Performance evaluation of an adaptive travel time prediction model

This paper presents a travel time prediction model and evaluates its performance and transferability. Advanced Travelers Information Systems (ATIS) are gaining more and more importance, increasing the need for accurate, timely and useful information to the travelers. Travel time information quantifies the traffic condition in an easy to understand way for the users. The proposed travel time prediction model is based on an efficient use of nearest neighbor search. The model is calibrated for optimal performance using Genetic Algorithms. Results indicate better performance by using the proposed model than the presently used naïve model.

Co-adaptive environments : investigation into computer and network enhanced adaptable, sustainable and participatory environments

This paper presents research in response to environmental concerns we face today. In a search for a better method to manage spaces and building resources consumed excessively through traditional top-down architectural solutions, the research began by speculating that the building spaces and resources can be managed by designing architectural systems that encourage a bottom-up approach. In other words, this research investigates how to design systems that encourage occupants and users of buildings to actively understand, manage and customise their own spaces. Specific attention is paid to the participation of building users because no matter how sophisticated the system is, the building will become as wasteful as conventional buildings if users cannot, or do not want to, utilise the system effectively. The research is still in its early stages. The intension of this paper is to provide a background to the issue, discuss researches and projects relevant to, but not necessarily about, architecture, and introduce a number of hypothesis and investigations to realise adaptable, participatory and sustainable environments for users.

Distributed orthogonal space-time block codes with adaptive diversity gain

In this paper we present a novel distributed coding protocol for multi-user cooperative networks. The proposed distributed coding protocol exploits the existing orthogonal space-time block codes to achieve higher diversity gain by repeating the code across time and space (available relay nodes). The achievable diversity gain depends on the number of relay nodes that can fully decode the signal from the source. These relay nodes then form space-time codes to cooperatively relay to the destination using number of time slots. However, the improved diversity gain is archived at the expense of the transmission rate. The design principles of the proposed space-time distributed code and the issues related to transmission rate and diversity trade off is discussed in detail. We show that the proposed distributed space-time coding protocol out performs existing distributed codes with a variable transmission rate.

Adaptive modelling of sustainable architecture design in entropy evolutionary

This paper argues a model of adaptive design for sustainable architecture within a framework of entropy evolution. The spectrum of sustainable architecture consists of efficient use of energy and material resource in the life-cycle of buildings, active involvement of the occupants into micro-climate control within the building, and the natural environment as the physical context. The interactions amongst all the parameters compose a complex system of sustainable architecture design, of which the conventional linear and fragmented design technologies are insufficient to indicate holistic and ongoing environmental performance. The latest interpretation of the Second Law of Thermodynamics states a microscopic formulation of an entropy evolution of complex open systems. It provides a design framework for an adaptive system evolves for the optimization in open systems, this adaptive system evolves for the optimization of building environmental performance. The paper concludes that adaptive modelling in entropy evolution is a design alternative for sustainable architecture.

Adaptive and maladaptive coping strategies predict posttrauma outcomes in ambulance personnel

Attending potentially dangerous and traumatic incidents is inherent in the role of emergency workers, yet there is a paucity of literature aimed at examining variables that impact on the outcomes of such exposure. Coping has been implicated in adjusting to trauma in other contexts, and this study explored the effectiveness of coping strategies in relation to positive and negative posttrauma outcomes in the emergency services environment. One hundred twenty-five paramedics completed a survey battery including the Posttraumatic Growth Inventory (PTGI; Tedeschi & Calhoun, 1996), the Impact of Events Scale–Revised (IES-R; Weiss & Marmar, 1997), and the Revised-COPE (Zuckerman & Gagne, 2003). Results from the regression analysis demonstrated that specific coping strategies were differentially associated with positive and negative posttrauma outcomes. The research contributes to a more comprehensive understanding regarding the effectiveness of coping strategies employed by paramedics in managing trauma, with implications for their psychological well-being as well as the training and support services available.

Adaptive GPS duty cycling and radio ranging for energy-efficient localization

This paper addresses the tradeoff between energy consumption and localization performance in a mobile sensor network application. The focus is on augmenting GPS location with more energy-efficient location sensors to bound position estimate uncertainty in order to prolong node lifetime. We use empirical GPS and radio contact data from a largescale animal tracking deployment to model node mobility, GPS and radio performance. These models are used to explore duty cycling strategies for maintaining position uncertainty within specified bounds. We then explore the benefits of using short-range radio contact logging alongside GPS as an energy-inexpensive means of lowering uncertainty while the GPS is off, and we propose a versatile contact logging strategy that relies on RSSI ranging and GPS lock back-offs for reducing the node energy consumption relative to GPS duty cycling. Results show that our strategy can cut the node energy consumption by half while meeting application specific positioning criteria.

