Evaluate User Preferences Research Articles

Utilization of Three Species of Mangrove Wood Branches for Briquettes and Its Preference as Alternative Solid Biofuel

Wood briquettes - one of the biomass energies - were produced from three species of mangrove wood branches, namely mata buaya (Bruguiera sexangula), buta-buta (Excoecaria agallocha), and bakau minyak (Rhizophora apiculata) using three types of starch adhesives made from tapioca, maize, and potato. This study aimed to evaluate user preferences for the kind of fuel, and responses to acceptance of the use of briquettes, and to analyze the effect of attributes on interest in using wood briquettes from mangrove wood branches. Cylindrical briquettes were produced in dimensions of 3 cm diameter and 4 cm thickness. After obtaining all the data, variance analysis and chi-square analysis were conducted. Before conducting the preference survey, evaluations of calorific value and color performance were carried out. The wood briquettes of this study have a calorific value that has met the ISO 17225-3:2-2020 standard of class A2. Even though the three types of wood briquettes differ in their L* (brightness), a* (red/green), and b* (blue/yellow) values, each briquette has a neutral color. A survey was conducted on 60 respondents using a questionnaire to evaluate user preferences for the type of fuel, responses to acceptance of the use of briquettes, and the effect of attributes on interest in using wood briquettes from mangrove wood branches. The results showed that gas is still the most preferred fuel by respondents, with as many as 35 people (58.33%). Meanwhile, 54 respondents (90%) stated they would use wood briquettes. Wood briquettes made of Bakau minyak wood species were the most preferred, with as many as 28 people (46.67%). The chi-square analysis results suggested no relationship between the preference for using wood briquettes from mangrove wood branches and fuel attributes. Based on this study, wood briquettes of mangrove wood branches have the potential to be an alternative fuel as long as their availability is easy to obtain.

A BERT-Based Sequential POI Recommender system in Social Media

Route schema is challenging for tourists because they must choose Points of Interest (POIs) in unknown areas that meet their preferences and limitations. Historically, sequential methods were utilized to generate recommendations based on previous user interactions. Despite their efficacy, however, such left-to-right unidirectional models are suboptimal due to the following factors: a) user behavior sequences are restricted in their ability to utilize hidden representations in unidirectional architectures; b) a rigidly ordered sequence is frequently assumed but is not always possible. This paper proposes a novel personalized sequential recommendation model, termed BERTSeqHybrid, which utilizes Bidirectional Encoder Representations from Transformers (BERT) to circumvent these limitations. In addition to contextual data from POIs, asymmetric schemas, and topic modeling are employed to improve the user-user similarity model. Furthermore, a novel method for evaluating user preferences is proposed utilizing explicit demographic data to mitigate the cold start problem. In the experimental evaluation, the developed methodology, which was applied to two different datasets (Yelp and Flickr), produced superior root mean square error RMSE, F-Score, mean average precision (MAP), and normalized discounted cumulative gain (NDCG) indexes.

Co-Creation of Breast Cancer Risk Communication Tools and an Assessment of Risk Factor Awareness: A Qualitative Study of Patients and the Public in India.

Low awareness of BC and its associated risk factors causes delays in diagnosis and impacts survival. It is critical to communicate BC risk to patients in a format that they are easily able to understand. Our study aim was to develop easy-to-follow transmedia prototypes to communicate BC risk and evaluate user preferences, alongside exploring awareness of BC and its risk factors. Prototypes of transmedia tools for risk communication were developed with multidisciplinary input. A qualitative in-depth online interview study was undertaken using a pre-defined topic guide of BC patients (7), their relatives (6), the general public (6), and health professionals (6). Interviews were analyzed using a thematic approach. Most participants preferred pictographic representations (frequency format) of lifetime risk and risk factors and storytelling using short animations and comic strips (infographics) for communicating genetic risk and testing: "In a short time, they explained it very well, and I liked it". Suggestions included minimizing technical terminology, decreasing the delivery speed, "two-way dialogue", and using local "language for different locations". There was low awareness of BC, with some understanding of age and hereditary risk factors but limited knowledge of reproductive factors. Our findings support use of multiple context-specific multimedia tools in communicating cancer risk in an easy-to-understand way. The preference for storytelling using animations and infographics is a novel finding and should be more widely explored.

