Distributed Saturation

The Saturation algorithm for symbolic state-space generation, has been a recent break-through in the exhaustive veri cation of complex systems, in particular globally-asyn- chronous/locally-synchronous systems. The algorithm uses a very compact Multiway Decision Diagram (MDD) encoding for states and the fastest symbolic exploration algo- rithm to date. The distributed version of Saturation uses the overall memory available on a network of workstations (NOW) to efficiently spread the memory load during the highly irregular exploration. A crucial factor in limiting the memory consumption during the symbolic state-space generation is the ability to perform garbage collection to free up the memory occupied by dead nodes. However, garbage collection over a NOW requires a nontrivial communication overhead. In addition, operation cache policies become critical while analyzing large-scale systems using the symbolic approach. In this technical report, we develop a garbage collection scheme and several operation cache policies to help on solving extremely complex systems. Experiments show that our schemes improve the performance of the original distributed implementation, SmArTNow, in terms of time and memory efficiency

Numerical Analysis on Color Preference and Visual Comfort from Eye Tracking Technique

Color preferences in engineering are very important, and there exists relationship between color preference and visual comfort. In this study, there are thirty university students who participated in the experiment, supplemented by pre- and posttest questionnaires, which lasted about an hour. The main purpose of this study is to explore the visual effects of different color assignment with subjective color preferences via eye tracking technology. Eye-movement data through a nonlinear analysis detect slight differences in color preferences and visual comfort, suggesting effective physiological indicators as extensive future research discussed. Results found that the average pupil size of eye-movement indicators can effectively reflect the differences of color preferences and visual comfort. This study more confirmed that the subjective feeling will make people have misjudgment

3D Magneto-Hydrodynamic Simulations of Parker Instability with Cosmic Rays

This study investigates Parker instability in an interstellar medium (ISM) near the Galactic plane using three-dimensional magneto-hydrodynamic simulations. Parker instability arises from the presence of a magnetic field in a plasma, wherein the magnetic buoyant pressure expels the gas and cause the gas to move along the field lines. The process is thought to induce the formation of giant molecular clouds in the Galaxy. In this study, the effects of cosmic-ray (CR) diffusion are examined. The ISM at equilibrium is assumed to comprise a plasma fluid and a CR fluid at various temperatures, with a uniform magnetic field passing through it in the azimuthal direction of the Galactic disk. After a small perturbation, the unstable gas aggregates at the footpoint of the magnetic fields and forms dense blobs. The growth rate of the instability increases with the strength of the CR diffusion. The formation of dense clouds is enhanced by the effect of cosmic rays (CRs), whereas the shape of the clouds depends sensitively on the initial conditions of perturbation.Comment: 4 pages, Computer Physics Communications 2011, 182, p177-17

Parallel symbolic state-space exploration is difficult, but what is the alternative?

State-space exploration is an essential step in many modeling and analysis problems. Its goal is to find the states reachable from the initial state of a discrete-state model described. The state space can used to answer important questions, e.g., "Is there a dead state?" and "Can N become negative?", or as a starting point for sophisticated investigations expressed in temporal logic. Unfortunately, the state space is often so large that ordinary explicit data structures and sequential algorithms cannot cope, prompting the exploration of (1) parallel approaches using multiple processors, from simple workstation networks to shared-memory supercomputers, to satisfy large memory and runtime requirements and (2) symbolic approaches using decision diagrams to encode the large structured sets and relations manipulated during state-space generation. Both approaches have merits and limitations. Parallel explicit state-space generation is challenging, but almost linear speedup can be achieved; however, the analysis is ultimately limited by the memory and processors available. Symbolic methods are a heuristic that can efficiently encode many, but not all, functions over a structured and exponentially large domain; here the pitfalls are subtler: their performance varies widely depending on the class of decision diagram chosen, the state variable order, and obscure algorithmic parameters. As symbolic approaches are often much more efficient than explicit ones for many practical models, we argue for the need to parallelize symbolic state-space generation algorithms, so that we can realize the advantage of both approaches. This is a challenging endeavor, as the most efficient symbolic algorithm, Saturation, is inherently sequential. We conclude by discussing challenges, efforts, and promising directions toward this goal

