Privacy concerns and purchase of travel product online

The travel and tourism industry has come to rely heavily on information and communication technologies to facilitate relations with consumers. Compiling consumer data profiles has become easier and it is widely thought that consumers place great importance on how that data is handled by firms. Lack of trust may cause consumers to have privacy concerns and may, in turn, have an adverse impact on consumers’ willingness to purchase online. Three specific aspects of privacy that have received attention from researchers are unauthorized use of secondary data, invasion of privacy, and errors. A survey study was undertaken to examine the effects of these factors on both prior purchase of travel products via the Internet and future purchase probability. Surprisingly, no significant relationships were found to indicate that such privacy concerns affect online purchase behavior within the travel industry. Implications for managers are discussed.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Stochastic ranking method for thermostatically controllable appliances to provide regulation services

Demand response can be used for providing regulation services in the electricity markets. The retailers can bid in a day-ahead market and respond to real-time regulation signal by load control. This paper proposes a new stochastic ranking method to provide regulation services via demand response. A pool of thermostatically controllable appliances (TCAs) such as air conditioners and water heaters are adjusted using direct load control method. The selection of appliances is based on a probabilistic ranking technique utilizing attributes such as temperature variation and statuses of TCAs. These attributes are stochastically forecasted for the next time step using day-ahead information. System performance is analyzed with a sample regulation signal. Network capability to provide regulation services under various seasons is analyzed. The effect of network size on the regulation services is also investigated.

Building the safety case for UAS operations in support of natural disaster response

A framework supporting the systematic development of safety cases for Unmanned Aircraft System (UAS) operations in a broad range of civil and commercial applications is presented. The case study application is the use of UAS for disaster response. In those States where regulations do not preclude UAS operations altogether, approvals for UAS operations can be granted on a case-by-case basis contingent on the provision of a safety case acceptable to the relevant National Airworthiness Authority (NAA). A safety case for UAS operations must show how the risks associated with the hazards have been managed to an acceptable level. The foundational components necessary for structuring and assessing these safety cases have not yet been proposed. Barrier-bow-tie models are used in this paper to structure the safety case for the two primary hazards of 1) a ground impact, and 2) a Mid-Air Collision (MAC). The models establish the set of Risk Control Variables (RCVs) available to reduce the risk. For the ground-impact risk model, seven RCVs are identified which in combination govern the probability of an accident. Similarly, ten RCVs are identified within the MAC model. The effectiveness of the RCVs and how they can implemented in terms of processes, policies, devices, practices, or other actions for each of the case-study applications are discussed. The framework presented can provide for the more systematic and consistent regulation of UAS through a "safety target" approach.

Use of physical activity self-management strategies by high school students

Teaching adolescents to use self-management strategies (SMS's) may be an effective approach to promoting lifelong physical activity (PA). However, the extent to which adolescents use SMS's and their impact on current PA have not been studied previously. The aims of this study were: 1) describe the prevalence of SMS use in adolescents; and 2) determine relationships between SMS use, PA self-efficacy, and PA participation. 197 students completed questionnaires measuring use of SMS's, self-efficacy, and PA behavior. The most prevalent SMS's (>30%) were thinking about the benefits of PA, making PA more enjoyable, choosing activities that are convenient, setting aside time to do PA, and setting goals to do PA. Less than 10% reported rewarding oneself for PA, writing planned activities in a book or calendar, and keeping charts of PA. SMS use was associated with increased self-efficacy (r = 0.47, P < .001) and higher levels of PA (r = 0.34 P < .001). A one unit difference in SMS scores was associated with a ~ 4-fold increase in the probability of being active (OR = 3.7, 95% CI = 1.8-7.4). Although strongly associated with PA, a relatively small percentage of adolescents routinely use SMS's.

Visit Perth, Fremantle and Rottnest Island in Western Australia

A travel article about Perth, Freemantle, and Rottnest Island. In all probability, the water at Cottesloe Beach is no bluer than at all the other beaches of Australia. But early afternoon on the day I arrive, that blue seems total, or perhaps even on the edge of some other colour spectrum still being invented. The blue of the west...

SEM, EDS and vibrational spectroscopic study of dawsonite NaAl(CO3)(OH)2

In this work we have studied the mineral dawsonite by using a combination of scanning electron microscopy with EDS and vibrational spectroscopy. Single crystals show an acicular habitus forming aggregates with a rosette shape. The chemical analysis shows a phase composed of C, Al, and Na. Two distinct Raman bands at 1091 and 1068 cm−1 are assigned to the CO32− ν1 symmetric stretching mode. Multiple bands are observed in both the Raman and infrared spectra in the antisymmetric stretching and bending regions showing that the symmetry of the carbonate anion is reduced and in all probability the carbonate anions are not equivalent in the dawsonite structure. Multiple OH deformation vibrations centred upon 950 cm−1 in both the Raman and infrared spectra show that the OH units in the dawsonite structure are non-equivalent. Raman bands observed at 3250, 3283 and 3295 cm−1 are assigned to OH stretching vibrations. The position of these bands indicates strong hydrogen bonding of the OH units in the dawsonite structure. The formation of the mineral dawsonite has the potential to offer a mechanism for the geosequestration of greenhouse gases.

