Single Computer Environment Research Articles

Plugin-based interoperability and ecosystem management for the MolSSI Driver Interface Project.

The MolSSI Driver Interface (MDI) Project is an effort to simplify and standardize the process of enabling tight interoperability between independently developed code bases and is supported by numerous software packages across the domain of chemical physics. It enables a wide variety of use cases, including quantum mechanics/molecular mechanics, advanced sampling, path integral molecular dynamics, machine learning, abinitio molecular dynamics, etc. We describe two major developments within the MDI Project that provide novel solutions to key interoperability challenges. The first of these is the development of the MDI Plugin System, which allows MDI-supporting libraries to be used as highly modular plugins, with MDI enforcing a standardized application programming interface across plugins. Codes can use these plugins without linking against them during their build process, and end-users can select which plugin(s) they wish to use at runtime. The MDI Plugin System features a sophisticated callback system that allows codes to interact with plugins on a highly granular level and represents a significant advancement toward increased modularity among scientific codes. The second major development is MDI Mechanic, an ecosystem management tool that utilizes Docker containerization to simplify the process of developing, validating, maintaining, and deploying MDI-supporting codes. Additionally, MDI Mechanic provides a framework for launching MDI simulations in which each interoperating code is executed within a separate computational environment. This eliminates the need to compile multiple production codes within a single computational environment, reducing opportunities for dependency conflicts and lowering the barrier to entry for users of MDI-enabled codes.

Big Data Storage Index Mechanism Based on Spatiotemporal Information Cloud Platform

In order to study the storage and management mechanism of raster data and vector data for different purposes in data services, a research method of big data storage and indexing mechanism based on spatiotemporal information cloud platform is proposed. This paper discusses the application of big data storage index in virtualization platform, cloud management software, and storage management, so that Hadoop cluster can use the dynamic expansion ability of cloud platform to obtain better expansion ability. High performance statistical applications for geographical conditions are constructed. A high performance geostatistical analysis system Hadoop-Geostatistics is designed and implemented. A variety of spatial statistics index calculation, flow, and MapReduce algorithm was realized. The experimental results show that in the cluster environment, the time consumption is basically the same as that of the single index calculation, while in the single computer environment, when the comprehensive index is calculated in parallel for 10,000 statistical objects, the system performance drops rapidly and reaches an early inflection point. In the comprehensive statistical concurrent calculation, when the time consumption reaches 5 × 10^7, the amount of calculation data is as high as 7000, which increases linearly. The experimental data show that the designed spatiotemporal information cloud platform model can store spatial big data, and the storage method is very accurate. By establishing a spatiotemporal information cloud platform, cloud computing technology can provide higher spatial information services.

Hidden Markov Pólya Trees for High-Dimensional Distributions

The Pólya tree (PT) process is a general-purpose Bayesian nonparametric model that has found wide application in a range of inference problems. It has a simple analytic form and the posterior computation boils down to beta-binomial conjugate updates along a partition tree over the sample space. Recent development in PT models shows that performance of these models can be substantially improved by (i) allowing the partition tree to adapt to the structure of the underlying distributions and (ii) incorporating latent state variables that characterize local features of the underlying distributions. However, important limitations of the PT remain, including (i) the sensitivity in the posterior inference with respect to the choice of the partition tree, and (ii) the lack of scalability with respect to dimensionality of the sample space. We consider a modeling strategy for PT models that incorporates a flexible prior on the partition tree along with latent states with Markov dependency. We introduce a hybrid algorithm combining sequential Monte Carlo (SMC) and recursive message passing for posterior sampling that can scale up to 100 dimensions. While our description of the algorithm assumes a single computer environment, it has the potential to be implemented on distributed systems to further enhance the scalability. Moreover, we investigate the large sample properties of the tree structures and latent states under the posterior model. We carry out extensive numerical experiments in density estimation and two-group comparison, which show that flexible partitioning can substantially improve the performance of PT models in both inference tasks. We demonstrate an application to a mass cytometry dataset with 19 dimensions and over 200,000 observations. Supplementary Materials for this article are available online.

