Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos

Insight into tourist travel behaviors is crucial for managers engaged in strategic planning and decision making to create a sustainable tourism industry. However, they continue to face significant challenges in fully capturing and understanding the behavior of international tourists. The challenges are primarily due to the inefficient data collection approaches currently in use. In this paper, we present a new approach to this task by exploiting the socially generated and user-contributed geotagged photos now made publicly available on the Internet. Our case study focuses on Hong Kong inbound tourism using 29,443 photos collected from 2100 tourists. We demonstrate how a dataset constructed from such geotagged photos can help address such challenges as well as provide useful practical implications for destination development, transportation planning, and impact management. This study has the potential to benefit tourism researchers worldwide from better understanding travel behavior and developing sustainable tourism industries.

Systematic approach to the quantitative voltammetric analysis of the Fe III /Fe II component of the [α 2 -Fe(OH 2 )P 2 W 17 O 61 ] 7-/8- reduction process in buffered and unbuffered aqueous media

The one-electron reduction of [α2-FeIII(OH2)P2W17O61]7- at a glassy carbon electrode was investigated using cyclic and rotating-disk-electrode voltammetry in buffered and unbuffered aqueous solutions over the pH range 3.45−7.50 with an ionic strength of approximately 0.6 M maintained. The behavior is well-described by a square-scheme mechanism P + e- ↔ Q (E10/ = −0.275 V, k10/ = 0.008 cm s-1, and α1 = 1/2), PH+ + e- ↔ QH+ (E20/ = −0.036 V, k20/ = 0.014 cm s-1, and α2 = 1/2), PH+ ↔ P + H+ (KP = 3.02 × 10-6 M), and QH+ ↔ Q + H+ (KQ = 2.35 × 10-10 M), where P, Q, PH+, and QH+ correspond to [α2-FeIII(OH)P2W17O61]8-, [α2-FeII(OH)P2W17O61]9-, [α2-FeIII(OH2)P2W17O61]7-, and [α2-FeII(OH2)P2W17O61]8-, respectively; E10‘ and E20‘ are the formal potentials, k10‘ and k20‘ are the formal (standard) rate constants, and KP and KQ are the acid dissociation constants for the relevant reactions. The analysis for the buffered media is based on the approach of Laviron who demonstrated that a square scheme with fully reversible protonations, reversible or quasi reversible electron transfers with the assumption that α1 = α2, can be well-described by the behavior of a simple redox couple, ox + e- ↔ red, whose formal potential, Eapp0‘, and standard rate constant, kapp0‘, are straightforwardly derived functions of pH, as are the values of E10‘, k10‘, E20‘, k20‘, and KP (only three of the four thermodynamic parameters in a square scheme can be specified). It was assumed that αapp = 1/2, and the simulation program DigiSim was used to determine the values of Eapp0‘ and kapp0‘, which are required to describe the cyclic voltammograms obtained in buffered media in the pH range from 3.45 to 7.52 (buffer-related reactions which effect general acid−base catalysis are included in the simulations). DigiSim simulations of cyclic voltammograms obtained in unbuffered media yielded the values of E10‘ and k10‘; KQ was then directly computed from thermodynamic constraints. These simulations included additional reactions between the redox species and H2O. The value of the diffusion coefficient of the [α2-FeIII(OH2)P2W17O61]7-, 2.92 × 10-6 cm2 s-1, was determined using DigiSim simulations of voltammograms at a rotating disk electrode in buffered and unbuffered media at pH 3.45. The diffusion coefficients of all redox species were assumed to be identical. When the pH is greater than 6, instability of P (i.e., [α2-FeIII(OH)P2W17O61]8-) led to the loss of the reactant and precluded lengthy experimentation.

Discovery of stop regions for understanding repeat travel behaviors of moving objects

GPS trajectory dataset with high sampling-rates is usually in large volume that challenges the processing efficiency. Most of the data points on trajectories are useless. This paper summarizes trajectories using stop points. We define a new concept of stay stability (i.e., time dividing distance or reciprocal of speed) between any two GPS points to detect stop points on individual trajectories. We propose a novel Mining Repeat Travel Behaviors Using Stop Regions (MRTBUSR) method. In MRTBUSR, a stop region is a popular region containing a certain number of close stop points that can be grouped into a cluster. We then retrieve common sequences of stop regions to denote repeat route patterns and further analyze the stop durations on a stop region to find repeat travel behaviors. The experiments on 20 labeled trajectories selected from GeoLife demonstrated the semantic effect, accuracy and near linear efficiency of our proposed method.

