Development of ADEM–CFD model for analyzing dynamic and breakage behavior of aggregates in wet ball milling

A new simulation method was developed for analyzing the grinding mechanisms of aggregates in wet ball milling. The calculation of the following five behaviors is needed in this case: dynamics and breakage behaviors of aggregates, collisions of aggregates, a motion of fluid including aggregates, ball-fluid interaction forces and aggregate-fluid interaction forces. The dynamic and breakage behaviors of aggregates were calculated by advanced discrete element method (ADEM). The collisions of aggregates were represented by DEM. The motion of fluid including aggregates was solved by spatially-averaged equations of the fluid with finite difference method (FDM). The ball-fluid interaction forces were calculated by immersed boundary method (IBM). The model for the aggregate-fluid interaction forces has not been established, so that a new simulation model for estimating them was developed and named ADEM-computational fluid dynamics (ADEM-CFD) model. The ADEM-CFD model was verified by comparing the fluid drag coefficients obtained by White’s equation. The new simulation method considering the five behaviors was validated by comparing with an experiment about dynamic and breakage behaviors of aggregates around a falling ball in liquid. It is found that the new simulation method proposed could analyze the dynamic and breakage behaviors of aggregates in wet ball milling.     

Higher order Cauchy–Born rule based multiscale cohesive zone model and prediction of fracture toughness of silicon thin films

In this work, we extend the multiscale cohesive zone model (MCZM) (Zeng and Li in Comput Methods Appl Mech Eng 199:547–556, 2010), in which interatomic potential is embedded into constitutive relation to express cohesive law in fracture process zone, to include the hierarchical Cauchy–Born rule in the process zone and to simulate three dimensional fracture in silicon thin films. The model has been applied to simulate fracture stress and fracture toughness of single-crystal silicon thin film by using the Tersoff potential. In this study, a new approach has been developed to capture inhomogeneous deformation inside the cohesive zone. For this purpose, we introduce higher order Cauchy–Born rules to construct constitutive relations for corresponding higher order process zone elements, and we introduce a sigmoidal function supported bubble mode in finite element shape function of those higher order cohesive zone elements to capture the nonlinear inhomogeneous deformation inside the cohesive zone elements. Benchmark tests with simple 3D models have confirmed that the present method can predict the fracture toughness of silicon thin films. Interestingly, this is accomplished without increasing of computational cost, because the present model does not require quadratic elements to represent heterogeneous deformation, which is the inherent weakness of the previous MCZM model. Quantitative comparisons with experimental results are performed by computing crack propagation in non-notched and initially notched silicon thin films, and it is found that our model can reproduce essential material properties, such as Young’s modulus, fracture stress, and fracture toughness of single-crystal silicon thin films.     

A method of gait coordination of hexapod robots using immune networks

Biological information processing systems can be regarded as one of the ultimate decentralized systems, and have been expected to provide various fruitful ideas in the engineering field. Among these systems, the immune system plays an important role in coping with dynamically changing environments by constructing self-nonself recognition networks among different species of antibodies, and has many interesting features from an engineering stand-point, such as learning, self-organizing abilities, and so on. However, it has not yet been applied to engineering fields. Therefore we pay close attention to the immune system and attempt to construct an artificial immune network for robot control. In this study we propose a new interpretation of the roles of antibodies in terms of self-assertion and subordination, and apply this idea to a gait coordination problem of a hexapod robot as a practical example. Several computer simulations are carried out, and the robustness against disturbances and the feasibility of our method are confirmed. This work was presented, in part, at the International Symposium on Artificial Life and Robotics, Oita, Japan, February 18–20, 1996     

A genetic network programming with learning approach for enhanced stock trading model

In this paper, an enhancement of stock trading model using Genetic Network Programming (GNP) with Sarsa Learning is described. There are three important points in this paper: First, we use GNP with Sarsa Learning as the basic algorithm while both Technical Indices and Candlestick Charts are introduced for efficient stock trading decision-making. In order to create more efficient judgment functions to judge the current stock price appropriately, Importance Index (IMX) has been proposed to tell GNP the timing of buying and selling stocks. Second, to improve the performance of the proposed GNP-Sarsa algorithm, we proposed a new method that can learn the appropriate function describing the relation between the value of each technical index and the value of the IMX. This is an important point that devotes to the enhancement of the GNP-Sarsa algorithm. The third point is that in order to create more efficient judgment functions, sub-nodes are introduced in each node to select appropriate stock price information depending on the situations and to determine appropriate actions (buying/selling). To confirm the effectiveness of the proposed method, we carried out the simulation and compared the results of GNP-Sarsa with other methods like GNP with Actor Critic, GNP with Candlestick Chart, GA and Buy&Hold method. The results shows that the stock trading model using GNP-Sarsa outperforms all the other methods.     

Molecular mechanics and molecular orbital simulations on specific interactions between peroxisome proliferator-activated receptor PPARalpha and plasticizer

Peroxisome proliferator-activated receptor α (PPARα) has various physiological functions such as lipid and glucose metabolism, inflammation and fibrosis in living organisms. Many types of ligand molecules such as phthalate and adipate esters control these physiological functions. In the present study, to elucidate the dependence of PPARα properties on ligand binding, we investigated stable structures and electronic properties for the complexes of PPARα and phthalate as well as adipate esters, which are used as a plasticizer, by molecular simulations based on molecular mechanics and molecular orbital methods. Furthermore, to elucidate the influence of these esters in vivo, we injected them into male mice and observed the change in the expression of PPARα-related enzymes. The comparison between the calculated and observed results indicates that the change in the expression has a correlation with the size of energy gaps between highest occupied and lowest unoccupied molecular orbitals of the complexes with mouse PPARα and esters.     

