Sensory imagery in craving : from cognitive psychology to new treatments for addiction

Sensory imagery is a powerful tool for inducing craving because it is a key component of the cognitive system that underpins human motivation. The role of sensory imagery in motivation is explained by Elaborated Intrusion (EI) theory. Imagery plays an important role in motivation because it conveys the emotional qualities of the desired event, mimicking anticipated pleasure or relief, and continual elaboration of the imagery ensures that the target stays in mind. We argue that craving is a conscious state, intervening between unconscious triggers and consumption, and summarise evidence that interfering with sensory imagery can weaken cravings. We argue that treatments for addiction can be enhanced by the application of EI theory to maintain motivation, and assist in the management of craving in high-risk situations.

On the commonly accepted assumptions regarding observed motor vehicle crash counts at transport system locations

Readily accepted knowledge regarding crash causation is consistently omitted from efforts to model and subsequently understand motor vehicle crash occurrence and their contributing factors. For instance, distracted and impaired driving accounts for a significant proportion of crash occurrence, yet is rarely modeled explicitly. In addition, spatially allocated influences such as local law enforcement efforts, proximity to bars and schools, and roadside chronic distractions (advertising, pedestrians, etc.) play a role in contributing to crash occurrence and yet are routinely absent from crash models. By and large, these well-established omitted effects are simply assumed to contribute to model error, with predominant focus on modeling the engineering and operational effects of transportation facilities (e.g. AADT, number of lanes, speed limits, width of lanes, etc.) The typical analytical approach—with a variety of statistical enhancements—has been to model crashes that occur at system locations as negative binomial (NB) distributed events that arise from a singular, underlying crash generating process. These models and their statistical kin dominate the literature; however, it is argued in this paper that these models fail to capture the underlying complexity of motor vehicle crash causes, and thus thwart deeper insights regarding crash causation and prevention. This paper first describes hypothetical scenarios that collectively illustrate why current models mislead highway safety researchers and engineers. It is argued that current model shortcomings are significant, and will lead to poor decision-making. Exploiting our current state of knowledge of crash causation, crash counts are postulated to arise from three processes: observed network features, unobserved spatial effects, and ‘apparent’ random influences that reflect largely behavioral influences of drivers. It is argued; furthermore, that these three processes in theory can be modeled separately to gain deeper insight into crash causes, and that the model represents a more realistic depiction of reality than the state of practice NB regression. An admittedly imperfect empirical model that mixes three independent crash occurrence processes is shown to outperform the classical NB model. The questioning of current modeling assumptions and implications of the latent mixture model to current practice are the most important contributions of this paper, with an initial but rather vulnerable attempt to model the latent mixtures as a secondary contribution.

The process and patterns of combining the use of traditional Chinese medicine and Western medicine in Taiwanese people with cancer

Objective: The nature of contemporary cancer therapy means that patients are faced with difficult treatment decisions about surgery, chemotherapy and radiotherapy. For some, this process may also involve consideration of therapies that sit outside the biomedical approach to cancer treatment, in our research, traditional Chinese medicine (TCM). Thus, it is important to explore how cancer patients in Taiwan incorporate TCM into their cancer treatment journey. This paper aims to explore of the patterns of combining the use of TCM and Western medicine into cancer treatment journey in Taiwanese people with cancer. Methods: The sampling was purposive and the data collected through in-depth interviews. Data collection occurred over an eleven month. The research was grounded in the premises of symbolic interactionism and adopted the methods of grounded theory. Twenty four participants who were patients receiving cancer treatment were recruited from two health care settings in Taiwan. Results: The study findings suggest that perceptions of health and illness are mediated through ongoing interactions with different forms of therapy. The participants in this study had a clear focus on “process and patterns of using TCM and Western medicine”. Further, ‘different importance in Western medicine and TCM’, ‘taken for granted to use TCM’, ‘each has specialized skills in Western medicine and TCM’ and ‘different symptoms use different approaches (Western medicine or TCM)’ may explicit how the participants in this study see CAM and Western medicine. Conclusions/Implications for practice: The descriptive frame of the study suggests that TCM and Western medicine occupy quite distinct domains in terms of decision making over their use. People used TCM based on interpretations of the present and against a background of an enduring cultural legacy grounded in Chinese philosophical beliefs about health and healthcare. The increasingly popular term of 'integrative medicine' obscures the complex contexts of the patterns of use of both therapeutic modalities. It is this latter point that is worthy of further exploration.

Prosecutorial guidelines for voluntary euthanasia and assisted suicide : autonomy, public confidence and high quality decision-making

This article proposes offence-specific guidelines for how prosecutorial discretion should be exercised in cases of voluntary euthanasia and assisted suicide. Similar guidelines have been produced in England and Wales but we consider them to be deficient in a number of respects, including that they lack a set of coherent guiding principles. In light of these concerns, we outline an approach to constructing alternative guidelines that begins with identifying three guiding principles that we argue are appropriate for this purpose: respect for autonomy, the need for high quality prosecutorial decision-making and the importance of public confidence in that decision-making.

