Current User Research Articles

Determinates of depressive disorder among adult patients with cardiovascular disease at outpatient cardiac clinic Jimma University Teaching Hospital, South West Ethiopia: cross-sectional study

BackgroundDepression and heart disease are an important public-health problem. Depression is one of the most prevalent and disabling psychiatric disorders with more than three times increased risk among patients with cardiovascular disorders.ObjectiveTo identify the prevalence and associated factors of depressive disorder among adult patients with cardiovascular disease.MethodsInstitution based cross-sectional study design was used to conduct this study on 293 study participants attending an outpatient cardiac clinic at Jimma University Teaching Hospital. All eligible patients were recruited into the study consecutively. Depression was assessed using patient health questionnaire-9. The patient health questionnaire-9 had a total score of 27, from which 0–4: no/minimal depression, 5–9: mild depression, 10–14: moderately depression, 15–19: moderately severe depression and 20–27 severe depression. The data was feed into Epi-data version 3.1 and lastly exported to SPSS version 21 for analysis. Bivariate analysis was used to analyze the statistical association of covariates of interest with depressive disorder among patients with cardiovascular disease. Then, logistic regression analysis was used as a final model to control confounders. The strength of association was measured by a 95% confidence interval.ResultsA total of 293 adult patients diagnosed with the cardiovascular disease were included in the study with 97% (n = 284) of response rate, 47.2% (n = 134) males and 52.8% (n = 150) females, making female to a male ratio around 1.1:1. The prevalence of depression was 52.8% (n = 150/284). Out of the subjects with depression 52.67% (n = 79), 36.0% (n = 54) and 11.33% (n = 17) were mild, moderate and severe depression, respectively. Variables such as employed, unemployed, physical activity, current cigarette user and poor social support were independently associated with depression in the final model.ConclusionsIn this study, depression was found to be highly prevalent psychiatric comorbidity in adult cardiovascular disease patients.

A longitudinal study of cannabis use increasing the use of asthma medication in young Norwegian adults

BackgroundA small number of studies have shown that the use of cannabis increases the risk of bronchial asthma. There is, however, a paucity of longitudinal studies which are able to control for known risk factors of bronchial asthma.MethodsSurvey data from a population-based longitudinal study encompassing 2602 young adults followed for 13 years were coupled with individual prescription data on asthma medication (β2-adrenergic receptor agonists and glucocorticoids for inhalation) from the Norwegian national prescription database, which covers the entire Norwegian population. Current cannabis use, gender, age, years of education, body mass index (BMI; kg/m2) and current smoking were measured.ResultsPrescription of asthma medication was associated with female gender, self-reported earlier asthma and allergies, daily tobacco smoking and current cannabis use. In a model adjusting for gender, age, years of education, BMI, earlier self-reported asthma and allergies and current tobacco smoking the odds ratio for a current cannabis user to fill prescriptions for asthma medication was 1.71 (95% CI: 1.06–2.77; p = 0.028).ConclusionsThis suggests that cannabis is a risk factor for bronchial asthma or use of asthma medication even when known risk factors are taken into consideration. Intake of cannabis through smoking should be avoided in persons at risk.

Variation-Aware Cloud Service Selection via Collaborative QoS Prediction

As the number of cloud services (CSs) offering similar functionality is growing, more attention has been payed on the quality of service (QoS) of CSs. However, in a dynamic cloud environment, the explicit and inherent variation of QoS causes the single CS selection via collaborative filtering techniques (CSS-CFT) to be challenging. A variation-aware approach via collaborative QoS prediction is proposed to select an optimal CS according to users’ non-functional requirements. Based on time series QoS data, this approach utilizes a set of specific cloud models to quantify the variation characteristics of QoS from the four aspects including central tendency, variation range, frequency of variation and period. To exactly identify the neighboring users for a current user, this paper employs the double Mahalanobis distances to measure the similarity of QoS cloud models. The variation-aware CSS-CFT is formulated as a multi-criteria decision-making problem, and an improved TOPSIS method is exploited to solve it, by considering both the objective QoS variation and subjective user preferences during different time periods. The experiments based on a real-world dataset demonstrate that the proposed approach can enhance the accuracy of CSS-CFT in a high-variance environment without noticeable increase of selection time, in comparison to the existing approaches.

