A modelling and networking architecture for distributed virtual environments with multiple servers.

Virtual Environments (VEs) attempt to give people the illusion of immersion that they are in a computer generated world. VEs allow people to actively participate in a synthetic environment. They range from a single-person running on a single computer, to multiple-people running on several computers connected through a network. When VEs are distributed on multiple computers across a network, we call this a Distributed Virtual Environment (DVE). Virtual Environments can benefit greatly from distributed strategies.A networked VE system based on the Client-Server model is the most commonly used paradigm in constructing DVE systems. In a Client-Server model, data can be distributed on several server computers. The server computers provide services to their own clients via networks. In some client-server models, however, a powerful server is required, or it will become a bottleneck. To reduce the amount of data and traffic maintained by a single server, the servers themselves can be distributed, and the virtual environment can be divided over a network of servers.The system described in this thesis, therefore, is based on the client-server model with multiple servers. This grouping is called a Distributed Virtual Environment System with Multiple- Servers (DVM). A DVM system shows a new paradigm of distributed virtual environments based on shared 3D synthetic environments. A variety of network elements are required to support large scale DVM systems. The network is currently the most constrained resource of the DVM system. Development of networking architectures is the key to solving the DVM challenge. Therefore, a networking architecture for implementing a DVM model is proposed. Finally, a DVM prototype system is described to demonstrate the validity of the modelling and network architecture of a DVM model

Oceanus.

v. 38, no.1 (1995

Just in Time: The Beyond-the-Hype Potential of E-Learning

Based on a year of conversations with more than 100 leading thinkers, practitioners, and entrepreneurs, this report explores the state of e-learning and the potential it offers across all sectors of our economy -- far beyond the confines of formal education. Whether you're a leader, worker in the trenches, or just a curious learner, imagine being able to access exactly what you need, when you need it, in a format that's quick and easy to digest and apply. Much of this is now possible and within the next decade, just-in-time learning will likely become pervasive.This report aims to inspire you to consider how e-learning could change the way you, your staff, and the people you serve transfer knowledge and adapt over time

Aerospace Medicine and Biology: A continuing bibliography with indexes, supplement 159

This bibliography lists 257 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1976

The 1990 Johnson Space Center bibliography of scientific and technical papers

Abstracts are presented of scientific and technical papers written and/or presented by L. B. Johnson Space Center (JSC) authors, including civil servants, contractors, and grantees, during the calendar year of 1990. Citations include conference and symposium presentations, papers published in proceedings or other collective works, seminars, and workshop results, NASA formal report series (including contractually required final reports), and articles published in professional journals