DoSTra: Discovering common behaviors of objects using the duration of staying on each location of trajectories

Since semantic trajectories can discover more semantic meanings of a user's interests without geographic restrictions, research on semantic trajectories has attracted a lot of attentions in recent years. Most existing work discover the similar behavior of moving objects through analysis of their semantic trajectory pattern, that is, sequences of locations. However, this kind of trajectories without considering the duration of staying on a location limits wild applications. For example, Tom and Anne have a common pattern of Home→Restaurant → Company → Restaurant, but they are not similar, since Tom works at Restaurant, sends snack to someone at Company and return to Restaurant while Anne has breakfast at Restaurant, works at Company and has lunch at Restaurant. If we consider duration of staying on each location we can easily to differentiate their behaviors. In this paper, we propose a novel approach for discovering common behaviors by considering the duration of staying on each location of trajectories (DoSTra). Our approach can be used to detect the group that has similar lifestyle, habit or behavior patterns and predict the future locations of moving objects. We evaluate the experiment based on synthetic dataset, which demonstrates the high effectiveness and efficiency of the proposed method.

Interpreting behaviors from accelerometry: a method combining simplicity and objectivity.

Quantifying the behavior of motile, free-ranging animals is difficult. The accelerometry technique offers a method for recording behaviors but interpretation of the data is not straightforward. To date, analysis of such data has either involved subjective, study-specific assignments of behavior to acceleration data or the use of complex analyses based on machine learning. Here, we present a method for automatically classifying acceleration data to represent discrete, coarse-scale behaviors. The method centers on examining the shape of histograms of basic metrics readily derived from acceleration data to objectively determine threshold values by which to separate behaviors. Through application of this method to data collected on two distinct species with greatly differing behavioral repertoires, kittiwakes, and humans, the accuracy of this approach is demonstrated to be very high, comparable to that reported for other automated approaches already published. The method presented offers an alternative to existing methods as it uses biologically grounded arguments to distinguish behaviors, it is objective in determining values by which to separate these behaviors, and it is simple to implement, thus making it potentially widely applicable. The R script coding the method is provided.

Living healthier for longer: comparative effects of three heart-healthy behaviors on life expectancy with and without cardiovascular disease

BACKGROUND: Non-smoking, having a normal weight and increased levels of physical activity are perhaps the three key factors for preventing cardiovascular disease (CVD). However, the relative effects of these factors on healthy longevity have not been well described. We aimed to calculate and compare the effects of non-smoking, normal weight and physical activity in middle-aged populations on life expectancy with and without cardiovascular disease.

METHODS: Using multi-state life tables and data from the Framingham Heart Study (n = 4634) we calculated the effects of three heart healthy behaviours among populations aged 50 years and over on life expectancy with and without cardiovascular disease. For the life table calculations, we used hazard ratios for 3 transitions (No CVD to CVD, no CVD to death, and CVD to death) by health behaviour category, and adjusted for age, sex, and potential confounders.

RESULTS: High levels of physical activity, never smoking (men), and normal weight were each associated with 20-40% lower risks of developing CVD as compared to low physical activity, current smoking and obesity, respectively. Never smoking and high levels of physical activity reduced the risks of dying in those with and without a history of CVD, but normal weight did not. Never-smoking was associated with the largest gains in total life expectancy (4.3 years, men, 4.1 years, women) and CVD-free life expectancy (3.8 and 3.4 years, respectively). High levels of physical activity and normal weight were associated with lesser gains in total life expectancy (3.5 years, men and 3.4 years, women, and 1.3 years, men and 1.0 year women, respectively), and slightly lesser gains in CVD-free life expectancy (3.0 years, men and 3.1 years, women, and 3.1 years men and 2.9 years women, respectively). Normal weight was the only behaviour associated with a reduction in the number of years lived with CVD (1.8 years, men and 1.9 years, women).