The MomConnect Nurses and Midwives Support Platform (NurseConnect): A Qualitative Process Evaluation.

BackgroundOver the past decade, mobile health has steadily increased in low-income and middle-income countries. However, few platforms have been able to sustainably scale up like the MomConnect program in South Africa. NurseConnect was created as a capacity building component of MomConnect, aimed at supporting nurses and midwives in maternal and child health. The National Department of Health has committed to expanding NurseConnect to all nurses across the country, and an evaluation of the current user experience was conducted to inform a successful scale up.ObjectiveThis study aims to evaluate the perception and use of NurseConnect by nurses and midwives to produce feedback that can be used to optimize the user experience as the platform continues to scale up.MethodsWe conducted focus group discussions and in-depth interviews with 110 nurses and midwives from 18 randomly selected health care facilities across South Africa. Questions focused on mobile phone use, access to medical information and their experience with NurseConnect registration, as well as the content and different platforms.ResultsAll participants had mobile phones and communication through calls and messaging was the main use in both personal and work settings. Of 110 participants, 108 (98.2%) had data-enabled phones, and the internet, Google, and apps (South African National Department of Health Guidelines, iTriage, Drugs.com) were commonly used, especially to find information in the work setting. Of 110 participants, 62 (56.4%) were registered NurseConnect users and liked the message content, especially listeriosis and motivational messages, which created behavioral change in some instances. The mobisite and helpdesk, however, were underutilized because of a lack of information surrounding these platforms. Some participants did not trust medical information from websites and had more confidence in apps, while others associated a “helpdesk” with a call-in service, not a messaging one. Many of the unregistered participants had not heard of NurseConnect, and some cited data and time constraints as barriers to both registration and uptake.ConclusionsMobile and smartphone penetration was very high, and participants often used their phone to find medical information. The NurseConnect messages were well-liked by all registered participants; however, the mobisite and helpdesk were underutilized owing to a lack of information and training around these platforms. Enhanced marketing and training initiatives that optimize existing social networks, as well as the provision of data and Wi-Fi, should be explored to ensure that registration improves, and that users are active across all platforms.

A survey on data storage and placement methodologies for Cloud-Big Data ecosystem

Currently, the data to be explored and exploited by computing systems increases at an exponential rate. The massive amount of data or so-called “Big Data” put pressure on existing technologies for providing scalable, fast and efficient support. Recent applications and the current user support from multi-domain computing, assisted in migrating from data-centric to knowledge-centric computing. However, it remains a challenge to optimally store and place or migrate such huge data sets across data centers (DCs). In particular, due to the frequent change of application and DC behaviour (i.e., resources or latencies), data access or usage patterns need to be analyzed as well. Primarily, the main objective is to find a better data storage location that improves the overall data placement cost as well as the application performance (such as throughput). In this survey paper, we are providing a state of the art overview of Cloud-centric Big Data placement together with the data storage methodologies. It is an attempt to highlight the actual correlation between these two in terms of better supporting Big Data management. Our focus is on management aspects which are seen under the prism of non-functional properties. In the end, the readers can appreciate the deep analysis of respective technologies related to the management of Big Data and be guided towards their selection in the context of satisfying their non-functional application requirements. Furthermore, challenges are supplied highlighting the current gaps in Big Data management marking down the way it needs to evolve in the near future.