Preference Model in the Context of Mobility as a Service: A Pilot Case Study

In this paper, a pilot study of a pre-test preference model in the context of mobility as a service (MaaS) is defined by following the steps required for transport system engineering: survey, specification, calibration, and validation. The availability of a MaaS preference model is crucial to support decision takers and decision makers before starting planning activities for new, sustainable transport services. In this paper, a pre-test model is proposed for evaluating user preferences. The pre-test model was specified with a Logit random utility model and the parameters were estimated using the maximum likelihood method. To define the preference model, a pilot survey was conducted in the Gioia Tauro area, an extra-urban area in southern Italy. For the pre-test model, a pilot sample of users was considered. In the area, a high percentage of users traveled by an individual transport system; this high percentage was also present in the survey, with 76% traveling by private car. Short- and long-distance scenarios were proposed to users. In the calibrated model, it emerged that bundles were more attractive for long-distance journeys and decreased with the cost of the package. The additional cost in the present scenario influenced the preference for bundle cost. Considering the parking cost in the present scenario (scenario 2), the MaaS preference probability started at higher probability values but increased less quickly. The pre-test model was defined starting from a pilot sample and represents the basis for a larger MaaS preference model built starting from a larger survey and a sample with a greater number of calibrated parameters.

Evaluating User Preferences, Comprehension, and Trust in Apps for Environmental Health Hazards: Qualitative Case Study.

Climate change is projected to increase environmental health hazard risks through fire-related air pollution and increased airborne pollen levels. To protect vulnerable populations, it is imperative that evidence-based and accessible interventions are available. The environmental health app, AirRater, was developed in 2015 in Australia to provide information on multiple atmospheric health hazards in near real time. The app allows users to view local environmental conditions, and input and track their personal symptoms to enable behaviors that protect health in response to environmental hazards. This study aimed to develop insights into users' perceptions of engagement, comprehension, and trust in AirRater to inform the future development of environmental health apps. Specifically, this study explored which AirRater features users engaged with, what additional features or functionality needs users felt they required, users' self-perception of understanding app information, and their level of trust in the information provided. A total of 42 adult AirRater users were recruited from 3 locations in Australia to participate in semistructured interviews to capture location- or context-specific experiences. Participants were notified of the recruitment opportunity through multiple avenues including newsletter articles and social media. Informed consent was obtained before participation, and the participants were remunerated for their time and perspectives. A preinterview questionnaire collected data including age range, any preexisting conditions, and location (postcode). All participant data were deidentified. Interviews were recorded, transcribed, and analyzed using thematic analysis in NVivo 12 (QSR International). Participants discussed app features and functionality, as well as their understanding of, and trust in, the information provided by the app. Most (26/42, 62%) participants used and valued visual environmental hazard features, especially maps, location settings, and hazard alerts. Most (33/42, 78%) found information in the app easy to understand and support their needs, irrespective of their self-reported literacy levels. Many (21/42, 50%) users reported that they did not question the accuracy of the data presented in the app. Suggested enhancements include the provision of meteorological information (eg, wind speed or direction, air pressure, UV rating, and humidity), functionality enhancements (eg, forecasting, additional alerts, and the inclusion of health advice), and clarification of existing information (eg, symptom triggers), including the capacity to download personal summary data for a specified period. Participants' perspectives can inform the future development of environmental health apps. Specifically, participants' insights support the identification of key elements for the optimal development of environmental health app design, including streamlining, capacity for users to customize, use of real time data, visual cues, credibility, and accuracy of data. The results also suggest that, in the future, iterative collaboration between developers, environmental agencies, and users will likely promote better functional design, user trust in the data, and ultimately better population health outcomes.

The Role of Machine Learning in E-Learning Using the Web and AI-Enabled Mobile Applications

For over two decades, e-learning has been recognized as a flexible and faster method compared to the other established methods, especially in enhancing knowledge. Concurrently, the expansion of information technology applications, such as mobile applications and Artificial Intelligence (AI), has provided well-grounded foundations for e-learning to be more reachable. In particular, education can be seen as the most beneficial sector of advancements in e-learning. Machine learning is considered a form of personalized learning that could be used to give each student a specific personal experience through which students are directed to gain their own experience. Web and AI-enabled mobile applications can be recognized as one of the most broadly used platforms for e-learning where machine learning technology can be applied to measure many influences and predictions regarding the quality of e-learning, but we cannot ignore the complexities of use. This study shows the role of machine learning in the user’s ability to make use of the course and its contents to measure ease and clarity. Based on a former study shown previously, this paper attempts to pinpoint realities and complexities associated with web and AI-enabled mobile applications by evaluating user preferences. This paper forms the second phase using two user groups (21–30 years) where data were attained using a survey questionnaire to investigate the user preferences when using an application for e-learning. The analysis shows that the future of e-learning has greater potential in web-based applications, as they have more scope for development and improvements compared to mobile applications. The paper concludes with a conceptual framework that works as a machine that stimulates different information and uses e-learning applications that support artificial intelligence techniques. This research provides a solid underpinning for further research into the future of AI-enabled e-learning education and its implication with respect to cost, quality, and usability.

