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Background: Serious games are risk-free environments training various medical competencies, such as clinical reasoning, without endangering patients' safety. Furthermore, serious games provide a context for training situations with unpredictable outcomes. Training these competencies is particularly important for healthcare professionals in emergency medicine. Methods: Based on these considerations, we designed, implemented, and evaluated a serious game in form of an emergency department, containing the features of a virtual patient generator, a chatbot for medical history taking with self-formulated questions, artificially generated faces based on an artificial intelligence algorithm, and feedback for students. The development process was based on an already existing framework resulting in an iterative procedure between development and evaluation. The serious game was evaluated using the System Usability Scale and the User Experience Questionnaire. Results: The System Usability Scale provided a substantial result for the usability. In terms of the user experience, four scales yielded positive results, whereas two scales yielded neutral results. Conclusion: The evaluation of both usability and user experience yielded overall positive results, while simultaneously identifying potential areas for improvement. Further studies will address the implementation of additional game design elements, and testing student learning outcome. [ABSTRACT FROM AUTHOR]

AbstractWe explored how interaction performance is affected by multi-depth VR targeting and button selection using two targeting methods: eye tracking with no UX modifications and feedback, or the controller with a visible cursor for targeting. Selections happened on a controller button press for both targeting modalities. Targets had a diameter of either 3, 4, or 5 degrees, placed in depths between 0.3 m-5m. When comparing conditions of a 1 m single depth vs. multi-depth environment, the eyes were less affected by depth than the controller. We found that performance decreased in multi-depth scenarios on targeting and selection for the controller as measured by Throughput (22% decrease), Movement Time (31% increase), and Misses (66% increase). Depth also affected eye tracking significantly, but to a lesser degree, for Throughput (4% decrease) and Movement Time (6% increase) but not Misses (5% increase). The eyes outperformed the controller in multi-depth scenarios, as measured by Throughput (2.86 bits/s vs. 2.56 bits/s), and were similar in Movement Time (1.10s vs. 1.10s) but had the most Misses (21% vs. 9%). Our study also shows that selecting consecutive targets that come closer to the user is more difficult than those that diverge away from the user, and that targets with larger depth distances take longer to select. Overall, this study provides further supporting evidence that eye tracking can play an important role in 3D interactions. [ABSTRACT FROM AUTHOR]

The evolution of museums has transitioned from being centered on “objects” to “people,” emphasizing the human experience as pivotal in museum education development. According to the cognitive characteristics and factors of museum visitors, this paper explores the key factors influencing visitor perception through quantitative and qualitative user studies. Subsequently, an innovative museum education system is devised, integrating robotics and mobile applications to provide guidance and companionship to visitors. Finally, the evaluation at the Zhejiang Provincial Museum validates the system’s efficacy in enhancing educational experiences. This study aims to analyze the key factors affecting perception and design an innovative system to optimize visitor learning experiences and learning outcomes, thereby enhancing the educational impact of Chinese museums and contributing to the revitalization of traditional culture. [ABSTRACT FROM AUTHOR]

Introduction: The learning experience has undergone significant changes recently, particularly with the adoption of advanced technology and online lectures to address challenges such as pandemics. In fields like engineering, where hands-on classes are essential, the online learning environment plays a crucial role in shaping students' experiences and satisfaction. Methods: This study aimed to explore the key factors affecting engineering students' satisfaction with online learning. A structured survey was administered to 263 students across various engineering disciplines and academic levels, all of whom had experienced both in-person learning before the pandemic and online learning during the pandemic. Factor analysis and multiple linear regression were employed to analyze the data. Results: The analysis identified interactions, services, and technology as the main factors positively influencing online learning satisfaction. The regression analysis further revealed that students' satisfaction is significantly dependent on the availability and quality of online learning services, assessment and interaction tools, and technology. Discussion: This study highlights the critical factors that enhance engineering students' satisfaction with online learning. It offers strategies for educators to improve online learning environments, emphasizing the importance of quality services, assessment, and interaction tools. These findings can guide the development of more effective online learning experiences in engineering education. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to compare the efficacy and utility of the McGrath™ videolaryngoscope, using the Macintosh-like McGrath™ MAC blade and the hyperangulated McGrath™ MAC Xblade with a conventional Macintosh blade under simulated resuscitation conditions. Methods: A prospective, randomized study under conditions mimicking ongoing chest compressions was conducted with 90 anesthesiologists. Intubation success rates, time-to-vocal cords, time-to-intubate, and time-to-ventilate were measured. Additionally, the study assessed the subjective ratings and the perceived workload using the 'NASA-task-load-index' during the procedure. Results: The overall intubation success rate was device dependent 99–100%. The McGrath™ MAC and McGrath™ MAC Xblade showed faster visualization times compared to conventional blades. The MAC blade demonstrated superior performance in time-to-intubate and time-to-ventilate compared to both conventional and MAC Xblades. Despite excellent visualization, the MAC Xblade posed challenges in tube placement, reflected in a prolonged intubation time of >120 seconds in one case. Both MAC and MAC Xblade reduced potential dental injuries and interruptions to chest compressions compared to conventional laryngoscopes. User experience significantly impacted intubation times with conventional laryngoscopes, but this effect was mitigated with videolaryngoscopy. Participants reported lower stress and effort when using videolaryngoscopes, with the MAC blade rated superior in perceived time pressure. Conclusion: The study supports the superiority of videolaryngoscopy with a Macintosh-like blade over conventional laryngoscopy during mechanical chest compressions, particularly for less experienced users. The McGrath™ MAC blade, in particular, offers advantages in intubation time, user-friendliness, and reduced stress. However, the MAC Xblade's challenges during tube placement highlight the need for further clinical validation. Continued research is essential to refine guidelines and improve resuscitation outcomes. [ABSTRACT FROM AUTHOR]

AbstractTouchable Holographic Solutions (THS) are an application type in Augmented and Mixed Reality (AR/MR) environments. Evaluating THS requires considering Usability and User eXperience (UX), particularly Immersion and Presence, often overlooked by current methods. A systematic mapping study and exploratory search revealed gaps, such as the lack of a unified instrument for THS usability and UX evaluation, methods that address THS-specific aspects, and challenges in adapting general evaluation technologies. To bridge these gaps, we present the Usability and User Experience Evaluation in Touchable Holograms (UUXE-ToH) questionnaire, with 67 Likert scale items and three open-ended questions. A validation study with 13 experts provided quantitative data and qualitative feedback. Results confirm the UUXE-ToH’s efficacy and offer insights for improvement. This research contributes to Human-Computer Interaction (HCI) by introducing a validated tool for evaluating THS, integrating hedonic and pragmatic aspects, and supporting early problem identification in development, accessible to professionals with diverse UX expertise. [ABSTRACT FROM AUTHOR]

