PDDRA: A new pre-fetching based dynamic data replication algorithm in data grids

In recent years, grid technology has had such a fast growth that it has been used in many scientific experiments and research centers. A large number of storage elements and computational resources are combined to generate a grid which gives us shared access to extra computing power. In particular, data grid deals with data intensive applications and provides intensive resources across widely distributed communities. Data replication is an efficient way for distributing replicas among the data grids, making it possible to access similar data in different locations of the data grid. Replication reduces data access time and improves the performance of the system. In this paper, we propose a new dynamic data replication algorithm named PDDRA that optimizes the traditional algorithms. Our proposed algorithm is based on an assumption: members in a VO (Virtual Organization) have similar interests in files. Based on this assumption and also file access history, PDDRA predicts future needs of grid sites and pre-fetches a sequence of files to the requester grid site, so the next time that this site needs a file, it will be locally available. This will considerably reduce access latency, response time and bandwidth consumption. PDDRA consists of three phases: storing file access patterns, requesting a file and performing replication and pre-fetching and replacement. The algorithm was tested using a grid simulator, OptorSim developed by European Data Grid projects. The simulation results show that our proposed algorithm has better performance in comparison with other algorithms in terms of job execution time, effective network usage, total number of replications, hit ratio and percentage of storage filled.     

Application of ion-engineered Persian Gulf seawater in EOR:effects of different ions on interfacial tension,contact angle,zeta potential,and oil recovery

In this study,we initially performed interfacial tension(IFT)tests to investigate the potential of using the Persian Gulf sea-water(PGSW)as smart water with dif...     

Scattering of elastic P- and SV-waves by a circular coated nanofiber based on Gurtin–Murdoch model of surface elasticity: Scattering cross section results

In this article, an analytical study of elastic P- and SV-wave scattering by a circular nanofiber is presented. The nanofiber is assumed to be surrounded by an inhomogeneous interphase layer, and Gurtin–Murdoch's model of surface elasticity is utilized to study the surface/interface effects in the regions between the fiber and interphase and also interphase and matrix. The simultaneous effects of surface elasticity and interphase inhomogeneity are considered here; by taking the inhomogeneous interphase to be composed of several sublayers, a transfer matrix approach is used to find the unknown field variables and, consequently, the scattering cross sections. The results indicate that considering the effects of surface elasticity and interphase inhomogeneity has a considerable impact on the calculated scattering cross sections.     

Association between antiinflammatory cytokine,IL‐10, and sleep quality in patients on maintenance hemodialysis

Elevated proinflammatory cytokines have been attributed to poor sleep quality in patients receiving hemodialysis. This is the first investigation about the relationship between sleep quality and circulating levels of antiinflammatory markers in these patients. A total of 72 patients who were receiving maintenance hemodialysis were enrolled in this cross‐sectional study. The Pittsburgh Sleep Quality Index (PSQI) was used to measure sleep quality. Patients were divided into two groups: good sleepers (PSQI score < 5) and poor sleepers (PSQI score ≥ 5). Assessments were made for serum biochemical parameters (albumin, parathyroid hormone), inflammatory (interleukin [IL]‐6, tumor necrosis factor‐alpha [TNF‐α], and high‐sensitivity c‐reactive protein [hs‐CRP] ) and antiinflammatory (IL‐10) markers. Fifty‐four patients (75%) were classified as poor sleepers. Poor sleepers showed significantly lower levels of serum IL‐10 and higher serum triglyceride and parathyroid hormone concentrations. These patients were more likely to have more comorbidities. The global PSQI score was significantly correlated with serum IL‐10 (p = 0.03) and triglyceride levels (p = 0.01). Multivariate logistic regression analysis showed a direct correlation between PSQI and having comorbidities (p = 0.011, odds ratio [OR] = 3.918; confidence interval 95% [CI] = 2.742–19.031), between PSQI and serum triglyceride (p = 0.027, OR = 1.027 [95% CI = 1.007–1.048] ), and an inverse correlation between PSQI and serum IL‐10 level (p = 0.021, OR = 0.424 [95% CI = 0.195–0.922]). Reduced circulating levels of the antiinflammatory cytokine IL‐10 were significantly associated with poor sleep quality in hemodialysis patients. Factors including serum IL‐10 and triglyceride concentrations and having comorbidities may predict patients prone to poor sleep quality.     

