The iPlant Collaborative: Cyberinfrastructure for Enabling Data to Discovery for the Life Sciences

The iPlant Collaborative provides life science research communities access to comprehensive, scalable, and cohesive computational infrastructure for data management; identity management; collaboration tools; and cloud, high-performance, high-throughput computing. iPlant provides training, learning material, and best practice resources to help all researchers make the best use of their data, expand their computational skill set, and effectively manage their data and computation when working as distributed teams. iPlant's platform permits researchers to easily deposit and share their data and deploy new computational tools and analysis workflows, allowing the broader community to easily use and reuse those data and computational analyses

Green Symbiotic Cloud Communications: Virtualized Transport Layer and Cognitive Decision Function

The evolution of the concept of cloud communications has posed a growing emphasis on virtual and abstract environments for the flow of information, structuring it in similitude to a natural cloud. The Green Symbiotic Cloud Communications (GSCC) paradigm created on this concept facilitates the use of multiple communication mediums concomitantly creating a first of its kind communication cloud. This paper specifically corroborates a virtualized transport layer and network ports and an abstracted Internet protocol scheme in defining the GSCC architecture. We further address the issue of formulating a cognitive decision function based on utility theory, which allows users with GSCC enabled devices to intelligently distribute its bandwidth requirement amongst the available communication mediums. Considering the multiple criteria associated with different networks we formulate an optimization problem to find the solution for this resource allocation problem for single user. We further address the multi-user scenario and formulate and solve the multi-objective optimization problem using goal attainment technique. Results in single and multiple user scenarios, demonstrate that by utilizing multiple mediums as per GSCC paradigm coupled with our proposed decision function improves the functionality of the communication cloud. The proposed architecture is dynamic and evolving, embedding greenness by efficiently utilizing the available resources as and when required. The multiple virtual links equate a linearly increasing relationship with the throughput achieved. Experimental results for both real time and static data through the proposed schematic are documented. The augmented paradigm enhances the quality of service, linearly increases throughput and increases the overall security in communications

Packet Size Optimization for Cognitive Radio Sensor Networks Aided Internet of Things

Cognitive Radio Sensor Networks (CRSN) is state of the art communication paradigm for power constrained short range data communication. It is one of the potential technology adopted for Internet of Things (IoT) and other futuristic Machine to Machine (M2M) based applications. Many of these applications are power constrained and delay sensitive. Therefore, CRSN architecture must be coupled with different adaptive and robust communication schemes to take care of the delay and energy-efficiency at the same time. Considering the tradeoff that exists in terms of energy efficiency and overhead delay for a given data packet length, it is proposed to transmit the physical layer payload with an optimal packet size (OPS) depending on the network condition. Furthermore, due to the cognitive feature of CRSN architecture overhead energy consumption due to channel sensing and channel handoff plays a critical role. Based on the above premises, in this paper we propose a heuristic exhaustive search based Algorithm-1 and a computationally efficient suboptimal low complexity Karuh-Kuhn- Tucker (KKT) condition based Algorithm-2 to determine the optimal packet size in CRSN architecture using variable rate m-QAM modulation. The proposed algorithms are implemented along with two main cognitive radio assisted channel access strategies based on Distributed Time Slotted-Cognitive Medium Access Control (DTS-CMAC) and Centralized Common Control Channel based Cognitive Medium Access Control (CC-CMAC) and their performances are compared. The simulation results reveals that proposed Algorithm-2 outperforms Algorithm-1 by a significant margin in terms of its implementation time. For the exhaustive search based Algorithm-1 the average time consumed to determine OPS for a given number of cognitive users is 1.2 seconds while for KKT based Algorithm-2 it is of the order of 5 to 10 ms. CC-CMAC with OPS is most efficient in terms of overall energy consumption but incurs more delay as compared to DTS-CMAC with OPS scheme

Packet Size Optimization for Multiple Input Multiple Output Cognitive Radio Sensor Networks aided Internet of Things