A new approach to spatial data interpolation using higher-order statistics

Interpolation techniques for spatial data have been applied frequently in various fields of geosciences. Although most conventional interpolation methods assume that it is sufficient to use first- and second-order statistics to characterize random fields, researchers have now realized that these methods cannot always provide reliable interpolation results, since geological and environmental phenomena tend to be very complex, presenting non-Gaussian distribution and/or non-linear inter-variable relationship. This paper proposes a new approach to the interpolation of spatial data, which can be applied with great flexibility. Suitable cross-variable higher-order spatial statistics are developed to measure the spatial relationship between the random variable at an unsampled location and those in its neighbourhood. Given the computed cross-variable higher-order spatial statistics, the conditional probability density function (CPDF) is approximated via polynomial expansions, which is then utilized to determine the interpolated value at the unsampled location as an expectation. In addition, the uncertainty associated with the interpolation is quantified by constructing prediction intervals of interpolated values. The proposed method is applied to a mineral deposit dataset, and the results demonstrate that it outperforms kriging methods in uncertainty quantification. The introduction of the cross-variable higher-order spatial statistics noticeably improves the quality of the interpolation since it enriches the information that can be extracted from the observed data, and this benefit is substantial when working with data that are sparse or have non-trivial dependence structures.

Combining opinions for use in Bayesian networks: A measurement error approach

Bayesian networks (BNs) are graphical probabilistic models used for reasoning under uncertainty. These models are becoming increasing popular in a range of fields including ecology, computational biology, medical diagnosis, and forensics. In most of these cases, the BNs are quantified using information from experts, or from user opinions. An interest therefore lies in the way in which multiple opinions can be represented and used in a BN. This paper proposes the use of a measurement error model to combine opinions for use in the quantification of a BN. The multiple opinions are treated as a realisation of measurement error and the model uses the posterior probabilities ascribed to each node in the BN which are computed from the prior information given by each expert. The proposed model addresses the issues associated with current methods of combining opinions such as the absence of a coherent probability model, the lack of the conditional independence structure of the BN being maintained, and the provision of only a point estimate for the consensus. The proposed model is applied an existing Bayesian Network and performed well when compared to existing methods of combining opinions.

Consumer phase identification in a three phase unbalanced LV distribution network

A new technique is presented for automatically identifying the phase connection of domestic customers. Voltage information from a reference three phase house is correlated with voltage information from other customer electricity meters on the same network to determine the highest probability phase connection. The techniques are purely based upon a time series of electrical voltage measurements taken by the household smart meters and no additional equipment is required. The method is demonstrated using real smart meter datasets to correctly identify the phase connections of 75 consumers on a low voltage distribution feeder.

Confidence measurement in the light of signal detection theory

We compare three alternative methods for eliciting retrospective confidence in the context of a simple perceptual task: the Simple Confidence Rating (a direct report on a numerical scale), the Quadratic Scoring Rule (a post-wagering procedure), and the Matching Probability (MP; a generalization of the no-loss gambling method). We systematically compare the results obtained with these three rules to the theoretical confidence levels that can be inferred from performance in the perceptual task using Signal Detection Theory (SDT). We find that the MP provides better results in that respect. We conclude that MP is particularly well suited for studies of confidence that use SDT as a theoretical framework.

Multilevel competing risk models to evaluate the risk of nosocomial infection

Introduction Risk factor analyses for nosocomial infections (NIs) are complex. First, due to competing events for NI, the association between risk factors of NI as measured using hazard rates may not coincide with the association using cumulative probability (risk). Second, patients from the same intensive care unit (ICU) who share the same environmental exposure are likely to be more similar with regard to risk factors predisposing to a NI than patients from different ICUs. We aimed to develop an analytical approach to account for both features and to use it to evaluate associations between patient- and ICU-level characteristics with both rates of NI and competing risks and with the cumulative probability of infection. Methods We considered a multicenter database of 159 intensive care units containing 109,216 admissions (813,739 admission-days) from the Spanish HELICS-ENVIN ICU network. We analyzed the data using two models: an etiologic model (rate based) and a predictive model (risk based). In both models, random effects (shared frailties) were introduced to assess heterogeneity. Death and discharge without NI are treated as competing events for NI. Results There was a large heterogeneity across ICUs in NI hazard rates, which remained after accounting for multilevel risk factors, meaning that there are remaining unobserved ICU-specific factors that influence NI occurrence. Heterogeneity across ICUs in terms of cumulative probability of NI was even more pronounced. Several risk factors had markedly different associations in the rate-based and risk-based models. For some, the associations differed in magnitude. For example, high Acute Physiology and Chronic Health Evaluation II (APACHE II) scores were associated with modest increases in the rate of nosocomial bacteremia, but large increases in the risk. Others differed in sign, for example respiratory vs cardiovascular diagnostic categories were associated with a reduced rate of nosocomial bacteremia, but an increased risk. Conclusions A combination of competing risks and multilevel models is required to understand direct and indirect risk factors for NI and distinguish patient-level from ICU-level factors.