EVALUATION OF USER PREFERENCES WHEN CHOOSING CLOUD SOFTWARE PRODUCTS

The article analyzes the assessment of user preferences when choosing cloud software products (CSP). The peculiarity of the choice of OPP is not only the choice of an enterprise - supplier of CSP of the necessary functionality, but also the choice of enterprises-intermediaries of cloud digitalization, which include an enterprise for designing a cloud information system (CIS) based on the selected CSP and a cloud data processing center during the operation of the CIS. A multidimensional characteristic of cloud digitalization enterprises used in the selection process is given. Since the criteria for choosing an CSP are vague, fuzzy numbers, an apparatus of fuzzy sets is proposed for evaluating user preferences.

A Hybrid Action-Related K-Nearest Neighbour (HAR-KNN) Approach for Recommendation Systems

Recommendation System (RS) has been broadly utilized in various areas and discovers product recommendations during an active user interaction in E-Commerce sites. Tremendous growth of users and products in recent years has faced some key challenges. There are numerous online sites that present many decisions to the user at once, which is strenuous. Moreover, finding active user or right product is an important task in RS. Existing works have been proposed to recommend a product by considering user inclination and socio-demographic behaviour. In this paper, we propose a Hybrid Action-Related K-Nearest Neighbour similarity (HAR-KNN) recommender that consolidates the simplicity of hybrid filtering to enrich user behaviour matrix with formation of the vector of features. It will classify the features using race classifiers from both quality and quantity aspects. The proposed approach also addresses the problems of the previous methods to efficiently evaluate user preference on products and balance feature analysis. The K-NN classification method has been qualified online and real-time to find user behaviour data coordinating to a specific user group containing the relationship between the similarity of many users and target users from a huge amount of data. The proposed experimental result is evaluated based on measures such as Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Squared Error (RMSE) with the lowest error of 0.7165, 0.7201 and 0.7322 separately. High predictive measures like Precision (P), Recall (R) and F1 are found to have values 0.8501, 0.2201 and 0.3507 respectively.

194 User Preferences for the Design of Wearable Technology Systems - A Scoping Review

Abstract Background Wearable technology is a fast developing area. Often, the focus of research is on accuracy, while the practicalities of using the device may be overlooked, despite the fact that this greatly influences utility. This scoping review therefore explored the design and usability preferences of people for wearable technology for health monitoring. Methods A scoping review was conducted of literature evaluating user preferences for the design of wearable technology systems, for people aged >50 years, with good health, or chronic diseases. Results A search of relevant databases yielded 628 potential studies (after duplicates removed). Following title/abstract and then full text screening, 17 papers were included. The most commonly reported theme related to design and user interface (13 studies). Users wanted a small, unobtrusive and light device which doesn’t snag on clothing or affect activities of daily living, but yet has a readable and easy-to-use interface, which may prove challenging for designers! Users were most happy to wear a device on the wrist and/or hip region, being considered the least obtrusive / most discrete. Users were open to the technology aspects of the device, but wanted specific training, or clear and readable instructions. Less commonly reported parameters included issues with privacy and ownership of data (two studies); cost (two studies); reliability and accuracy (three studies), including being accurate overnight and in the shower, etc.; and clinical usefulness, i.e. the data being effectively linked with other healthcare data. Where considered, participants didn’t want to wear a device by night (two studies). Safety of wearable devices was not a theme in any study. Conclusion Overall, user needs seem to be rarely considered in the design of wearable technology for health monitoring. However, the limited studies do highlight important user concerns, which should be considered by the technology designers and prescribers.

Maternal and Child Acceptability of a Proposed Guided Imagery Therapy Mobile App Designed to Treat Functional Abdominal Pain Disorders in Children: Mixed-Methods Predevelopment Formative Research.