CONCLUSIONS: Achieving high levels of physical activity, normal weight, and never smoking, are effective ways to prevent cardiovascular disease and to extend total life expectancy and the number of years lived free of CVD. Increasing the prevalence of normal weight could further reduce the time spent with CVD in the population.

A new multi-agent system to simulate the foraging behaviors of Physarum

Physarum Polycephalum is a unicellular and multi-headed slime mold, which can form high efficient networks connecting spatially separated food sources in the process of foraging. Such adaptive networks exhibit a unique characteristic in which network length and fault tolerance are appropriately balanced. Based on the biological observations, the foraging process of Physarum demonstrates two self-organized behaviors, i.e., search and contraction. In this paper, these two behaviors are captured in a multi-agent system. Two types of agents and three transition rules are designed to imitate the search and the contraction behaviors of Physarum based on the necessary and the sufficient conditions of a self-organized computational system. Some simulations of foraging process are used to investigate the characteristics of our system. Experimental results show that our system can autonomously search for food sources and then converge to a stable solution, which replicates the foraging process of Physarum. Specially, a case study of maze problem is used to estimate the path-finding ability of the foraging behaviors of Physarum. What’s more, the model inspired by the foraging behaviors of Physarum is proposed to optimize meta-heuristic algorithms for solving optimization problems. Through comparing the optimized algorithms and the corresponding traditional algorithms, we have found that the optimization strategies have a higher computational performance than their corresponding traditional algorithms, which further justifies that the foraging behaviors of Physarum have a higher computational ability.

A revised Australian dietary guideline index and its association with key sociodemographic factors, health behaviors and body mass index in peri-retirement aged adults

The Dietary Guideline Index, a measure of diet quality, was updated to reflect the 2013 Australian Dietary Guidelines. This paper describes the revision of the index (DGI-2013) and examines its use in older adults. The DGI-2013 consists of 13 components reflecting food-based daily intake recommendations of the Australian Dietary Guidelines. In this cross-sectional study, the DGI-2013 score was calculated using dietary data collected via an 111-item food frequency questionnaire and additional food-related behaviour questions. The DGI-2013 score was examined in Australian adults (aged 55-65 years; n = 1667 men; 1801 women) according to sociodemographics, health-related behaviours and BMI. Women scored higher than men on the total DGI-2013 and all components except for dairy. Those who were from a rural area (men only), working full-time (men only), with lower education, smoked, did not meet physical activity guidelines, and who had a higher BMI, scored lower on the DGI-2013, highlighting a group of older adults at risk of poor health. The DGI-2013 is a tool for assessing compliance with the Australian Dietary Guidelines. We demonstrated associations between diet quality and a range of participant characteristics, consistent with previous literature. This suggests that the DGI-2013 continues to demonstrate convergent validity, consistent with the original Dietary Guideline Index.

Statistically modelling the relationships between Type D personality and social support, health behaviors and symptom severity in chronic illness groups

OBJECTIVE: The study aimed to develop a predictive model of how Type D personality influences health behaviours, social support and symptom severity and assess its generalisability to a range of chronic illnesses.

DESIGN: Participants were classified as either healthy (n = 182) or having a chronic illness (n = 207). Participants completed an online survey measuring Type D and a range of health-related variables. Chronic illness participants were classified as having either a functional somatic syndrome (i.e. chronic fatigue syndrome or fibromyalgia), where the underlying pathological processes were unclear, or illnesses such as type 2 diabetes, osteoarthritis or rheumatoid arthritis, where the causes are well understood.

MAIN OUTCOME MEASURES: Outcome measures were health behaviours, social support and both physical and psychological symptoms.

RESULTS: The rate of Type D was higher in chronic illness participants (53%) than in healthy controls (39%). Negative affectivity (NA) and social inhibition (SI) both correlated with outcome measures, although NA was generally the stronger predictor. Using NA and SI as independent subscales led to superior prediction of health outcomes than using categorical or continuous representations.

CONCLUSION: Findings suggest that the relationship between Type D and health outcomes may generalise across different chronic illnesses.

