Integrating Computing Resources on Multiple Grid-Enabled Job Scheduling Systems Through a Grid RPC System

We present a framework for a parallel programming model by remote procedure calls, which bridge large-scale computing resource pools managed by multiple Grid-enabled job scheduling systems. With this system, the user can exploit not only remote servers and clusters, but also the computing resources provided by Grid-enabled job scheduling systems located on different sites. This framework requires a Grid remote procedure call (RPC) system to decouple the computation in a remote node from the Grid RPC mechanism and uses document-based communication rather than connection-based communication. We implemented the proposed framework as an extension of the OmniRPC system, which is a Grid RPC system for parallel programming. We designed a general interface to easily adapt the OmniRPC system to various Grid-enabled job scheduling systems, including XtremWeb, CyberGRIP, Condor and Grid Engine. We show the preliminary performance of these implementations using a phylogenetic application. We found that the proposed system can achieve approximately the same performance as OmniRPC and can handle interruptions in worker programs on remote nodes. Yoshihiro Nakajima is a Research Fellow of the Japan Society for the Promotion of Science     

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts

Instance selection is becoming increasingly relevant due to the huge amount of data that is constantly being produced in many fields of research. Although current algorithms are useful for fairly large datasets, scaling problems are found when the number of instances is in the hundreds of thousands or millions. When we face huge problems, scalability becomes an issue, and most algorithms are not applicable.Thus, paradoxically, instance selection algorithms are for the most part impracticable for the same problems that would benefit most from their use. This paper presents a way of avoiding this difficulty using several rounds of instance selection on subsets of the original dataset. These rounds are combined using a voting scheme to allow good performance in terms of testing error and storage reduction, while the execution time of the process is significantly reduced. The method is particularly efficient when we use instance selection algorithms that are high in computational cost. The proposed approach shares the philosophy underlying the construction of ensembles of classifiers. In an ensemble, several weak learners are combined to form a strong classifier; in our method several weak (in the sense that they are applied to subsets of the data) instance selection algorithms are combined to produce a strong and fast instance selection method.An extensive comparison of 30 medium and large datasets from the UCI Machine Learning Repository using 3 different classifiers shows the usefulness of our method. Additionally, the method is applied to 5 huge datasets (from three hundred thousand to more than a million instances) with good results and fast execution time.     

Continuous synthesis of Zn2SiO4:Mn fine particles in supercritical water at temperatures of 400-500 °C and pressures of 30-35 MPa

Sub-micron sized Zn₂SiO₄:Mn²⁺ phosphors particles were continuously synthesized in supercritical water with a flow reactor. Colloidal silica or sodium silicate was used as the Si source. Zn and Mn sources were chosen from their nitrates, sulfates, and acetates. The syntheses were carried out at temperatures from 400 to 500 °C, at pressures from 30 to 35 MPa, at NaOH concentrations from 0.014 to 0.025 M, and for residence times from 0.025 to 0.18 s. Sodium silicate formed α- and β-Zn₂SiO₄:Mn²⁺ phases regardless of the Zn and Mn sources, while colloidal silica formed phases dependent on the type of Zn and Mn sources used in addition to the use of alkali. As the reaction temperature increased, the crystallinity of α-Zn₂SiO₄:Mn²⁺ phase increased and the Mn substitution into the Zn sites of the α-Zn₂SiO₄ phase decreased. Of the conditions studied, the most highly crystalline α-Zn₂SiO₄:Mn²⁺ was produced at a temperature of 400 °C, a pressure of 30 MPa, a NaOH concentration of 0.14 M, and a residence time of 0.13 s with Zn and Mn sulfates and colloidal silica as starting materials. The α-Zn₂SiO₄:Mn²⁺ fine particles synthesized were round in shape, had an average diameter of 268 nm, and exhibited a green-emission with a peak wavelength of 524 nm.     

Finite element and dimensional analysis algorithm for the prediction of mechanical properties of bulk materials and thin films

In this work, the applicability of a new algorithm for the estimation of mechanical properties from instrumented indentation data was studied for thin films. The applicability was analyzed with the aid of both three-dimensional finite element simulations and experimental indentation tests. The numerical approach allowed studying the effect of the substrate on the estimation of mechanical properties of the film, which was conducted based on the ratio h_max/l between maximum indentation depth and film thickness. For the experimental analysis, indentation tests were conducted on AISI H13 tool steel specimens, plasma nitrated and coated with TiN thin films. Results have indicated that, for the conditions analyzed in this work, the elastic deformation of the substrate limited the extraction of mechanical properties of the film/substrate system. This limitation occurred even at low h_max/l ratios and especially for the estimation of the values of yield strength and strain hardening exponent. At indentation depths lower than 4% of the film thickness, the proposed algorithm estimated the mechanical properties of the film with accuracy. Particularly for hardness, precise values were estimated at h_max/l lower than 0.1, i.e. 10% of film thickness.     

Dehydration of d-glucose in high temperature water at pressures up to 80 MPa

Reaction of d-glucose in water to yield 5-hydroxymethylfurfural (5-HMF), 1,2,4-benzenetriol (BTO) and furfural was studied at high temperatures (up to 400 °C) and high pressures (up to 80 MPa) using a continuous flow reactor. Maximum temperature and pressure conditions gave maximum furfural yield. Increasing pressure from 40 to 70 and 80 MPa enhanced dehydration reactions to 5-HMF, but also enhanced hydrolysis of 5-HMF leading to the production of BTO and thus lead to lower yields of 5-HMF (below 10%). Remarkably, the dehydration reaction to 5-HMF and the hydrolysis of 5-HMF were both enhanced by the increase in water density at 400 °C.     

