1,722,876 research outputs found
Oblikovanje nabavnih cen zemeljskega plina in njihov vpliv na uspešnost izbranega podjetja
No full textHBV DNA preC/C amplification v2
No full textPreC-C Primers encompass described PreC-C mutations: mutation G1896A in the PreC region and mutations A1762T and G1764A in C-gene promotor. Primers were modified according to literature-based alignment, in order to optimize the amplification of all HBV genotypes. Sequence analysis at position 1762, 1764 et 1896 allows to determine sample wild type or mutant sequence (i.e. HBeAg expression or not, respectively). It is also possible to describe others rare punctual mutations. Furthermore, the sequence analysis also allows assessing HBV genotyping. </p
GPROF PREC DATA
No full textNASA prec data with GPROF prediction and our MLP test.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV
PREC: Practical Root Exploit Containment for Android Devices
No full textApplication markets such as the Google Play Store and the Apple App Store have become the de facto method of distributing software to mobile devices. While official markets dedicate significant resources to detecting malware, stateof-the-art malware detection can be easily circumvented using logic bombs or checks for an emulated environment. We present a Practical Root Exploit Containment (PREC) framework that protects users from such conditional malicious behavior. PREC can dynamically identify system calls from high-risk components (e.g., third-party native libraries) and execute those system calls within isolated threads. Hence, PREC can detect and stop root exploits with high accuracy while imposing low interference to benign applications. We have implemented PREC and evaluated our methodology on 140 most popular benign applications and 10 root exploit malicious applications. Our results show that PREC can successfully detect and stop all the tested malware while reducing the false alarm rates by more than one order of magnitude over traditional malware detection algorithms. PREC is light-weight, which makes it practical for runtime on-device root exploit detection and containment
Constrained dependence parameter estimates (a) and (b) of the conditional distribution of (<i>YieldRefl</i>|<i>Prec</i>) conditional on <i>Prec</i> > <i>qu</i><sub><i>Prec</i></sub>, with <i>qu</i><sub><i>Prec</i></sub> being the 90<sup><i>th</i></sup> quantile of the conditioning variable <i>Prec</i>, correspond to the maximum of the profile likelihood surface using maize data of Eastern Africa.
No full textConstrained dependence parameter estimates (a) and (b) of the conditional distribution of (YieldRefl|Prec) conditional on Prec > quPrec, with quPrec being the 90th quantile of the conditioning variable Prec, correspond to the maximum of the profile likelihood surface using maize data of Eastern Africa.</p
Correlation of rsRAI with aDMN-pDMN/prec functional connectivity.
No full text<p>rsRAI: resource allocation index in resting state; aDMN: anterior default mode network; pDMN/prec: precuneus part of posterior default mode network.</p
PREC: semantic translation of property graphs
No full textInternational audienceConverting property graphs to RDF graphs allows to enhance the interoperability of knowledge graphs. But existing tools perform the same conversion for every graph, regardless of its content. In this paper, we propose PREC, a user-configured conversion of property graphs to RDF graphs to better capture the semantics of the content
Association of the PreC/CP mutational patterns with clinical status (A) and HBV-DNA level (B).
No full text<p>Four patterns (preC−/CP−, preC+/CP−, preC−/CP+, and preC+/CP+) were defined based on the presence (+) or absence (−) of pre-C mutation G1896A and CP A1762T/G1764A double mutation. For HBV-DNA levels, the data are presented as box plots, illustrated as the median (horizontal line) and the range from the 25th to the 75th percentiles. Group 1 <i>vs</i> 2, <i>p</i> = 0.09, Group 3 <i>vs</i> 1, <i>p</i> = 0.009; Group 3 <i>vs</i> 2, <i>p</i> = 0.02; Group 3 <i>vs</i> 4, <i>p</i> = 0.627; Group 4 <i>vs</i> 2, <i>p</i> = 0.02.</p
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