The light field (LF) imaging technique can capture 3D scene information in 4D by recording both 2D intensity and 2D direction of incoming light rays. Due to this capability, LF has shown a great interest in virtual reality (VR) and augmented reality (AR) for enhanced immersion, improved depth perception and reconstruction of realistic 3D environments. This paper presents a comprehensive review of LF imaging technology and other approaches used for VR content creation. The applications of LF technology beyond VR and AR are also discussed. The challenges and limitations of other approaches for VR content creation are examined. State‐of‐the‐art research has focused on how VR experiences benefit from LF technology and identified the challenges to creating comfortable, immersive and realistic VR content such as (1) image size and resolution, (2) processing speed, (3) precise calibration and (4) depth reconstruction. Recommendations that can be considered for creating immersive VR content are provided to enhance user experience. These recommendations aim to contribute to developing more comfortable and realistic VR content, extending the potential applications of LF imaging technology in diverse fields. [ABSTRACT FROM AUTHOR]

The development of metaverse systems is marked by uncertainty regarding their technical architecture and the scope of their capabilities. Current social virtual reality (SVR) systems offer glimpses of future metaverse environments but remain fragmented, with limited interoperability, poor discoverability, and challenges in preserving assets and social capital across different platforms. Drawing parallels with the evolution of the Internet and World Wide Web, the essay argues that while certain metaverse standards may emerge naturally, fundamental challenges persist, particularly concerning client software. The essay highlights three overlooked yet critical issues for user experience: interoperability, scalable awareness, and accessibility. These challenges, often underemphasized in current standards discussions, are crucial for the practical development of user-friendly metaverse systems. [ABSTRACT FROM AUTHOR]

In recent years, Virtual Reality (VR) has emerged as a promising tool for enhancing training responses in high-stress professions, notably among police officers. This study investigates the psychophysiological responses and subjective user experience of active police officers undergoing Mental Health Crisis Response (MHCR) training using an immersive full-body VR system. A total of 10 active police officers with Special Weapons and Tactics (SWAT) training participated in our controlled study. Officers independently took part in one VR training session lasting 7-12 min involving an avatar in crisis portrayed by an actor. Officers wore integrated cardiovascular and electrodermal activity measurement devices for physiological monitoring. VR user experience aspects such as induced symptoms or game mechanics were investigated upon completing the training, aiming to evaluate the officer's perceptions of the technology. We used the DePICT™ scale to evaluate the de-escalation skills of officers, coded by a research professional. Our findings revealed significant differences in heart rate and heart rate variability responses between baseline and VR scenario immersion, suggesting heightened stress regulation during the MHCR simulation using fullbody VR. Arousal measurements also revealed measurable responses during the training in VR. Additionally, the user experience assessment indicated a positive reception to the VR training, with minimal VR-induced symptoms. A "Defensive-Dynamics-Dichotomy" was revealed highlighting dominant autonomic responses linked to defensive actions (e.g., officers who drew a weapon; those who kept their weapons holstered) and their respective implications for stress management and cognitive function. A unique constellation of de-escalation skills was revealed among officers who relied on weapons relative to those who did not, to resolve the scenario. The study highlighted the perceived utility of physiological monitoring technologies in enhancing police training outcomes. In conclusion, our research underscores the potential of VR as an effective tool for de-escalation training following MHCR simulated scenarios among active police officers, offering insights into its psychophysiological impact and user experience. The findings contribute to improving our understanding of the physiology associated with decision-making in police officers to draw a weapon, emphasizing the role of advanced simulation and physiological monitoring technology in developing evidence-based training programs for public safety. [ABSTRACT FROM AUTHOR]

This study investigates the key factors influencing the usability of smartphone graphical user interfaces (GUIs) among elderly users in Shanxi Province, China. A total of 1,111 healthy individuals aged 60-75 years participated in the study. Quantitative analysis methods, including descriptive statistics, Pearson correlation, regression analysis, and mediation and moderation analysis, were employed to examine the interactions between user characteristics (age, education, experience, health status) and GUI design factors (interface layout, text and icon design, feedback mechanisms, information presentation) about usability metrics (cognitive load, interaction barriers, user experience). The analysis revealed that both user characteristics and GUI design factors significantly impact usability outcomes. Specifically, GUI design factors were found to mediate the relationship between user characteristics and usability, while user characteristics moderated the effects of GUI design on usability. These findings underscore the necessity of tailored interface design to enhance user experience and promote digital inclusion among elderly populations. Practical recommendations include the use of larger and more readable text, simplified iconography, and clear, immediate feedback to users. Such adjustments can significantly improve usability and foster greater digital inclusion. This research contributes valuable insights to both theoretical understanding and practical applications in the design of age-appropriate mobile interfaces. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos del Centro de Estudios de Diseño y Comunicación is the property of Cuadernos del Centro de Estudios de Diseno y Comunicacion and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Every texture in the real world provides us with the essential information to identify the physical characteristics of real objects. In addition to sight, humans use the sense of touch to explore their environment. Through haptic interaction we obtain unique and distinct information about the texture and the shape of objects. In this paper, we enhance X3D 3D graphics files with haptic features to create 3D objects with haptic feedback. We propose haptic attributes such as static and dynamic friction, stiffness, and maximum altitude that provide the optimal user experience in a virtual haptic environment. After numerous optimization attempts on the haptic textures, we propose various haptic geometrical textures for creating a virtual 3D haptic environment for the tactile Internet. These tangible geometrical textures can be attached to any geometric shape, enhancing the haptic sense. We conducted a study of user interaction with a virtual environment consisting of 3D objects enhanced with haptic textures to evaluate performance and user experience. The goal is to evaluate the realism and recognition accuracy of each generated texture. The findings of the study aid visually impaired individuals to better understand their physical environment, using haptic devices in conjunction with the enhanced haptic textures. [ABSTRACT FROM AUTHOR]