Dietary fiber from coconut flour: A functional food

To determine the effectiveness of dietary fiber present in coconut flour as a functional food, the following studies were conducted: (a) Dietary Fiber Composition and Fermentability of Coconut Flour; (b) The Effect of Coconut Flour on Mineral Availability from Coconut Flour Supplemented Foods; (c) Glycemic Index of Coconut Flour Supplemented Foods in Normal and Diabetic Subjects; and (d) The Cholesterol Lowering Effect of Coconut Flakes in Moderately Raised Cholesterol Levels of Humans. The dietary fiber content of coconut flour was 60.0 ± 1.0 g/100 g sample, 56% insoluble and 4% soluble. Fermentation of coconut flour produced short chain fatty acids with butyrate (1.73 ± 0.07 mmol/g fiber isolate) > acetate (1.40 ± 0.12; (P < 0.05) > propionate (0.47 ± 0.01; P < 0.05). Iron and zinc availability were highest for carrot cake (Fe, 33.3 ± 0.7%; Zn, 12.6 ± 0.1%) supplemented with 20% coconut flour while multigrain loaf supplemented with 10% and macaroons with 25% coconut flour were highest for calcium availability (63.4 ± 8.0% and 38.7 ± 1.1%, respectively). Increasing concentrations of dietary fiber from coconut flour did not affect mineral availability from all test foods. The significantly low glycemic index foods (< 60 mmol × min/l) investigated were: macaroons (45.7 ± 3.0), carrot cake (51.8 ± 3.3) and brownies (60.1 ± 5.4) with 20–25% coconut flour. The test foods containing 15% coconut flour has a glycemic index ranging from 61 to 77 mmol × min/l. Among the test foods, pan de sal (87.2 ± 5.5) and multigrain loaf (85.2 ± 6.8) gave significantly higher glycemic index with 5% and 10% coconut flour. On the other hand, granola bar and cinnamon which contained 5% and 10% coconut flour, respectively gave a glycemic index ranging from 62 to 76 mmol × min/l and did not differ significantly from the test foods containing 15% coconut flour (P < 0.05). A very strong negative correlation (r = − 85, n = 11, P < 0.005) was observed between the glycemic index and dietary fiber content of the test foods supplemented with coconut. There was a significant reduction (%) in serum total and LDL cholesterol for: oat bran flakes, 8.4 ± 1.4 and 8.8 ± 6.7, respectively; 15% coconut flakes, 6.9 ± 1.1 and 11.0 ± 4.0, respectively; and 25% coconut flakes, 10.8 ± 1.3 and 9.2 ± 5.4, respectively (P < 0.05). Serum triglycerides were significantly reduced for all test foods: corn flakes, 14.5 ± 6.3%; oat bran flakes, 22.7 ± 2.9%; 15% coconut flakes, 19.3 ± 5.7%; and 25% coconut flakes, 21.8 ± 6.0% (P < 0.05). Results from the above study can be a basis in the development of coconut flour as a functional food.

Industrial relevance

The functionality of coconut flour in terms of prevention for risk of chronic diseases, e.g. diabetes mellitus, cardiovascular diseases (CVD) and colon cancer, revealed increase production of coconut and coconut flour. The production of coconut flour is very economical because it can be produced in a small or large scale. The raw material is obtained from the by-product (waste) of the coconut milk industry and the process and equipment used in its production is simple and cheap. Coconut flour as a good source of dietary fiber can be added to bakery products, recipes and other food products for good health.     

First international two-way satellite time and frequency transfer experiment employing dual pseudo-random noise codes

Two-way satellite time and frequency transfer (TWSTFT) is one of the main techniques used to compare atomic time scales over long distances. To both improve the precision of TWSTFT and decrease the satellite link fee, a new software-defined modem with dual pseudo-random noise (DPN) codes has been developed. In this paper, we demonstrate the first international DPN-based TWSTFT experiment over a period of 6 months. The results of DPN exhibit excellent performance, which is competitive with the Global Positioning System (GPS) precise point positioning (PPP) technique in the short-term and consistent with the conventional TWSTFT in the long-term. Time deviations of less than 75 ps are achieved for averaging times from 1 s to 1 d. Moreover, the DPN data has less diurnal variation than that of the conventional TWSTFT. Because the DPN-based system has advantages of higher precision and lower bandwidth cost, it is one of the most promising methods to improve international time-transfer links.     

实验室模拟控制轧制和调质处理对桥梁钢Q690q组织和性能的影响

研究了920℃精轧,830℃终轧以12℃/s冷至590℃,空冷的TMCP控制轧制工艺和TMCP+940℃淬火-630℃回火两工艺的桥梁钢Q690q(/%:0.05C、0.30Si、1.40Mn、1.10Cu、0.50Cr、0.80Ni、0.07V、0.55Mo,焊接冷裂纹敏感指数P_cm≤0.267)15mm板组织和力学性能。结果表明,TMCP工艺生产的桥梁钢Q690q组织主要由粒状贝氏体和少量铁素体组成,TMCP+调质处理后的组织为多边形铁素体和少量渗碳体,其屈服强度R_p0.2为845～870MPa,抗拉强度Rm895～900MPa,-20℃冲击功153～186J, -40℃为141～155 J。调质处理减小了钢材的M/A岛尺寸和位错密度,使Q690_q钢保持高强度的同时也具有较好的冲击韧性。