BackgroundFunctional abdominal pain disorders are chronic abdominal pain conditions, which affect up to 20% of children worldwide. Of the various functional abdominal pain disorder treatment modalities, psychological therapies such as guided imagery therapy appear most effective. However, there are significant barriers to receiving psychological therapies, including access to trained therapists. Alternatively, remotely delivered psychological therapies for functional abdominal pain disorders have been efficacious.ObjectiveThe objective of our study was to assess acceptability of a proposed guided imagery therapy app designed to treat functional abdominal pain disorders through remote delivery of prerecorded audio sessions and to evaluate user preferences for using such an app.MethodsUsing a mixed-methods approach, we conducted a predevelopment formative study among children aged 7 to 12 years with a functional abdominal pain disorder and their parents. The parents completed our modified Technology Acceptance Model (TAM) questionnaire, which quantified behavioral intention and related factors for using a guided imagery therapy app. Dyads participated in separate in-person semistructured interviews to assess their attitudes toward and preferences for a guided imagery therapy app. Questionnaire and interview findings were collected concurrently, analyzed separately, and then integrated through methods triangulation.ResultsAmong the 15 participating parent-child dyads, 5 (33%) children were Hispanic and 11 (73%) had irritable bowel syndrome. They had diverse socioeconomic status. All parent participants were mothers. The TAM questionnaire indicated that mothers scored favorably on behavioral intention to use a guided imagery therapy app (mean score 12.0, SD 2.6, possible range 3-15). Scores for the TAM factors perceived usefulness, perceived ease of use, hedonic motivation, compatibility, and habit also were favorable. Maternal interviews confirmed positive attitudes toward the proposed app. They advocated a visual component to hold their child’s attention during the guided imagery therapy sessions; recommended incorporating background sounds into the sessions; favored session reminder notifications from the app; and thought the best time for their child to listen to the sessions would be in the evening or before bed. The child interviews also confirmed positive attitudes toward the proposed app. They suggested guided imagery therapy session topics such as sports and adventures; listening to sessions in their bedroom; and the need for parental supervision to install the app on their mobile device. Integration of the quantitative and qualitative methods findings complimented one another on acceptability. The favorable behavioral intention TAM score aligned well with expressed positive maternal and child attitudes toward the app and can be explained by the desire to avoid medications. The questionnaire and interviews also confirmed therapeutic benefit as an intrinsic motivator to promote routine use.ConclusionsA guided imagery therapy app designed to treat pediatric patients with functional abdominal pain disorders appears to be acceptable to both mothers and children. Incorporating parent and child preferences into a guided imagery therapy app could promote therapeutic compliance and increase access to optimal care.

Mixed Similarity Diffusion for Recommendation on Bipartite Networks

In recommender systems, collaborative filtering technology is an important method to evaluate user preference through exploiting user feedback data, and has been widely used in industrial areas. Diffusion-based recommendation algorithms inspired by diffusion phenomenon in physical dynamics are a crucial branch of collaborative filtering technology, which use a bipartite network to represent collection behaviors between users and items. However, diffusion-based recommendation algorithms calculate the similarity between users and make recommendations by only considering implicit feedback but neglecting the benefits from explicit feedback data, which would be a significant feature in recommender systems. This paper proposes a mixed similarity diffusion model to integrate both explicit feedback and implicit feedback. First, cosine similarity between users is calculated by explicit feedback, and we integrate it with resource-allocation index calculated by implicit feedback. We further improve the performance of the mixed similarity diffusion model by considering the degrees of users and items at the same time in diffusion processes. Some sophisticated experiments are designed to evaluate our proposed method on three real-world data sets. Experimental results indicate that recommendations given by the mixed similarity diffusion perform better on both the accuracy and the diversity than that of most state-of-the-art algorithms.

Applying event related potentials to evaluate user preferences toward smartphone form design

Product forms can affect user preference and play an important role in user's purchasing decisions. Neuroimaging methods can provide an improved understanding of the mechanisms of decision making, which enhance the ability of enterprises to effectively design their products. Hence event related potentials (ERPs) were applied to explore the brain activity evoked by variety of product forms when users made preference among them. Smartphone product forms were displayed with equiprobability randomly. Participants were asked to click the left mouse button when they preferred one product form, else the right button for nonpreferred. The brain signals of each participant were recorded by Curry 7.0. Finally, brain signals were processed by using Curry 7.0 SBA and SPSS 18.0 software. The results showed that preferred product forms evoked enhanced N2, P2 and P3. Moreover, there were significant correlation between ERPs and behavioural data, participants devoted more attention and had faster responding time to preferred products than to nonpreferred. These results indicate that the differences of ERPs can be used to evaluate user preference. Relevance to industryThe integration of customer preferences is nowadays a challenge in new product development. Hence a thorough research on the inherent mechanism of preference formation can provide an accurate measurement method of user's perception. The differences of brain signals evoked by product forms can also provide technical support for product designers, which in turn can meet with user experience. Moreover, the results can be taken as evaluating indicators of product design.

