Formal home health care, informal care, and family decision making

We use the 1993 wave of the Assets and Health Dynamics Among the Oldest Old (AHEAD) data set to estimate a game-theoretic model of families' decisions concerning the provision of informal and formal care for elderly individuals. The outcome is the Nash equilibrium where each family member jointly determines her consumption, transfers for formal care, and allocation of time to informal care, market work, and leisure. We use the estimates to decompose the effects of adult children's opportunity costs, quality of care, and caregiving burden on their propensities to provide informal care. We also simulate the effects of a broad range of policies of current interest. © (2009) by the Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.

Degradation modeling and monitoring of machines using operation-specific hidden Markov models

In this paper, a novel data-driven approach to monitoring of systems operating under variable operating conditions is described. The method is based on characterizing the degradation process via a set of operation-specific hidden Markov models (HMMs), whose hidden states represent the unobservable degradation states of the monitored system while its observable symbols represent the sensor readings. Using the HMM framework, modeling, identification and monitoring methods are detailed that allow one to identify a HMM of degradation for each operation from mixed-operation data and perform operation-specific monitoring of the system. Using a large data set provided by a major manufacturer, the new methods are applied to a semiconductor manufacturing process running multiple operations in a production environment.

Reversible and semi-blind relational database watermarking

Database watermarking has received significant research attention in the current decade. Although, almost all watermarking models have been either irreversible (the original relation cannot be restored from the watermarked relation) and/or non-blind (requiring original relation to detect the watermark in watermarked relation). This model has several disadvantages over reversible and blind watermarking (requiring only watermarked relation and secret key from which the watermark is detected and original relation is restored) including inability to identify rightful owner in case of successful secondary watermarking, inability to revert the relation to original data set (required in high precision industries) and requirement to store unmarked relation at a secure secondary storage. To overcome these problems, we propose a watermarking scheme that is reversible as well as blind. We utilize difference expansion on integers to achieve reversibility. The major advantages provided by our scheme are reversibility to high quality original data set, rightful owner identification, resistance against secondary watermarking attacks, and no need to store original database at a secure secondary storage.

Towards a phylogeny of the Tenebrionoidea (Coleoptera)

The phylogenetic relationships of the beetle superfamily Tenebrionoidea are investigated using the most comprehensive genetic data set compiled to date. With ∼34,000 described species in approximately 1250 genera and 28 families, Tenebrionoidea represent one of the most diverse and species-rich superfamilies of beetles. The interfamilial relationships of the Tenebrionoidea are poorly known; previous morphological and molecular phylogenies recovered few well-supported and often conflicting relationships between families. Here we present a molecular phylogeny of Tenebrionoidea based on genes commonly used to resolve family and superfamily-level phylogenies of beetles (18S, 28S, 16S, 12S, tRNA Val and COI). The alignment spanned over 6.5 KB of DNA sequence and over 300 tenebrionoid genera from 24 of the 28 families were sampled. Maximum Likelihood and Bayesian analysis could not resolve deeper level divergences within the superfamily and very few relationships between families were supported. Increasing gene coverage in the alignment by removing taxa with missing data did not improve clade support but when rogue taxa were removed increased resolution was recovered. Investigation of signal strength suggested conflicting phylogenetic signal was present in the standard genes used for beetle phylogenetics, even when rogue taxa were removed. Our study of Tenebrionoidea highlights that even with relatively comprehensive taxon sampling within a lineage, this standard set of genes is unable to resolve relationships within this superfamily.

Effect of Oil Price on Emissions Trading Market: VAR Analysis

In this study, we investigated the relationship of European Union carbon dioxide CO2 allowances EUAs prices and oil prices by employing a VAR analysis, Granger causality test and impulse response function. If oil price continues increasing, companies will decrease dependency on fossil fuels because of an increase in energy costs. Therefore, the price of EUAs may be affected by variations in oil prices if the greenhouse gases discharged by the consumption of alternative energy are less than that of fossil fuels. There are no previous studies that investigated these relationships. In this study, we analyzed eight types of EUAs EUA05 to EUA12 with a time series daily data set during 2005-2007 collected from a European Climate Exchange time series data set. Differentiations in these eight types were redemption period. We used the New York Mercantile Exchange light sweet crude price as an oil price. From our examination, we found that only the EUA06 and EUA07 types of EUAs Granger-cause oil prices and vice versa and other six types of EUAs do not Granger-cause oil price. These results imply that the earlier redemption period types of EUAs are more sensitive to oil price. In employing the impulse response function, the results showed that a shock to oil price has a slightly positive effect on all types of EUAs for a very short period. On the other hand, we found that a shock to price of EUA has a slightly negative effect on oil price following a positive effect in only EUA06 and EUA07 types. Therefore, these results imply that fluctuations in EUAs prices and oil prices have little effect on each other. Lastly, we did not consider the substitute energy prices in this study, so we plan to include the prices of coal and natural gas in future analyses.