Analyzing epigenomic data in a large-scale context

While large amounts of epigenomic data are publicly available, their retrieval in a form suitable for downstream analysis is a bottleneck in current research. In a typical analysis, users are required to download huge files that span the entire genome, even if they are only interested in a small subset (e.g., promoter regions) or an aggregation thereof. Moreover, complex operations on genome-level data are not always feasible on a local computer due to resource limitations. The DeepBlue Epigenomic Data Server mitigates this issue by providing a robust server that affords a powerful API for searching, filtering, transforming, aggregating, enriching, and downloading data from several epigenomic consortia. Furthermore, its main component implements scalable data storage and Manipulation methods that scale with the increasing amount of epigenetic data, thereby making it the ideal resource for researchers that seek to integrate epigenomic data into their analysis workflow. This work also presents companion tools that utilize the DeepBlue API to enable users not proficient in scripting or programming languages to analyze epigenomic data in a user-friendly way: (i) an R/Bioconductor package that integrates DeepBlue into the R analysis workflow. The extracted data are automatically converted into suitable R data structures for downstream analysis and visualization within the Bioconductor frame- work; (ii) a web portal that enables users to search, select, filter and download the epigenomic data available in the DeepBlue Server. This interface provides elements, such as data tables, grids, data selections, developed for empowering users to find the required epigenomic data in a straightforward interface; (iii) DIVE, a web data analysis tool that allows researchers to perform large-epigenomic data analysis in a programming-free environment. DIVE enables users to compare their datasets to the datasets available in the DeepBlue Server in an intuitive interface, which summarizes the comparison of hundreds of datasets in a simple chart. Furthermore, these tools are integrated, being capable of sharing results among themselves, creating a powerful large-scale epigenomic data analysis environment. The DeepBlue Epigenomic Data Server and its ecosystem was well received by the International Human Epigenome Consortium and already attracted much attention by the epigenomic research community with currently 160 registered users and more than three million anonymous workflow processing requests since its release.Während große Mengen epigenomischer Daten öffentlich verfügbar sind, ist ihre Abfrage in einer für die Downstream-Analyse geeigneten Form ein Engpass in der aktuellen Forschung. Bei einer typischen Analyse müssen Benutzer riesige Dateien herunterladen, die das gesamte Genom umfassen, selbst wenn sie nur an einer kleinen Teilmenge (z.B., Promotorregionen) oder einer Aggregation davon interessiert sind. Darüber hinaus sind komplexe Vorgänge mit Daten auf Genomebene aufgrund von Ressourceneinschränkungen auf einem lokalen Computer nicht immer möglich. Der DeepBlue Epigenomic Data Server behebt dieses Problem, indem er eine leistungsstarke API zum Suchen, Filtern, Umwandeln, Aggregieren, Anreichern und Herunterladen von Daten verschiedener epigenomischer Konsortien bietet. Darüber hinaus implementiert der DeepBlue-Server skalierbare Datenspeicherungs- und manipulationsmethoden, die der zunehmenden Menge epigenetischer Daten gerecht werden. Dadurch ist der DeepBlue Server ideal für Forscher geeignet, die die aktuellen epigenomischen Ressourcen in ihren Analyse-Workflow integrieren möchten. In dieser Arbeit werden zusätzlich Begleittools vorgestellt, die die DeepBlue-API verwenden, um Benutzern, die sich mit Scripting oder Programmiersprachen nicht auskennen, die Möglichkeit zu geben, epigenomische Daten auf benutzerfreundliche Weise zu analysieren: (i) ein R/ Bioconductor-Paket, das DeepBlue in den R-Analyse-Workflow integriert. Die extrahierten Daten werden automatisch in geeignete R-Datenstrukturen für die Downstream-Analyse und Visualisierung innerhalb des Bioconductor-Frameworks konvertiert; (ii) ein Webportal, über das Benutzer die auf dem DeepBlue Server verfügbaren epigenomischen Daten suchen, auswählen, filtern und herunterladen können. Diese Schnittstelle bietet Elemente wie Datentabellen, Raster, Datenselektionen, mit denen Benutzer die erforderlichen epigenomischen Daten in einer einfachen Schnittstelle finden können; (iii) DIVE, ein Webdatenanalysetool, mit dem Forscher umfangreiche epigenomische Datenanalysen in einer programmierungsfreien Umgebung durchführen können. Mit DIVE können Benutzer ihre Datensätze mit den im Deep- Blue Server verfügbaren Datensätzen in einer intuitiven Benutzeroberfläche vergleichen. Dabei kann der Vergleich hunderter Datensätze in einem Diagramm ausgedrückt werden. Aufgrund der großen Datenmenge, die in DIVE verfügbar ist, werden Methoden bereitgestellt, mit denen die ähnlichsten Datensätze für eine vergleichende Analyse vorgeschlagen werden können. Alle zuvor genannten Tools sind miteinander integriert, so dass sie die Ergebnisse untereinander austauschen können, wodurch eine leistungsstarke Umgebung für die Analyse epigenomischer Daten entsteht. Der DeepBlue Epigenomic Data Server und sein Ökosystem wurden vom International Human Epigenome Consortium äußerst gut aufgenommen und erreichten seit ihrer Veröffentlichung große Aufmerksamkeit bei der epigenomischen Forschungsgemeinschaft mit derzeit 160 registrierten Benutzern und mehr als drei Millionen anonymen Verarbeitungsanforderungen

Web-based interface for environmental niche modelling

Marine species are subject to anthropogenic impacts, such as noise pollution, marine litter, and direct impact collisions. While there are efforts in the marine community and crowd-sourcing to report the occurrence of marine species, not enough projects explore the prediction of where such animals may be. This dissertation analyzes the state of the art in species distribution model ing (SDM) systems, capable of reporting and predicting marine biodiversity. The proposal implements the algorithms for predicting species through publicly avail able repositories of data, provides means to ease the upload and management of occurrence points as well as methods for prediction analysis. A web-based user interface is proposed using Ecological Niche Modelling (ENM) as an automated alerting mechanism towards ecological awareness. Performed user studies evaluate marine biodiversity concerns from fisherman and whale-watching sea-vessels, assessing attitudes, threats, values, and motiva tion of both samples. Further, biologists and experts on ENMs will evaluate the workflow and interface, reviewing the proposal’s potential to enable ecologists to create solutions for their custom problems using simple protocols without the need for any third-party entities and extensive knowledge in programming.Espécies marinhas estão sujeitas a impactos antropogênicos, tais como poluição sonora, lixo marinho, e colisões com tráfego marinho. Apesar de existirem al guns esforços da comunidade marinha e crowdsourcing relativamente ao registo de ocorrências de biodiversidade marinha, não existem projeto suficientes que exploram as previsões de onde estas espécies poderão estar. Esta dissertação analisa o estado da arte em sistemas de modelação de dis tribuição de espécies, capazes de relatar e prever biodiversidade marinha. A pro posta implementa os algoritmos para prever espécies por meio de repositórios consolidados de dados disponíveis online, fornece meios para facilitar o carrega mento e gestão de pontos de ocorrência, bem como métodos para análise das previsões. Uma interface web de utilizador é proposta utilizando Ecological Niche Modeling como um mecanismo de alerta automatizado para incrementar a con sciência ecológica. Os estudos do sistema irão avaliar as preocupações relativas a biodiversi dade marinha de embarcações de pesca e navios de observação de baleias. Desta forma é possível determinar atitudes, ameaças, valores e motivação de ambas as amostras para com a biodiversidade marinha. Além disso, biólogos e espe cialistas nesta tipologia de sistemas, avaliarão o fluxo de trabalho e a inter face desenvolvida, avaliando o potencial do sistema, permitindo aos ecologistas criar soluções personalizados através de protocolos simples, sem a necessidade de quaisquer entidades terceirizadas e conhecimento em programação