Use Of Standard Software Research Articles

The unified system for storing and accessing geophysical data. Traditions and new approaches

The features of the development of data collection systems within the Information Processing Center (IPC) of the Geophysical Survey of the Russian Academy of Sciences (GS RAS) are presenting. Historical information is given, including technical details related to the systematization of the archive and the evolution of geophysical data formats. The historical, territorial features, as well as the experience of deploying such information systems within the Federal Research Center of the Unified State Social Service of the Russian Academy of Sciences and abroad are taken into account. A new concept of building an information system is proposed, which takes into account the requirements for scalability, reproducibility at various objects and the use of standard software. A lot of work was done to form a homogeneous archive of waveforms and an inventory of metadata for seismic stations, which resulted in the possibility of including the GS RAS in the international centers for processing geophysical information based on FDSN. Creation of a distributed collection and processing system using a cloud service allows abstracting from the territorial features of collecting and storing geophysical information, which increases the performance of the data access service and the degree of technical readiness of key system nodes.

Use of the Shor’s r-Algorithm in Linear Robust Optimization Problems

The paper is devoted to the description of a new approach to the construction of algorithms for solving linear programming problems (LP-problems), in which the number of constraints is much greater than the number of variables. It is based on the use of a modification of the r-algorithm to solve the problem of minimizing a nonsmooth function, which is equivalent to LP problem. The advantages of the approach are demonstrated on the linear robust optimization problem and the robust parameters estimation problem using the least moduli method. The developed octave programs are designed to solve LP problems with a very large number of constraints, for which the use of standard software from linear programming is either impossible or impractical, because it requires significant computing resources. The material of the paper is presented in three sections. In the first section for the problem of minimizing a convex function we describe a modification of the r-algorithm with a constant coefficient of space dilation in the direction of the difference of two successive subgradients and an adaptive method for step size adjustment in the direction of the antisubgradient in the transformed space of variables. The software implementation of this modification is presented in the form of Octave function ralgb5a, which allows to find or approximation of the minimum point of a convex function, or approximation of the maximum point of the concave function. The code of the ralgb5a function is given with a brief description of its input and output parameters. In the second section, a method for solving the LP problem is presented using a nonsmooth penalty function in the form of maximum function and the construction of an auxiliary problem of unconstrained minimization of a convex piecewise linear function. The choice of the finite penalty coefficient ensures equivalence between the LP-problem and the auxiliary problem, and the latter is solved using the ralgb5a program. The results of computational experiments in GNU Octave for solving test LP-problems with the number of constraints from two hundred thousand to fifty million and the number of variables from ten to fifty are presented. The third section presents least moduli method that is robust to abnormal observations or "outliers". The method uses the problem of unconstrained minimization of a convex piecewise linear function, and is solved using the ralgb5a program. The results of computational experiments in GNU Octave for solving test problems with a large number of observations (from two hundred thousand to five million) and a small number of unknown parameters (from ten to one hundred) are presented. They demonstrate the superiority of the developed programs over well-known linear programming software such as the GLPK package. Keywords: robust optimization, linear programming problem, nonsmooth penalty function, r-algorithm, least modulus method, GNU Octave.

Capacity Management as a Service for Enterprise Standard Software

Capacity management approaches optimize component utilization from a strong technical perspective. In fact, the quality of involved services is considered implicitly by linking it to resource capacity values. This practice hinders to evaluate design alternatives with respect to given service levels that are expressed in user-centric metrics such as the mean response time for a business transaction. We argue that utilized historical workload traces often contain a variety of performance-related information that allows for the integration of performance prediction techniques through machine learning. Since enterprise applications excessively make use of standard software that is shipped by large software vendors to a wide range of customers, standardized prediction models can be trained and provisioned as part of a capacity management service which we propose in this article. Therefore, we integrate knowledge discovery activities into well-known capacity planning steps, which we adapt to the special characteristics of enterprise applications. Using a real-world example, we demonstrate how prediction models that were trained on a large scale of monitoring data enable cost-efficient measurement-based prediction techniques to be used in early design and redesign phases of planned or running applications. Finally, based on the trained model, we demonstrate how to simulate and analyze future workload scenarios. Using a Pareto approach, we were able to identify cost-effective design alternatives for an enterprise application whose capacity is being managed.

