A block coprocessor for user data rate improvements to GPRS coding scheme 4

The general packet radio service (GPRS) has been developed to allow packet data to be transported efficiently over an existing circuit-switched radio network, such as GSM. The main application of GPRS are in transporting Internet protocol (IP) datagrams from web servers (for telemetry or for mobile Internet browsers). Four GPRS baseband coding schemes are defined to offer a trade-off in requested data rates versus propagation channel conditions. However, data rates in the order of > 100 kbits/s are only achievable if the simplest coding scheme is used (CS-4) which offers little error detection and correction (EDC) (requiring excellent SNR) and the receiver hardware is capable of full duplex which is not currently available in the consumer market. A simple EDC scheme to improve the GPRS block error rate (BLER) performance is presented, particularly for CS-4, however gains in other coding schemes are seen. For every GPRS radio block that is corrected by the EDC scheme, the block does not need to be retransmitted releasing bandwidth in the channel and improving the user's application data rate. As GPRS requires intensive processing in the baseband, a viable field programmable gate array (FPGA) solution is presented in this paper.

Jpowder: a Java-based program for the display and examination of powder diffraction data

The ability to display and inspect powder diffraction data quickly and efficiently is a central part of the data analysis process. Whilst many computer programs are capable of displaying powder data, their focus is typically on advanced operations such as structure solution or Rietveld refinement. This article describes a lightweight software package, Jpowder, whose focus is fast and convenient visualization and comparison of powder data sets in a variety of formats from computers with network access. Jpowder is written in Java and uses its associated Web Start technology to allow ‘single-click deployment’ from a web page, http://www.jpowder.org. Jpowder is open source, free and available for use by anyone.

Ocean data dissemination: new challenges for data integration

In the decade since OceanObs `99, great advances have been made in the field of ocean data dissemination. The use of Internet technologies has transformed the landscape: users can now find, evaluate and access data rapidly and securely using only a web browser. This paper describes the current state of the art in dissemination methods for ocean data, focussing particularly on ocean observations from in situ and remote sensing platforms. We discuss current efforts being made to improve the consistency of delivered data and to increase the potential for automated integration of diverse datasets. An important recent development is the adoption of open standards from the Geographic Information Systems community; we discuss the current impact of these new technologies and their future potential. We conclude that new approaches will indeed be necessary to exchange data more effectively and forge links between communities, but these approaches must be evaluated critically through practical tests, and existing ocean data exchange technologies must be used to their best advantage. Investment in key technology components, cross-community pilot projects and the enhancement of end-user software tools will be required in order to assess and demonstrate the value of any new technology.

Data management for the ocean sciences - perspectives for the next decade

There is remarkable agreement in expectations today for vastly improved ocean data management a decade from now -- capabilities that will help to bring significant benefits to ocean research and to society. Advancing data management to such a degree, however, will require cultural and policy changes that are slow to effect. The technological foundations upon which data management systems are built are certain to continue advancing rapidly in parallel. These considerations argue for adopting attitudes of pragmatism and realism when planning data management strategies. In this paper we adopt those attitudes as we outline opportunities for progress in ocean data management. We begin with a synopsis of expectations for integrated ocean data management a decade from now. We discuss factors that should be considered by those evaluating candidate “standards”. We highlight challenges and opportunities in a number of technical areas, including “Web 2.0” applications, data modeling, data discovery and metadata, real-time operational data, archival of data, biological data management and satellite data management. We discuss the importance of investments in the development of software toolkits to accelerate progress. We conclude the paper by recommending a few specific, short term targets for implementation, that we believe to be both significant and achievable, and calling for action by community leadership to effect these advancements.