Statistical education at university level faces significant challenges, particularly with the rapid advancements in technology and evolving teaching methods. Updating teaching methodologies for statistics in higher education is essential. Information technologies can greatly enhance the learning of statistical content, but many existing platforms are not well-suited for university contexts, either due to language barriers or how the content is presented (often confusing, with limited explanations or lacking context). It is crucial to have a student-centered platform that addresses these issues, ensuring an effective and efficient learning experience. This study introduces "EstApp", an innovative mobile application prototype designed for teaching and learning descriptive statistics in university courses. The application was developed using the Design Thinking methodology, which emphasizes user experience and focuses on the needs of the end users. The design process involved stages of empathizing, defining, ideating, prototyping, and testing, culminating in validation through user tests with university students and professors. This study concludes that EstApp's functionalities enhance understanding of (1) statistical models through interactive graphs and data visualizers; (2) probability concepts via a probability calculator; and (3) descriptive statistics through real-time data generation and visualizers. [ABSTRACT FROM AUTHOR]

In recent years, recommender systems—which provide personalized recommendations by analyzing users' historical behavior to infer their preferences—have become essential tools across various domains, including e-commerce, streaming media, and social platforms. Recommender systems play a crucial role in enhancing user experience by mining vast amounts of data to identify what is most relevant to users. Among these, deep learning-based recommender systems have demonstrated exceptional recommendation performance. However, these "black-box" systems lack reasonable explanations for their recommendation results, which reduces their impact and credibility. To address this situation, an effective strategy is to provide a personalized textual explanation along with the recommendation. This approach has received increasing attention from researchers because it can enhance users' trust in recommender systems through intuitive explanations. In this context, our paper introduces a novel explainable recommendation model named GCLTE. This model integrates Graph Contrastive Learning with transformers within an Encoder–Decoder framework to perform rating prediction and reason generation simultaneously. In addition, we cleverly combine the neural network layer with the transformer using a straightforward information enhancement operation. Finally, our extensive experiments on three real-world datasets demonstrate the effectiveness of GCLTE in both recommendation and explanation. The experimental results show that our model outperforms the top existing models. [ABSTRACT FROM AUTHOR]

The V-Cockpit platform aims to transform the design, testing, and validation of car infotainment systems from the physical realm to virtual reality. It uniquely integrates various aspects of the creative phases—from conceptualization to evaluation—streamlining the process and reducing time and costs compared to traditional methods that focus on individual activities and rely heavily on physical prototyping. This technical note provides a comprehensive overview of the main platform's aspects, highlighting the integration of hardware and behavioral analysis algorithms to improve user experience and detect potential design flaws early on. The V-Cockpit platform, composed of six key components, leverages virtual reality and digital twins, promising significant cost savings and enhanced design efficiency. This work details the system architecture, implementation, and first benefits of this innovative approach through the analysis of three use cases. [ABSTRACT FROM AUTHOR]

AbstractWith the proliferation of AI conversational agents, the design of preset prompts—text suggestions that guide user interactions—has become crucial for enhancing user experience. This study investigates the impact of different preset prompt language styles (social-oriented vs. task-oriented) on user satisfaction. Utilizing two empirical studies, we examined how these language styles influence user perceptions of an AI agent’s warmth and competence, and how these perceptions mediate overall satisfaction. In the first study, participants interacted with an AI agent using either social-oriented or task-oriented prompts under conditions of service success or failure. The results indicated that social-oriented prompts significantly enhance user satisfaction by increasing perceptions of warmth, but not competence. However, this positive effect diminishes in the event of service failure. In the second study, we explored the moderating effect of task urgency. Findings revealed that the positive impact of social-oriented prompts on satisfaction is significant in low-urgency tasks but not in high-urgency scenarios. These insights underscore the importance of prompt language style in AI interactions and provide practical implications for designing more effective AI communication strategies, especially in customer service contexts. [ABSTRACT FROM AUTHOR]

AbstractAppearance-based gaze estimation, which uses only a regular camera to estimate human gaze, is important in various application fields. While the technique faces data bias issues, data collection protocol is often demanding, and collecting data from a wide range of participants is difficult. It is an important challenge to design opportunities that allow a diverse range of people to participate while ensuring the quality of the training data. To tackle this challenge, we introduce a novel gamified approach for collecting training data. In this game, two players communicate words via eye gaze through a transparent letter board. Images captured during gameplay serve as valuable training data for gaze estimation models. The game is designed as a physical installation that involves communication between players, and it is expected to attract the interest of diverse participants. We assess the game’s significance on data quality and user experience through a comparative user study. [ABSTRACT FROM AUTHOR]

Background: Digital phenotyping, the in-situ collection of passive (phone sensor) and active (daily surveys) data using a digital device, may provide new insights into the complex relationship between daily behaviour and mood for people with type 2 diabetes. However, there are critical knowledge gaps regarding its use in people with type 2 diabetes. This study assessed feasibility, tolerability, and user experience of digital phenotyping in people with and without type 2 diabetes after participation in a 2-month digital phenotyping study in Ireland. At study completion, participants rated methodology elements from "not a problem" to a "serious problem" on a 5-point scale and reported their comfort with the potential future use of digital phenotyping in healthcare, with space for qualitative expansion. Results: Eighty-two participants completed baseline. Attrition was 18.8%. Missing data ranged from 9–44% depending on data stream. Sixty-eight participants (82.9%) completed the user experience questionnaire (51.5% with type 2 diabetes; 61.8% female; median age-group 50–59). Tolerability of digital phenotyping was high, with "not a problem" being selected 76.5%—89.7% of the time across questions. People with type 2 diabetes (93.9%) were significantly more likely to be comfortable with their future healthcare provider having access to their digital phenotyping data than those without (53.1%), χ2 (1) = 14.01, p = <.001. Free text responses reflected a range of positive and negative experiences with the study methodology. Conclusions: An uncompensated, 2-month digital phenotyping study was feasible among people with and without diabetes, with low attrition and reasonable missing data rates. Participants found digital phenotyping to be acceptable, and even enjoyable. The potential benefits of digital phenotyping for healthcare may be more apparent to people with type 2 diabetes than the general population. [ABSTRACT FROM AUTHOR]

Technical communication aids in helping people envision the usability of technology. A similar need exists for constructing the usability of data sets, but the rhetorical work is less well documented. This article reports on how writers attribute meaning and usability to a geolocation data set and its derivative objects. This rhetorical work connects the data to existing and potential social realities, which provide a framework for thinking of that data set’s usability. [ABSTRACT FROM AUTHOR]

AbstractIntelligent environments are rapidly gaining ground, propelled by a rich sensor infrastructure, the Internet of Things, sophisticated reasoning capabilities, and Artificial Intelligence. In this complex technological landscape, crafting usable intelligent environments and assessing the user experience (UX) demands a thorough understanding of the concepts involved and the parameters that need to be studied. This paper carries out a review of usability and UX evaluation methods and frameworks, elaborating on fundamental concepts and presenting in detail approaches and methods reported in the literature. It additionally examines evaluation approaches in adaptive and ubiquitous computing systems, which are closely associated with intelligent environments, and presents UX challenges and evaluation frameworks in intelligent environments. Finally, the findings are synthesized and consolidated to produce a comprehensive overview of the field and the challenges that lie ahead. [ABSTRACT FROM AUTHOR]

