Demand response for residential appliances via Customer Reward Scheme

This paper proposes a reward based demand response algorithm for residential customers to shave network peaks. Customer survey information is used to calculate various criteria indices reflecting their priority and flexibility. Criteria indices and sensitivity based house ranking is used for appropriate load selection in the feeder for demand response. Customer Rewards (CR) are paid based on load shift and voltage improvement due to load adjustment. The proposed algorithm can be deployed in residential distribution networks using a two-level hierarchical control scheme. Realistic residential load model consisting of non-controllable and controllable appliances is considered in this study. The effectiveness of the proposed demand response scheme on the annual load growth of the feeder is also investigated. Simulation results show that reduced peak demand, improved network voltage performance, and customer satisfaction can be achieved.

Selling new neighborhoods as good for walking : issues for measuring self-selection

Background: Self-selection-whether individuals inclined to walk more seek to live in walkable environments-must be accounted for when studying built environment influences on walking. The way neighborhoods are marketed to future residents has the potential to sway residential location choice, and may consequently affect measures of self-selection related to location preferences. We assessed how walking opportunities are promoted to potential buyers, by examining walkability attributes in marketing materials for housing developments. Methods: A content analysis of marketing materials for 32 new housing developments in Perth, Australia was undertaken, to assess how walking was promoted in the text and pictures. Housing developments designed to be pedestrian-friendly (LDs) were compared with conventional developments (CDs). Results: Compared with CDs, LD marketing materials had significantly more references to 'public transport,' small home sites,' walkable parks/open space,' ease of cycling,' safe environment,' and 'boardwalks.' Other walkability attributes approached significance. Conclusion: Findings suggest the way neighborhoods are marketed may contribute to self-reported reasons for choosing particular neighborhoods, especially when attributes are not present at the time of purchase. The marketing of housing developments may be an important factor to consider when measuring self-selection, and its influence on the built environment and walking relationship.

Selection and monitoring of navigation modes for an autonomous rover

Considering the wide spectrum of situations that it may encounter, a robot navigating autonomously in outdoor environments needs to be endowed with several operating modes, for robustness and efficiency reasons. Indeed, the terrain it has to traverse may be composed of flat or rough areas, low cohesive soils such as sand dunes, concrete road etc... Traversing these various kinds of environment calls for different navigation and/or locomotion functionalities, especially if the robot is endowed with different locomotion abilities, such as the robots WorkPartner, Hylos [4], Nomad or the Marsokhod rovers.

Selection and monitoring of navigation modes for an autonomous rover

Considering the wide spectrum of situations that it may encounter, a robot navigating autonomously in outdoor environments needs to be endowed with several operating modes, for robustness and efficiency reasons. Indeed, the terrain it has to traverse may be composed of flat or rough areas, low cohesive soils such as sand dunes, concrete road etc. . .Traversing these various kinds of environment calls for different navigation and/or locomotion functionalities, especially if the robot is endowed with different locomotion abilities, such as the robots WorkPartner, Hylos [4], Nomad or the Marsokhod rovers. Numerous rover navigation techniques have been proposed, each of them being suited to a particular environment context (e.g. path following, obstacle avoidance in more or less cluttered environments, rough terrain traverses...). However, seldom contributions in the literature tackle the problem of selecting autonomously the most suited mode [3]. Most of the existing work is indeed devoted to the passive analysis of a single navigation mode, as in [2]. Fault detection is of course essential: one can imagine that a proper monitoring of the Mars Exploration Rover Opportunity could have avoided the rover to be stuck during several weeks in a dune, by detecting non-nominal behavior of some parameters. But the ability to recover the anticipated problem by switching to a better suited navigation mode would bring higher autonomy abilities, and therefore a better overall efficiency. We propose here a probabilistic framework to achieve this, which fuses environment related and robot related information in order to actively control the rover operations.

Selection et controle de modes de deplacement pour un robot mobile autonome en environnements naturels

Autonomous navigation and locomotion of a mobile robot in natural environments remain a rather open issue. Several functionalities are required to complete the usual perception/decision/action cycle. They can be divided in two main categories : navigation (perception and decision about the movement) and locomotion (movement execution). In order to be able to face the large range of possible situations in natural environments, it is essential to make use of various kinds of complementary functionalities, defining various navigation and locomotion modes. Indeed, a number of navigation and locomotion approaches have been proposed in the literature for the last years, but none can pretend being able to achieve autonomous navigation and locomotion in every situation. Thus, it seems relevant to endow an outdoor mobile robot with several complementary navigation and locomotion modes. Accordingly, the robot must also have means to select the most appropriate mode to apply. This thesis proposes the development of such a navigation/locomotion mode selection system, based on two types of data: an observation of the context to determine in what kind of situation the robot has to achieve its movement and an evaluation of the behavior of the current mode, made by monitors which influence the transitions towards other modes when the behavior of the current one is considered as non satisfying. Hence, this document introduces a probabilistic framework for the estimation of the mode to be applied, some navigation and locomotion modes used, a qualitative terrain representation method (based on the evaluation of a difficulty computed from the placement of the robot's structure on a digital elevation map), and monitors that check the behavior of the modes used (evaluation of rolling locomotion efficiency, robot's attitude and configuration watching. . .). Some experimental results obtained with those elements integrated on board two different outdoor robots are presented and discussed.