Towards data warehousing and mining of protein unfolding simulation data

OBJECTIVES: The prediction of protein structure and the precise understanding of protein folding and unfolding processes remains one of the greatest challenges in structural biology and bioinformatics. Computer simulations based on molecular dynamics (MD) are at the forefront of the effort to gain a deeper understanding of these complex processes. Currently, these MD simulations are usually on the order of tens of nanoseconds, generate a large amount of conformational data and are computationally expensive. More and more groups run such simulations and generate a myriad of data, which raises new challenges in managing and analyzing these data. Because the vast range of proteins researchers want to study and simulate, the computational effort needed to generate data, the large data volumes involved, and the different types of analyses scientists need to perform, it is desirable to provide a public repository allowing researchers to pool and share protein unfolding data. METHODS: To adequately organize, manage, and analyze the data generated by unfolding simulation studies, we designed a data warehouse system that is embedded in a grid environment to facilitate the seamless sharing of available computer resources and thus enable many groups to share complex molecular dynamics simulations on a more regular basis. RESULTS: To gain insight into the conformational fluctuations and stability of the monomeric forms of the amyloidogenic protein transthyretin (TTR), molecular dynamics unfolding simulations of the monomer of human TTR have been conducted. Trajectory data and meta-data of the wild-type (WT) protein and the highly amyloidogenic variant L55P-TTR represent the test case for the data warehouse. CONCLUSIONS: Web and grid services, especially pre-defined data mining services that can run on or 'near' the data repository of the data warehouse, are likely to play a pivotal role in the analysis of molecular dynamics unfolding data.

Adding Centaur publication data into CMS pages

Guidance for University of Reading staff wishing to add CentAUR publication lists to CMS web pages: staff profile page; department page; standard page. Log in with University of Reading (staff) username and password when prompted.

Design and baseline characteristics of the Food4Me study: a web-based randomised controlled trial of personalised nutrition in seven European countries

Improving lifestyle behaviours has considerable potential for reducing the global burden of non-communicable diseases, promoting better health across the life-course and increasing well-being. However, realising this potential will require the development, testing and implementation of much more effective behaviour change interventions than are used conventionally. Therefore, the aim of this study was to conduct a multi-centre, web-based, proof-of-principle study of personalised nutrition (PN) to determine whether providing more personalised dietary advice leads to greater improvements in eating patterns and health outcomes compared to conventional population-based advice. A total of 5,562 volunteers were screened across seven European countries; the first 1,607 participants who fulfilled the inclusion criteria were recruited into the trial. Participants were randomly assigned to one of the following intervention groups for a 6-month period: Level 0-control group-receiving conventional, non-PN advice; Level 1-receiving PN advice based on dietary intake data alone; Level 2-receiving PN advice based on dietary intake and phenotypic data; and Level 3-receiving PN advice based on dietary intake, phenotypic and genotypic data. A total of 1,607 participants had a mean age of 39.8 years (ranging from 18 to 79 years). Of these participants, 60.9 % were women and 96.7 % were from white-European background. The mean BMI for all randomised participants was 25.5 kg m(-2), and 44.8 % of the participants had a BMI ≥ 25.0 kg m(-2). Food4Me is the first large multi-centre RCT of web-based PN. The main outcomes from the Food4Me study will be submitted for publication during 2015.

Stated willingness-to-pay for organic fruit and pesticide ban: an evaluation using both web-based and face-to-face interviewing

Income growth in highly industrialised countries has resulted in consumer choice of foodstuffs no longer being primarily influenced by basic factors such as price and organoleptic features. From this perspective, the present study sets out to evaluate how and to what extent consumer choice is influenced by the possible negative effects on health and environment caused by the consumption of fruit containing deposits of pesticides and chemical products. The study describes the results of a survey which explores and estimates consumer willingness to pay in two forms: a yearly contribution for the abolition of the use of pesticides on fruit, and a premium price for organically grown apples guaranteed by a certified label. The same questionnaire was administered to two samples. The first was a conventional face-to-face survey of customers of large retail outlets located around Bologna (Italy); the second was an Internet sample. The discrete choice data were analysed by means of probit and tobit models to estimate the utility consumers attribute to organically grown fruit and to a pesticide ban. The research also addresses questions of validity and representativeness as a fundamental problem in web-based surveys.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.