Successful weight maintenance after weight loss is challenging. Digital interventions may facilitate behaviour change to prevent weight regain, however little is known about the acceptance and use of digital interventions in support of weight maintenance. This mixed methods study aims to evaluate user experiences, system use, and preliminary efficacy of eCHANGE, an application based self-management intervention for weight loss maintenance. A 3-month multi-site single group feasibility pilot study was conducted among adults (N = 60) with BMI ≥30 kg/m2, aiming to maintain weight after weight loss of ≥8%. User experiences and system use were examined through validated questionnaires, system use log data, and individual interviews (n = 15). Preliminary efficacy testing included body weight and patient reported outcome measures. Participants rated eCHANGE usability and usefulness as good in support of weight maintenance, with variation in usage. Analysis indicated that higher behavioural engagement scores were statistically significantly associated with frequency of technology usage. Weight loss was maintained by 83% of the participants at 3-months (i.e. defined as weight change of <3% of baseline body weight (kg)). Digital interventions, combining persuasive system design principles and behaviour change techniques, supporting self-regulation and maintenance of health behaviours, have the potential to facilitate weight maintenance after weight loss.Trial Registration: ClinicalTrials.gov NCT04537988 https://clinicaltrials.gov/ct2/show/NCT04537988 [ABSTRACT FROM AUTHOR]

AbstractIn-vehicle Augmented Reality Head-Up Displays (AR-HUDs) offer drivers integrated navigation and vehicle status information, thereby improving the driving experience. Many studies explored the effects of AR-HUD navigation design and information presentation on driving performance; however, these studies neglected the occurrence of inattentional blindness, which may lead to potential safety hazards. This study used a two-factor within-subjects design to investigate how dynamic effects (pulsation, blinking, movement) and color transparency levels (T1, T0.75, T0.5, T0.35) of AR-HUD navigation graphics influence inattentional blindness and user experience. The results indicated that color transparency significantly affects the drivers’ inattentional blindness behavior and user experience. Specifically, T0.75 resulted in the lowest inattentional blindness, while T0.5 was associated with the shortest reaction time (without blindness) and highest user satisfaction. Dynamic effects did not significantly impact inattentional blindness, but blinking reduced reaction time (without blindness). This study provides design insights for vehicle AR-HUD navigation interfaces. [ABSTRACT FROM AUTHOR]

The rapid advancement of virtual reality (VR) technology has brought many immersive experiences, each designed to transport users into captivating virtual worlds. While these experiences aim to provide a sense of presence and engagement, the factors contributing to a truly immersive experience are often complex and multifaceted. Existing scholarship has predominantly focused on specific aspects of user experience, such as psychological factors (e.g., sense of presence), emotional factors (e.g., enjoyment), or design-related factors (e.g., interface usability). This fragmented approach has impeded a comprehensive understanding of the overall quality of VR experiences. To address this, we propose a multidimensional taxonomy encompassing five essential qualities: immersivity, interactivity, explorability, plausibility, and believability. The framework aims to disentangle the complex, interrelated facets shaping VR experiences for a more systematic evaluation. Immersivity refers to the subjective sense of presence and "being there" in a virtual environment. Interactivity denotes the ability to interact with virtual objects, promoting engagement dynamically. Explorability refers to users' freedom to navigate and discover new elements. Plausibility examines the logical congruence of the virtual environment's rules and behaviors. Finally, believability relates to the world-building and internal coherence of the VR world. This taxonomy provides a structured approach to look at VR experiences holistically, assessing the interplay of these facets to facilitate a more objective, comprehensive evaluation, capturing the multidimensional nature of VR experiences. In summary, our proposed taxonomy marks an essential step toward systematic VR evaluation, providing guidance for researchers and highlighting factors integral to VR quality. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Exercise therapy is an important rehabilitation method for knee osteoarthritis (KOA) patients. This paper developed an exercise game(exergame) system based on Azure Kinect to help KOA patients accurately perform exercise prescriptions and improve their compliance with active exercise. The proposed system digitizes the KOA exercise prescription formulated by the physician and designs a personalized exergame, guiding the KOA patients to perform the correct rehabilitation exercise. Besides, the system employs the Azure Kinect to collect the user's main joint position information and assist in recognizing the user's rehabilitation training actions. We recruited 3 rehabilitation physicians and 30 KOA patients (experimental group: 20, control group: 10) to conduct a clinical trial aimed at evaluating the usability of the system and user satisfaction. Based on the data recorded by the system and evaluations conducted by the physicians, we found that the experimental group demonstrated an impressive accuracy rate of 97.47% in executing actions, which was significantly higher than the control group. These results demonstrate the effectiveness of the system in guiding the rehabilitation training of KOA patients. The system achieved an accuracy of 95.42% for action recognition, demonstrating that the exergame is reliably interactive. According to the analysis of the UEQ-S questionnaire, the average satisfaction score of the experimental group with the system was (1.68±0.893). These results indicate that the system is capable of providing an exceptional user experience, ultimately increasing patients' willingness and compliance to engage in active exercises. The above experiments confirmed that the proposed exergame system has high usability and an ideal user experience and is suitable for home active exercise therapy for KOA patients. [ABSTRACT FROM AUTHOR]

Smartphone applications are one of the main delivery modalities in digital health. Many of these mHealth apps use gamification to engage users, improve user experience, and achieve better health outcomes. Yet, it remains unclear whether gamified approaches help to deliver effective, safe, and clinically beneficial products to users. This study examines the compliance of 69 gamified mHealth apps with the EU Medical Device Regulation and assesses the specific risks arising from the gamified nature of these apps. Of the identified apps, 32 (46.4%) were considered non-medical devices; seven (10.1%) were already cleared/approved by the regulatory authorities, and 31 (44.9%) apps were assessed as likely non-compliant or potentially non-compliant with regulatory requirements. These applications and one approved application were assessed as on the market without the required regulatory approvals. According to our analysis, a higher proportion of these apps would be classified as medical devices in the US. The level of risk posed by gamification remains ambiguous. While most apps showed only a weak link between the degree of gamification and potential risks, this link was stronger for those apps with a high degree of gamification or an immersive game experience. [ABSTRACT FROM AUTHOR]