Using entropy for similarity measures in collaborative filtering

Collaborative filtering has been successfully implemented in many commercial recommender systems. These systems recommend items favored by other users with similar preference history to the current user. As finding similar users is critical to the performance of the system, various techniques have been suggested to develop similarity measures. However, there are still much to be improved, because existing similarity measures simply utilize additional heuristic information and seldom reflect the global rating behaviors on items. This paper aims to improve the previous similarity measures by employing the information entropy of user ratings so that the user’s global rating behavior on items can be reflected. The efficiency of the proposed method is examined through extensive experiments to demonstrate its superior performance over the previous similarity measures especially in small-scaled and sparse datasets.

Mobile User Indoor-Outdoor Detection Through Physical Daily Activities.

An automatic, fast, and accurate switching method between Global Positioning System and indoor positioning systems is crucial to achieve current user positioning, which is essential information for a variety of services installed on smart devices, e.g., location-based services (LBS), healthcare monitoring components, and seamless indoor/outdoor navigation and localization (SNAL). In this study, we proposed an approach to accurately detect the indoor/outdoor environment according to six different daily activities of users including walk, skip, jog, stay, climbing stairs up and down. We select a number of features for each activity and then apply ensemble learning methods such as Random Forest, and AdaBoost to classify the environment types. Extensive model evaluations and feature analysis indicate that the system can achieve a high detection rate with good adaptation for environment recognition. Empirical evaluation of the proposed method has been verified on the HASC-2016 public dataset, and results show 99% accuracy to detect environment types. The proposed method relies only on the daily life activities data and does not need any external facilities such as the signal cell tower or Wi-Fi access points. This implies the applicability of the proposed method for the upper layer applications.

Performance analysis of cloud computing using series of queues with Erlang service

In this paper we have proposed a priority based Erlang service distribution with k-phases for cloud computing architecture. The multiple users from the public cloud entering into two serially connected M/M/s and M/Ek/1 (Erlang service queue) queues are served based on the non-pre-emptive priority discipline. We have assumed each user of different priority class i, (i ≥ 2) desired to wait until the current user is being served if priority of the service is similar, as per FCFS policy. If the servers are free, users can enter into the M/M/s queue, then enter into the M/Ek/1 queue with probability ϕ and leave the system after service completion or leave the system with probability (1 - ϕ) without entering into the ESQ. We have obtained waiting time for both the queues in the cloud system and shown numerically the total waiting time is lower than the existing system.

Consensus Opinion Model in Online Social Networks Based on Influential Users

A framework to consensus opinion model within a networked social group is put forward. The current research in opinion formation within the groups is largely based on the opinion aggregation of each user of the network. However, the consistency of users in aggregation, social power, and the impact of each individual user of the group for opinion formation are not considered. In this paper, we investigate a consensus opinion model in social groups based on the impact of influential users and aggregation methods. In order to reach the consensus model, we aggregate the users' opinions. To maintain consistency, we propagate the opinion through the users to reach an agreement. This propagation will consider the influential users' impacts that have a crucial effect on its process. A novel method is proposed to detect the influential users and opinion propagation based on them to derive the opinion toward the networked social group. In particular, we applied optimism and pessimism scores as the users' personality to discover the influential users in the network. Considering that, we propagate the opinion based on two facts: 1) the impact of influential users, derived by the presence of an extremely confident individual in the network and 2) the impact of neighbors, induced by the presence of the users who have a connection with the current user. Then, we proposed the opinion aggregation of the group induced by the weighted averaging operator and fuzzy techniques. In order to evaluate the validity of the method, we used enormous data sets of Epinions and Etsy which are signed and unsigned, respectively.

Immersive Virtual Reality-Based Interfaces for Character Animation

Virtual Reality (VR) has increasingly attracted the attention of the computer animation community in search of more intuitive and effective alternatives to the current sophisticated user interfaces. Previous works in the literature already demonstrated the higher affordances offered by VR interaction, as well as the enhanced spatial understanding that arises thanks to the strong sense of immersion guaranteed by virtual environments. These factors have the potential to improve the animators’ job, which is tremendously skill-intensive and time-consuming. The present paper explores the opportunities provided by VR-based interfaces for the generation of 3D animations via armature deformation. To the best of the authors’ knowledge, for the first time a tool is presented which allows users to manage a complete pipeline supporting the above animation method, by letting them execute key tasks such as rigging, skinning and posing within a well-known animation suite using a customizable interface. Moreover, it is the first work to validate, in both objective and subjective terms, character animation performance in the above tasks and under realistic work conditions involving different user categories. In our experiments, task completion time was reduced by 26%, on average, while maintaining almost the same levels of accuracy and precision for both novice and experienced users.