Information practices of teen content creators : the intersection of action and experiences a grounded theory study

What are the information practices of teen content creators? In the United States over two thirds of teens have participated in creating and sharing content in online communities that are developed for the purpose of allowing users to be producers of content. This study investigates how teens participating in digital participatory communities find and use information as well as how they experience the information. From this investigation emerged a model of their information practices while creating and sharing content such as film-making, visual art work, story telling, music, programming, and web site design in digital participatory communities. The research uses grounded theory methodology in a social constructionist framework to investigate the research problem: what are the information practices of teen content creators? Data was gathered through semi-structured interviews and observation of teen’s digital communities. Analysis occurred concurrently with data collection, and the principle of constant comparison was applied in analysis. As findings were constructed from the data, additional data was collected until a substantive theory was constructed and no new information emerged from data collection. The theory that was constructed from the data describes five information practices of teen content creators. The five information practices are learning community, negotiating aesthetic, negotiating control, negotiating capacity, and representing knowledge. In describing the five information practices there are three necessary descriptive components, the community of practice, the experiences of information and the information actions. The experiences of information include information as participation, inspiration, collaboration, process, and artifact. Information actions include activities that occur in the categories of gathering, thinking and creating. The experiences of information and information actions intersect in the information practices, which are situated within the specific community of practice, such as a digital participatory community. Finally, the information practices interact and build upon one another and this is represented in a graphic model and explanation.

Organisational culture in airworthiness management programs : developing a measurement model

All civil and private aircraft are required to comply with the airworthiness standards set by their national airworthiness authority and throughout their operational life must be in a condition of safe operation. Aviation accident data shows that over twenty percent of all fatal accidents in aviation are due to airworthiness issues, specifically aircraft mechanical failures. Ultimately it is the responsibility of each registered operator to ensure that their aircraft remain in a condition of safe operation, and this is done through both effective management of airworthiness activities and the effective program governance of safety outcomes. Typically, the projects within these airworthiness management programs are focused on acquiring, modifying and maintaining the aircraft as a capability supporting the business. Program governance provides the structure through which the goals and objectives of airworthiness programs are set along with the means of attaining them. Whilst the principal causes of failures in many programs can be traced to inadequate program governance, many of the failures in large scale projects can have their root causes in the organisational culture and more specifically in the organisational processes related to decision-making. This paper examines the primary theme of project and program based enterprises, and introduces a model for measuring organisational culture in airworthiness management programs using measures drawn from 211 respondents in Australian airline programs. The paper describes the theoretical perspectives applied to modifying an original model to specifically focus it on measuring the organisational culture of programs for managing airworthiness; identifying the most important factors needed to explain the relationship between the measures collected, and providing a description of the nature of these factors. The paper concludes by identifying a model that best describes the organisational culture data collected from seven airworthiness management programs.

Introduction to grounded theory

Grounded theory, first developed by Glaser and Strauss in the 1960s, was introduced into nursing education as a distinct research methodology in the 1970s. The theory is grounded in a critique of the dominant contemporary approach to social inquiry, which imposed "enduring" theoretical propositions onto study data. Rather than starting from a set theoretical framework, grounded theory relies on researchers distinguishing meaningful constructs from generated data and then identifying an appropriate theory. Grounded theory is thus particularly useful in investigating complex issues and behaviours not previously addressed and concepts and relationships in particular populations or places that are still undeveloped or weakly connected. Grounded theory data analysis processes include open, axial and selective coding levels. The purpose of this article was to explore the grounded theory research process and provide an initial understanding of this methodology.