The use of collaborative robots in industries is growing rapidly. To ensure the successful implementation of these devices, it is essential to consider the user experience (UX) during their design process. This study is aimed at testing the UX goals that emerge when users interact with a collaborative robot during the programming and collaborating phases. A framework on UX goals will be tested, in the geographical context of Portugal. For that, an experimental setup was introduced in the form of a laboratory case study in which the human–robot collaboration (HRC) was evaluated by the combination of both quantitative (applying the User Experience Questionnaire [UEQ]) and qualitative (semistructured interviews) metrics. The sample was constituted by 19 university students. The quantitative approach showed positive overall ratings for the programming phase UX, with attractiveness having the highest average value (M = 2.21; SD = 0.59) and dependability the lowest (M = 1.64; SD = 0.65). For the collaboration phase, all UX ratings were positive, with attractiveness having the highest average value (M = 2.46; SD = 0.78) and efficiency the lowest (M = 1.93; SD = 0.77). Only perspicuity showed significant differences between the two phases (t(18) = −4.335, p = 0.002). The qualitative approach, at the light of the framework used, showed that efficiency, inspiration, and usability are the most mentioned UX goals emerging from the content analysis. These findings enhance manufacturing workers' well‐being by improving cobot design in organizations. [ABSTRACT FROM AUTHOR]

Background: More efficient methods to detect and treat precancerous lesions of the cervix at a single visit, such as low-cost confocal microscopy, could improve early diagnosis and hence outcomes. We piloted a prototype smartphone-compatible confocal micro-endoscope (SCME) among women presenting to a public cervical cancer screening clinic in Kampala, Uganda. We describe the piloting of the SCME device at an urban clinic used by lower cadre staff. Methods: We screened women aged 18 and 60 years, who presented for cervical cancer screening at the Kawempe National Referral Hospital Kampala, and evaluated the experience of their providers (nurses). Nurses received a 2-day training by the study doctors on how to use the SCME, which was added to the standard Visual Inspection with Acetic acid (VIA)-based cervical cancer screening. The SCME was used to take colposcopy images before and after VIA at positions 12 and 6 O'clock if VIA negative, and on precancer-suspicious lesions if VIA positive. We used questionnaires to assess the women's experiences after screening, and the experience of the nurses who operated the SCME. Results: Between November 2021 and July 2022, we screened 291 women with a median age of 36 years and 65.7% were HIV positive. Of the women screened, 146 were eligible for VIA, 123 were screened with the SCME, and we obtained confocal images from 103 women. Of those screened with the SCME, 60% found it comfortable and 81% were willing to screen again with it. Confocal images from 79% of the women showed distinguishable cellular features, while images from the remaining 21% were challenging to analyze. Nurses reported a mean score of 85% regarding the SCME's usefulness to their work, 71% regarding their satisfaction and willingness to use it again, 63% in terms of ease of use, and 57% concerning the ease of learning how to operate the SCME. Conclusion: Our findings demonstrate the feasibility of using the SCME by lower cadre staff in low-resource settings to aid diagnosis of precancerous lesions. However, more work is needed to make it easier for providers to learn how to operate the SCME and capture high-quality confocal images. [ABSTRACT FROM AUTHOR]

Although live streaming is indispensable, live-streaming e-business requires accurate and timely sales-volume prediction to ensure a healthy supply–demand balance for companies. Practically, because various factors can significantly impact sales results, the development of a powerful, interpretable model is crucial for accurate sales prediction. In this study, we propose SaleNet, a deep-learning model designed for sales-volume prediction. Our model achieved correct prediction results on our private, real operating data. The mean absolute percentage error (MAPE) of our model's performance fell as low as 11.47% for a + 1.5-days forecast. Even for a 1-week forecast (+ 6 days), the MAPE was only 19.79%, meeting actual business needs and practical requirements. Notably, our model demonstrated robust interpretability, as evidenced by the feature contribution results which are consistent with prevailing research findings and industry expertise. Our findings provided a theoretical foundation for predicting shopping behavior in live-broadcast e-commerce and offered valuable insights for designing live-broadcast content and optimizing the user experience. [ABSTRACT FROM AUTHOR]

Introduction Traumatic brain injury (TBI) can impact language processing, necessitating language-tailored approaches. Telehealth may expand rural Veterans' access but has unknown feasibility for language preferences. This study explored telehealth/Veterans Video Connect satisfaction for Spanish/English TBI screening. Materials and Methods The study was approved by the VA Caribbean Healthcare System Institutional Review Board and the Research and Development Committee. Mixed methods evaluated telehealth satisfaction in Veterans receiving TBI assessments from October 2021 to October 2023. Surveys included the 16-item Clinical Video Telehealth (CVT) questionnaire on communication, technical factors, coordination, and overall satisfaction, and the 21-item Telemedicine Satisfaction and Usefulness Questionnaire (TSUQ) examining usefulness, ease of use, manners, quality, and satisfaction. Mean domain/item scores were calculated among 57 Veterans, 12 English, and 45 Spanish-speaking. Semi-structured interviews also elicited user experiences from 4 providers and 5 Veterans. Transcripts underwent qualitative coding for themes using Atlas.ti.8. Results On CVT (0-5 scale), overall satisfaction averaged 4.50 (English) and 4.69 (Spanish). Lowest scoring item for English users was easy video connection (4.25), while unclear expectations had the lowest Spanish score (3.60). For TSUQ, overall mean scores were 4.50 (English) and 4.67 (Spanish), with improved health post-telehealth having the lowest average (English 3.33, Spanish 3.67). Qualitatively, Veterans and providers noted strengths like access and communication but weaknesses around connectivity, care delays, and privacy. Differences emerged regarding convenience (Veterans) versus operational barriers (providers). There was a strong positive correlation for Spanish surveys and a moderate correlation for English surveys (r  = 0.71 Spanish surveys, r  = 0.69 English surveys) between TSUQ and CVT for individual respondents. Conclusions Patients conveyed positive experiences, but qualitative data revealed actionable targets for optimization like infrastructure and coordination improvements. Key limitations include small samples and lack of comparison to in-person care. Still, high satisfaction coupled with specific user feedback highlights telehealth's potential while directing enhancements. The results found high Veteran satisfaction with Spanish/English TBI telehealth, but mixed methods illuminated salient domains for better accommodating user needs, particularly regarding logistics and technology. Rigorously integrating experiences with metrics over expanded diverse samples and modalities can further guide refinements to enhance telehealth with a language-tailored approach. [ABSTRACT FROM AUTHOR]