The determination of Optimal Packet Size (OPS) for Cognitive Radio assisted Sensor Networks (CRSNs) architecture is non-trivial. State of the art in this area describes various complex techniques to determine OPS for CRSNs. However, it is observed that under high interference from the surrounding users, it is not possible to determine a feasible optimal packet size of data transmission under the simple point-to-point CRSN network topology. This is contributed primarily due to the peak transmit power constraint of the cognitive nodes. To address this specific challenge, this paper proposes a Multiple Input Multiple Output based Cognitive Radio Sensor Networks (MIMO-CRSNs) architecture for futuristic technologies like Internet of Things (IoT) and machine-to-machine (M2M) communications. A joint optimization problem is formulated taking into account network constraints like the overall end to end latency, interference duration caused to the non-cognitive users, average BER and transmit power.We propose our Algorithm-1 based on generic exhaustive search technique blue to solve the optimization problem. Furthermore, a low complexity suboptimal Algorithm-2 based on solving classical Karush-Kuhn-Tucker (KKT) conditions is proposed. These algorithms for MIMO-CRSNs are implemented in conjunction with two different channel access schemes. These channel access schemes are Time Slotted Distributed Cognitive Medium Access Control denoted as MIMO-DTS-CMAC and CSMA/CA assisted Centralized Common Control Channel based Cognitive Medium Access Control denoted as MIMO-CC-CMAC. Simulations reveal that the proposed MIMO based CRSN network outperforms the conventional point-to-point CRSN network in terms of overall energy consumption. Moreover, the proposed Algorithm-1 and Algorithm2 shows perfect match and the implementation complexity of Algorithm-2 is much lesser than Algorithm-1. Algorithm-1 takes almost 680 ms to execute and provides OPS value for a given number of users while Algorithm- 2 takes 4 to 5 ms on an average to find the optimal packet size for the proposed MIMO-CRSN framework

Effect of various dopant elements on primary graphite growth

Five spheroidal graphite cast irons were investigated, a usual ferritic grade and four pearlitic alloys containing Cu and doped with Sb, Sn and Ti. These alloys were remelted in a graphite crucible, leading to volatilization of the magnesium added for spheroidization and to carbon saturation of the liquid. The alloys were then cooled down and maintained at a temperature above the eutectic temperature. During this step, primary graphite could develop showing various features depending on the doping elements added. The largest effects were that of Ti which greatly reduces graphite nucleation and growth, and that of Sb which leads to rounded agglomerates instead of lamellar graphite. The samples have been investigated with secondary ion mass spectrometry to enlighten distribution of elements in primary graphite. SIMS analysis showed almost even distribution of elements, including Mg and Al (from the inoculant) in the ferritic grade, while uneven distribution was evident in all doped alloys. Investigations are going on to clarify if the uneven distribution is associated with structural defects in the graphite precipitates

The Chlamydomonas genome project: A decade on

The green alga Chlamydomonas reinhardtii is a popular unicellular organism for studying photosynthesis, cilia biogenesis, and micronutrient homeostasis. Ten years since its genome project was initiated an iterative process of improvements to the genome and gene predictions has propelled this organism to the forefront of the omics era. Housed at Phytozome, the plant genomics portal of the Joint Genome Institute (JGI), the most up-to-date genomic data include a genome arranged on chromosomes and high-quality gene models with alternative splice forms supported by an abundance of whole transcriptome sequencing (RNA-Seq) data. We present here the past, present, and future of Chlamydomonas genomics. Specifically, we detail progress on genome assembly and gene model refinement, discuss resources for gene annotations, functional predictions, and locus ID mapping between versions and, importantly, outline a standardized framework for naming genes

Ecofeminism in the 21st Century

This paper considers the influence of ecofeminism on policy concerning gender (in)equality and the environment during the past 20 years. It reviews the broad contours of the ecofeminist debate before focusing on the social construction interpretation of women's relationship with the environment. It will argue that there have been substantial policy shifts in Europe and the UK in both the environmental and equalities fields, and that this is in part a result of lobbying at a range of scales by groups informed by ecofeminist debates. Nevertheless, the paper cautions that these shifts are largely incremental and operate within existing structures, which inevitably limit their capacity to create change. As policy addresses some of the concerns highlighted by ecofeminism, academic discourse and grass roots activity have been moving on to address other issues, and the paper concludes with a brief consideration of contemporary trajectories of ecofeminism and campaigning on issues that link women's, feminist and environment concerns

Synthesis of the elements in stars: forty years of progress

Forty years ago Burbidge, Burbidge, Fowler, and Hoyle combined what we would now call fragmentary evidence from nuclear physics, stellar evolution and the abundances of elements and isotopes in the solar system as well as a few stars into a synthesis of remarkable ingenuity. Their review provided a foundation for forty years of research in all of the aspects of low energy nuclear experiments and theory, stellar modeling over a wide range of mass and composition, and abundance studies of many hundreds of stars, many of which have shown distinct evidence of the processes suggested by B2FH. In this review we summarize progress in each of these fields with emphasis on the most recent developments

Data mining and wireless sensor network for agriculture pest/disease predictions

Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely related and interdependent pest (Thrips) - disease (Bud Necrosis) dynamics of groundnut crop. Data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest/ disease continuum. These dynamics obtained from the data mining techniques and trained through mathematical models were validated with corresponding surveillance data. Results obtained from 2009 & 2010 kharif seasons (monsoon) and 2009-10 & 2010-11 rabi seasons (post monsoon) data could be used to develop a real to near real-time decision support system for pest/disease predictions