Rapid idea of located defects on grounding systems

This study gives an original way to have a rapid idea of the position of the located defects on grounding systems. For determining electromagnetic fields' radiation, we use an easy method, based on analytical formula, transmission line theory, and modified images theory, instead of complex method of Summerfield's integrals. The cartography of the electromagnetic fields radiated at the soil—air interface, by energized grounding systems (electrode or grid), can be used to detect and localize a defect on these systems. This model is aimed at helping in electromagnetic compatibility and lightning protection studies. Copyright © 2011 John Wiley & Sons, Ltd.     

Computation of an Emptiable Minimal Siphon in a Subclass of Petri Nets Using Mixed-Integer Programming

Deadlock resolution strategies based on siphon control are widely investigated.Their computational efficiency largely depends on siphon computation.Mixed-integer programming(MIP)can be utilized for the computation of an emptiable siphon in a Petri net(PN).Based on it,deadlock resolution strategies can be designed without requiring complete siphon enumeration that has exponential complexity.Due to this reason,various MIP methods are proposed for various subclasses of PNs.This work proposes an innovative MIP method to compute an emptiable minimal siphon(EMS)for a subclass of PNs named S⁴PR.In particular,many particular structural characteristics of EMS in S4 PR are formalized as constraints,which greatly reduces the solution space.Experimental results show that the proposed MIP method has higher computational efficiency.Furthermore,the proposed method allows one to determine the liveness of an ordinary S⁴PR.     

Properties of hardened highly refractory basic cements as a function of in relation to their phase composition

The phase, composition of the previously prepared three hardened highly refractory basic cements (dolomitic magnesite, periclase and dolomitic magnesite chrome) was established by the aid of d.t.a., t.g.a., microscopic examination, electron microscopy and X-ray analysis. The following properties of these cements were determined: compressive strength, apparent porosity, linear shrinkage and ignition loss. These parameters were measured for each cement after 3 days air curing at ～ 60% r.h. and after heating these cements to 110°, 450°, 600°, 800° and 1200°C. The variation in these parameters as a function of temperature was correlated in the light of their component phases.     

Document Similarity Using a Phrase Indexing Graph Model

Document clustering techniques mostly rely on single term analysis of text, such as the vector space model. To better capture the structure of documents, the underlying data model should be able to represent the phrases in the document as well as single terms. We present a novel data model, the Document Index Graph, which indexes Web documents based on phrases rather than on single terms only. The semistructured Web documents help in identifying potential phrases that when matched with other documents indicate strong similarity between the documents. The Document Index Graph captures this information, and finding significant matching phrases between documents becomes easy and efficient with such model. The model is flexible in that it could revert to a compact representation of the vector space model if we choose not to index phrases. However, using phrase indexing yields more accurate document similarity calculations. The similarity between documents is based on both single term weights and matching phrase weights. The combined similarities are used with standard document clustering techniques to test their effect on the clustering quality. Experimental results show that our phrase-based similarity, combined with single-term similarity measures, gives a more accurate measure of document similarity and thus significantly enhances Web document clustering quality.     

MSEBAG: a dynamic classifier ensemble generation based on ‘minimum-sufficient ensemble' and bagging

In this paper, we propose a dynamic classifier system, MSEBAG, which is characterised by searching for the ‘minimum-sufficient ensemble’ and bagging at the ensemble level. It adopts an ‘over-generation and selection’ strategy and aims to achieve a good bias–variance trade-off. In the training phase, MSEBAG first searches for the ‘minimum-sufficient ensemble’, which maximises the in-sample fitness with the minimal number of base classifiers. Then, starting from the ‘minimum-sufficient ensemble’, a backward stepwise algorithm is employed to generate a collection of ensembles. The objective is to create a collection of ensembles with a descending fitness on the data, as well as a descending complexity in the structure. MSEBAG dynamically selects the ensembles from the collection for the decision aggregation. The extended adaptive aggregation (EAA) approach, a bagging-style algorithm performed at the ensemble level, is employed for this task. EAA searches for the competent ensembles using a score function, which takes into consideration both the in-sample fitness and the confidence of the statistical inference, and averages the decisions of the selected ensembles to label the test pattern. The experimental results show that the proposed MSEBAG outperforms the benchmarks on average.     

Identification of Novel Insulin Resistance Related ceRNA Network in T2DM and Its Potential Editing by CRISPR/Cas9

Background: Type 2 diabetes mellitus is one of the leading causes of morbidity and mortality worldwide and is derived from an accumulation of genetic and epigenetic changes. In this study, we aimed to construct Insilco, a competing endogenous RNA (ceRNA) network linked to the pathogenesis of insulin resistance followed by its experimental validation in patients’, matched control and cell line samples, as well as to evaluate the efficacy of CRISPR/Cas9 as a potential therapeutic strategy to modulate the expression of this deregulated network. By applying bioinformatics tools through a two-step process, we identified and verified a ceRNA network panel of mRNAs, miRNAs and lncRNA related to insulin resistance, Then validated the expression in clinical samples (123 patients and 106 controls) and some of matched cell line samples using real time PCR. Next, two guide RNAs were designed to target the sequence flanking LncRNA/miRNAs interaction by CRISPER/Cas9 in cell culture. Gene editing tool efficacy was assessed by measuring the network downstream proteins GLUT4 and mTOR via immunofluorescence. Results: LncRNA-RP11-773H22.4, together with RET, IGF1R and mTOR mRNAs, showed significant upregulation in T2DM compared with matched controls, while miRNA (i.e., miR-3163 and miR-1) and mRNA (i.e., GLUT4 and AKT2) expression displayed marked downregulation in diabetic samples. CRISPR/Cas9 successfully knocked out LncRNA-RP11-773H22.4, as evidenced by the reversal of the gene expression of the identified network at RNA and protein levels to the normal expression pattern after gene editing. Conclusions: The present study provides the significance of this ceRNA based network and its related target genes panel both in the pathogenesis of insulin resistance and as a therapeutic target for gene editing in T2DM.     

Characterization of the seed oil and meal from apricot,cherry, nectarine,peach and plum

The oil and meal from apricot, cherry, nectarine, peach and plum seeds were characterized for their physico-chemical properties. The wt% seed/fruit ranged from 2.8–7.6% and the wt% kernel/seed ranged from 6.8–31.6%. Kernel moisture ranged from 38.8–72.4%. The proximate composition of whole seeds on a dry weight basis ranged from 1.3–6.9% protein, 0.6–14.5% fat, 51.0–72.3% fiber, 0.4–1.2% ash, and 18.1–27.9% carbohydrate (by difference). The kernels contained 41.9–49.3% fat, and the resulting meals contained 31.7–38.7% protein. The major fatty acids were oleic (52.9–66.3%) and linoleic (26.8%–35.0%). The major essential amino acids were arginine (21.7–30.5 mmoles/100 g meal) and leucine (16.2–21.6), and the predominant nonessential amino acid was glutamic acid (49.9–68.0). The iodine values ranged from 105 to 113, hydroxyl value from 5.5 to 7.0 and the unsaponifiables from 0.56–0.80%. The mineral composition (Cu, Fe, Ca, Mg, Zn, P) was also determined on the meals.