Long-tailed bats' use of a Pinus radiata stand in Kinleith Forest : Recommendations for monitoring

Targeted monitoring of threatened species within plantations is becoming more important due to forest certification programmes’ requirement to consider protection of threatened species, and to increase knowledge of the distribution of species. To determine patterns of long-tailed bat (Chalinolobus tuberculatus) activity in different habitat structures, with the aim of improving the likelihood of detection by targeting monitoring, we monitored one stand of 26 year-old Pinus radiata over seven months between December 2007 and June 2008 in Kinleith Forest, an exotic plantation forest centred around Tokoroa, South Waikato, New Zealand. Activity was determined by acoustic recording equipment, which is able to detect and record bats’ echolocation calls. We monitored activity from sunset to sunrise along a road through the stand, along stand edges, and in the interior of the stand. Bats were recorded on 80% of the 35 nights monitored. All activity throughout the monitoring period was detected on the edge of the stand or along the road. No bats were detected within the interior of the stand. Bat activity was highest along the road through the stand (40.4% of all passes), followed by an edge with stream running alongside (35.2%), along the road within a skidsite (19.8%), and along an edge without a stream (4.6%). There was a significant positive relationship between bat pass rate (bat passes h-1) and the feeding buzz rate (feeding buzzes h-1) indicating that bat activity was associated with feeding and not just commuting. Bat feeding activity was also highest along the road through the stand (59.2% of feeding buzzes), followed by the road within the skidsite (30.6%), and along the stream-side edge (10.2%). No feeding buzzes were recorded in either the interior or along the edge without the stream. Differences in overall feeding activity were significant only between the road and edge and between edges with and without a stream. Bat activity was detected each month and always by the second night of monitoring, and in this stand was highest during April. We recommend targeted monitoring for long-tailed bats be focused on road-side and stand edge habitat, and along streams, and that monitoring take place for at least three nights to maximise probability of detection.

Predictors of chronic bronchitis in South African adults

SETTING National household survey of adults in South Africa, a middle income country. OBJECTIVE To determine the prevalence and predictors of chronic bronchitis. DESIGN A stratified national probability sample of households was selected. All adults in the selected households were interviewed. Chronic bronchitis was defined as chronic productive cough. Socio-demographic predictors were wealth, education, race, age and urban residence. Personal and exposure variables included history of tuberculosis, domestic exposure to smoky fuels, occupational exposures, smoking and body mass index. RESULTS The overall prevalence of chronic bronchitis was 2.3% in men and 2.8% in women. The strongest predictor of chronic bronchitis was a history of tuberculosis (men, odds ratio [OR] 4.9; 95% confidence interval [CI] 2.6-9.2; women, OR 6.6; 95% CI 3.7-11.9). Other risk factors were smoking, occupational exposure (in men), domestic exposure to smoky fuel (in women) and (in univariate analysis only) being underweight. Wealth and particularly education were protective. CONCLUSION The pattern of chronic bronchitis in South Africa suggests a combination of risk factors that includes not only smoking but also tuberculosis, occupational exposures in men and domestic fuel exposure in women. Control of these risk factors requires public health action across a broad front. The protective role of education requires elucidation.

Wheeze, asthma diagnosis and medication use : a national adult survey in a developing country

Background As relatively little is known about adult wheeze and asthma in developing countries, this study aimed to determine the predictors of wheeze, asthma diagnosis, and current treatment in a national survey of South African adults. Methods A stratified national probability sample of households was drawn and all adults (>14 years) in the selected households were interviewed. Outcomes of interest were recent wheeze, asthma diagnosis, and current use of asthma medication. Predictors of interest were sex, age, household asset index, education, racial group, urban residence, medical insurance, domestic exposure to smoky fuels, occupational exposure, smoking, body mass index, and past tuberculosis. Results A total of 5671 men and 8155 women were studied. Although recent wheeze was reported by 14.4% of men and 17.6% of women and asthma diagnosis by 3.7% of men and 3.8% of women, women were less likely than men to be on current treatment (OR 0.6; 95% confidence interval (CI) 0.5 to 0.8). A history of tuberculosis was an independent predictor of both recent wheeze (OR 3.4; 95% CI 2.5 to 4.7) and asthma diagnosis (OR 2.2; 95% CI 1.5 to 3.2), as was occupational exposure (wheeze: OR 1.8; 95% CI 1.5 to 2.0; asthma diagnosis: OR 1.9; 95% CI 1.4 to 2.4). Smoking was associated with wheeze but not asthma diagnosis. Obesity showed an association with wheeze only in younger women. Both wheeze and asthma diagnosis were more prevalent in those with less education but had no association with the asset index. Independently, having medical insurance was associated with a higher prevalence of diagnosis. Conclusions Some of the findings may be to due to reporting bias and heterogeneity of the categories wheeze and asthma diagnosis, which may overlap with post tuberculous airways obstruction and chronic obstructive pulmonary disease due to smoking and occupational exposures. The results underline the importance of controlling tuberculosis and occupational exposures as well as smoking in reducing chronic respiratory morbidity. Validation of the asthma questionnaire in this setting and research into the pathophysiology of post tuberculous airways obstruction are also needed.