Quantifying microbial utilization of petroleum hydrocarbons in salt-marsh sediments using the ^(13)C content of bacterial rRNA

Natural remediation of oil spills is catalyzed by complex microbial consortia. Here we take a whole-community approach to investigate bacterial incorporation of petroleum hydrocarbons from a simulated oil spill. We utilized the natural difference in carbon-isotopic abundance between a salt marsh ecosystem supported by the ^(13)C-enriched C4 grass, Spartina alterniflora, and the ^(13)C-depleted composition of petroleum to monitor changes in the ^(13)C content of biomass. Magnetic-bead capture methods for the selective recovery of bacterial RNA were used to monitor the ^(13)C content of bacterial biomass during a two-week experiment. The data show that by the end of the experiment, up to 26% of bacterial biomass derived from consumption of the freshly-spilled oil. The results contrast with the inertness of a nearby relict spill, which occurred in 1969 in West Falmouth, MA. Sequences of 16S rRNA genes from our experimental samples also were consistent with previous reports suggesting the importance of {gamma}- and {delta}-Proteobacteria and Firmicutes in the remineralization of hydrocarbons. The magnetic-bead capture approach makes it possible to quantify uptake of petroleum hydrocarbons by microbes in-situ. Although employed here at the Domain level, RNA-capture procedures can be highly specific. The same strategy could be used with genus-level specificity, something which is not currently possible using the ^(13)C content of biomarker lipids

Agent-oriented Programming in Defence Domain

Research in distributed artificial intelligence has given rise to agent-oriented programming (AOP), an advanced software modelling paradigm. It has several benefits when compared with the existing development approaches, in particular, the ability to let agents represent high-level abstractions of active entities in a software system. Although still young and under evolution, this paradigm has already shown particular promise in a number of areas. This paper gives an overview of this paradigm, its benefits over the other conventional programming paradigms being used. It also proposes the decision support system model for the military domain.In the proposed system there are certain critical issues, which need to be focused upon. The existing conventional paradigms are inadequate to deal with these issues. This paper identifies these critical issues and discusses how AOP can address these issues

Notes on the Implementation of a Remote Fork Mechanism

We describe a method for implementing a remote fork, a primitive with the semantics of a UNIX fork() call which begins the execution of the child process on a remote machine. We begin by examining the subject of process migration, and conclude that most of the relevant process state can be captured and transferred to a remote system without operating system support. We then show how our implementation is easily optimized to achieve a performance improvement of greater than 10 times when measuring execution time. We conclude with some comments on limitations and applications of the remote fork mechanism

Disaster Recovery Services in Intercloud using Genetic Algorithm Load Balancer

Paradigm need to shifts from cloud computing to intercloud for disaster recoveries, which can outbreak anytime and anywhere. Natural disaster treatment includes radically high voluminous impatient job request demanding immediate attention. Under the disequilibrium circumstance, intercloud is more practical and functional option. There are need of protocols like quality of services, service level agreement and disaster recovery pacts to be discussed and clarified during the initial setup to fast track the distress scenario. Orchestration of resources in large scale distributed system having muli-objective optimization of resources, minimum energy consumption, maximum throughput, load balancing, minimum carbon footprint altogether is quite challenging. Intercloud where resources of different clouds are in align, plays crucial role in resource mapping. The objective of this paper is to improvise and fast track the mapping procedures in cloud platform and addressing impatient job requests in balanced and efficient manner. Genetic algorithm based resource allocation is proposed using pareto optimal mapping of resources to keep high utilization rate of processors, high througput and low carbon footprint.  Decision variables include utilization of processors, throughput, locality cost and real time deadline. Simulation results of load balancer using first in first out and genetic algorithm are compared under similar circumstances

State of the art survey of network operating systems development

The results of the State-of-the-Art Survey of Network Operating Systems (NOS) performed for Goddard Space Flight Center are presented. NOS functional characteristics are presented in terms of user communication data migration, job migration, network control, and common functional categories. Products (current or future) as well as research and prototyping efforts are summarized. The NOS products which are revelant to the space station and its activities are evaluated

Distributed systems status and control

Concepts are investigated for an automated status and control system for a distributed processing environment. System characteristics, data requirements for health assessment, data acquisition methods, system diagnosis methods and control methods were investigated in an attempt to determine the high-level requirements for a system which can be used to assess the health of a distributed processing system and implement control procedures to maintain an accepted level of health for the system. A potential concept for automated status and control includes the use of expert system techniques to assess the health of the system, detect and diagnose faults, and initiate or recommend actions to correct the faults. Therefore, this research included the investigation of methods by which expert systems were developed for real-time environments and distributed systems. The focus is on the features required by real-time expert systems and the tools available to develop real-time expert systems

Predicting parking space availability based on heterogeneous data using Machine Learning techniques

Abstract. These days, smart cities are focused on improving their services and bringing quality to everyday life, leveraging modern ICT technologies. For this reason, the data from connected IoT devices, environmental sensors, economic platforms, social networking sites, governance systems, and others can be gathered for achieving such goals. The rapid increase in the number of vehicles in major cities of the world has made mobility in urban areas difficult, due to traffic congestion and parking availability issues. Finding a suitable parking space is often influenced by various factors such as weather conditions, traffic flows, and geographical information (markets, hospitals, parks, and others). In this study, a predictive analysis has been performed to estimate the availability of parking spaces using heterogeneous data from Cork County, Ireland. However, accumulating, processing, and analysing the produced data from heterogeneous sources is itself a challenge, due to their diverse nature and different acquisition frequencies. Therefore, a data lake has been proposed in this study to collect, process, analyse, and visualize data from disparate sources. In addition, the proposed platform is used for predicting the available parking spaces using the collected data from heterogeneous sources. The study includes proposed design and implementation details of data lake as well as the developed parking space availability prediction model using machine learning techniques

2014 Projects Day Booklet

https://scholarworks.seattleu.edu/projects-day/1029/thumbnail.jp