The effect of airtightness required in building energy conservation regulations on indoor and outdoor originated pollutants

The contradiction of indoor air quality (IAQ) and energy conservation by isolating the indoor environment from the outdoor through airtightness is one of the challenges of the building sector. The key issue is, what are the optimum airtightness limits that can ensure IAQ in naturally ventilated buildings, taking into account the paradoxical effect of house leakages on the infiltration of outdoor pollutants and accumulation of indoor-generated pollutants? For this purpose, the effect of different levels of airtightness required in energy-compliant, low-energy, and very low-energy buildings on the concentration of two pollutants with outdoor and indoor origin, PM2.5 and formaldehyde, respectively, were studied. This study used a multizone model, CONTAM(W), which was validated using measured data to study the distribution of selected pollutants in a typical relatively old dwelling, to investigate the situation in Iran. Subsequently, we conducted simulations based on different combinations of scenarios for airtightness, user behavior, source strength, and meteorological parameters. The results showed that increasing the airtightness from the baseline scenario (ACH50 = 11.11/h) to 3, 1.5, and 0.75 in closed window conditions reduced the PM2.5 by 15%, 38%, and 58%, respectively, and elevated formaldehyde by 23%, 77%, and 169%, correspondingly. Under normal outdoor PM2.5 pollution, indoor formaldehyde levels exceeded the permissible limit only in closed window conditions, and IAQ remained acceptable in other scenarios. However, there is no indication that IAQ can be ensured by any degree of airtightness under severe outdoor air pollution, demanding specific solutions, such as those proposed in this work.

Psychological antecedents of telehealth acceptance: A technology readiness perspective

The ongoing coronavirus pandemic has been threatening the healthcare system. In this context, telehealth is a potential solution to deliver effective and safe health care to the public. To facilitate the application and acceptance of telehealth, a good understanding of psychological determinants is of great importance. Therefore, this study aims to examine the public's positive and negative mindsets towards telehealth. A theoretical model was established by employing the technology readiness model and perceived value theory. To empirically test the relationships between constructs, a total of 500 responses from residents in Singapore were collected; thereafter, structural equation modeling was performed. The results indicate that discomfort negatively impacts perceived value whereas optimism and innovativeness positively impact users' perceived value. Further, perceived value positively impacts the acceptance of telehealth via attitude. Demographic factors (i.e. internet literacy, age, education) can also influence certain aspects of technology readiness (e.g. innovativeness, optimism). Moreover, social influence is an important moderator between perceived value and the acceptance of telehealth. The empirical findings enhance the understanding of users' psychology concerning telehealth and provide policy recommendations regarding the development of telehealth to improve public health.

Developing personas for live streaming commerce platforms with user survey data

Live streaming commerce has emerged as a novel form of online marketing that offers live streaming commerce platforms a means of meeting different user groups' needs. The objective of this article is to examine the effects of age and gender on live streaming commerce platform usage and investigate user characteristics of these platforms in China. This study adopted a data-driven persona construction method combining quantitative and qualitative methods through the use of survey and interview. The survey involved 506 participants (age range = 19-70), and the interview involved 12 participants. The survey findings showed that age significantly affected users' livestream platform usage, while gender did not. Younger users had higher device proficiency and operation numbers. With more trust and device use, older users used the platforms later in the day than younger users. Interview findings revealed that gender affected users' motivations and value focus. Women tended to use the platforms as a means of entertainment. Women valued service quality and enjoyment more, while men focused on the accuracy of product information more. Four personas with significant differences were then constructed: Dedicated, Dependent, Active and Lurker. Their various needs, motivations and behavior patterns can be considered by designers to elevate the interaction of live streaming commerce platforms.

Measurement data on the window opening behavior and climate in a strongly daylit office building

The long-term measurement data presented in this article result were collected in a strongly daylit office building under real working conditions and include temperature and wind speed of the outdoor situation as well as climatic variables of the indoor space, such as temperature and relative humidity. In addition to the measurement of environmental variables, the window opening behavior was also logged. The entire data acquisition was implemented via the building control system and was performed with a one-minute resolution. An exception to this is the recording of the window openings, which were logged on change of state. The measurement data obtained can be combined with other measurement data to provide an improved data basis for energy building simulations, prediction models and energy potential assessments.