This study explores content optimization methods by analysing the interplay between user actions and the visual attributes within digital immersive environments, aiming to optimize visual content by utilizing user interaction data analysis to fulfil user experience requirements. At first, the paper illustrates the main principle of user interaction. Then it describes that it was used in visual research with an autistic disorder person at the Zhongshan District Rehabilitation Centre for Mental Disorders in Dalian, focusing on the colour preferences and cognitive abilities of the autism community. By integrating user interaction models and visual immersion strategies, the goal is to optimize the digital space experience for individuals with autism. Firstly, we analysed the visual preferences and behavioural responses of autistic users, especially their strong preferences for colours, finding that over 89.7% of the autistic population prefers yellow, which can significantly improve their comfort and satisfaction levels. Secondly, based on user interaction data, a modular visual immersion environment was established to observe the behavioural patterns of individuals with autism within this space. The study shows that combining visual immersion strategies and user interaction models can effectively enhance the experience of users with autism. This research not only provides practical strategies for designing digital spaces with colour preferences friendly to individuals with autism but also expands the application range of visual immersion technology among special needs groups to enhance user immersion experience and meet their specific needs. [ABSTRACT FROM AUTHOR]

Building on the success of e-bikes, sales of e-cargo bikes are rapidly increasing in several countries. Cargo bikes fill an important gap in the urban transport market by combining the advantages of cycling with the greater transport capacity of a family car. Whereas most research on cargo bikes has focused on logistics, this paper addresses their use for personal transport. It is based on a large-scale survey in Switzerland among both proprietary cargo bike owners (CBO) and users of cargo bike sharing (CBS) (N = 955). A principal component analysis finds 3 families of motivations for using cargo bikes: transporting children, staying active, and reducing car use. Based on these 3 components, we use hierarchical clustering to identify 4 user segments: cargo transporters, enthusiasts, multimodals, and sustainable parents. Our results suggest that owned and shared cargo bikes are complementary and have the potential to attract new audiences to cycling and reduce car use. They could become a central component in a low-carbon/post-car urban mobility strategy. However, user experiences indicate that lacking safety, road infrastructure and parking provisions remain barriers to wider cargo bike use. We conclude by proposing a future research agenda for cargo bike research. [ABSTRACT FROM AUTHOR]

Two decades ago, Gaston and O'Neill (2004) deliberated on why automated species identification had not become widely employed. We no longer have to wonder: This AI-based technology is here, embedded in numerous web and mobile apps used by large audiences interested in nature. Now that automated species identification tools are available, popular, and efficient, it is time to look at how the apps are developed, what they promise, and how users appraise them. Delving into the automated species identification apps landscape, we found that free and paid apps differ fundamentally in presentation, experience, and the use of biodiversity and personal data. However, these two business models are deeply intertwined. Going forward, although big tech companies will eventually take over the landscape, citizen science programs will likely continue to have their own identification tools because of their specific purpose and their ability to create a strong sense of belonging among naturalist communities. [ABSTRACT FROM AUTHOR]

The proliferation of diverse frequency bands, from millimeter-wave to terahertz, in 6G networks brings both opportunities and challenges. The use of adaptive beamforming and resource allocation algorithms is necessary to optimize the benefits of each band while minimizing its drawbacks. This study addresses several machine learning-based methods that dynamically choose frequency bands, modify beamforming patterns, and distribute resources according to real-time data analysis, channel circumstances and user requests. In comparison with traditional resource allocation schemes, remarkable enhancement in user experience and network efficiency has been established by previous researchers. By utilizing channel modeling and mitigation methods for this high-frequency range, the challenge of terahertz communication has been covered. Our results indicate the efficiency of resource allocation and dynamic, data-driven beamforming in enabling heterogeneous 6G networks to reach their maximum potential. [ABSTRACT FROM AUTHOR]

In the digital era, where choices saturate daily life, the phenomenon of choice overload becomes a significant concern in consumer behavior and psychology. Recommender systems, exemplified by Netflix's sophisticated model, play a transformative role in navigating the vast landscape of digital entertainment. This qualitative study examines the impact of recommender systems on choice overload through 12 semi-structured interviews with Netflix users, revealing the intricate dynamics between personalization algorithms and user decision-making processes. The study is guided by the following research questions: (1) How does the Netflix recommendation system influence users' experiences of choice overload and ease of decision-making? (2) To what extent do users perceive Netflix's recommended content as appealing and diverse, and how reliant are they on these recommendations for content selection? (3) How do user interactions with Netflix's recommendation system, including user feedback, impact variables such as search time, choice effort, and choice satisfaction? The findings reveal a notable absence of explicit user feedback and the presence of choice overload in Netflix users. This is evident in prolonged search times, heightened choice effort, and moderate satisfaction levels, coupled with perceptions of unattractiveness and limited diversity in the recommended content. Negative emotional responses during content selection further underscore the challenges users face on the platform. Paradoxically, this gives rise to a potential "user's dilemma," as the study exposes a high reliance and trust in recommendation lists. However, this reliance also results in users frequently experiencing frustration and disappointment when recommendations fail to meet expectations. The study provides valuable insights into the nuanced interactions between users and the Netflix platform and offers a foundational framework for ongoing refinement of recommender systems in the ever-evolving landscape of streaming services and emphasizes the need for recommendation lists to strike a delicate balance between effective guidance and user exploration. [ABSTRACT FROM AUTHOR]

The use of artificial intelligence (AI) is already redefining the ways we engage with various types of information and data. In the wake of AI's unprecedented influence, its impact on grey literature (GL) remains an important yet underexplored domain. It is up to all GL professionals to integrate AI into their work and harness its power. This paper delves into the innovative applications of AI to bolster the efficiency of GL management, elevate metadata quality, and enhance user experiences. Four fundamental GL management facets, namely collection of GL, metadata creation, summarization, and user experience, offer great potential and a good starting point for using the power of AI. By harnessing AI's transformative capabilities, GL professionals can spearhead a paradigm shift in managing grey literature, promising extensive and far-reaching implications for the field. [ABSTRACT FROM AUTHOR]

In this paper, we aim to provide readers with three critical concepts that can maximize inclusivity of learner experience (LX) and user experience (UX) design: accessibility, usability, and universal design for learning (UDL). Although recent K-12 and postsecondary education have experienced rapid change in its student population with the growing awareness of the equal opportunities for all, general design methods of addressing such diversity across formal and informal learning contexts are still prone to retrofitted changes to predefined instructions in favor of standardization. This is due in part to the limited interpretation of accessibility as the concept for the medical model of disabilities where learners with disabilities are not regarded as users or consumers, but as clients or patients. Moreover, LX and UX have been widely designed with the usability of dominant group in mind while trading off exclusion of those who cannot fit themselves into it. However, accessibility and usability are not mutually exclusive; rather, a truly inclusive learning design (i.e. UDL) comes from the interplay between them. Through the paper, we provide definition and description of each concept and provide practical examples of how UDL principles can be applied to inclusive learning design. [ABSTRACT FROM AUTHOR]

