Association of hearing impairment and subsequent driving mobility in older adults

- Purpose of the study Hearing impairment (HI) is associated with driving safety (e.g., increased crashes and poor on-road driving performance). However, little is known about HI and driving mobility. This study examined the longitudinal association of audiometric hearing with older adults’ driving mobility over three years. - Design and Methods Secondary data analyses were conducted of 500 individuals (63-90 years) from the Staying Keen in Later Life (SKILL) study. Hearing (pure tone average of 0.5, 1, and 2 kHz) was assessed in the better hearing ear and categorized into normal hearing <25 dB HL; mild HI 26-40 dB HL; or moderate and greater HI>41 dB HL. The Useful Field of View Test (UFOV) was used to estimate the risk for adverse driving events. MANCOVA compared driving mobility between HI levels across time, adjusting for age, sex, race, hypertension, and stroke. Adjusting for these same covariates, Cox regression analyses examined incidence of driving cessation by HI across three years. - Results Individuals with moderate or greater HI performed poorly on the UFOV, indicating increased risk for adverse driving events (p<.001). No significant differences were found among older adults with varying levels of HI for driving mobility (ps>.05), including driving cessation rates (p=.38), across time. - Implications Although prior research indicates older adults with HI may be at higher risk for crashes, they may not modify driving over time. Further exploration of this issue is required to optimize efforts to improve driving safety and mobility among older adults.

Business process performance mining with staged process flows

Existing process mining techniques provide summary views of the overall process performance over a period of time, allowing analysts to identify bottlenecks and associated performance issues. However, these tools are not de- signed to help analysts understand how bottlenecks form and dissolve over time nor how the formation and dissolution of bottlenecks – and associated fluctua- tions in demand and capacity – affect the overall process performance. This paper presents an approach to analyze the evolution of process performance via a notion of Staged Process Flow (SPF). An SPF abstracts a business process as a series of queues corresponding to stages. The paper defines a number of stage character- istics and visualizations that collectively allow process performance evolution to be analyzed from multiple perspectives. The approach has been implemented in the ProM process mining framework. The paper demonstrates the advantages of the SPF approach over state-of-the-art process performance mining tools using two real-life event logs publicly available.

An experimental analysis of information acquisition in prediction markets

We study which factors in terms of trading environment and trader characteristics determine individual information acquisition in experimental asset markets. Traders with larger endowments, existing inconclusive information, lower risk aversion, and less experience in financial markets tend to acquire more information. Overall, we find that traders overacquire information, so that informed traders on average obtain negative profits net of information costs. Information acquisition and the associated losses do not diminish over time. This overacquisition phenomenon is inconsistent with predictions of rational expectations equilibrium, and we argue it resembles the overdissipation results from the contest literature. We find that more acquired information in the market leads to smaller differences between fundamental asset values and prices. Thus, the overacquisition phenomenon is a novel explanation for the high forecasting accuracy of prediction markets.

Continuous after-the-fact leakage-resilient eCK-secure key exchange

Security models for two-party authenticated key exchange (AKE) protocols have developed over time to capture the security of AKE protocols even when the adversary learns certain secret values. Increased granularity of security can be modelled by considering partial leakage of secrets in the manner of models for leakage-resilient cryptography, designed to capture side-channel attacks. In this work, we use the strongest known partial-leakage-based security model for key exchange protocols, namely continuous after-the-fact leakage eCK (CAFL-eCK) model. We resolve an open problem by constructing the first concrete two-pass leakage-resilient key exchange protocol that is secure in the CAFL-eCK model.

Destination image: identifying baseline perceptions of Brazil, Argentina and Chile in the nascent Australian long haul travel market

While the popularity of destination image research has increased exponentially in the literature, there has been relatively little published about perceptions held by international consumers of destinations in South America. The purpose of this paper is to report the findings of a research project that aimed to identify the baseline market perceptions of Brazil, Argentina and Chile amongst Australian residents, at the time of the emergence of this long haul market. Of interest was the extent to which Australians differentiate the three distinct countries versus perceiving the continent as a gestalt. These baseline perceptions enable the effectiveness of future marketing communications in Australia by the three national tourism offices to be monitored over time. Importance-Performance Analysis (IPA) is used as a practical analytical tool to guide decision makers. In terms of operationalising destination image, a key research finding was the very high ratio or participants using the ‘Don’t know’ (DK) option for each destination performance scale item. This finding has practical implications for the destination marketers, as well as for researchers engaged in destination image research in long haul and/or emerging markets.

Harnessing information from injury narrative in the 'big data' era: Understanding and applying machine learning for injury surveillance

Objective Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. Methods This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. Results The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. Conclusion The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.

The relationship between the determinants of rental housing satisfaction and considering moving in Finland

Purpose The purpose of this paper is to discuss the relation between dissatisfaction with housing conditions and considering moving among residents of Finnish rental multifamily buildings. The paper examines physical attributes, socioeconomic factors, and subjective opinions related to housing conditions and satisfaction with housing. Design/methodology/approach Logistic regression analysis is used to examine survey data to analyse which factors contribute to dissatisfaction with the housing unit and the apartment building and whether dissatisfaction is related to consideration of moving. Findings The findings indicate that dissatisfaction with the building and individual housing unit are associated with greater probability of considering moving. Satisfaction with kitchen, living room, storage, and building age are the most important indicators of satisfaction with the housing unit, and satisfaction with living room, bathroom, storage, and building age are associated with satisfaction with the apartment building. These are the areas in which landlords could invest in renovations to increase satisfaction in an attempt to reduce turnover. Research limitations/implications The study is conducted with Finnish data only. The sample is not a representative sample of the Finnish population. A longitudinal study would be needed to determine whether dissatisfied residents indending to move actually change residence. Originality/value This study is the first of its kind in the Finnish housing market. It tests a general model that has been suggested to be customized to local conditions. In addition, much of the research on this topic is more than 20 years old. Examination of the model under current housing and socioeconomic conditions is necessary to determine if relationships have changed over time.