A maximum likelihood approach to density estimation with semidefinite programming

Density estimation plays an important and fundamental role in pattern recognition, machine learning, and statistics. In this article, we develop a parametric approach to univariate (or low-dimensional) density estimation based on semidefinite programming (SDP). Our density model is expressed as the product of a nonnegative polynomial and a base density such as normal distribution, exponential distribution, and uniform distribution. When the base density is specified, the maximum likelihood estimation of the polynomial is formulated as a variant of SDP that is solved in polynomial time with the interior point methods. Since the base density typically contains just one or two parameters, computation of the maximum likelihood estimate reduces to a one- or two-dimensional easy optimization problem with this use of SDP. Thus, the rigorous maximum likelihood estimate can be computed in our approach. Furthermore, such conditions as symmetry and unimodality of the density function can be easily handled within this framework. AIC is used to choose the best model. Through applications to several instances, we demonstrate flexibility of the model and performance of the proposed procedure. Combination with a mixture approach is also presented. The proposed approach has possible other applications beyond density estimation. This point is clarified through an application to the maximum likelihood estimation of the intensity function of a nonstationary Poisson process.     

Challenges for Next Generation Network Operations and Service Management: a Report on APNOMS 2008

This article presents a report on APNOMS 2008, which was held October 22–24, 2008 in Beijing, China. The theme of APNOMS 2008 was “Challenges for Next Generation Network Operations and Service Management.”     

Finding Related Search Engine Queries by Web Community Based Query Enrichment

The conventional approaches of finding related search engine queries rely on the common terms shared by two queries to measure their relatedness. However, search engine queries are usually short and the term overlap between two queries is very small. Using query terms as a feature space cannot accurately estimate relatedness. Alternative feature spaces are needed to enrich the term based search queries. In this paper, given a search query, first we extract the Web pages accessed by users from Japanese Web access logs which store the users individual and collective behavior. From these accessed Web pages we usually can get two kinds of feature spaces, i.e, content-sensitive (e.g., nouns) and content-ignorant (e.g., URLs), to enrich the expressions of search queries. Then, the relatedness between search queries can be estimated on their enriched expressions. Our experimental results show that the URL feature space produces much lower precision scores than the noun feature space which, however, is not applicable in non-text pages, dynamic pages and so on. It is crucial to improve the quality of the URL (content-ignorant) feature space since it is generally available in all types of Web pages. We propose a novel content-ignorant feature space, called Web community which is created from a Japanese Web page archive by exploiting link analysis. Experimental results show that the proposed Web community feature space generates much better results than the URL feature space.     

High-temperature fatigue crack propagation mechanism of orthogonal 3-D woven amorphous SiC Fiber/SiC/YSi2-Si matrix composites

To elucidate degradation mechanisms attributable to high-temperature fatigue crack propagation, a study was conducted of 3-D woven SiC_f/SiC CMC in which amorphous SiC fiber was used as a reinforcement material and in which a matrix was formed through low-temperature melt infiltration. From a high-temperature fatigue test conducted at 1373 K in the atmosphere with stress of 142 MPa or more, the fracture lifetime of newly developed SiC_f/SiC CMC was found to be longer than that of SiC_f/SiC CMC, which uses crystalline SiC fiber. Furthermore, repeatedly applying high temperatures during high-temperature fatigue tests and using X-ray computed tomography, fatigue cracks were found to propagate in a direction across 0-degree fiber bundles that undergo stress. Electron mapping of regions with crack propagation revealed that oxidation eliminates boron nitride (BN), which has a crack deflection effect. The SiC fibers and matrix are fixed through the formation of oxides. Cracks propagate because of the consequent decrease in toughness of the SiC_f/SiC CMC. In regions without crack propagation, fracture surfaces were not covered with oxides. These regions underwent forcible fracture in the final stage of the high-temperature fatigue tests. From the test results presented above, SiC_f/SiC CMC is considered to undergo fracture when the effective cross-sectional area is reduced because of crack propagation accompanying oxidation and when the test load exceeds the tensile strength of the residual cross-sectional area. However, some cracks in the matrix produced by a low-temperature melt infiltration process were closed by oxides derived from YSi₂. Because of crack closing, crack propagation is presumed to be avoided. Also, LMI-CMC showed excellent high-temperature fatigue properties at pressures higher than 150 MPa, which exceeds the proportional limit.     

Towards a Treatment for Neuroinflammation in Epilepsy: Interleukin-1 Receptor Antagonist,Anakinra, as a Potential Treatment in Intractable Epilepsy

Febrile Infection-Related Epilepsy Syndrome (FIRES) is a unique catastrophic epilepsy syndrome, and the development of drug-resistant epilepsy (DRE) is inevitable. Recently, anakinra, an interleukin-1 receptor antagonist (IL-1RA), has been increasingly used to treat DRE due to its potent anticonvulsant activity. We here summarized its effects in 38 patients (32 patients with FIRES and six with DRE). Of the 22 patients with FIRES, 16 (73%) had at least short-term seizure control 1 week after starting anakinra, while the remaining six suspected anakinra-refractory cases were male and had poor prognoses. Due to the small sample size, an explanation for anakinra refractoriness was not evident. In all DRE patients, seizures disappeared or improved, and cognitive function improved in five of the six patients following treatment. Patients showed no serious side effects, although drug reactions with eosinophilia and systemic symptoms, cytopenia, and infections were observed. Thus, anakinra has led to a marked improvement in some cases, and functional deficiency of IL-1RA was indicated, supporting a direct mechanism for its therapeutic effect. This review first discusses the effectiveness of anakinra for intractable epileptic syndromes. Anakinra could become a new tool for intractable epilepsy treatment. However, it does not currently have a solid evidence base.