This article aims to assess the usability of selected map portals with a checklist. The methods employed allowed the author to conduct user experience tests from a longer temporal perspective against a retrospective analysis of the evolution of design techniques for presenting spatial data online. The author performed user experience tests on three versions of Tomice Municipality's geoportal available on the Internet. The desktop and mobile laboratory tests were performed by fourteen experts following a test scenario. The study employs the exploratory approach, inspection method, and System Usability Scale (SUS). The author calculated the Geoportal Overall Quality (GOQ) index to better illustrate the relationships among the subjective perceptions of the usability quality of the three geoportals. The usability results were juxtaposed with performance measurements. Normalised and aggregated results of user experience demonstrated that the expert assessments of the usability of geoportals G1 and G3 on mobile devices were similar despite significant development differences. The overall results under the employed research design have confirmed that geoportal G2 offers the lowest usability in both mobile and desktop modes. The study has demonstrated that some websites can retain usability even considering the dynamic advances in hardware and software despite their design, which is perceived as outdated today. Users still expect well-performing and quick map applications, even if this means limited functionality and usability. Moreover, the results indirectly show that the past resolution of the 'large raster problem' led to the aggravation of the issue of 'large scripts'. [ABSTRACT FROM AUTHOR]

Aim: The study aimed to evaluate the reliability and usability of the CARE‐Radiology checklist in assessing radiological case reports and provide a basis for its broader adoption and optimization. Methods: Ten randomly selected radiological case reports published in scientific journals in 2020 were evaluated using the CARE‐Radiology checklist. Twenty‐six experts from 10 countries were invited to independently assess all ten reports. The reliability of the checklist was measured using Fleiss' Kappa, and Cronbach's alpha coefficient. Usability was evaluated by recording the time taken to complete the assessments and requesting the evaluators to rate each item on a Likert scale for its easiness of use. Results: The median time for evaluating one radiological case report was 15 min. The overall agreement among evaluators showed moderate reliability with a Kappa value of 0.47 and a Cronbach's alpha of 0.51. The mean compliance rate for the items of CARE‐Radiology was 61.8%, with some items exceeding 90% compliance. Items related to abstracts and keywords had the lowest compliance rates. The evaluators found most items easy to understand, with a few exceptions. Conclusions: The CARE‐Radiology checklist is relatively easy for researchers to use and understand. Continuous feedback is necessary for future revisions and updates, to enhance the effectiveness of the checklist, and to improve user experience. [ABSTRACT FROM AUTHOR]

As voice user interfaces (VUIs) rapidly transform the landscape of human–computer interaction, their potential to revolutionize user engagement is becoming increasingly evident. This paper aims to advance the field of human–computer interaction by conducting a bibliometric analysis of the user experience associated with VUIs. It proposes a classification framework comprising six research categories to systematically organize the existing literature, analyzes the primary research streams, and identifies future research directions within each category. This systematic literature review provides a comprehensive analysis of the development and effectiveness of VUIs in facilitating natural human–machine interaction. It offers critical insights into the user experience of VUIs, contributing to the refinement of VUI design to optimize overall user interaction and satisfaction. [ABSTRACT FROM AUTHOR]

Game reviews heavily influence public perception. User feedback is crucial for developers, offering valuable insights to enhance game quality. In this research, Metacritic game reviews for Elden Ring were analyzed for topic modeling using Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and a hybrid model combining both to identify effective methods for extracting underlying themes in player feedback. We analyzed and interpreted these models' outputs to learn the game reviews. We aimed to identify the differences, similarities, and variations between the three to determine which provided more valuable and instructive information. Our findings indicate that each method successfully identified keywords with some similarities in identified words. The LDA model had the highest silhouette score, indicating the most distinct clustering. The LDA-BERT model had a 1% higher coherence score than LDA, indicating more meaningful topics. [ABSTRACT FROM AUTHOR]

The International Telecommunication Union - Telecommunication Standardization Sector (ITU-T) Study Group 16 (SG16) meeting was held in Rennes, France, in April 2024 with online participation (hybrid). NTT Group made a proposal to Question 8 to initiate a new work item on "first-person transfer immersive live experience," which enables users to share (feel) the sensation of the performer at a remote event site (e.g. the piano player in a music concert). This article mainly reports on the discussion and result of Question 8. [ABSTRACT FROM AUTHOR]

Person pose transfer has shown great application prospects and potential in recent years. However, this research mainly focuses on posture transfer and does not pay much attention to other human body attributes. In addition, there are problems such as blurred facial expressions and inconsistent facial texture. In order to solve these problems and improve user experience, this paper proposes a multi-feature transfer person image generation model, which combines facial expression transfer and pose transfer and completes the lighting reconstruction of person images. Our model can simultaneously migrate and supplement facial expressions while migrating a person's posture, ensuring the consistency of characters' overall migration. Our model can freely switch personal body lighting to adapt to different scenes, greatly improving the user experience. The experimental results show that our method's preservation rate for face identity is improved by nearly 35% compared with the benchmark, and the similarity index for the overall person body is improved by nearly 14%. The evaluation results indicate that our method performs well in person posture transfer. The experimental results show that the proposed method is superior to the existing algorithms in person posture transformation. [ABSTRACT FROM AUTHOR]

As social virtual reality (VR) continues to grow as a medium for digital communication, sustaining presence among communicators remains one of the main constructs that researchers and practitioners use to assess the quality of user experience. In the present paper, we explore language patterns as a behavioral link to presence. We accomplished this through an exploratory text analysis of over 4,800 min of conversation in social VR, consisting of over 130,000 spoken words from 126 participants. We observed that the use of self-references and collective references positively correlated to social presence and spatial presence. Furthermore, median interpersonal distance between communicators was positively associated with using impersonal pronouns, suggesting that participants who stood farther apart from their interlocutors tended to speak in more impersonal terms. Our work sheds light on the possible psychological mechanisms behind presence and the potential of using speech data to help build systems that enhance user engagement. [ABSTRACT FROM AUTHOR]