Capturing and sharing our collective expertise on climate data: the CHARMe project

For users of climate services, the ability to quickly determine the datasets that best fit one's needs would be invaluable. The volume, variety and complexity of climate data makes this judgment difficult. The ambition of CHARMe ("Characterization of metadata to enable high-quality climate services") is to give a wider interdisciplinary community access to a range of supporting information, such as journal articles, technical reports or feedback on previous applications of the data. The capture and discovery of this "commentary" information, often created by data users rather than data providers, and currently not linked to the data themselves, has not been significantly addressed previously. CHARMe applies the principles of Linked Data and open web standards to associate, record, search and publish user-derived annotations in a way that can be read both by users and automated systems. Tools have been developed within the CHARMe project that enable annotation capability for data delivery systems already in wide use for discovering climate data. In addition, the project has developed advanced tools for exploring data and commentary in innovative ways, including an interactive data explorer and comparator ("CHARMe Maps") and a tool for correlating climate time series with external "significant events" (e.g. instrument failures or large volcanic eruptions) that affect the data quality. Although the project focuses on climate science, the concepts are general and could be applied to other fields. All CHARMe system software is open-source, released under a liberal licence, permitting future projects to re-use the source code as they wish.

Effects of a web-based personalized intervention on physical activity in European adults: a randomized controlled trial

Background: The high prevalence of physical inactivity worldwide calls for innovative and more effective ways to promote physical activity (PA). There are limited objective data on the effectiveness of Web-based personalized feedback on increasing PA in adults. Objective: It is hypothesized that providing personalized advice based on PA measured objectively alongside diet, phenotype, or genotype information would lead to larger and more sustained changes in PA, compared with nonpersonalized advice. Methods: A total of 1607 adults in seven European countries were randomized to either a control group (nonpersonalized advice, Level 0, L0) or to one of three personalized groups receiving personalized advice via the Internet based on current PA plus diet (Level 1, L1), PA plus diet and phenotype (Level 2, L2), or PA plus diet, phenotype, and genotype (Level 3, L3). PA was measured for 6 months using triaxial accelerometers, and self-reported using the Baecke questionnaire. Outcomes were objective and self-reported PA after 3 and 6 months. Results: While 1270 participants (85.81% of 1480 actual starters) completed the 6-month trial, 1233 (83.31%) self-reported PA at both baseline and month 6, but only 730 (49.32%) had sufficient objective PA data at both time points. For the total cohort after 6 months, a greater improvement in self-reported total PA (P=.02) and PA during leisure (nonsport) (P=.03) was observed in personalized groups compared with the control group. For individuals advised to increase PA, we also observed greater improvements in those two self-reported indices (P=.006 and P=.008, respectively) with increased personalization of the advice (L2 and L3 vs L1). However, there were no significant differences in accelerometer results between personalized and control groups, and no significant effect of adding phenotypic or genotypic information to the tailored feedback at month 3 or 6. After 6 months, there were small but significant improvements in the objectively measured physical activity level (P<.05), moderate PA (P<.01), and sedentary time (P<.001) for individuals advised to increase PA, but these changes were similar across all groups. Conclusions: Different levels of personalization produced similar small changes in objective PA. We found no evidence that personalized advice is more effective than conventional “one size fits all” guidelines to promote changes in PA in our Web-based intervention when PA was measured objectively. Based on self-reports, PA increased to a greater extent with more personalized advice. Thus, it is crucial to measure PA objectively in any PA intervention study.

Photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7

We present a catalogue of galaxy photometric redshifts and k-corrections for the Sloan Digital Sky Survey Data Release 7 (SDSS-DR7), available on the World Wide Web. The photometric redshifts were estimated with an artificial neural network using five ugriz bands, concentration indices and Petrosian radii in the g and r bands. We have explored our redshift estimates with different training sets, thus concluding that the best choice for improving redshift accuracy comprises the main galaxy sample (MGS), the luminous red galaxies and the galaxies of active galactic nuclei covering the redshift range 0 < z < 0.3. For the MGS, the photometric redshift estimates agree with the spectroscopic values within rms = 0.0227. The distribution of photometric redshifts derived in the range 0 < z(phot) < 0.6 agrees well with the model predictions. k-corrections were derived by calibration of the k-correct_v4.2 code results for the MGS with the reference-frame (z = 0.1) (g - r) colours. We adopt a linear dependence of k-corrections on redshift and (g - r) colours that provide suitable distributions of luminosity and colours for galaxies up to redshift z(phot) = 0.6 comparable to the results in the literature. Thus, our k-correction estimate procedure is a powerful, low computational time algorithm capable of reproducing suitable results that can be used for testing galaxy properties at intermediate redshifts using the large SDSS data base.