Evaluating user preferences for video transfer methods from a mobile device to a TV screen

This article reports on the evaluation of four methods for transferring video content from a mobile device to a fixed television. The methods have been investigated in a Wizard-of-Oz approach through two consecutive studies. The first experiment aimed at collecting general opinions toward such a feature as well as preferences in terms of usability. In general, participants preferred methods that resemble already known interaction paradigms, and argued against the need of moving physically. From the comments collected in the first study, six potential indicators of preference were generated and investigated in a second experiment. The results indicate that familiarity, convenience and annoyance are strong predictors of the preference level of methods for transferring video content from a mobile- to a fixed device. A discussion and potential design guidelines are finally proposed.

A survey of the optimal handle position for boxes with different sizes and manual handling positions

Handles on objects are very important for enhancing the safety and efficiency of manual handling for people who use them. In this study, four different prototype boxes with auxiliary handles were designed to determine the optimal handle position of a box based on the evaluated user preferences and body part discomfort (BPD). Twenty male students participated in the experiment. Likert-5 point summated rating was applied to evaluate user preferences for the provided boxes with handles in upper, middle, and lower positions, in four different sizes and manual handling positions. Ten additional subjects were asked to indicate their BPD on a body chart after performing a similar experiment. The results show that the subjects preferred the upper part of the handle on a small box regardless of handling position; while the mid to upper parts of the handle on a big box were preferred for handling above the waist height. BPD also indicated that an upper handle was less stressful for a relatively smaller box than a big one; and mid to upper handles were less comfortable for a big box. The optimal handle positions depending on box size and handling position were suggested based on the results of the evaluation. It is thus recommended that a box provides a handle according to its relevant position, depending on size and manual handling condition, to reduce the musculoskeletal stress and in turn to increase user satisfaction.

Evaluation of proper height for squatting stool

Many jobs and activities in people's daily lives have them in squatting postures. Jobs such as housekeeping, farming and welding require various squatting activities. It is speculated that prolonged squatting without any type of supporting stool would gradually and eventually impose musculoskeletal injuries on workers. This study aims to examine the proper height of the stool according to the position of working materials for the squatting worker. A total of 40 male and female college students and 10 female farmers participated in the experiment to find the proper stool height. Student participants were asked to sit and work in three different positions: floor level of 50 mm; ankle level of 200 mm; and knee level of 400 mm. They were then provided with stools of various heights and asked to maintain a squatting work posture. For each working position, they were asked to write down their thoughts on a preferred stool height. A Likert summated rating method as well as pairwise ranking test was applied to evaluate user preference for provided stools under conditions of different working positions. Under a similar experimental procedure, female farmers were asked to indicate their body part discomfort (BPD) on a body chart before and after performing the work. Statistical analysis showed that comparable results were found from both evaluation measures. When working position is below 50 mm, the proper stool height is 100 or should not be higher than 150 mm. When working position is 200 mm, the proper stool height is 150 mm. When working position is 400 mm, the proper stool height is 200 mm. Thus, it is strongly recommended to use proper height of stools with corresponding working position. Moreover, a wearable chair prototype was designed so that workers in a squatting posture do not have to carry and move the stool from one place to another. This stool should ultimately help to relieve physical stress and hence promote the health of squatting workers. This study sought to evaluate and make suggestions on user preference and BPD of proper stool height, at which work can be done comfortably, according to squatting work position. In short, results showed that proper stool height differed according to working position, even with similar squatting tasks.

Resolution versus Speckle Relative to Geologic Interpretability of Spaceborne Radar Images: A Survey of User Preference

A survey was conducted to evaluate user preference for resolution versus speckle relative to the geologic interpretability of spaceborne radar images. Thirteen different resolution/looks combinations were simulated from Seasat synthetic-aperture radar (SAR) data of each of three test sites. The SAR images were distributed with questionnarires for analysis by eighty-five earth scientists. The relative discriminability of geologic targets at each test site for each simulation of resolution and speckle on the images is determined here from a survey of the evaluations. A large majority of the analysts indicate that for most targets a two-look image at the highest simulated resolution is best. For a constant data rate a higher resolution is more important for target discrimination than a greater number of looks. Sand dunes require more looks than other geologic targets. Multiple-look images are preferred over the corresponding single-look image at all resolutions. The number of multiple looks that is optimal for discriminating geologic logic targets is in general inversely related to the simulated resolution.