The precise and robust localization of pupils is crucial for advancing medical diagnostics and enhancing user experience. Currently, the predominant method for determining the center of the pupil relies on the principles of multi-view geometry, necessitating the simultaneous operation of multiple sensors at different angles. This study introduces a single-stage pupil localization network named ResDenseDilateNet, which is aimed at utilizing a single sensor for pupil localization and ensuring accuracy and stability across various application environments. Our network utilizes near-infrared (NIR) imaging to ensure high-quality image output, meeting the demands of most current applications. A unique technical highlight is the seamless integration of the efficient characteristics of the Deep Residual Network (ResNet) with the Dense Dilated Convolutions Merging Module (DDCM), which substantially enhances the network's performance in precisely capturing pupil features, providing a deep and accurate understanding and extraction of pupil details. This innovative combination strategy greatly improves the system's ability to handle the complexity and subtleties of pupil detection, as well as its adaptability to dynamic pupil changes and environmental factors. Furthermore, we have proposed an innovative loss function, the Contour Centering Loss, which is specifically designed for irregular or partially occluded pupil scenarios. This method innovatively calculates the pupil center point, significantly enhancing the accuracy of pupil localization and robustness of the model in dealing with varied pupil morphologies and partial occlusions. The technology presented in this study not only significantly improves the precision of pupil localization but also exhibits exceptional adaptability and robustness in dealing with complex scenarios, diverse pupil shapes, and occlusions, laying a solid foundation for the future development and application of pupil localization technology. [ABSTRACT FROM AUTHOR]

Pose estimation is crucial for ensuring passenger safety and better user experiences in semi- and fully autonomous vehicles. Traditional methods relying on pose estimation from regular color images face significant challenges due to a lack of three-dimensional (3D) information and the sensitivity to occlusion and lighting conditions. Depth images, which are invariant to lighting issues and provide 3D information about the scene, offer a promising alternative. However, there is a lack of strong work in 3D pose estimation from such images due to the time-consuming process of annotating depth images with 3D postures. In this paper, we present a novel approach to 3D human posture estimation using depth and infrared (IR) images. Our method leverages a three-stage fine-tuning process involving simulation data, approximated data, and a limited set of manually annotated samples. This approach allows us to effectively train a model capable of accurate 3D pose estimation with a median error of under 10 cm across all joints, using fewer than 100 manually annotated samples. To the best of our knowledge, this is the first work focusing on vehicle occupant posture detection utilizing only depth and IR data. Our results demonstrate the feasibility and efficacy of this approach, paving the way for enhanced passenger safety in autonomous vehicle systems. [ABSTRACT FROM AUTHOR]

Accurate 6DoF (degrees of freedom) pose and focal length estimation are important in extended reality (XR) applications, enabling precise object alignment and projection scaling, thereby enhancing user experiences. This study focuses on improving 6DoF pose estimation using single RGB images of unknown camera metadata. Estimating the 6DoF pose and focal length from an uncontrolled RGB image, obtained from the internet, is challenging because it often lacks crucial metadata. Existing methods such as FocalPose and Focalpose++ have made progress in this domain but still face challenges due to the projection scale ambiguity between the translation of an object along the z-axis ( t z ) and the camera's focal length. To overcome this, we propose a two-stage strategy that decouples the projection scaling ambiguity in the estimation of z-axis translation and focal length. In the first stage, t z is set arbitrarily, and we predict all the other pose parameters and focal length relative to the fixed t z . In the second stage, we predict the true value of t z while scaling the focal length based on the t z update. The proposed two-stage method reduces projection scale ambiguity in RGB images and improves pose estimation accuracy. The iterative update rules constrained to the first stage and tailored loss functions including Huber loss in the second stage enhance the accuracy in both 6DoF pose and focal length estimation. Experimental results using benchmark datasets show significant improvements in terms of median rotation and translation errors, as well as better projection accuracy compared to the existing state-of-the-art methods. In an evaluation across the Pix3D datasets (chair, sofa, table, and bed), the proposed two-stage method improves projection accuracy by approximately 7.19%. Additionally, the incorporation of Huber loss resulted in a significant reduction in translation and focal length errors by 20.27% and 6.65%, respectively, in comparison to the Focalpose++ method. [ABSTRACT FROM AUTHOR]

The current transition to the electric car by all users is advancing steadily, and hence the design of these products occupies a prominent place in the car design sector. Likewise, the user experience is one of the determining aspects for the choice of a model. The main objective of this work is to determine, through eye tracking, which areas of an image (in this case, images of the front of cars) are more influential in the perception of the vehicle as either electric or combustion-powered, by segmenting the most important areas of a vehicle and users' perceptions thereof according to gender and other socio-demographic variables. This work provides an experimental design and a way to analyse and draw conclusions from an oculometry study for vehicles, as well as an initial design briefing for the generation of the visual identity of electric vehicles based on the perception of users for different elements of the vehicle. A methodology is proposed that includes the use of tools such as ANOVAs, eye-tracking, heat maps, tracking maps and fixation times. Through this data it is possible to develop optimal designs aligned with the user's perception. [ABSTRACT FROM AUTHOR]

The existing methods for sentiment classification normally ignore that the past experiences retrieved by users under particular situations would affect their sentiment expressions. Furthermore, related research may underutilize user personality to personalize the analysis of storage and retrieval of past experiences. Inspired by the cognition process of human memory, we propose a Personality-Driven Experience Storage and Retrieval (PDESR) model for sentiment classification. Concretely, we first selectively store the user's past experiences in her/his experience bank via personalized input and forget gates. We then adopt personalized output gate to retrieve past experiences from the experience bank. Finally, we integrate the current experience with the retrieved past experiences to classify user sentiment. Specifically, personality is used to personalize the control of which past experiences should be stored in experience bank and which past experiences should be retrieved from experience bank. The experimental results show that PDESR model outperforms the related models in accuracy. [ABSTRACT FROM AUTHOR]

Online vendors often deploy limited-time and limited-quantity cues on their e-commerce sites to influence consumers purchase decisions. Although these scarcity cues can reflect genuine restrictions in the availability of goods, they are increasingly considered as ill-intentioned nudges or 'dark patterns' due to their omnipresence and success in persuading consumers. In an online experiment (N = 202), we examined the effects of limited-time and limited-quantity cues on perceived task load, perceived benevolence, and user experience. Results suggest that participants associated scarcity cues with a lack of benevolence from online vendors. E-commerce site design without scarcity cues provided participants with a superior hedonic and pragmatic user experience. In the case of limited-time scarcity cues, participants reported frustration-related negative emotions. We discuss the implications of these findings from the perspectives of dark pattern researchers, designers, and online vendors. [ABSTRACT FROM AUTHOR]