Modal choice between air and rail : a social efficiency benchmarking analysis that considers CO2 emissions

The problem of modal choice between rail and air arises as public awareness of carbon dioxide (CO2) emissions by the transportation sector rises. In this paper, we answer this question quantitatively by performing an efficiency benchmarking analysis that takes into account life-cycle CO2 emission due to transport service provision. The paper employs nonparametric efficiency estimation methods, namely a slacks-based inefficiency measure, as well as a more conventional directional distance function approach. We apply them to a panel data set for three major railway companies and the aviation sector in Japan for the period from 1999 to 2007. Results shows that, contrary to the common argument, air transport can still be more socially efficient than rail transport, even when the environmental load due to CO2 emission is incorporated. This is due to the aviation sector's extremely low user cost, measured in terms of in-vehicle time. In other words, aviation is a necessary transportation mode for those with a very high willingness to pay for their time.

Improving the outcomes of foot and ankle surgery through the audit cycle : a case study

The direct costs of managing adverse outcomes from Australian health care are estimated to be $2 billion. The audit cycle is considered an important tool to assist in the preventive management of adverse outcomes.Australian guidelines for audit cycle design allow for comparison of data sets derived from similar surgical specialities. However a lack of data set standardisation inhibits meaningful comparisons of foot and ankle surgical audits. This research will assist development of a best practice model for auditing foot and ankle surgery. Data derived from this model will improve the safety and quality of foot and ankle surgery. The preliminary phase of this process is to identify and understand the attitudes and behaviours of how and why surgeons participate in the audit cycle. A descriptive embedded multiple case study research design is planned to provide an intense focus on a single phenomenon (the audit cycle) within its real life context (clinical governance). The measures to be included in the case study have been identified by the Balanced Patient Safety Measurement Framework. These include: audit and peer review activity, provider attitudes to patient safety, safety learning, action and performance. A purposive sample of 6 to 8 surgeons (units of analysis) from 3 to 4 specialities (cases) will undergo semi-structured interview. This will investigate: current audit tools and processes; attitudes; and behaviours of surgeons to the audit cycle. Similarities in and differences between the units of analysis will indicate which identified measures function as barriers or enablers of the audit cycle. Reliability and validity (external and construct) will be assessed using established methods for case studies. The descriptive embedded multiple case study will reveal how and why foot and ankle surgeons participate in the audit cycle. This will inform further research to improve the outcomes of foot and ankle surgery through development of an audit tool.

Approximate Bayesian Computation for astronomical model analysis : a case study in galaxy demographics and morphological transformation at high redshift

Approximate Bayesian Computation’ (ABC) represents a powerful methodology for the analysis of complex stochastic systems for which the likelihood of the observed data under an arbitrary set of input parameters may be entirely intractable – the latter condition rendering useless the standard machinery of tractable likelihood-based, Bayesian statistical inference [e.g. conventional Markov chain Monte Carlo (MCMC) simulation]. In this paper, we demonstrate the potential of ABC for astronomical model analysis by application to a case study in the morphological transformation of high-redshift galaxies. To this end, we develop, first, a stochastic model for the competing processes of merging and secular evolution in the early Universe, and secondly, through an ABC-based comparison against the observed demographics of massive (Mgal > 1011 M⊙) galaxies (at 1.5 < z < 3) in the Cosmic Assembly Near-IR Deep Extragalatic Legacy Survey (CANDELS)/Extended Groth Strip (EGS) data set we derive posterior probability densities for the key parameters of this model. The ‘Sequential Monte Carlo’ implementation of ABC exhibited herein, featuring both a self-generating target sequence and self-refining MCMC kernel, is amongst the most efficient of contemporary approaches to this important statistical algorithm. We highlight as well through our chosen case study the value of careful summary statistic selection, and demonstrate two modern strategies for assessment and optimization in this regard. Ultimately, our ABC analysis of the high-redshift morphological mix returns tight constraints on the evolving merger rate in the early Universe and favours major merging (with disc survival or rapid reformation) over secular evolution as the mechanism most responsible for building up the first generation of bulges in early-type discs.

Locality-sensitive hashing for protein classification

Determination of sequence similarity is a central issue in computational biology, a problem addressed primarily through BLAST, an alignment based heuristic which has underpinned much of the analysis and annotation of the genomic era. Despite their success, alignment-based approaches scale poorly with increasing data set size, and are not robust under structural sequence rearrangements. Successive waves of innovation in sequencing technologies – so-called Next Generation Sequencing (NGS) approaches – have led to an explosion in data availability, challenging existing methods and motivating novel approaches to sequence representation and similarity scoring, including adaptation of existing methods from other domains such as information retrieval. In this work, we investigate locality-sensitive hashing of sequences through binary document signatures, applying the method to a bacterial protein classification task. Here, the goal is to predict the gene family to which a given query protein belongs. Experiments carried out on a pair of small but biologically realistic datasets (the full protein repertoires of families of Chlamydia and Staphylococcus aureus genomes respectively) show that a measure of similarity obtained by locality sensitive hashing gives highly accurate results while offering a number of avenues which will lead to substantial performance improvements over BLAST..