Acceptance and use of virtual reality games: an extension of HMSAM

Virtual reality (VR) is considered as one of the technological megatrends of 2020s, and today, VR systems are used in various settings, digital gaming being among the most popular ones. However, there has been a dearth of understanding regarding the central factors behind VR gaming acceptance and use. The present study therefore aimed to explain the factors that drive the use and acceptance of VR games. We extended the hedonic-motivation system acceptance model with utilitarian and inconvenience factors to capture the pertinent features of VR systems more holistically. We proposed a theoretical model and analyzed it through covariance-based structural equation modeling using an online survey sample of 473 VR gamers. Our findings help explain the role of different antecedents behind VR gaming acceptance and demonstrate that VR gaming is driven more by the hedonic gaming aspects than by the utilitarian health and well-being aspects of VR games, enjoyment being the strongest driver behind VR gaming intention and immersion. Moreover, findings also suggested that use intentions and immersion levels are not significantly diminished by physical discomfort and VR sickness. The findings, which potentially extend to other VR systems as well, also pose important implications for the providers of VR games. As the main contribution, based on our empirical findings, we provide a greater theoretical understanding on VR gaming acceptance and use.

Analysis and modeling of changes in online shopping behavior due to Covid-19 pandemic: A Florida case study

The emergence of eCommerce and online shopping commenced a new episode in human life and changed trading patterns. Online shopping provided access to a broader range of products and facilitated their delivery, which increased demand. To respond to the increased demand, more heavy commercial vehicles need to be on the roads to deliver orders. This is while the road infrastructure is not ready for such a swift shift, and most roads and bridges were planned and constructed during the 19s when online shopping was not coined yet. The continued increase of heavy vehicles on roads can intensify the deterioration of roads and structures such as bridges. Therefore, there is a significant need for an update on new shopping trends, especially changes in people's behavior due to the ongoing Covid-19 pandemic, and to assess if the pandemic permanently changed the trends of in-store and online shopping. This study first examines the NHTS 2017 data to find the attributes that are significant to online shoppers' behavior. Then a survey is developed to scrutinize Covid-19 effects on the online shopping behavior of users before, during, and after the Covid-19 pandemic. 206 records of data are interpreted through descriptive analysis and discrete choice modeling of users' responses to find the most significant attributes affecting their online shopping behavior. The findings of discrete choice modeling and descriptive analysis support that people tend to go back to stores after the pandemic. The findings of this study show that online and in-store shopping would be balanced after the pandemic and would pursue their normal trends as they were before the pandemic. Based on the findings of this study, it is hard to state that online shopping can vanish in-store shopping due to Covid-19. People still need to go to stores to fulfill their needs for the joy of shopping, interactions with other people, and touching the products they would like to buy. Therefore, transportation stakeholders need to pay special attention to both in-store and online shopping for their planning and operation management of ground transportation infrastructure.

User behavior, influence factors, and impacts on real-world pollutant emissions from the household heating stoves in rural China

Household heating stoves are commonly used for heating in rural China during winter and are responsible for a large portion of the particulate matter in the atmosphere. Pollutant emissions from household stoves are influenced by user behaviors in actual use, in addition to purely technological reasons (i.e., type and age of appliance) and installation conditions (i.e., the natural draft of chimney system). The variability in user behavior is one of the reasons for uncertainty in household emission inventories. In this study, household stove user behaviors, including ignition frequency, heating with or without cooking, smoldering duration, and fuel-adding times, were investigated through face-to-face surveys in Shanxi province, north China. The survey of user behaviors showed that the majority of the RHS users (81.3%) and approximately half of the WHS users (49.4%) used their stoves for both cooking and heating, whereas the remaining users used their stoves just for heating. Approximately 80% of surveyed households (97.4% for water heating stove and 68.7% for radiant heating stove) kept the stoves smoldering at night, whereas the remaining users ignited their stoves every day. The highest frequency of smoldering duration and highest frequency of fuel-adding operation were 8-9 h and 4-7 times, respectively. Principal component analysis showed that stove type, permanent population, and annual income are the potential influencing factors of user behavior. The smoldering duration was positively related to indoor air pollutant concentrations and fuel-adding times had a significant impact on outdoor PM_2.5 emission factors. The results from this research will be beneficial for understanding the cause of fluctuation in emissions and designing heating appliances for real-life operations.