A Food Recommender System Considering Nutritional Information and User Preferences

The World Health Organization identifies the overall increasing of noncommunicable diseases as a major issue, such as premature heart diseases, diabetes, and cancer. Unhealthy diets have been identified as the important causing factor of such diseases. In this context, personalized nutrition emerges as a new research field for providing tailored food intake advices to individuals according to their physical, physiological data, and further personal information. Specifically, in the last few years, several types of research have proposed computational models for personalized food recommendation using nutritional knowledge and user data. This paper presents a general framework for daily meal plan recommendations, incorporating as main feature the simultaneous management of nutritional-aware and preference-aware information, in contrast to the previous works which lack this global viewpoint. The proposal incorporates a pre-filtering stage that uses AHPSort as multi-criteria decision analysis tool for filtering out foods which are not appropriate to the current user characteristics. Furthermore, it incorporates an optimization-based stage for generating a daily meal plan whose goal is the recommendation of food highly preferred by the user, not consumed recently, and satisfying his/her daily nutritional requirements. A case study is developed for testing the performance of the recommender system.

English Education Game using Non-Player Character Based on Natural Language Processing

Game education is a game that aims for educational and entertainment. But the educational game is not popular to users. Therefore, it takes innovation and other ways to improve the educational game itself. Based on the current user survey, games that have dynamic elements such as artificial intelligence are sufficiently requested by users. For this reason, this study aims to build educational games that implement artificial intelligent (AI). In a game, AI is often implemented as Non-Player Character (NPC). The method used for NPCs in this game is based on the Natural Language Processing approach. This is so that the NPC can answer questions about English automatically. The results show that the educational game that has this NPC gets an average score above 75% of users. In addition, users also provide positive feedback on the game itself apart from the questionnaire. So it can be concluded that the presence of NPCs in educational games can increase user interest. With this new approach, it is hoped that it can also increase the popularity ranking of the educational game genre.

Improving Satisfaction through the Analysis of Decision-Making Criteria of Meteorological Information User

It is important to understand how forecast information is recognized and used in order to effectively facilitate service delivery to the meteorological community. This study was conducted to quantitatively estimate users’ dissatisfaction level with forecasting errors and to evaluate whether the distribution of user's decision criteria of probabilistic forecasts is appropriate. Correlation analysis was conducted between the satisfaction value of forecasts and the user-perceived accuracy to estimate the level of dissatisfaction regarding inaccurate forecasts. Further, satisfaction value was compared between an actual and a virtual group to investigate how forecast users are effectively utilizing the information. The analysis showed that the users who responded to the questionnaire generally did not recognize the effective probability threshold as being able to provide a better value. These users were estimated to be very dissatisfied when adverse weather was not predicted in advance. Meanwhile, the value of the forecast to younger users (19–39 years)can be increased by adjusting their current user threshold. However, those over 60 years did not benefit from this adjustment. To improve user satisfaction with forecasts, it is deemed necessary to educate users regarding the meaning of probabilistic forecasts and the probability threshold pattern based on age group. By quantitatively analyzing user dissatisfaction, it is possible to estimate which forecast elements and forecasting errors garner user dissatisfaction. Through such analysis, moreover, we can help the meteorological community to develop better services by considering the characteristics of the user group.