Adaptive Bayesian compound designs for dose finding studies

We consider the problem of how to efficiently and safely design dose finding studies. Both current and novel utility functions are explored using Bayesian adaptive design methodology for the estimation of a maximum tolerated dose (MTD). In particular, we explore widely adopted approaches such as the continual reassessment method and minimizing the variance of the estimate of an MTD. New utility functions are constructed in the Bayesian framework and are evaluated against current approaches. To reduce computing time, importance sampling is implemented to re-weight posterior samples thus avoiding the need to draw samples using Markov chain Monte Carlo techniques. Further, as such studies are generally first-in-man, the safety of patients is paramount. We therefore explore methods for the incorporation of safety considerations into utility functions to ensure that only safe and well-predicted doses are administered. The amalgamation of Bayesian methodology, adaptive design and compound utility functions is termed adaptive Bayesian compound design (ABCD). The performance of this amalgamation of methodology is investigated via the simulation of dose finding studies. The paper concludes with a discussion of results and extensions that could be included into our approach.

An assessment of timber dwellings typical of the Queenslander era, and constraints associated with their relocation, component and adaptive reuse when considering 'regeneration'

This paper presents an explanation of why the reuse of building components after demolition or deconstruction is critical to the future of the construction industry. An examination of the historical cause and response to climate change sets the scene as to why governance is becoming increasingly focused on the built environment as a mechanism to controlling waste generation associated with the process of demolition, construction and operation. Through an annotated description to the evolving design and construction methodology of a range of timber dwellings (typically 'Queenslanders' during the eras of 1880-1900, 1900-1920 & 1920-1940) the paper offers an evaluation to the variety of materials, which can be used advantageously by those wishing to 'regenerate' a Queenslander. This analysis of 'regeneration' details the constraints when considering relocation and/ or reuse by adaption including deconstruction of building components against the legislative framework requirements of the Queensland Building Act 1975 and the Queensland Sustainable Planning Act 2009, with a specific examination to those of the Building Codes of Australia. The paper concludes with a discussion of these constraints, their impacts on 'regeneration' and the need for further research to seek greater understanding of the practicalities and drivers of relocation, adaptive and building components suitability for reuse after deconstruction.

Fault diagnosis of induction motor based on decision trees and adaptive neuro-fuzzy inference

This paper presents a fault diagnosis method based on adaptive neuro-fuzzy inference system (ANFIS) in combination with decision trees. Classification and regression tree (CART) which is one of the decision tree methods is used as a feature selection procedure to select pertinent features from data set. The crisp rules obtained from the decision tree are then converted to fuzzy if-then rules that are employed to identify the structure of ANFIS classifier. The hybrid of back-propagation and least squares algorithm are utilized to tune the parameters of the membership functions. In order to evaluate the proposed algorithm, the data sets obtained from vibration signals and current signals of the induction motors are used. The results indicate that the CART–ANFIS model has potential for fault diagnosis of induction motors.

The link between age, career goals, and adaptive development for work-related learning among local government employees

More recently, lifespan development psychology models of adaptive development have been applied to the workforce to investigate ageing worker and lifespan issues. The current study uses the Learning and Development Survey (LDS) to investigate employee selection and engagement of learning and development goals and opportunities and constraints for learning at work in relation to demographics and career goals. It was found that mature age was associated with perceptions of preferential treatment of younger workers with respect to learning and development. Age was also correlated with several career goals. Findings suggest that younger workers’ learning and development options are better catered for in the workplace. Mature aged workers may compensate for unequal learning opportunities at work by studying for an educational qualification or seeking alternate job opportunities. The desire for a higher level job within the organization or educational qualification was linked to engagement in learning and development goals at work. It is suggested that an understanding of employee perceptions in the workplace in relation to goals and activities may be important in designing strategies to retain workers.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

Margin-adaptive model selection in statistical learning

A classical condition for fast learning rates is the margin condition, first introduced by Mammen and Tsybakov. We tackle in this paper the problem of adaptivity to this condition in the context of model selection, in a general learning framework. Actually, we consider a weaker version of this condition that allows one to take into account that learning within a small model can be much easier than within a large one. Requiring this “strong margin adaptivity” makes the model selection problem more challenging. We first prove, in a general framework, that some penalization procedures (including local Rademacher complexities) exhibit this adaptivity when the models are nested. Contrary to previous results, this holds with penalties that only depend on the data. Our second main result is that strong margin adaptivity is not always possible when the models are not nested: for every model selection procedure (even a randomized one), there is a problem for which it does not demonstrate strong margin adaptivity.