Role of twitter user profile features in retweet prediction for big data streams

To study the various factors influencing the process of information sharing on Twitter is a very active research area. This paper aims to explore the impact of numerical features extracted from user profiles in retweet prediction from the real-time raw feed of tweets. The originality of this work comes from the fact that the proposed model is based on simple numerical features with the least computational complexity, which is a scalable solution for big data analysis. This research work proposes three new features from the tweet author profile to capture the unique behavioral pattern of the user, namely "Author total activity", "Author total activity per year", and "Author tweets per year". The features set is tested on a dataset of 100 million random tweets collected through Twitter API. The binary labels regression gave an accuracy of 0.98 for user-profile features and gave an accuracy of 0.99 when combined with tweet content features. The regression analysis to predict the retweet count gave an R-squared value of 0.98 with combined features. The multi-label classification gave an accuracy of 0.9 for combined features and 0.89 for user-profile features. The user profile features performed better than tweet content features and performed even better when combined. This model is suitable for near real-time analysis of live streaming data coming through Twitter API and provides a baseline pattern of user behavior based on numerical features available from user profiles only.

News recommender system: a review of recent progress, challenges, and opportunities

Nowadays, more and more news readers read news online where they have access to millions of news articles from multiple sources. In order to help users find the right and relevant content, news recommender systems (NRS) are developed to relieve the information overload problem and suggest news items that might be of interest for the news readers. In this paper, we highlight the major challenges faced by the NRS and identify the possible solutions from the state-of-the-art. Our discussion is divided into two parts. In the first part, we present an overview of the recommendation solutions, datasets, evaluation criteria beyond accuracy and recommendation platforms being used in the NRS. We also talk about two popular classes of models that have been successfully used in recent years. In the second part, we focus on the deep neural networks as solutions to build the NRS. Different from previous surveys, we study the effects of news recommendations on user behaviors and try to suggest possible remedies to mitigate those effects. By providing the state-of-the-art knowledge, this survey can help researchers and professional practitioners have a better understanding of the recent developments in news recommendation algorithms. In addition, this survey sheds light on the potential new directions.

Understanding consumer behavior in the multimedia context: incorporating gamification in VR-enhanced web system for tourism e-commerce

This study aims to investigate how gamification and virtual reality (VR)-enhanced web services can be integrated to influence consumer behavior in the context of tourism e-commerce. A gamified VR-enhanced tourism web system (VRTWS) was designed and developed for this investigation, while a research framework with 12 hypotheses was proposed and empirically tested by adopting PLS-SEM approach to analyze 208 valid data collected from survey. The results reveal that both Enjoyment and Activation in Gamification significantly and positively affected Media Richness. Additionally, Media Richness significantly and positively affected both Usefulness and Ease of Use in using VR technology with gamification. Also, a user's Perceived Value is not only positively affected by Usefulness and Ease of Use but also Interactivity and Immersion in a gamified VRTWS. Immersion was found to be positively affected by Presence. Through the positive effect on Satisfaction, user's Perceived Value had positive effect on the Intention toward adoption. The proposed gamified VRTWS and the study results with implications are expected to be referenced by the researchers and practitioners for managing incorporation of gamification into designing, developing, and managing their VR-enhanced service in tourism e-commerce.

Using Participatory Design Methods to Explore Changes in the Self-Efficacy of Students with Visual Impairments After Playing Exergames