A “Low-Molecular” Approach to the Identification of Microorganisms by MALDI Mass Spectrometry

A new approach to the identification of microorganisms is presented. It includes the transformation of their MALDI mass spectra aimed at reducing mass scale by one order of magnitude and the use of standard software for building mass spectral libraries of low-molecular compounds and library searches. A library of 728 transformed (“rescaled”) mass spectra of 182 strains for some Streptococcus species was built. With this library, the rate of true microorganism identification was estimated by cross-comparison between library mass spectra (internal validation of the approach). The true identification rate was 84% for three Streptococcus species, which corresponds to the average trueness of species identification by MALDI as found in the literature. The proposed approach to identification can be considered as a method of choice for solving identification problems under consideration.

Method of generation of casual numbers on the basis of the use of apparatus of the computer plugged in the Internet

In systems or voting poll on the Internet, with a view to ensuring the conditions for the free expression of respondents thought necessary secrecy of the vote. For this purpose cryptographic protection of information that needs to generate random numbers. It is known that the standard computer functions to generate random numbers, which are based on mathematical transformations, give a pseudorandom generator that resemble random, but random really. Knowing the methods of obtaining such numbers, hackers can crack the security system. In this paper makes recommendations on the principles of construction of computer programs generating real (not pseudo) casual numbers in the terminal nodes of the Internet, provided the exclusive use of standard software and hardware (without any additional physical devices). The experimental results of the proposed principles of random number generation. In theory, proved the impossibility of foreseeing the numbers received, indicating that the futility of trying to use cryptanalysis funds to disclose them

Methods for meta-analysis of pharmacodynamic dose-response data with application to multi-arm studies of alogliptin.

Standard methods for meta-analysis of dose-response data in epidemiology assume a model with a single scalar parameter, such as log-linear relationships between exposure and outcome; such models are implicitly unbounded. In contrast, in pharmacology, multi-parameter models, such as the widely used Emax model, are used to describe relationships that are bounded above and below. We propose methods for estimating the parameters of a dose-response model by meta-analysis of summary data from the results of randomized controlled trials of a drug, in which each trial uses multiple doses of the drug of interest (possibly including dose 0 or placebo). We assume that, for each randomized arm of each trial, the mean and standard error of a continuous response measure and the corresponding allocated dose are available. We consider weighted least squares fitting of the model to the mean and dose pairs from all arms of all studies, and a two-stage procedure in which scalar inverse-variance meta-analysis is performed at each dose, and the dose-response model is fitted to the results by weighted least squares. We then compare these with two further methods inspired by network meta-analysis that fit the model to the contrasts between doses. We illustrate the methods by estimating the parameters of the Emax model to a collection of multi-arm, multiple-dose, randomized controlled trials of alogliptin, a drug for the management of diabetes mellitus, and further examine the properties of the four methods with sensitivity analyses and a simulation study. We find that all four methods produce broadly comparable point estimates for the parameters of most interest, but a single-stage method based on contrasts between doses produces the most appropriate confidence intervals. Although simpler methods may have pragmatic advantages, such as the use of standard software for scalar meta-analysis, more sophisticated methods are nevertheless preferable for their advantages in estimation.

CMS conditions data access using FroNTier

The CMS experiment at the LHC has established an infrastructure using the FroNTier framework to deliver conditions (i.e. calibration, alignment, etc.) data to processing clients worldwide. FroNTier is a simple web service approach providing client HTTP access to a central database service. The system for CMS has been developed to work with POOL which provides object relational mapping between the C++ clients and various database technologies. Because of the read only nature of the data, Squid proxy caching servers are maintained near clients and these caches provide high performance data access. Several features have been developed to make the system meet the needs of CMS including careful attention to cache coherency with the central database, and low latency loading required for the operation of the online High Level Trigger. The ease of deployment, stability of operation, and high performance make the FroNTier approach well suited to the GRID environment being used for CMS offline, as well as for the online environment used by the CMS High Level Trigger. The use of standard software, such as Squid and various monitoring tools, makes the system reliable, highly configurable and easily maintained. We describe the architecture, software, deployment, performance, monitoring and overall operational experience for the system.

