation of the Switching Regulator for the BTF Power Supply of DAFNE Injector

The BTF power supply is used to extract from the beam transfer line the particles pulses coming from the LINAC which normally fill the DAFNE collider at Frascati INFN Lab. This new power converter has been designed to feed a bending magnet with 447 A at nominal conditions and with a total ramp up and down of 20 ms. This power converter can run either in pulsed mode or in a conventional DC current mode. In pulsed mode the flat top can be streched from 5  ms up to 960 ms allowing a multi-pulse extraction. The heart of the system is based on a static converter with an H bridge topology made of IGBTs with integrated free wheel diodes. On one hand this paper presents the analysis of the thermal behaviour, the fatigue estimation and the impact on the power converter life time. On the other hand the design of a forced air cooling system is presented with the final results

The BTF (Beam Transfer Facility) DC/Pulsed 50 kW Power Supply for DAFNE injector

CERN has designed and built a special power supply for the INFN National laboratory of Frascati. This power supply is used to deflect the particle pulses coming from the Linac, which would normally go and fill the DAFNE collider, from the beam transfer line. The Linac repetition time is 20 ms, thus the power supply feeding the switchyard magnet must be able to ramp up and down in less than 20 ms and have a minimum ON flat top of 5 ms. In pulsed mode the flat top can be stretched to 0.96 s to allow a multi-pulse extraction. In addition this power supply must be able to operate as a conventional DC current generator. Finally as the Linac can produce both electrons and positrons it must be possible to reverse the polarity of the magnetic field, i.e. of the current.This paper describes the design and the commissioning of this power supply which was done by the CERN team

Prompting the data transformation activities for cluster analysis on collections of documents

In this work we argue towards a new self-learning engine able to suggest to the analyst good transformation methods and weighting schemas for a given data collection. This new generation of systems, named SELF-DATA (SELF-learning DAta TrAnsformation) relies on an engine capable of exploring different data weighting schemas (e.g., normalized term frequencies, logarithmic entropy) and data transformation methods (e.g., PCA, LSI) before applying a given data mining algorithm (e.g., cluster analysis), evaluating and comparing solutions through different quality indices (e.g., weighted Silhouette), and presenting the 3-top solutions to the analyst. SELF-DATA will also include a knowledge database storing results of experiments on previously processed datasets, and a classification algorithm trained on the knowledge base content to forecast the best methods for future analyses. SELF-DATA’s current implementation runs on Apache Spark, a state-of-the-art distributed computing framework. The preliminary validation performed on 4 collections of documents highlights that the TF-IDF and logarithmic entropy weighting methods are effective to measure item relevance with sparse datasets, and the LSI method outperforms PCA in the presence of a larger feature domain

On integrating a proprietary and a commercial architecture for optimal BIST performances in SoCs

This paper presents the integration of a proprietary hierarchical and distributed test access mechanism called HD2BIST and a BIST insertion commercial tool. The paper briefly describes the architecture and the features of both the environments and it presents some experimental results obtained on an industrial So

A System for Series Magnetic Measurements of the LHC Main Quadrupoles

More than 400 twin aperture lattice quadrupoles are needed for the Large Hadron Collider (LHC) which is under construction at CERN. The main quadrupole is assembled with correction magnets in a common cryostat called the Short Straight Section (SSS). We plan to measure all SSS's in cold conditions with an unprecedented accuracy: integrated gradient of the field within 150 ppm, harmonics in a range of 1 to 5 ppm, magnetic axis of all elements within 0.1 mm and their field direction within 0.2 mrad. In this paper we describe the automatic measurement system that we have designed, built and calibrated. Based on the results obtained on the two first prototypes of the SSS's (SSS3 and SSS4) we show that this system meets all above requirements

NEMICO: Mining network data through cloud-based data mining techniques

Thanks to the rapid advances in Internet-based applications, data acquisition and storage technologies, petabyte-sized network data collections are becoming more and more common, thus prompting the need for scalable data analysis solutions. By leveraging today’s ubiquitous many-core computer architectures and the increasingly popular cloud computing paradigm, the applicability of data mining algorithms to these large volumes of network data can be scaled up to gain interesting insights. This paper proposes NEMICO, a comprehensive Big Data mining system targeted to network traffic flow analyses (e.g., traffic flow characterization, anomaly detection, multiplelevel pattern mining). NEMICO comprises new approaches that contribute to a paradigm-shift in distributed data mining by addressing most challenging issues related to Big Data, such as data sparsity, horizontal scaling, and parallel computation

HD2BIST: a hierarchical framework for BIST scheduling, data patterns delivering and diagnosis in SoCs

Proposes HD2BIST, a complete hierarchical framework for BIST scheduling, data patterns delivering, and diagnosis of a complex system including embedded cores with different test requirements as full scan cores, partial scan cores, or BIST-ready cores. The main goal of HD2BIST is to maximize and simplify the reuse of the built-in test architectures, giving the chip designer the highest flexibility in planning the overall SoC test strategy. HD2BIST defines a test access method able to provide a direct “virtual” access to each core of the system, and can be conceptually considered as a powerful complement to the P1500 standard, whose main target is to make the test interface of each core independent from the vendo