Objective: To explore changes in task/scheduling self-efficacy in children with visual impairments after playing exergames, using the participatory design (PD) method to guide students with visual impairments to engage in the design and test of exergames. Materials and Methods: A pre-/post-test was used with two experimental groups (nine high school students with visual impairments aged 18-21, nine junior high school students with visual impairments aged 14-17). Data were analyzed by an independent-/paired-sample t-test to assess changes in task/scheduling self-efficacy of different groups after gameplay. Data of three dimensions (psychological emotion, physical activity, and social interaction) were collected through texts, participative observations, live notetaking, and video recordings. These dimensions provide opportunities for children with visual impairments to improve their self-efficacy. Results: Exergames helped children with visual impairments to improve their task and scheduling self-efficacy. It confirmed that exergames can be useful to promote their psychological emotion, enhance levels of positive physical activity, and increase social opportunities to improve self-efficacy. Conclusion: Participants of various ages who engaged in exergames reported an improvement in both task and scheduling self-efficacy in terms of psychological emotion, physical activity, and social interaction. The PD approach may be useful in the design of products for people with visual impairments and may ultimately be helpful in supporting the social and physical needs of people with disabilities.

Tick trails: the role of online recreational trail reviews in identifying risk factors and behavioral recommendations associated with tick encounters in Indiana

BACKGROUND

Recreational trails abound across the United States and represent high risk areas for tick exposure. Although online reviews represent a rich source of user information, they have rarely been used in determining the risk of tick exposure during recreational trail use. Based on online user reviews and comments, the purpose of this study was to determine risk factors and behavioral recommendations associated with tick encounters (Tick Presence) on recreational trails in the state of Indiana, U.S.

METHODS

We reviewed 26,016 user comments left on AllTrails.com for 697 Indiana trails. Reviews were evaluated to determine Tick Presence/Absence, the total number of Tick Presence Reviews per trail, and multiple trail and user behavioral characteristics. We used hot spot (Getis-Ord Gi*) analysis to test the hypothesis of whether there are clusters in the number of Tick Presence Reviews. Pearson chi-square tests of independence evaluated whether tick presence was associated with several trail characteristics. Finally, negative binomial regression evaluated the strength of the association between the number of Tick Presence Reviews and several trail characteristics.

RESULTS

Tick Presence was recorded at 10% (n = 65) of trails and occurred most frequently in May. Hot spot analysis revealed statistically significant clusters of Tick Presence Reviews on trails in the Southern Indiana State Region. Results of χ2 tests indicated significant associations between Tick Presence Reviews and (a) State Region and (b) Land Management Type; Mann-Whitney U tests detected significant differences in Tick Presence Reviews based on Trail Length and Elevation Gain. Subsequent results of a negative binomial regression model indicated that Southern Indiana State Region, Federal and Private Land Management Type, and Elevation Gain were factors significantly associated with Tick Presence Reviews. Content of user reviews indicated several behaviors employed to prevent tick encounters, particularly Repellent Application and Recreational Deterrence; 25% included a behavior Recommendation to others.

CONCLUSIONS

Online, user-generated trail reviews have the potential to serve as rich data sources for identifying recreational trails, where 1) the risk of tick exposure is great, 2) more robust active tick and tick-borne pathogen surveillance may be warranted, and 3) tailored prevention interventions are needed.

Demand for integrative medicine among women with breast and gynecological cancer: a multicenter cross-sectional study in Southern and Northern Germany

Purpose

The aim of this multicenter cross-sectional study was to analyze a cohort of breast (BC) and gynecological cancer (GC) patients regarding their interest in, perception of and demand for integrative therapeutic health approaches.

Methods

BC and GC patients were surveyed at their first integrative clinic visit using validated standardized questionnaires. Treatment goals and potential differences between the two groups were evaluated.

Results

340 patients (272 BC, 68 GC) participated in the study. The overall interest in IM was 95.3% and correlated with older age, recent chemotherapy, and higher education. A total of 89.4% were using integrative methods at the time of enrolment, primarily exercise therapy (57.5%), and vitamin supplementation (51.4%). The major short-term goal of the BC patients was a side-effects reduction of conventional therapy (70.4%); the major long-term goal was the delay of a potential tumor progression (69.3%). In the GC group, major short-term and long-term goals were slowing tumor progression (73.1% and 79.1%) and prolonging survival (70.1% and 80.6%). GC patients were significantly more impaired by the side-effects of conventional treatment than BC patients [pain (p = 0.006), obstipation (< 0.005)].