Asset health prediction using the explicit hazard model

The ability to estimate the asset reliability and the probability of failure is critical to reducing maintenance costs, operation downtime, and safety hazards. Predicting the survival time and the probability of failure in future time is an indispensable requirement in prognostics and asset health management. In traditional reliability models, the lifetime of an asset is estimated using failure event data, alone; however, statistically sufficient failure event data are often difficult to attain in real-life situations due to poor data management, effective preventive maintenance, and the small population of identical assets in use. Condition indicators and operating environment indicators are two types of covariate data that are normally obtained in addition to failure event and suspended data. These data contain significant information about the state and health of an asset. Condition indicators reflect the level of degradation of assets while operating environment indicators accelerate or decelerate the lifetime of assets. When these data are available, an alternative approach to the traditional reliability analysis is the modelling of condition indicators and operating environment indicators and their failure-generating mechanisms using a covariate-based hazard model. The literature review indicates that a number of covariate-based hazard models have been developed. All of these existing covariate-based hazard models were developed based on the principle theory of the Proportional Hazard Model (PHM). However, most of these models have not attracted much attention in the field of machinery prognostics. Moreover, due to the prominence of PHM, attempts at developing alternative models, to some extent, have been stifled, although a number of alternative models to PHM have been suggested. The existing covariate-based hazard models neglect to fully utilise three types of asset health information (including failure event data (i.e. observed and/or suspended), condition data, and operating environment data) into a model to have more effective hazard and reliability predictions. In addition, current research shows that condition indicators and operating environment indicators have different characteristics and they are non-homogeneous covariate data. Condition indicators act as response variables (or dependent variables) whereas operating environment indicators act as explanatory variables (or independent variables). However, these non-homogenous covariate data were modelled in the same way for hazard prediction in the existing covariate-based hazard models. The related and yet more imperative question is how both of these indicators should be effectively modelled and integrated into the covariate-based hazard model. This work presents a new approach for addressing the aforementioned challenges. The new covariate-based hazard model, which termed as Explicit Hazard Model (EHM), explicitly and effectively incorporates all three available asset health information into the modelling of hazard and reliability predictions and also drives the relationship between actual asset health and condition measurements as well as operating environment measurements. The theoretical development of the model and its parameter estimation method are demonstrated in this work. EHM assumes that the baseline hazard is a function of the both time and condition indicators. Condition indicators provide information about the health condition of an asset; therefore they update and reform the baseline hazard of EHM according to the health state of asset at given time t. Some examples of condition indicators are the vibration of rotating machinery, the level of metal particles in engine oil analysis, and wear in a component, to name but a few. Operating environment indicators in this model are failure accelerators and/or decelerators that are included in the covariate function of EHM and may increase or decrease the value of the hazard from the baseline hazard. These indicators caused by the environment in which an asset operates, and that have not been explicitly identified by the condition indicators (e.g. Loads, environmental stresses, and other dynamically changing environment factors). While the effects of operating environment indicators could be nought in EHM; condition indicators could emerge because these indicators are observed and measured as long as an asset is operational and survived. EHM has several advantages over the existing covariate-based hazard models. One is this model utilises three different sources of asset health data (i.e. population characteristics, condition indicators, and operating environment indicators) to effectively predict hazard and reliability. Another is that EHM explicitly investigates the relationship between condition and operating environment indicators associated with the hazard of an asset. Furthermore, the proportionality assumption, which most of the covariate-based hazard models suffer from it, does not exist in EHM. According to the sample size of failure/suspension times, EHM is extended into two forms: semi-parametric and non-parametric. The semi-parametric EHM assumes a specified lifetime distribution (i.e. Weibull distribution) in the form of the baseline hazard. However, for more industry applications, due to sparse failure event data of assets, the analysis of such data often involves complex distributional shapes about which little is known. Therefore, to avoid the restrictive assumption of the semi-parametric EHM about assuming a specified lifetime distribution for failure event histories, the non-parametric EHM, which is a distribution free model, has been developed. The development of EHM into two forms is another merit of the model. A case study was conducted using laboratory experiment data to validate the practicality of the both semi-parametric and non-parametric EHMs. The performance of the newly-developed models is appraised using the comparison amongst the estimated results of these models and the other existing covariate-based hazard models. The comparison results demonstrated that both the semi-parametric and non-parametric EHMs outperform the existing covariate-based hazard models. Future research directions regarding to the new parameter estimation method in the case of time-dependent effects of covariates and missing data, application of EHM in both repairable and non-repairable systems using field data, and a decision support model in which linked to the estimated reliability results, are also identified.

Improved foreground detection via block-based classifier cascade with probabilistic decision integration

Background subtraction is a fundamental low-level processing task in numerous computer vision applications. The vast majority of algorithms process images on a pixel-by-pixel basis, where an independent decision is made for each pixel. A general limitation of such processing is that rich contextual information is not taken into account. We propose a block-based method capable of dealing with noise, illumination variations, and dynamic backgrounds, while still obtaining smooth contours of foreground objects. Specifically, image sequences are analyzed on an overlapping block-by-block basis. A low-dimensional texture descriptor obtained from each block is passed through an adaptive classifier cascade, where each stage handles a distinct problem. A probabilistic foreground mask generation approach then exploits block overlaps to integrate interim block-level decisions into final pixel-level foreground segmentation. Unlike many pixel-based methods, ad-hoc postprocessing of foreground masks is not required. Experiments on the difficult Wallflower and I2R datasets show that the proposed approach obtains on average better results (both qualitatively and quantitatively) than several prominent methods. We furthermore propose the use of tracking performance as an unbiased approach for assessing the practical usefulness of foreground segmentation methods, and show that the proposed approach leads to considerable improvements in tracking accuracy on the CAVIAR dataset.