ZnO nanocones with high-index {101̅1} facets for enhanced energy conversion efficiency of dye-sensitized solar cells

ZnO is a promising photoanode material for dye-sensitized solar cells (DSCs) due to its high bulk electron mobility and because different geometrical structures can easily be tailored. Although various strategies have been taken to improve ZnO-based DSC efficiencies, their performances are still far lower than TiO2 counterparts, mainly because low conductivity Zn2+–dye complexes form on the ZnO surfaces. Here, cone-shaped ZnO nanocrystals with exposed reactive O-terminated {101̅1} facets were synthesized and applied in DSC devices. The devices were compared with DSCs made from more commonly used rod-shaped ZnO nanocrystals where {101̅0} facets are predominantly exposed. When cone-shaped ZnO nanocrystals were used, DSCs sensitized with C218, N719, and D205 dyes universally displayed better power conversion efficiency, with the highest photoconversion efficiency of 4.36% observed with the C218 dye. First-principles calculations indicated that the enhanced DSCs performance with ZnO nanocone photoanodes could be attributed to the strength of binding between the dye molecules and reactive O-terminated {101̅1} ZnO facets and that more effective use of dye molecules occurred due to a significantly less dye aggregation on these ZnO surfaces compared to other ZnO facets.

Australasian nutrition research for prevention and management of child obesity : innovation and progress in the last decade

The Food and Nutrition stream of Australasian Child and Adolescent Obesity Research Network (ACAORN) aims to improve the quality of dietary methodologies and the reporting of dietary intake within Australasian child obesity research (http://www.acaorn.org.au/streams/nutrition/). With 2012 marking ACAORN’s 10th anniversary, this commentary profiles a selection of child obesity nutrition research published over the last decade by Food and Nutrition Stream members. In addition, stream activities have included the development of an online selection guide to assist researchers in their selection of appropriate dietary intake methodologies (http://www.acaorn.org.au/streams/nutrition/dietary-intake/index.php). The quantity and quality of research to guide effective child obesity prevention and treatment has increased substantially over the last decade. ACAORN provides a successful case study of how research networks can provide a collegial atmosphere to foster and co-ordinate research efforts in an otherwise competitive environment.

Export readiness index : construction and analysis

The internationalisation process of firms has attracted much research interest since the 1970s. It is noted, however, that a significant research gap exists in studies with a primary focus on the pre-internationalisation behaviour of firms. This paper proposes the incorporation of a pre-internationalisation phase into the traditional Uppsala model of firm internationalisation to address the issue of export readiness. Through extensive literature review, the concepts fundamental to the ability of an Uppsala type firm to begin internationalisation through an export entry mode are identified: exposure to stimuli factors, attitudinal commitment of decision makers towards exporting, the firm’s resource capabilities, as well as the moderating effect of lateral rigidity. The concept of export readiness is operationalised in this study through the construction of an export readiness index (ERI) using exploratory and confirmatory factor analysis. The index is then applied to some representative cases and tested using logistic regression to establish its validity as a diagnostic tool. The proposed ERI presents not only a more practical approach towards analysing firms’ export readiness but has also major public policy implications as a possible tool for government export promotion agencies.

Exploring the international market selection of small to medium sized enterprises

Research into the international market selection (IMS) of small to medium sized enterprises (SMEs) commonly identifies psychic distance and networks as being the most important determinants of a firm’s IMS. Whether regional factors, such as bilateral and multilateral regional integration, are important as determinants of IMS is not well understood. This paper utilises a multiple case study method through in-depth interviews to investigate, in the context of the current business environment, how important regionalisation, psychic distance and networks are as determinants of IMS among SMEs in the food and beverage industries within Australia and Malaysia. The study found regional considerations to be important to the IMS of Malaysian but not Australian firms, while psychic distance was considered an important determinant on IMS by only half of the sampled firms. The role of networks, however, was considered the most important determinant of IMS among all the sampled firms.

Consultant engagement for computer system selection: a pro-active client role in small enterprises

Because of their limited number of senior positions and fewer alternative career paths, small businesses have a more difficult time attracting and retaining skilled information systems (IS) staff and are thus dependent upon external expertise. Small businesses are particularly dependent on outside expertise when first computerizing. Because small businesses suffer from severe financial constraints. it is often difficult to justify the cost of custom software. Hence. for many small businesses, engaging a consultant to help with identifying suitable packaged software and related hardware, is their first critical step toward computerization. This study explores the importance of proactive client involvement when engaging a consultant to assist with computer system selection in small businesses. Client involvement throughout consultant engagement is found to be integral to project success and frequently lacking due to misconceptions of small businesses regarding their role. Small businesses often overestimate the impact of consultant and vendor support in achieving successful computer system selection and implementation. For consultant engagement to be successful, the process must be viewed as being directed toward the achievement of specific organizational results where the client accepts responsibility for direction of the process.