Protein–Protein Interaction Prediction for Targeted Protein Degradation

Protein–protein interactions (PPIs) play a fundamental role in various biological functions; thus, detecting PPI sites is essential for understanding diseases and developing new drugs. PPI prediction is of particular relevance for the development of drugs employing targeted protein degradation, as their efficacy relies on the formation of a stable ternary complex involving two proteins. However, experimental methods to detect PPI sites are both costly and time-intensive. In recent years, machine learning-based methods have been developed as screening tools. While they are computationally more efficient than traditional docking methods and thus allow rapid execution, these tools have so far primarily been based on sequence information, and they are therefore limited in their ability to address spatial requirements. In addition, they have to date not been applied to targeted protein degradation. Here, we present a new deep learning architecture based on the concept of graph representation learning that can predict interaction sites and interactions of proteins based on their surface representations. We demonstrate that our model reaches state-of-the-art performance using AUROC scores on the established MaSIF dataset. We furthermore introduce a new dataset with more diverse protein interactions and show that our model generalizes well to this new data. These generalization capabilities allow our model to predict the PPIs relevant for targeted protein degradation, which we show by demonstrating the high accuracy of our model for PPI prediction on the available ternary complex data. Our results suggest that PPI prediction models can be a valuable tool for screening protein pairs while developing new drugs for targeted protein degradation.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

Inspection and Monitoring of Concrete Structures via Radiography and Weighted Nuclear Norm Minimization Method

Russian Journal of Nondestructive Testing - The location of reinforcement bar in concrete, the bar corrosion, diameter and the depth below the surface are important factors in the evaluation of the...     

An experimental study of the synergistic effects of BMIM.BF4, BMIM.DCA and TEACl aqueous solutions on methane hydrate formation

In this work, the effects of three ionic liquids (ILs), namely, 1-butyl-3-methylimidazolium tetrafluoroborate, 1-butyl-3-methylimidazolium dicyanamide and tetraethyl-ammonium chloride, on methane hydrate formation and dissociation kinetic parameters were studied. The kinetic parameters including the initial rate of hydrate formation, hydrate stability at atmospheric pressure and hydrate storage capacity were evaluated. The experimental measurements were performed in an initial pressure range of 3.5–7.1 MPa. It was found that both of ILs with imidazolium-based cation increase the initial methane hydrate formation rate while the IL with ammonium-based cation leads to a decrease in the initial methane hydrate formation rate. It was also interpreted from the results that all of the three studied ILs decrease methane hydrate stability at atmospheric pressure and increase methane hydrate storage capacity. Finally, both of ILs with imidazolium-based cations were found to have higher impacts on decreasing hydrate stability at atmospheric pressure and increasing the methane hydrate storage capacity than the applied IL with ammonium-based cation.     

A dynamic search pattern motion estimation algorithm using prioritized motion vectors

Motion estimation is one of the critical parts in video compression standards with a high computational load. Many motion estimation algorithms have been developed to reduce the number of search points compared to a full-search algorithm without losing the quality considerably. Most of them use fixed search patterns in their first step which may suffer from trapping into local minima or searching unnecessary blocks due to inappropriate size and type of search patterns. In this paper, a new dynamic search pattern using motion vectors of spatial and temporal neighboring blocks is proposed. The motion vectors of neighboring blocks are prioritized, in order to efficiently use of halfway stop technique. The simulation results indicate that proposed algorithm is very close to the full-search algorithm in quality, compared to other rivals. Moreover, the average number of searches is often less than other algorithms.     

Functionalization of Graphene Oxide Films with Au and MoOx Nanoparticles as Efficient p‐Contact Electrodes for Inverted Planar Perovskite Solar Cells

A graphene oxide (GO) film is functionalized with metal (Au) and metal‐oxide (MoO_x) nanoparticles (NPs) as a hole‐extraction layer for high‐performance inverted planar‐heterojunction perovskite solar cells (PSCs). These NPs can increase the work function of GO, which is confirmed with X‐ray photoelectron spectra, Kelvin probe force microscopy, and ultraviolet photoelectron spectra measurements. The down‐shifts of work functions lead to a decreased level of potential energy and hence increased V_oc of the PSC devices. Although the GO‐AuNP film shows rapid hole extraction and increased V_oc, a J_sc improvement is not observed because of localization of the extracted holes inside the AuNP that leads to rapid charge recombination, which is confirmed with transient photoelectric measurements. The power conversion efficiency (PCE) of the GO‐AuNP device attains 14.6%, which is comparable with that of the GO‐based device (14.4%). In contrast, the rapid hole extraction from perovskite to the GO‐MoO_x layer does not cause trapping of holes and delocalization of holes in the GO film accelerates rapid charge transfer to the indium tin oxide substrate; charge recombination in the perovskite/GO‐MoO_x interface is hence significantly retarded. The GO‐MoO_x device consequently shows significantly enhanced V_oc and J_sc, for which its device performance attains PCE of 16.7% with great reproducibility and enduring stability.