Intelligent wireless web services: context-aware computing in construction-logistics supply chain

The construction industry has incurred a considerable amount of waste as a result of poor logistics supply chain network management. Therefore, managing logistics in the construction industry is critical. An effective logistic system ensures delivery of the right products and services to the right players at the right time while minimising costs and rewarding all sectors based on value added to the supply chain. This paper reports on an on-going research study on the concept of context-aware services delivery in the construction project supply chain logistics. As part of the emerging wireless technologies, an Intelligent Wireless Web (IWW) using context-aware computing capability represents the next generation ICT application to construction-logistics management. This intelligent system has the potential of serving and improving the construction logistics through access to context-specific data, information and services. Existing mobile communication deployments in the construction industry rely on static modes of information delivery and do not take into account the worker’s changing context and dynamic project conditions. The major problems in these applications are lack of context-specificity in the distribution of information, services and other project resources, and lack of cohesion with the existing desktop based ICT infrastructure. The research works focus on identifying the context dimension such as user context, environmental context and project context, selection of technologies to capture context-parameters such wireless sensors and RFID, selection of supporting technologies such as wireless communication, Semantic Web, Web Services, agents, etc. The process of integration of Context-Aware Computing and Web-Services to facilitate the creation of intelligent collaboration environment for managing construction logistics will take into account all the necessary critical parameters such as storage, transportation, distribution, assembly, etc. within off and on-site project.

The impact of an intelligent classroom on pupils' interactive behaviour

Purpose – The purpose of this research is to determine whether new intelligent classrooms will affect the behaviour of children in their new learning environments. Design/methodology/approach – A multi-method study approach was used to carry out the research. Behavioural mapping was used to observe and monitor the classroom environment and analyse usage. Two new classrooms designed by INTEGER (Intelligent and Green) in two different UK schools provided the case studies to determine whether intelligent buildings (learning environments) can enhance learning experiences. Findings – Several factors were observed in the learning environments: mobility, flexibility, use of technology, interactions. Relationships among them were found indicating that the new environments have positive impact on pupils' behaviour. Practical implications – A very useful feedback for the Classrooms of the Future initiative will be provided, which can be used as basis for the School of the Future initiative. Originality/value – The behavioural analysis method described in this study will enable an evaluation of the “Schools of the Future” concept, under children's perspective. Using a real life laboratory gives contribution to the education field by rethinking the classroom environment and the way of teaching.

Coordinated management of intelligent pervasive spaces

The content of this paper is a snapshot of a current project looking at producing a real-time sensor-based building assessment tool, and a system that personalises workspaces using multi-agent technology. Both systems derive physical environment information from a wireless sensor network that allows clients to subscribe to real-time sensed data. The principal ideologies behind this project are energy efficiency and well-being of occupants; in the context of leveraging the current state-of-the-art in agent technology, wireless sensor networks and building assessment systems to enable the optimisation and assessment of buildings. Participants of this project are from both industry (construction and research) and academia.

Volatile flavour profile of goat meat extracted by three widely used techniques

Three procedures for the isolation of volatiles from grilled goat meat were compared: dynamic headspace entrainment on Tenax TA, simultaneous steam distillation-extraction, and solid-phase microextraction. Headspace entrainment on Tenax TA extracted the highest number of Maillard-derived volatile compounds. Two hundred and three volatile components were identified: 159 are reported for the first time in goat meat. Most of the volatiles detected (155) were lipid oxidation products, such as hydrocarbons, aldehydes, alcohols, ketones, carboxylic acids and esters. Forty-eight Maillard-derived compounds were identified. comprising pyrazines, pyrroles, thiophenes, furanthiol derivatives, alkyl and alicyclic sulphides, pyridines, and thiazoles. Some reported character impact compounds of cooked meat, e.g., 12-methyltridecanal, (EE)-2,4-decadienal, methional, and dimethyl trisulphide were identified in the volatile profile of goat meat, together with a series of C-2 to C-5 alkylformylcyclopentenes, which have been reported in cooked chicken, pork, beef and lamb, as being important for the characteristic flavour impression of different animal species. (C) 2009 Published by Elsevier Ltd.