Analysis on the Development Status and Prospects of the Intelligent Pension Model in Tianjin

The issue of old-age care has become a topic of widespread concern. With the development of technology, the intelligent mode of old-age care has emerged. Based on the online intelligent mode, Tianjin Leling Intelligent Care Co., Ltd. has added and strengthened offline services, creating a new way of old-age care: O2O intelligent care mode. Delegated by the company, this study is based on the company's existing 19 community centers in Tianjin and the elderly (less than 60 years old) who live within a radius of 5 kilometers and have self-care ability are surveyed. By the investigation, this paper finds that the current situation of O2O intelligent care is as follows: this mode has a certain degree of recognition, but not high; this mode is not enough for advertisement; this mode has the best satisfaction with the elderly, but the price of O2O intelligent care is not satisfactory. For the current user, the situation is as follows: the age of user prefers for younger; the choice of O2O intelligent care has nothing to do with the annual income of the elderly family; it is less satisfied with font size and operational difficulty; the objective elderly is the main customer of O2O intelligent care. In conclusion, O2O intelligent care is the trend of old-age care in the future and it has great potential in the market. Based on the above analyses, this paper gives detailed suggestion for the company.

Decentralized Iot Edge Nanoservice Architecture for Future Gadget-Free Computing

In the envisioned ubiquitous world, services will follow users as they move across smart surroundings. Services are instantiated to users through the environment, appearing and disappearing as they move, which reduces the need for personal communication devices such as smartphones or tablets. To facilitate this development, service architectures need to support virtualized, on-demand service composition based on the hardware and software resources available at the current user location. The technical context for this type of user interaction with digital services through smart surroundings is called Internet of Everything (IoE). Today's service architectures will be too inflexible in this highly decentralized and dynamic environment. Hence, in this article we propose a novel service model called nanoEdge, where nodes collaboratively provide needed functions for virtual services that need to be deployed locally due to performance, efficiency or reliability requirements, for example. The main contributions of this article are the nanoEdge conceptual model and its proof-of-concept (PoC) implementation to show that the model is feasible with regard to performance and resource-efficiency. The successful demonstration of PoC implementation exemplifies future IoE service scenarios with today's hardware components.

Contraceptive Use Related Health Benefits as Women’s Experiences Explored Phenomenologicaly in the Rural Part of Southern Ethiopia

Despite the existence of notable remedies for maternal and child health, mothers and children in developing nations are affected disproportionately. Contraceptive service has been contributing for the betterment of maternal and child health by averting unwanted and unplanned pregnancies. However, the effect is not uniform across the nation and the globe. This study is conducted with the purpose of exploring the experiences of current contraceptive users and cascade to the non-users to enhance the benefits of contraceptive use for maternal and child health. The study employed an interpretive phenomenology to explore the lived experiences of current user. Focus group discussions and individual in-depth interview methods used to collect information. Data were analyzed using interpretive phenomenological analysis. The finding indicates that contraceptive use has created great changes both in the lives of mothers and children when compared to the non-use period. They acclaimed that they got time to care for themselves and their children by averting unplanned pregnancies. Based on the study finding it is concluded that women are able to control their body, their fertility and hence relieved from the trap of unplanned pregnancies. The experiences of the current user women can be best lesson for the non-user, hence, further improve the health status of mothers and children in rural part of the country.

Toward business process recommendation-based collaborative filtering

Existing process recommendation methods cannot meet the various needs of personalised users. To address this problem, this paper proposed a personalised process recommendation method that is based on user behaviour preference. This method combines traditional process recommendation with user behaviour similarity and mines user behaviour preference according to the historical tracks of processes that were performed by users. In the execution of a process, the execution trace of a behaviour-similar user and executable candidate activities to be recommended that are provided by conventional process recommendation are analysed. Then, activities or recommended activities for the current user are selected to realise the automatic construction of the entire process to meet the personalised needs of users. The experimental results show that the proposed method outperforms other methods in terms of accuracy and efficiency.