A frontier functions approach to optimal scales of sustainable production

This paper translates the concepts of sustainable production to three dimensions of economic, environmental and ecological sustainability to analyze optimal production scales by solving optimizing problems. Economic optimization seeks input-output combinations to maximize profits. Environmental optimization searches for input-output combinations that minimize the polluting effects of materials balance on the surrounding environment. Ecological optimization looks for input-output combinations that minimize the cumulative destruction of the entire ecosystem. Using an aggregate space, the framework illustrates that these optimal scales are often not identical because markets fail to account for all negative externalities. Profit-maximizing firms normally operate at the scales which are larger than optimal scales from the viewpoints of environmental and ecological sustainability; hence policy interventions are favoured. The framework offers a useful tool for efficiency studies and policy implication analysis. The paper provides an empirical investigation using a data set of rice farms in South Korea.

Reduced complexity on-line estimation of hidden Markov model parameters

In this paper we propose and study low complexity algorithms for on-line estimation of hidden Markov model (HMM) parameters. The estimates approach the true model parameters as the measurement noise approaches zero, but otherwise give improved estimates, albeit with bias. On a nite data set in the high noise case, the bias may not be signi cantly more severe than for a higher complexity asymptotically optimal scheme. Our algorithms require O(N3) calculations per time instant, where N is the number of states. Previous algorithms based on earlier hidden Markov model signal processing methods, including the expectation-maximumisation (EM) algorithm require O(N4) calculations per time instant.

Superstars as drivers of organizational identification : empirical findings from professional soccer

This paper examines the effect of superstars on external stakeholders’ organizational identification through the lens of sport. Drawing on social identity theory and the concept of organizational identification, as well as on role model theories and superstar economics, several hypotheses are developed regarding the influence of soccer stars on their fans’ degree of team identification. Using a proprietary data set that combines archival data on professional German soccer players and clubs with survey data on more than 1,400 soccer fans, this study finds evidence for a positive effect of superstar characteristics and role model perception. Moreover, it is found that players who qualify for the definition of a superstar are more important to fans of established teams than to fans of unsuccessful teams. The player's club tenure, however, seems to have no influence on fans’ team identification. It is further argued that the effect of soccer stars on their fans is comparable to that of executives on external stakeholders, and hence, the results are applied to the business domain. The results of this study contribute to existing research by extending the list of personnel-related determinants of organizational identification.

Superstars and disloyal football fans : factors influencing attraction to competition

Purpose – The purpose of this paper is to examine the effect of superstars (and other factors) on football fans’ attraction to competition (i.e. disloyal behavior). Design/methodology/approach – A proprietary data set including archival data on professional German football players and clubs as well as survey data of more than 900 football fans is used. The hypotheses are tested with two-sample mean-comparison t-tests and multivariate probit models. Findings – This study provides evidence that superstars both attract new fans and contribute to the retention of existing fans. While the presence of superstars, team loyalty and team identification prevent football fans from being attracted to competition, the team's recent performance seems to have no effect. Fans who select their favorite player from a competing team rather choose superstars, young players, players who are known for exemplary behavior and defenders. Originality/value – This paper contributes to existing research by expanding the list of antecedents of disloyalty and by being the first to employ independent, quantitative data for the assessment of superstar characteristics in the context of team loyalty.

Happiness online : supporting the emotions of emergent teachers in a virtual community

Extending Lash and Urry's (1994) notion of new "imagined communities" through information and communication structures, I ask the question: Are emergent teachers happy when they interact in online learning environments? This question is timely in the context of the ubiquity of online media and its pervasiveness in teachers' everyday work and lives. The research is important nationally and internationally, because the current research is contradictory. On the one hand, feelings of isolation and frustration have been cited as common emotions experienced in many online environments (Su, Bonk, Magjuka, Liu, & Lee, 2005). Yet others report that online communities encourage a sense of belonging and support (Mills, 2011). Emotions are inherently social, are central to learning and online interaction (Shen, Wang, & Shen, 2009). The presentations reports the use of e-motion blogs to explore emotional states of emergent primary teachers in an online learning context as they transition into their first field experience in schools. The original research was conducted with a graduate class of 64 secondary science pre-service teachers in Science Education Curriculum Studies in a large Australian university, including males and females from a variety of cultural backgrounds, aged 17-55 years. Online activities involved the participants watching a series of streamed live lectures within a course of 8 weeks duration, providing a varied set of learning experiences, such as viewing live teaching demonstrations. Each week, participants provided feedback on learning by writing and posting an e-motion diary or web log about their emotional response. The blogs answered the question: What emotions you experience during this learning experience? The descriptive data set included 284 online posts, with students contributing multiple entries. The Language of Appraisal framework, following Martin and White (2005), was used to cluster the discrete emotions within six affect groups. The findings demonstrated that the pre-service teachers' emotional responses tended towards happiness and satisfaction within the typology of affect groups - un/happiness, in/security, and dis/satisfaction. Fewer participants reported that online learning mode triggered negative feelings of frustration, and when this occurred, it often pertained expectations of themselves in the forthcoming field experience in schools or as future teachers. The findings primarily contribute new understanding about emotional states in online communities, and recommendations are provided for supporting the happiness and satisfaction of emergent teachers as they interact in online communities. It demonstrates that online environments can play an important role in fulfilling teachers' need for social interaction and inclusion.