Arabidopsis thaliana response to extracellular dna: Self versus nonself exposure

The inhibitory effect of extracellular DNA (exDNA) on the growth of conspecific individuals was demonstrated in different kingdoms. In plants, the inhibition has been observed on root growth and seed germination, demonstrating its role in plant\u2013soil negative feedback. Several hypotheses have been proposed to explain the early response to exDNA and the inhibitory effect of conspecific exDNA. We here contribute with a whole-plant transcriptome profiling in the model species Arabidopsis thaliana exposed to extracellular self-(conspecific) and nonself-(heterologous) DNA. The results highlight that cells distinguish self-from nonself-DNA. Moreover, confocal microscopy analyses reveal that nonself-DNA enters root tissues and cells, while self-DNA remains outside. Specifically, exposure to self-DNA limits cell permeability, affecting chloroplast functioning and reactive oxygen species (ROS) production, eventually causing cell cycle arrest, consistently with macroscopic observations of root apex necrosis, increased root hair density and leaf chlorosis. In contrast, nonself-DNA enters the cells triggering the activation of a hypersensitive response and evolving into systemic acquired resistance. Complex and different cascades of events emerge from exposure to extracellular selfor nonself-DNA and are discussed in the context of Damage-and Pathogen-Associated Molecular Patterns (DAMP and PAMP, respectively) responses

Using data mining for prediction of hospital length of stay: an application of the CRISP-DM Methodology

Hospitals are nowadays collecting vast amounts of data related with patient records. All this data hold valuable knowledge that can be used to improve hospital decision making. Data mining techniques aim precisely at the extraction of useful knowledge from raw data. This work describes an implementation of a medical data mining project approach based on the CRISP-DM methodology. Recent real-world data, from 2000 to 2013, were collected from a Portuguese hospital and related with inpatient hospitalization. The goal was to predict generic hospital Length Of Stay based on indicators that are commonly available at the hospitalization process (e.g., gender, age, episode type, medical specialty). At the data preparation stage, the data were cleaned and variables were selected and transformed, leading to 14 inputs. Next, at the modeling stage, a regression approach was adopted, where six learning methods were compared: Average Prediction, Multiple Regression, Decision Tree, Artificial Neural Network ensemble, Support Vector Machine and Random Forest. The best learning model was obtained by the Random Forest method, which presents a high quality coefficient of determination value (0.81). This model was then opened by using a sensitivity analysis procedure that revealed three influential input attributes: the hospital episode type, the physical service where the patient is hospitalized and the associated medical specialty. Such extracted knowledge confirmed that the obtained predictive model is credible and with potential value for supporting decisions of hospital managers

WHOLE-GENOME RE-SEQUENCING OF TWO TOMATO LANDRACES REVEALS SEQUENCE VARIATIONS UNDERPINNING KEY ECONOMICALLY IMPORTANT TRAITS

In the post-genomic era, one of the major challenges is the identification of alleles directly responsible for phenotype variation among different genotypes within the same species. Tomato is a model crop for understanding the development and ripening of climacteric fleshy fruits, and it is also known to be an important source of health-promoting compounds. In addition, cultivated tomato germplasm shows a high phenotypic variation despite its very low genetic diversity. Toward the identification of sequence variations responsible for stress tolerance, high fruit quality and long shelf life, we re-sequenced the genomes of two traditional landraces grown in the Campania region (Southern Italy). Crovarese, belonging to the Corbarino type (COR), and Lucariello (LUC) are typically grown under low water regimes and produce highly appreciated fruits, which can be stored up to 4-8 months. We generated 65.8M and 56.4M of paired-end 30-150 bp reads with an average insert size of 380 bp (± 52bp) and 364 bp (± 49bp) for COR and LUC, respectively. A referenceguided assembly was performed using 'Heinz 1706' as a reference genome. We estimated a mean coverage depth of ~15X for COR and 13X for LUC. Comparing the genomes of COR and LUC with that of 'Heinz 1706' we found a similar distribution of SNPs (68.8% vs. 69.9%, respectively), small deletions (8.9% vs. 8.6%) and small insertions (22.1% vs. 21.3%). Through a de novo assembly of the unmapped reads we identified 29 and 36 new contigs in COR and LUC, respectively. The new contigs could be assigned to the chromosomes thanks to the use of a splitread approach. On average, the contigs inserted in COR were 654bp, whereas those inserted in LUC were 616bp. Using custom RNA-seq data, a total of 43054 and 44576 gene loci were annotated in COR and LUC, corresponding to 62369 and 65094 transcripts, respectively. Among the genes showing a similar structure in COR and LUC compared to 'Heinz 1706', we identified ~2000 and 1700 SNPs causing potentially disruptive effects on the function of 1371 and 1201 genes in COR and LUC, respectively. Interesting GO categories highly represented in genes affected by sequence changes were identified. Major variations were present in stress-responsive genes as well as in fruit quality and development-related genes. From a practical perspective, the identified SNPs and InDels are candidate polymorphisms to track DNA variations associated to key traits of economic interest