Improvement of Business Analysis Method of E-Commerce System from the Perspective of Intelligent Recommendation System

In recent years, with the continuous development of the country’s Internet platforms, China has gradually entered the e-commerce era of national online shopping, and more and more e-commerce platforms and stores have adopted intelligent recommendation systems to increase transaction rates. However, it is not easy for consumers to filter out the products they want from a large amount of information. The emergence of intelligent recommendation systems provides great convenience for people to screen out personalized products that meet their own characteristics. However, the algorithms used in traditional recommendation technology focus on the single-computer environment and do not consider the performance of the recommendation method when distributed parallel processing is required in the big data environment, so it cannot meet the personalized needs of users in the big data environment. Aiming at the new requirements for the development of e-commerce intelligent recommendation technology in the big data environment, this paper uses the big data processing technology based on cloud computing and focuses on the realization technology of the e-commerce intelligent recommendation algorithm and the comprehensive evaluation method of the recommendation system in the big data environment. A prototype system of personalized intelligent recommendation based on cloud computing has been developed, which is of great importance to meet the needs of e-commerce personalized intelligent recommendation in the big data environment, improve the effectiveness, scale, and real-time performance of the personalized intelligent recommendation system, and improve the level of personalized precision marketing., which is of theoretical significance and economic value.

An assessment of the transient effect on helicopter main rotor stability and power demand

The researched object os a helicopter main rotor with blades of variable geometric twist characteristics. Variable torsion refers to systems of actuators made of shape memory alloys. The presented numerical analyses allow for evaluating both the dynamics of the rotor in transient states, i.e. in the zone between the static phase and the full activation phase and the impact of the change on the pulsation of the amplitude of the necessary power generated by the rotor corresponding the flight state, and thus covering the demand by the disposable power generated by the engine. This study follows a methodology of numerical analyses based on Multi Body Dynamics and the Finite Element Method and uses fluid mechanics elements and algorithms to analyze lift generation, compiled in a single computational environment referring to the same period of time.

FastMapping: Software to create field maps and identify management zones in precision agriculture

Diverse on-farm agronomic data, gathered via precision agriculture (PA), require depuration and joint analysis of multiple georeferenced field characteristics such as crop yields, and soil and topographic aspects. Multivariate analysis of spatial variability has been recommended to understand variation of data within fields and classify sites into zones of broad similarity to support crop management. However, most of the statistical techniques available for spatial data require advanced skills. Cleaning and statistical analysis of big spatial data can only be effectively implemented if there are easy-to-use computer programs integrating the analytical steps. This paper explains the development and implementation of FastMapping, an interactive web application to automatically clean PA raw data, generate spatial variability field maps, and delineate multivariate management zones. The application uses an interface developed in R language that automatically depurates datafiles; in addition, through automatic spatial interpolation of each variable, it allows us to merge data layers on the same spatial grid in such a way that each site within the agricultural field has values of all the measured variables. On this grid, FastMapping identifies homogeneous zones, in a multivariate and spatial way, and provides data reports including validation of the delineated zones. FastMapping outputs for zone delineation were compared with results from Management Zone Analyst (MZA) software in several fields. The new software yielded a similar zoning to that of MZA and added graphs and statistical results that are lacking in most PA software tools. The flexibility of FastMapping to import, clean and visualize PA data in a single computer environment is highly encouraging. FastMapping can be used by the PA community to support site-specific agricultural management.