Conclusion

Our data demonstrate a high overall interest in and use of IM in BC and GC patients. This supports the need for specialized IM counseling and the implementation of integrative treatments into conventional oncological treatment regimes in both patient groups. Primary tumor site, cancer diagnosis, treatment phase, and side effects had a relevant impact on the demand for IM in our study population.

A study on users' preference towards diabetes-related video clips on YouTube

Background

Social media has arisen to be a new and important channel for information users for seeking and creating user-generated content. For health consumers, social media has long been regarded and employed as an important source to find health-related information and emotional support. This study investigated the characteristics of diabetes-related videos posted on YouTube, one of the most popular video-based social media platforms, and explored the factors influencing users’ preference towards the investigated videos.

Methods

A mixed research method including coding and negative binomial regression test was applied. Coding was utilized to identify the status of the diabetes-related video clips and the factors related to users’ attitude to them. Negative binomial regression approach was employed to detect significant relationships among the factors and users’ attitude.

Results

The researchers selected eight factors (e.g. number of views, post period, presenters’ gender, and subject) to represent the characteristics of the diabetes-related video clips. Eleven subjects were identified by examining the diabetes-related videos and three subjects, Treatment, Sign & Symptom, and Social & Culture, appeared the most frequently. Media type, presentation setting, post period, presenter role, and presenters’ gender affect the users’ positive attitude significantly. Post period, presenter role, and the Sign & Symptom subject and the Nutrient subject have significant influence on the users’ negative attitude.

Conclusions

Treatment, Sign & Symptom, and Social & Culture are the most popular subjects of the investigated video clips. The users are less likely to show their attitude to old videos. They prefer journalists and patients on videos but dislike male presenters compared with other presenters, and show more negative attitude towards the videos about nutrients. The findings of this study can be used to enhance the content creation of diabetes-related video clips for video contributors, the design and organization of the diabetes-related content for multimedia-based social media Website designers, and the information seeking and communication among health information users.

Prototyping for context: exploring stakeholder feedback based on prototype type, stakeholder group and question type

Engineering designers frequently use prototypes to gather input from stakeholders. Design guidelines recommend the use of quick and simple prototypes early and often in a design process. However, the type and quality of a prototype can influence how stakeholders perceive a new design concept and can therefore impact their responses. Additionally, different levels of experience, expertise, and preparedness for providing input to designers may lead stakeholders from different geographical or cultural settings to provide different responses, making the format of a prototype even more influential. Although design practitioners are known to intentionally align their prototyping approach with the specific design question to be answered, it is unclear the extent to which prototyping approaches should vary based on the stakeholders, context, and setting of a design project. To investigate how the format and quality of prototypes influence stakeholders' responses, we conducted a field study with various medical professionals in Ghana. We presented prototypes for a medical device in different formats to stakeholders and collected responses to the design through semi-structured interviews. We found that professional expertise, prototype format, and question type influenced the types of responses that stakeholders provided. These findings suggest that designers seeking input from stakeholders on new concepts should consider context-specific prototyping strategies, especially when designing at distance and across cultures.

Usage of a Responsible Gambling Tool: A Descriptive Analysis and Latent Class Analysis of User Behavior

Gambling is a common pastime around the world. Most gamblers can engage in gambling activities without negative consequences, but some run the risk of developing an excessive gambling pattern. Excessive gambling has severe negative economic and psychological consequences, which makes the development of responsible gambling strategies vital to protecting individuals from these risks. One such strategy is responsible gambling (RG) tools. These tools track an individual's gambling history and supplies personalized feedback and might be one way to decrease excessive gambling behavior. However, research is lacking in this area and little is known about the usage of these tools. The aim of this article is to describe user behavior and to investigate if there are different subclasses of users by conducting a latent class analysis. The user behaviour of 9528 online gamblers who voluntarily used a RG tool was analysed. Number of visits to the site, self-tests made, and advice used were the observed variables included in the latent class analysis. Descriptive statistics show that overall the functions of the tool had a high initial usage and a low repeated usage. Latent class analysis yielded five distinct classes of users: self-testers, multi-function users, advice users, site visitors, and non-users. Multinomial regression revealed that classes were associated with different risk levels of excessive gambling. The self-testers and multi-function users used the tool to a higher extent and were found to have a greater risk of excessive gambling than the other classes.