Temporal-spatial Analysis of Breast Cancer Risk on Upper Cape Cod, Massachusetts, USA

P-665 Introduction: The reason for elevated breast cancer rates on upper Cape Cod, Massachusetts remains unknown despite epidemiological studies to investigate possible environmental risk factors. Two existing population-based breast cancer case-control studies of upper Cape Cod (1983–1993) provide a geocoded residential history and information on confounders for all subjects. Combining the two studies creates an invaluable dataset for temporal-spatial analysis of subjects’ residences from 1943 to 1993. Methods: Geographic Information Systems (GIS) have allowed the use of spatial analytic techniques in public health studies previously not feasible. Generalized Additive Models (GAMs) are an effective approach to mapping spatial distributions of data that combines: smoothing of geographical location and residency duration, inclusion of risk factors, selection of optimum degree of smoothing (bandwidth), hypothesis testing, ability to handle various types of outcome data, and use of standard software. The combination of mapping and statistical modeling provides a powerful tool for visualizing disease risk. Case-control data were modeled with GAMs and GIS to create breast cancer risk maps that are smoothed over time and space using a bivariate smooth term for location and a univariate smooth term time for residency duration. We also examined years of residency by applying the GAM model to the dataset restricted to residences for given time periods. A series of maps were used to create movies of changing breast cancer risk over time. The movies allow us to visualize changes in magnitude, geographic size, and location of elevated risk for the fifty years of residential history. Results: Maps show a large area of elevated breast cancer risk near the Massachusetts Military Reservation (MMR) beginning in the late 1940s and remaining consistent in size and location until early 1960s. Throughout much of the 1970s and 1980s, the risk map is relatively flat. In the late 1980s, an area of elevated risk is present in the northeast region of the study area. In the maps of residency duration, an area of elevated breast cancer risk north of the MMR appears after ten years of duration and increases in magnitude and size with longer duration. Maps of promoter periods showed very little spatial variation. Discussion and Conclusions: Temporal-spatial analysis of the breast cancer data may help identify new exposure hypotheses that warrant a future epidemiologic investigation. By determining the location, residency years, and duration associated with elevated breast cancer risk, we may gain a better understanding of any environmental factors involved.

A compartmental model of hepatic disposition kinetics: 1. Model development and application to linear kinetics.

The conventional convection-dispersion model is widely used to interrelate hepatic availability (F) and clearance (Cl) with the morphology and physiology of the liver and to predict effects such as changes in liver bloodflow on F and Cl. The extension of this model to include nonlinear kinetics and zonal heterogeneity of the liver is not straightforward and requires numerical solution of partial differential equation, which is not available in standard nonlinear regression analysis software. In this paper, we describe an alternative compartmental model representation of hepatic disposition (including elimination). The model allows the use of standard software for data analysis and accurately describes the outflow concentration-time profile for a vascular marker after bolus injection into the liver. In an evaluation of a number of different compartmental models, the most accurate model required eight vascular compartments, two of them with back mixing. In addition, the model includes two adjacent secondary vascular compartments to describe the tail section of the concentration-time profile for a reference marker. The model has the added flexibility of being easy to modify to model various enzyme distributions and nonlinear elimination. Model predictions of F, MTT, CV2, and concentration-time profile as well as parameter estimates for experimental data of an eliminated solute (palmitate) are comparable to those for the extended convection-dispersion model.

Efficient evaluation of treatment effects in the presence of missing covariate values

In clinical trials, treatment comparisons are often performed by models that incorporate important prognostic factors. Since these models require complete covariate information on all patients, statisticians frequently resort to complete case analysis or to omission of an important covariate. A probability imputation technique (PIT) is proposed that involves substituting conditional probabilities for missing covariate values when the covariate is qualitative. Simulation results are presented which demonstrate that the method neither violates the size of the treatment test nor introduces additional bias for the estimation of the treatment effect. It allows use of standard software. A clinical trial of breast cancer treatment, in which an important covariate was partly missing, was analysed by Cox's model. The use of PIT resulted in smaller observed error probability compared with case deletion, and sensitivity analysis supported these results.

The use of standard software in real time programming: An example demonstrating the integration of ADA, ORACLE and GKS

In the development of an operation guidance and recovery and maintenance planning system for a large telecommunication network the use of commercial standard software has been chosen. A network analysis system together with decision support functions for the operator is being programmed in an ADA environment, data storage is organized around an ORACLE data base, and man machine communication is done almost exclusively in graphics using GKS. We show how the standard packages have been integrated to guarantee both minimum development effort for the project, and fast real time response. Various compatibility problems and their solutions will be discussed, as well as criteria which standard software to choose in which cases: e.g. Pascal vs. ADA, ORACLE vs. RDB.