Efficient method for simulating quantum electron dynamics under the time dependent Kohn-Sham equation

A numerical scheme for solving the time-evolution of wave functions under the time dependent Kohn-Sham equation has been developed. Since the effective Hamiltonian depends on the wave functions, the wave functions and the effective Hamiltonian should evolve consistently with each other. For this purpose, a self-consistent loop is required at every time-step for solving the time-evolution numerically, which is computationally expensive. However, in this paper, we develop a different approach expressing a formal solution of the TD-KS equation, and prove that it is possible to solve the TD-KS equation efficiently and accurately by means of a simple numerical scheme without the use of any self-consistent loops.Comment: 5 pages, 3 figures. Physical Review E, 2002, in pres

Plasma Dynamics

Contains reports on two research projects.U. S. Energy Research and Development Administration (Contract E(l1-1)-3070)National Science Foundation (Grant ENG75-06242

Plasma Dynamics

Contains reports on eight research projects split into two sections.National Science Foundation (Grant ENG79-07047)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143D)U.S. Department of Energy (Contract DE-ACO2-78ET-51013)U.S. Department of Energy (Contract DE-ACO2-78ET-53073.AO02)U.S. Department of Energy (Contract DE-ACO2-78ET-53074)U.S. Department of Energy (Contract DE-ACO2-78ET-53076)U.S. Department of Energy (Contract DE-ACO2-78ET-51002

Plasma Dynamics

Contains reports on twenty research projects split into two sections.National Science Foundation (Grant ENG75-06242-A01)U. S. Energy Research and Development Administration (Contract E(11-1)-2766)U. S. Energy Research and Development Administration (Contract E(11-1)-3070

Plasma Dynamics

Contains reports on ten research projects split into two sections.National Science Foundation (Grant ENG77-00340)U.S. Department of Energy (Contract EY-76-S-02-2766)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Department of Energy (Contract ET-78-C-01-3019)U.S. Department of Energy (Contract ET-78-S-02-4681)U.S. Department of Energy (Contract ET-78-S-02-4682)U.S. Department of Energy (Grant EG-77-G-01-4107)U.S. Department of Energy (Contract ET-78-S-02-4714)U.S. Department of Energy (Contract ET-78-S-02-4886)U.S. Department of Energy (Contract ET-78-S-02-4690

Plasma Dynamics

Contains reports on ten research projects divided into two sections.National Science Foundation (Grant ENG79-07047)U.S. Air Force - Office of Scientific Research (Grant AFOSR-77-3143)U.S. Department of Energy (Contract DE-ACO2-78ET51013)U.S. Department of Energy (Contract DE-ASO2-78ET53073.AO02)U.S. Department of Energy (Contract ET-78-S-02-4682)U.S. Department of Energy (Contract DE-AS02-78ET53074)U.S. Department of Energy (Contract DE-ASO2-78ET53050)U.S. Department of Energy (Contract DE-AS02-78ET51002)U.S. Department of Energy (Contract DE-ASO2-78ET53076

Plasma Dynamics

Contains research objectives and summary of research on eighteen research projects split into seven sections and reports on four research projects.U.S. Atomic Energy Commission (Contract AT(l1-1)-3070)National Science Foundation (Grant GK-37979X1

Automating Genomic Data Mining via a Sequence-based Matrix Format and Associative Rule Set

There is an enormous amount of information encoded in each genome – enough to create living, responsive and adaptive organisms. Raw sequence data alone is not enough to understand function, mechanisms or interactions. Changes in a single base pair can lead to disease, such as sickle-cell anemia, while some large megabase deletions have no apparent phenotypic effect. Genomic features are varied in their data types and annotation of these features is spread across multiple databases. Herein, we develop a method to automate exploration of genomes by iteratively exploring sequence data for correlations and building upon them. First, to integrate and compare different annotation sources, a sequence matrix (SM) is developed to contain position-dependant information. Second, a classification tree is developed for matrix row types, specifying how each data type is to be treated with respect to other data types for analysis purposes. Third, correlative analyses are developed to analyze features of each matrix row in terms of the other rows, guided by the classification tree as to which analyses are appropriate. A prototype was developed and successful in detecting coinciding genomic features among genes, exons, repetitive elements and CpG islands

The Genopolis Microarray Database

<p>Abstract</p> <p>Background</p> <p>Gene expression databases are key resources for microarray data management and analysis and the importance of a proper annotation of their content is well understood.</p> <p>Public repositories as well as microarray database systems that can be implemented by single laboratories exist. However, there is not yet a tool that can easily support a collaborative environment where different users with different rights of access to data can interact to define a common highly coherent content. The scope of the Genopolis database is to provide a resource that allows different groups performing microarray experiments related to a common subject to create a common coherent knowledge base and to analyse it. The Genopolis database has been implemented as a dedicated system for the scientific community studying dendritic and macrophage cells functions and host-parasite interactions.</p> <p>Results</p> <p>The Genopolis Database system allows the community to build an object based MIAME compliant annotation of their experiments and to store images, raw and processed data from the Affymetrix GeneChip^®platform. It supports dynamical definition of controlled vocabularies and provides automated and supervised steps to control the coherence of data and annotations. It allows a precise control of the visibility of the database content to different sub groups in the community and facilitates exports of its content to public repositories. It provides an interactive users interface for data analysis: this allows users to visualize data matrices based on functional lists and sample characterization, and to navigate to other data matrices defined by similarity of expression values as well as functional characterizations of genes involved. A collaborative environment is also provided for the definition and sharing of functional annotation by users.</p> <p>Conclusion</p> <p>The Genopolis Database supports a community in building a common coherent knowledge base and analyse it. This fills a gap between a local database and a public repository, where the development of a common coherent annotation is important. In its current implementation, it provides a uniform coherently annotated dataset on dendritic cells and macrophage differentiation.</p