Urinary concentrations of monohydroxylated polycyclic aromatic hydrocarbons in adults from the U.S. Population Assessment of Tobacco and Health (PATH) Study Wave 1 (2013–2014)

BackgroundPolycyclic aromatic hydrocarbons (PAHs) are environmental pollutants formed from incomplete combustion of organic matter; some PAHs are carcinogens. Smoking, diet, and other activities contribute to exposure to PAHs. Exposure data to PAHs among combustible tobacco product users (e.g. cigarette smokers) exist; however, among non-combustible tobacco products users (e.g., e-cigarette users), such data are rather limited. ObjectivesWe sought to evaluate exposure to PAHs among participants in Wave 1 (2013–2014) of the Population Assessment of Tobacco and Health (PATH) Study based on the type of tobacco product (combustible vs non-combustible), and frequency and intensity of product use. MethodsWe quantified seven PAH urinary biomarkers in 11,519 PATH Study participants. From self-reported information, we categorized 8327 participants based on their use of tobacco products as never-tobacco user (never user, n = 1700), exclusive current established combustible products user (combustible products user, n = 5767), and exclusive current established non-combustible products user (non-combustible products user, n = 860). We further classified tobacco users as exclusive cigarette user (cigarette user, n = 3964), exclusive smokeless product user (SLT user, n = 509), and exclusive e-cigarette user (e-cigarette user, n = 280). Last, we categorized frequency of product use (everyday vs some days) and time since use (last hour, within 3 days, over 3 days). We calculated geometric mean (GM) concentrations, and evaluated associations between tobacco product user categories and PAH biomarkers concentrations. ResultsCombustible products users had significantly higher GMs of all biomarkers than non-combustible products users and never users; non-combustible products users had significantly higher GMs than never users for four of seven biomarkers. For all biomarkers examined, cigarette users had the highest GMs compared to other tobacco-product users. Interestingly, GMs of 2-hydroxyfluorene, 3-hydroxyfluorene and ∑2,3-hydroxyphenanthrene were significantly higher in SLT users than in e-cigarette users; 3-hydroxyfluorene and 1-hydroxypyrene were also significantly higher in e-cigarette and SLT users than in never users. Everyday cigarette and SLT users had significantly higher GMs for most biomarkers than some days' users; cigarette and SLT users who used the product in the last hour had significantly higher GMs of most biomarkers than other occasional cigarette or SLT users respectively. By contrast, everyday e-cigarette users' GMs of most biomarkers did not differ significantly from those in some days' e-cigarette users; we did not observe clear trends by time of last use among e-cigarette users. ConclusionsUsers of tobacco products had higher PAH urinary biomarker concentrations compared to never users, and concentrations differed by type and frequency of tobacco product use.

Literature vs. Twitter: Empirical insights on customer needs in e-mobility

Knowledge about current user preferences and needs regarding electric vehicles is key for developing convenient solutions in the field of e-mobility. They are decisive for a successful market uptake of electric vehicles. In this article, we analyze German Twitter data as fast and unbiased feedback from customers on current products. We demonstrate how this new data source can be applied in the field of e-mobility. We compare our analysis to the traditional approach of a literature survey. Results show that for the German-speaking region the frequency distribution of needs derived from the literature survey differs significantly from the frequency distribution derived from the sample of tweets. Price-related needs and needs concerning Car characteristics are comparably overrepresented in the literature survey. On the other hand, Charging-related needs in particular as well as needs classified in the society cluster are overrepresented in the Twitter data set. We derive interpretations from these insights and illustrate possibilities for stakeholders and policymakers to increase acceptance.

Net neutrality from legislative, technical and marketing perspective

Net neutrality is the principle in which Internet service providers (ISPs) treat all data traffic equally, and none of them slows down or accelerates data transfer speeds depending on who the current user is, what kind of content is being transferred, from which web site it comes, or from which platform, application or communication model. So far this kind of ISPs behavior has been a standard, but by changing certain rules, new moments which can significantly change the way the Internet is use are coming. Also, the way that we do business over Internet might change as well. The elimination of net neutrality can be seen from several perspectives, and we will pay attention to net neutrality from legislative, technical and marketing one.