Understanding the participatory library through a grounded theory study

Lankes and Silverstein (2006) introduced the “participatory library” and suggested that the nature and form of the library should be explored. In the last several years, some attempts have been made in order to develop contemporary library models that are often known as Library 2.0. However, little research has been based on empirical data and such models have had a strong focus on technical aspects but less focus on participation. The research presented in this paper fills this gap. A grounded theory approach was adopted for this study. Six librarians were involved in in-depth individual interviews. As a preliminary result, five main factors of the participatory library emerged including technological, human, educational, social-economic, and environmental. Five factors influencing the participation in libraries were also identified: finance, technology, education, awareness, and policy. The study’s findings provide a fresh perspective on contemporary library and create a basis for further studies on this area.

Self-determination theory as applied to the design of a software learning system using whole-body controls

Whole-body computer control interfaces present new opportunities to engage children with games for learning. Stomp is a suite of educational games that use such a technology, allowing young children to use their whole body to interact with a digital environment projected on the floor. To maximise the effectiveness of this technology, tenets of self-determination theory (SDT) are applied to the design of Stomp experiences. By meeting user needs for competence, autonomy, and relatedness our aim is to increase children's engagement with the Stomp learning platform. Analysis of Stomp's design suggests that these tenets are met. Observations from a case study of Stomp being used by young children show that they were highly engaged and motivated by Stomp. This analysis demonstrates that continued application of SDT to Stomp will further enhance user engagement. It also is suggested that SDT, when applied more widely to other whole-body multi-user interfaces, could instil similar positive effects.

On the reliability of classifying programming tasks using a Neo-Piagetian theory of cognitive development

Recent research has proposed Neo-Piagetian theory as a useful way of describing the cognitive development of novice programmers. Neo-Piagetian theory may also be a useful way to classify materials used in learning and assessment. If Neo-Piagetian coding of learning resources is to be useful then it is important that practitioners can learn it and apply it reliably. We describe the design of an interactive web-based tutorial for Neo-Piagetian categorization of assessment tasks. We also report an evaluation of the tutorial's effectiveness, in which twenty computer science educators participated. The average classification accuracy of the participants on each of the three Neo-Piagetian stages were 85%, 71% and 78%. Participants also rated their agreement with the expert classifications, and indicated high agreement (91%, 83% and 91% across the three Neo-Piagetian stages). Self-rated confidence in applying Neo-Piagetian theory to classifying programming questions before and after the tutorial were 29% and 75% respectively. Our key contribution is the demonstration of the feasibility of the Neo-Piagetian approach to classifying assessment materials, by demonstrating that it is learnable and can be applied reliably by a group of educators. Our tutorial is freely available as a community resource.

Adaptive stepsize based on control theory for stochastic differential equations

The numerical solution of stochastic differential equations (SDEs) has been focused recently on the development of numerical methods with good stability and order properties. These numerical implementations have been made with fixed stepsize, but there are many situations when a fixed stepsize is not appropriate. In the numerical solution of ordinary differential equations, much work has been carried out on developing robust implementation techniques using variable stepsize. It has been necessary, in the deterministic case, to consider the "best" choice for an initial stepsize, as well as developing effective strategies for stepsize control-the same, of course, must be carried out in the stochastic case. In this paper, proportional integral (PI) control is applied to a variable stepsize implementation of an embedded pair of stochastic Runge-Kutta methods used to obtain numerical solutions of nonstiff SDEs. For stiff SDEs, the embedded pair of the balanced Milstein and balanced implicit method is implemented in variable stepsize mode using a predictive controller for the stepsize change. The extension of these stepsize controllers from a digital filter theory point of view via PI with derivative (PID) control will also be implemented. The implementations show the improvement in efficiency that can be attained when using these control theory approaches compared with the regular stepsize change strategy.

Climate of fear in organisational settings : construct definition, measurement and a test of theory

This paper reports a study that explored a new construct: ‘climate of fear’. We hypothesised that climate of fear would vary across work sites within organisations, but not across organisations. This is in contrast a to measures of organisational culture, which were expected to vary both within and across organisations. To test our hypotheses, we developed a new 13-item measure of perceived fear in organisations and tested it in 20 sites across two organisations (N ≡ 209). Culture variables measured were innovative leadership culture, and communication culture. Results were that climate of fear did vary across sites in both organisations, while differences across organisations were not significant, as we anticipated. Organisational culture, however, varied between the organisations, and within one of the organisations. The climate of fear scale exhibited acceptable psychometric properties

Smart demand side management of low-voltage distribution networks using multi-objective decision making

A novel intelligent online demand side management system is proposed for peak load management in low-voltage distribution networks. This method uses low-cost controllers with low-bandwidth two-way communication installed in custumers’ premises and at distribution transformers to manage the peak load while maximising customer satisfaction. A multi-objective decision making process is proposed to select the load(s) to be delayed or controlled. The efficacy of the proposed control system is verified by simulation of three different feeder types.