Dynamic recrystallization behaviors of a Mg-4Y-2Nd-0.2Zn-0.5Zr alloy and the resultant mechanical properties after hot compression

The development behaviors of ultrafine grains (UFGs) due to continuous dynamic recrystallization (cDRX) were investigated in hot compression of a Mg-4Y-2Nd-0.2Zn-0.5Zr alloy pretreated in solution and subsequently peak-aging. In the aging sample containing statically precipitated particles (SPPs), the occurrence of cDRX starts to take place at medium to high strains, and finally a stable size of UFGs are fully developed in a whole volume. In the as-solution sample with no SPPs, by contrast, the size of UFGs evolved increases rapidly at lower strains, slowly at medium strains and then finally shows a bimodal distribution in high strain. In the latter, smaller grains accompanying with an incomplete formation of UFGs are developed by any effect of dynamically precipitated particles (DPPs). The microtexture evolved is effectively randomized in the regions of UFGs, leading to the formation of a weaker texture. The tensile elongation of the aging sample, with SPPs and fully developed UFGs, was around 17.4%. This was much higher than that of the as-solution one, with no SPPs and incompletely developed UFGs, that was 11.8%, which might result from the more randomized texture due to fully developed UFGs.

Modern screen-use behaviors: the effects of single- and multi-screen use on energy intake

PURPOSE: The effects of using multiple screens at once on energy intake (EI) are unknown. This study compared EI of participants with access to multiple screens (television + iPad + smartphone) versus a single screen (television). METHODS: A laboratory-based, randomized, two-arm parallel (multi-screen vs. single-screen) trial was conducted in 78 adolescents (ages, 13-18 years). Food and drink were available during a 1-hour exposure period. Total EI was the primary outcome. Linear regression analyses were conducted to test the treatment difference, adjusting for age, sex, body mass index, and appetite at baseline. RESULTS: Total EI did not differ significantly between the two groups (multi-screen, 758 kcal [standard error = 75] vs. single-screen, 681 kcal [standard error = 75]; difference, +77 kcal; 95% confidence interval, -166 to +320). CONCLUSIONS: EI did not differ between adolescents with access to multiple screens and those with access to a single screen; however, limitations in the study design may have decreased the power of the study.

mHealth technologies to influence physical activity and sedentary behaviors: behavior change techniques, systematic review and meta-analysis of randomized controlled trials

BACKGROUND: mHealth programs offer potential for practical and cost-effective delivery of interventions capable of reaching many individuals.

PURPOSE: To (1) compare the effectiveness of mHealth interventions to promote physical activity (PA) and reduce sedentary behavior (SB) in free-living young people and adults with a comparator exposed to usual care/minimal intervention; (2) determine whether, and to what extent, such interventions affect PA and SB levels and (3) use the taxonomy of behavior change techniques (BCTs) to describe intervention characteristics.

METHODS: A systematic review and meta-analysis following PRISMA guidelines was undertaken to identify randomized controlled trials (RCTs) comparing mHealth interventions with usual or minimal care among individuals free from conditions that could limit PA. Total PA, moderate-to-vigorous intensity physical activity (MVPA), walking and SB outcomes were extracted. Intervention content was independently coded following the 93-item taxonomy of BCTs.

RESULTS: Twenty-one RCTs (1701 participants-700 with objectively measured PA) met eligibility criteria. SB decreased more following mHealth interventions than after usual care (standardised mean difference (SMD) -0.26, 95 % confidence interval (CI) -0.53 to -0.00). Summary effects across studies were small to moderate and non-significant for total PA (SMD 0.14, 95 % CI -0.12 to 0.41); MVPA (SMD 0.37, 95 % CI -0.03 to 0.77); and walking (SMD 0.14, 95 % CI -0.01 to 0.29). BCTs were employed more frequently in intervention (mean = 6.9, range 2 to 12) than in comparator conditions (mean = 3.1, range 0 to 10). Of all BCTs, only 31 were employed in intervention conditions.

CONCLUSIONS: Current mHealth interventions have small effects on PA/SB. Technological advancements will enable more comprehensive, interactive and responsive intervention delivery. Future mHealth PA studies should ensure that all the active ingredients of the intervention are reported in sufficient detail.