Techniques for quantifying effects of dietary antioxidants on transcription factor translocation and nitric oxide production in cultured cells

Dietary antioxidants can affect cellular processes relevant to chronic inflammatory diseases such as atherosclerosis. We have used non- standard techniques to quantify effects of the antioxidant soy isoflavones genistein and daidzein on translocation of Nuclear Factor-KB (NF-KB) and nitric oxide (NO) production, which are important in these diseases. Translocation was quantified using confocal immunofluoresecence microscopy and ratiometric image analysis. NO was quantified by an electrochemical method after reduction of its oxidation products in cell culture supernatants. Activation of the RAW 264.7 murine monocyte/macrophage cell line increased the ratio of nuclear to cytoplasmic immunostaining for NF-kB. The increase was exacerbated by pre-treatment with genistein or daidzein. To show that decreases could also be detected, pre-treatment with the pine bark extract Pycnogenol (R) r was examined, and found to reduce translocation. NO production was also increased by activation, but was reduced by pre-treatment with genistein or daidzein. In the EA. hy926 human endothelial cell line, constitutive production was detectable and was increased by thrombin. The confocal and electrochemical methods gave data that agreed with results obtained using the established electromobility shift and Griess assays, but were more sensitive, more convenient, gave more detailed information and avoided the use of radioisotopes.

Application of semiquantitative proteomics techniques to the Maillard reaction

Proteomic tools-in particular, mass spectrometry (MS)-have advanced significantly in recent years, and the identification of proteins within complex mixtures is now a routine procedure. Quantitative methods of analysis are less well advanced and continue to develop. These include the use of stable isotope ratio approaches, isotopically labeled peptide standards, and nonlabeling methods. This paper summarizes the use of MS as a proteomics tool to identify and semiquantify proteins and their modified forms by using examples of relevance to the Maillard reaction. Finally, some challenges for the future are presented.

The DREAM Framework: using a network of scalable ontologies for intelligent indexing and retrieval of visual content

There are still major challenges in the area of automatic indexing and retrieval of digital data. The main problem arises from the ever increasing mass of digital media and the lack of efficient methods for indexing and retrieval of such data based on the semantic content rather than keywords. To enable intelligent web interactions or even web filtering, we need to be capable of interpreting the information base in an intelligent manner. Research has been ongoing for a few years in the field of ontological engineering with the aim of using ontologies to add knowledge to information. In this paper we describe the architecture of a system designed to automatically and intelligently index huge repositories of special effects video clips, based on their semantic content, using a network of scalable ontologies to enable intelligent retrieval.

The SAFEE on-board threat detection system

Under the framework of the European Union Funded SAFEE project(1), this paper gives an overview of a novel monitoring and scene analysis system developed for use onboard aircraft in spatially constrained environments. The techniques discussed herein aim to warn on-board crew about pre-determined indicators of threat intent (such as running or shouting in the cabin), as elicited from industry and security experts. The subject matter experts believe that activities such as these are strong indicators of the beginnings of undesirable chains of events or scenarios, which should not be allowed to develop aboard aircraft. This project aimes to detect these scenarios and provide advice to the crew. These events may involve unruly passengers or be indicative of the precursors to terrorist threats. With a state of the art tracking system using homography intersections of motion images, and probability based Petri nets for scene understanding, the SAFEE behavioural analysis system automatically assesses the output from multiple intelligent sensors, and creates. recommendations that are presented to the crew using an integrated airborn user interface. Evaluation of the system is conducted within a full size aircraft mockup, and experimental results are presented, showing that the SAFEE system is well suited to monitoring people in confined environments, and that meaningful and instructive output regarding human actions can be derived from the sensor network within the cabin.

A sparse kernel density estimation algorithm using forward constrained regression

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection. The jackknife parameter estimator subject to positivity constraint check is used for the parameter estimation of a single parameter at each forward step. As such the proposed approach is simple to implement and the associated computational cost is very low. An illustrative example is employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to that of the classical Parzen window estimate.