Inferring Genetic Interactions via a Data-Driven Second Order Model

Genetic/transcriptional regulatory interactions are shown to predict partial components of signaling pathways, which have been recognized as vital to complex human diseases. Both activator (A) and repressor (R) are known to coregulate their common target gene (T). Xu et al. (2002) proposed to model this coregulation by a fixed second order response surface (called the RS algorithm), in which T is a function of A, R, and AR. Unfortunately, the RS algorithm did not result in a sufficient number of genetic interactions (GIs) when it was applied to a group of 51 yeast genes in a pilot study. Thus, we propose a data-driven second order model (DDSOM), an approximation to the non-linear transcriptional interactions, to infer genetic and transcriptional regulatory interactions. For each triplet of genes of interest (A, R, and T), we regress the expression of T at time t + 1 on the expression of A, R, and AR at time t. Next, these well-fitted regression models (viewed as points in R3) are collected, and the center of these points is used to identify triples of genes having the A-R-T relationship or GIs. The DDSOM and RS algorithms are first compared on inferring transcriptional compensation interactions of a group of yeast genes in DNA synthesis and DNA repair using microarray gene expression data; the DDSOM algorithm results in higher modified true positive rate (about 75%) than that of the RS algorithm, checked against quantitative RT-polymerase chain reaction results. These validated GIs are reported, among which some coincide with certain interactions in DNA repair and genome instability pathways in yeast. This suggests that the DDSOM algorithm has potential to predict pathway components. Further, both algorithms are applied to predict transcriptional regulatory interactions of 63 yeast genes. Checked against the known transcriptional regulatory interactions queried from TRANSFAC, the proposed also performs better than the RS algorithm

The ACR11 encodes a novel type of chloroplastic ACT domain repeat protein that is coordinately expressed with GLN2 in Arabidopsis

<p>Abstract</p> <p>Background</p> <p>The ACT domain, named after bacterial aspartate kinase, chorismate mutase and TyrA (prephenate dehydrogenase), is a regulatory domain that serves as an amino acid-binding site in feedback-regulated amino acid metabolic enzymes. We have previously identified a novel type of ACT domain-containing protein family, the ACT domain repeat (ACR) protein family, in <it>Arabidopsis</it>. Members of the ACR family, ACR1 to ACR8, contain four copies of the ACT domain that extend throughout the entire polypeptide. Here, we describe the identification of four novel ACT domain-containing proteins, namely ACR9 to ACR12, in <it>Arabidopsis</it>. The ACR9 and ACR10 proteins contain three copies of the ACT domain, whereas the ACR11 and ACR12 proteins have a putative transit peptide followed by two copies of the ACT domain. The functions of these plant ACR proteins are largely unknown.</p> <p>Results</p> <p>The ACR11 and ACR12 proteins are predicted to target to chloroplasts. We used protoplast transient expression assay to demonstrate that the <it>Arabidopsis </it>ACR11- and ACR12-green fluorescent fusion proteins are localized to the chloroplast. Analysis of an <it>ACR11 </it>promoter-β-glucuronidase (GUS) fusion in transgenic <it>Arabidopsis </it>revealed that the GUS activity was mainly detected in mature leaves and sepals. Interestingly, coexpression analysis revealed that the <it>GLN2</it>, which encodes a chloroplastic glutamine synthetase, has the highest mutual rank in the coexpressed gene network connected to <it>ACR11</it>. We used RNA gel blot analysis to confirm that the expression pattern of <it>ACR11 </it>is similar to that of <it>GLN2 </it>in various organs from 6-week-old <it>Arabidopsis</it>. Moreover, the expression of <it>ACR11 </it>and <it>GLN2 </it>is highly co-regulated by sucrose and light/dark treatments in 2-week-old <it>Arabidopsis </it>seedlings.</p> <p>Conclusions</p> <p>This study reports the identification of four novel ACT domain repeat proteins, ACR9 to ACR12, in <it>Arabidopsis</it>. The ACR11 and ACR12 proteins are localized to the chloroplast, and the expression of <it>ACR11 </it>and <it>GLN2 </it>is highly coordinated. These results suggest that the <it>ACR11 </it>and <it>GLN2 </it>genes may belong to the same functional module. The <it>Arabidopsis </it>ACR11 protein may function as a regulatory protein that is related to glutamine metabolism or signaling in the chloroplast.</p

Shrimp shell as a potential sorbent for removal of arsenic from aqueous solution

Abstract This study examined the ability of shrimp shell to remove arsenic (As) from aqueous solutions. The shells of two species of shrimp, black tiger shrimp Penaeus monodon and white shrimp Litopenaeus vannamei, were chosen to be the sorbents. Laboratory exposure experiments estimated uptake and depuration rate constants (i.e., k 1 and k 2 ) as well as the bioconcentration factors (BCF) of the shells of the two shrimps. A first-order one-compartment model was presented to describe the uptake kinetics of As in shrimp shell. The resulting k 1 , k 2 , and BCF values of black tiger shrimp were 0.034-1.722 ml/g/day, 0.007-0.345 g/g/day, and 5.08 ± 1.56 ml/g, while those for white shrimp were 0.053-0.523 ml/g/day, 0.011-0.237 g/g/day, and 3.95 ± 1.88 ml/g, respectively. The sorption capacities of black tiger shrimp shell and white shrimp shell were 1.08 9 10 -4 -6.66 9 10 -3 and 1.04 9 10 -4 -3.26 9 10 -3 mmol/g, respectively. The sorption capacity of shrimp shell increased with the initial As concentration in water. Shrimp shell, as a waste material, could be potentially used for the removal of As from an aqueous medium. Although the As-removal capacity of shrimp shell was lower than those of natural and chemical sorbents, using shrimp shells as sorbents is less expensive and could increase the additional value of shrimp products