A Satellite Selection Algorithm for Achieving High Reliability of Ambiguity Resolution with GPS and Beidou Constellations

Reliability of carrier phase ambiguity resolution (AR) of an integer least-squares (ILS) problem depends on ambiguity success rate (ASR), which in practice can be well approximated by the success probability of integer bootstrapping solutions. With the current GPS constellation, sufficiently high ASR of geometry-based model can only be achievable at certain percentage of time. As a result, high reliability of AR cannot be assured by the single constellation. In the event of dual constellations system (DCS), for example, GPS and Beidou, which provide more satellites in view, users can expect significant performance benefits such as AR reliability and high precision positioning solutions. Simply using all the satellites in view for AR and positioning is a straightforward solution, but does not necessarily lead to high reliability as it is hoped. The paper presents an alternative approach that selects a subset of the visible satellites to achieve a higher reliability performance of the AR solutions in a multi-GNSS environment, instead of using all the satellites. Traditionally, satellite selection algorithms are mostly based on the position dilution of precision (PDOP) in order to meet accuracy requirements. In this contribution, some reliability criteria are introduced for GNSS satellite selection, and a novel satellite selection algorithm for reliable ambiguity resolution (SARA) is developed. The SARA algorithm allows receivers to select a subset of satellites for achieving high ASR such as above 0.99. Numerical results from a simulated dual constellation cases show that with the SARA procedure, the percentages of ASR values in excess of 0.99 and the percentages of ratio-test values passing the threshold 3 are both higher than those directly using all satellites in view, particularly in the case of dual-constellation, the percentages of ASRs (>0.99) and ratio-test values (>3) could be as high as 98.0 and 98.5 % respectively, compared to 18.1 and 25.0 % without satellite selection process. It is also worth noting that the implementation of SARA is simple and the computation time is low, which can be applied in most real-time data processing applications.

Quantitative estimation of bioturbation based on digital image analysis

Quantitative determination of modification of primary sediment features, by the activity of organisms (i.e., bioturbation) is essential in geosciences. Some methods proposed since the 1960s are mainly based on visual or subjective determinations. The first semiquantitative evaluations of the Bioturbation Index, Ichnofabric Index, or the amount of bioturbation were attempted, in the best cases using a series of flashcards designed in different situations. Recently, more effective methods involve the use of analytical and computational methods such as X-rays, magnetic resonance imaging or computed tomography; these methods are complex and often expensive. This paper presents a compilation of different methods, using Adobe® Photoshop® software CS6, for digital estimation that are a part of the IDIAP (Ichnological Digital Analysis Images Package), which is an inexpensive alternative to recently proposed methods, easy to use, and especially recommended for core samples. The different methods — “Similar Pixel Selection Method (SPSM)”, “Magic Wand Method (MWM)” and the “Color Range Selection Method (CRSM)” — entail advantages and disadvantages depending on the sediment (e.g., composition, color, texture, porosity, etc.) and ichnological features (size of traces, infilling material, burrow wall, etc.). The IDIAP provides an estimation of the amount of trace fossils produced by a particular ichnotaxon, by a whole ichnocoenosis or even for a complete ichnofabric. We recommend the application of the complete IDIAP to a given case study, followed by selection of the most appropriate method. The IDIAP was applied to core material recovered from the IODP Expedition 339, enabling us, for the first time, to arrive at a quantitative estimation of the discrete trace fossil assemblage in core samples.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.

Supramolecular selection in molecular and framework co-crystals

An ongoing challenge in chemistry and crystal engineering is the synthesis of functional materials with predictable structures and customisable properties. This may be achieved by crystallising mixtures of different compounds. Co-crystals formed through this method have predictable structures and their properties may be tuned by varying the ratio of the compounds in the crystallising solution. This thesis examines single crystals formed by the co-crystallisation of metal complexes that have similar structures but different physical or chemical properties. A variety of new compounds with interesting properties were prepared, characterised and their significance in the context of crystal engineering was explored.

Predicting freeway pavement construction cost using a back-propagation neural network : a case study in Henan, China

The objective of this research was to develop a model to estimate future freeway pavement construction costs in Henan Province, China. A comprehensive set of factors contributing to the cost of freeway pavement construction were included in the model formulation. These factors comprehensively reflect the characteristics of region and topography and altitude variation, the cost of labour, material, and equipment, and time-related variables such as index numbers of labour prices, material prices and equipment prices. An Artificial Neural Network model using the Back-Propagation learning algorithm was developed to estimate the cost of freeway pavement construction. A total of 88 valid freeway cases were obtained from freeway construction projects let by the Henan Transportation Department during the period 1994−2007. Data from a random selection of 81 freeway cases were used to train the Neural Network model and the remaining data were used to test the performance of the Neural Network model. The tested model was used to predict freeway pavement construction costs in 2010 based on predictions of input values. In addition, this paper provides a suggested correction for the prediction of the value for the future freeway pavement construction costs. Since the change in future freeway pavement construction cost is affected by many factors, the predictions obtained by the proposed method, and therefore the model, will need to be tested once actual data are obtained.