Clinical trial metadata:Defining and extracting metadata on the design, conduct, results and costs of 125 randomised clinical trials funded by the National Institute for Health Research Health Technology Assessment programme

Background:  By 2011, the Health Technology Assessment (HTA) programme had published the results of over 100 trials with another 220 in progress. The aim of the project was to develop and pilot ‘metadata’ on clinical trials funded by the HTA programme.   Objectives: The aim of the project was to develop and pilot questions describing clinical trials funded by the HTA programme in terms of it meeting the needs of the NHS with scientifically robust studies. The objectives were to develop relevant classification systems and definitions for use in answering relevant questions and to assess their utility.   Data sources: Published monographs and internal HTA documents.   Review methods: A database was developed, ‘populated’ using retrospective data and used to answer questions under six prespecified themes. Questions were screened for feasibility in terms of data availability and/or ease of extraction. Answers were assessed by the authors in terms of completeness, success of the classification system used and resources required. Each question was scored to be retained, amended or dropped.    Results: One hundred and twenty-five randomised trials were included in the database from 109 monographs. Neither the International Standard Randomised Controlled Trial Number nor the term ‘randomised trial’ in the title proved a reliable way of identifying randomised trials. Only limited data were available on how the trials aimed to meet the needs of the NHS. Most trials were shown to follow their protocols but updates were often necessary as hardly any trials recruited as planned. Details were often lacking on planned statistical analyses, but we did not have access to the relevant statistical plans. Almost all the trials reported on cost-effectiveness, often in terms of both the primary outcome and quality-adjusted life-years. The cost of trials was shown to depend on the number of centres and the duration of the trial. Of the 78 questions explored, 61 were well answered, 33 fully with 28 requiring amendment were the analysis updated. The other 17 could not be answered with readily available data.   Limitations: The study was limited by being confined to 125 randomised trials by one funder.   Conclusions: Metadata on randomised controlled trials can be expanded to include aspects of design, performance, results and costs. The HTA programme should continue and extend the work reported here

Validating a Web Service Security Abstraction by Typing

An XML web service is, to a first approximation, an RPC service in which requests and responses are encoded in XML as SOAP envelopes, and transported over HTTP. We consider the problem of authenticating requests and responses at the SOAP-level, rather than relying on transport-level security. We propose a security abstraction, inspired by earlier work on secure RPC, in which the methods exported by a web service are annotated with one of three security levels: none, authenticated, or both authenticated and encrypted. We model our abstraction as an object calculus with primitives for defining and calling web services. We describe the semantics of our object calculus by translating to a lower-level language with primitives for message passing and cryptography. To validate our semantics, we embed correspondence assertions that specify the correct authentication of requests and responses. By appeal to the type theory for cryptographic protocols of Gordon and Jeffrey's Cryptyc, we verify the correspondence assertions simply by typing. Finally, we describe an implementation of our semantics via custom SOAP headers.Comment: 44 pages. A preliminary version appears in the Proceedings of the Workshop on XML Security 2002, pp. 18-29, November 200

Traditional Knowledge and Biodiversity in South Africa : CSIR case

The focus of this paper is traditional knowledge (TK) and indigenous biological resources protection in South Africa, through the analysis of the existing policies and legislations, in order to provide a useful insight for a developed country such as Japan which has recently adopted the guidelines for the protection of TK and biological resources and promotion of access and benefit sharing (ABS). South Africa is the 3rd most diverse country in terms of natural resources, culture and traditions, languages and geology and its comprehensive legislative framework system shows the country\u27s seriousness to safeguard TK and conserve biological resources for future generations. The paper uses the South Africa\u27s government owned research and technology development institution, Council for Scientific and Industrial Research (CSIR), as an example to demonstrate the application of the TK protection and biodiversity conservation (including access and benefit sharing) laws, through case studies approach for lessons learned for other African countries, contemplating creation of their own TK protection and environmental conservation. Due to the repositioning of CSIR within the local and global research and develop, the organisation has adopted Industrialisation Strategy, and TK will play a significant role in technology development and new business models in rural agroprocessing and production to enhance inclusive development (through benefit sharing) and support economic growth. The paper concludes that TK and indigenous biological resources protection through the relevant government laws, as well as value addition to TK and biodiversity through research and development supported by government funding, is necessary for socioeconomic attainment, especially for local and indigenous communities and rural agroprocessing businesses as part of benefit sharing

A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now

Trust with Private and Common Property: Effects of Stronger Property Right Entitlements

Is mutually beneficial cooperation in trust games more prevalent with private property or common property? Does the strength of property right entitlement affect the answer? Cox, Ostrom, Walker, et al. [1] report little difference between cooperation in private and common property trust games. We assign stronger property right entitlements by requiring subjects to meet a performance quota in a real effort task to earn their endowments. We find that cooperation is lower in common property trust games than in private property trust games, which is an idiosyncratic prediction of revealed altruism theory [2].

Machine learning for early detection of traffic congestion using public transport traffic data

The purpose of this project is to provide better knowledge of how the bus travel times is affected by congestion and other problems in the urban traffic environment. The main source of data for this study is second-level measurements coming from all buses in the Linköping region showing the location of each vehicle.The main goal of this thesis is to propose, implement, test and optimize a machine learning algorithm based on data collected from regional buses from Sweden so that it is able to perform predictions on the future state of the urban traffic.El objetivo principal de este proyecto es proponer, implementar, probar y optimizar un algoritmo de aprendizaje automático basado en datos recopilados de autobuses regionales de Suecia para que poder realizar predicciones sobre el estado futuro del tráfico urbano.L'objectiu principal d'aquest projecte és proposar, implementar, provar i optimitzar un algoritme de machine learning basat en dades recollides a partir d'autobusos regionals de Suècia de manera per poder realitzar prediccions sobre l'estat futur del trànsit urbà