Workload characterization of JVM languages

Originally developed with a single language in mind, the JVM is now targeted by numerous programming languages—its automatic memory management, just‐in‐time compilation, and adaptive optimizations—making it an attractive execution platform. However, the garbage collector, the just‐in‐time compiler, and other optimizations and heuristics were designed primarily with the performance of Java programs in mind. Consequently, many of the languages targeting the JVM, and especially the dynamically typed languages, are suffering from performance problems that cannot be simply solved at the JVM side. In this article, we aim to contribute to the understanding of the character of the workloads imposed on the JVM by both dynamically typed and statically typed JVM languages. To this end, we introduce a new set of dynamic metrics for workload characterization, along with an easy‐to‐use toolchain to collect the metrics. We apply the toolchain to applications written in six JVM languages (Java, Scala, Clojure, Jython, JRuby, and JavaScript) and discuss the findings. Given the recently identified importance of inlining for the performance of Scala programs, we also analyze the inlining behavior of the HotSpot JVM when executing bytecode originating from different JVM languages. As a result, we identify several traits in the non‐Java workloads that represent potential opportunities for optimization. © 2015 The Authors. Software: Practice and Experience Published by John Wiley & Sons Ltd.     

Identifying Novel Osteoarthritis-Associated Genes in Human Cartilage Using a Systematic Meta-Analysis and a Multi-Source Integrated Network

Osteoarthritis, the most common joint disorder, is characterised by deterioration of the articular cartilage. Many studies have identified potential therapeutic targets, yet no effective treatment has been determined. The aim of this study was to identify and rank osteoarthritis-associated genes and micro-RNAs to prioritise those most integral to the disease. A systematic meta-analysis of differentially expressed mRNA and micro-RNAs in human osteoarthritic cartilage was conducted. Ingenuity pathway analysis identified cellular senescence as an enriched pathway, confirmed by a significant overlap (p < 0.01) with cellular senescence drivers (CellAge Database). A co-expression network was built using genes from the meta-analysis as seed nodes and combined with micro-RNA targets and SNP datasets to construct a multi-source information network. This accumulated and connected 1689 genes which were ranked based on node and edge aggregated scores. These bioinformatic analyses were confirmed at the protein level by mass spectrometry of the different zones of human osteoarthritic cartilage (superficial, middle, and deep) compared to normal controls. This analysis, and subsequent experimental confirmation, revealed five novel osteoarthritis-associated proteins (PPIB, ASS1, LHDB, TPI1, and ARPC4-TTLL3). Focusing future studies on these novel targets may lead to new therapies for osteoarthritis.     

Application of fusion property estimation within the multisolid wax prediction model on Kazakhstani crude

Precipitation of deposited wax on pipe walls is one of the complex flow assurance problems that cause a decrease and complete blockage of oil production rates by reducing the cross-sectional area of the flow in the pipelines. In addition, surface facilities require higher energy consumption and equipment failure due to paraffin plugs. The purpose of the research is to use the assessment of melting properties in the model of multisolid (MS) forecasting of paraffin in Kazakhstani oil. This article presents the calculation and modification of the fusion properties for a certain Kazakhstani oil for the subsequent calculation of the MS model for predicting wax deposition; numerous approaches have been developed to predict and prevent the flow assurance problems in both science and industry. Most of them are based on predicting the temperature of crystallization of paraffin considering the temperature dependence of the solubility parameters of individual components in the liquid and solid phases, as well as the molar volumes of individual components. Wax deposition can occur anywhere from a reservoir to surface facilities and pipelines. One of the main limitations of the existing models is their applicability to a wide range of crude oil types, while this modified paraffin predicting model will target Kazakhstani crude oil.     

On discovering co-location patterns in datasets: a case study of pollutants and child cancers

We intend to identify relationships between cancer cases and pollutant emissions by proposing a novel co-location mining algorithm. In this context, we specifically attempt to understand whether there is a relationship between the location of a child diagnosed with cancer with any chemical combinations emitted from various facilities in that particular location. Co-location pattern mining intends to detect sets of spatial features frequently located in close proximity to each other. Most of the previous works in this domain are based on transaction-free apriori-like algorithms which are dependent on user-defined thresholds, and are designed for boolean data points. Due to the absence of a clear notion of transactions, it is nontrivial to use association rule mining techniques to tackle the co-location mining problem. Our proposed approach is focused on a grid based transactionization? of the geographic space, and is designed to mine datasets with extended spatial objects. It is also capable of incorporating uncertainty of the existence of features to model real world scenarios more accurately. We eliminate the necessity of using a global threshold by introducing a statistical test to validate the significance of candidate co-location patterns and rules. Experiments on both synthetic and real datasets reveal that our algorithm can detect a considerable amount of statistically significant co-location patterns. In addition, we explain the data modelling framework which is used on real datasets of pollutants (PRTR/NPRI) and childhood cancer cases.