Spark-based intelligent parameter inversion method for prestack seismic data

Seismic exploration is an oil exploration method by utilizing seismic information. Useful reservoir parameter information can be gained through inversion of seismic information to effectively carry out exploration work. Prestack data are characterized by large data size and rich information. Rich reservoir parameter information can be obtained through inversion of prestack data. Due to mass prestack seismic data, existing single computer environment cannot satisfy computation requirement of huge data size. Thus, an efficient and fast method is urgently needed to solve the inversion problem of prestack seismic big data. Since local optimum may be easily caught when genetic algorithm is used to optimize elastic parameters, the inversion effect is not obvious. In particular, the optimization effect for the density parameters is not good. An intelligent optimization algorithm is proposed in this paper for elastic parameter inversion of prestack seismic data. The algorithm improves genetic manipulation. The improved algorithm has been used for model trial for log data, and good inversion effect has been achieved. The inverted elastic parameters well fit with the log curve of the theoretical model. The improved algorithm effectively improves the inversion accuracy of density parameters. In this paper, the algorithm has been implemented on Spark model, and the results show that the parallel model can effectively reduce operation time of the algorithm.

Climate Data Analysis Tools Facilitate Scientific Investigations

Insightful analysis of geophysical phenomena often depends on the choice of software tools to access, manipulate, and visualize data sets of interest. These data exploration tasks can be efficiently executed in a single computational environment by the freely distributed Climate Data Analysis Tools (CDAT; http://www‐pcmdi.llnl.gov/software‐portal/). Although CDAT has been designed primarily for climate science applications, its capabilities are increasingly being enhanced to address the data needs of other geophysical sciences.The CDAT software is based on the object‐oriented Python computer language, but the software extends existing constructs to achieve capabilities that are relevant to any gridded geophysical data. Depending on the users preference, CDAT can be fully deployed by either a script or graphical user interface.

Application of orthogonal collocation and regression techniques for recovering parameters of a two-pathway transdermal drug-delivery model

This work focuses on the estimation of model parameters for a two-pathway transdermal drug-delivery system in which molecules are allowed to transfer from one pathway to the other. Orthogonal collocation, implemented in Mathematica ® (Wolfram Research Inc.), was used to reduce the original system into a set of ordinary differential equations (ODEs). After selecting the optimum number of internal collocation points, a sensitivity analysis was performed to determine the process parameters that significantly affected the cumulative amount of drug released. “In silico” experiments were conducted using randomly generated values of the key input variables. A study was designed to explore the ability of an orthogonal collocation-based optimization scheme to recover the true parameter values from simulation data. Although the solution of such an inverse problem is non-unique, the methodology was successful at identifying the process values, in most cases. This approach avoids the use of elaborate numerical inversion techniques of Laplace transforms, uses a single computational environment for generating the ODEs and estimating the parameters and does not require an analytical solution of the model. The computational approach was applied to elucidate the transport mechanism of caffeine through excised human skin in the presence of several permeation enhancers. The method shows that (i) the route of penetration of the drug across the skin is determined by the type of enhancers, (ii) the notable increase in steady-state transdermal flux, in comparison with control, is reflected in the estimated model parameters.

Laser-Doppler Flowmetry in Neurosurgery

Laser-Doppler flowmetry (LDF) provides a reliable measurement of local cerebral blood flow (lCBF) as demonstrated by multiple validation studies. This article evaluates the clinical applications of LDF in neurosurgery. With the availability of modified probes, it is possible to carry out LDF monitoring of neurosurgical patients in the intensive care unit. Currently, there are multiple systemic and intracranial parameters that are interactive and separately monitored. A multi-channeled digital data acquisition system allows these data to be compiled in a single computer environment for the interpretation of lCBF changes. Guidelines are suggested for the clinical use of LDF monitoring, and the technical features of monitoring including the interpretation of data are summarized.

IMAGE ANALYSIS AND DIGITAL CARTOGRAPHY WITHIN THE NERC THEMATIC INFORMATION SERVICE: THE DEVELOPMENT OF A UNIFIED SPATIAL MODEL

AbstractThe paper presents research and development at the Thematic Information Service of the Natural Environment Research Council (NERC) on the topic of integrated geographic information systems. The term integrated is used to describe the incorporation of vector digitised map data and raster scanned remotely sensed data within a single computational environment and with a single interface to the user. Point sampled and statistical data can also be handled by the system. The general structure and working of the Thematic Information Service (TIS) is described only to the extent that it is relevant to providing the background to the research or to highlighting why the specific approach described has been taken.