Average User Rate Research Articles

A heuristic approach to energy efficient user association in ultra‐dense HetNets using intermittent scheduling strategies

AbstractThe ultra‐dense deployment of pico cells in 5G heterogeneous networks (HetNets) has raised serious concerns regarding interference and energy consumption. Both industry and academia are focusing on enhancing network energy efficiency (EE) while maintaining satisfactory quality of service (QoS) levels. However, finding an optimal solution to NEE is very challenging, especially in ultra‐dense HetNets. Here, a user association and power management algorithm is presented that follows a heuristic approach and aims to maximize EE while satisfying other network requirements. The proposed algorithm associates users based on criteria that consider the users’ EE and minimizes energy consumption by intermittently switching into sleep mode base stations with the highest impact on overall network EE. The performance of this solution is evaluated in a realistic multi‐cell two‐tier scenario considering both co‐tier and cross‐tier interference by comparing it with two other solutions: a heuristic approach based on standardized eICIC, and an optimization approach based on Lagrangian dual decomposition. The simulation results show that our solution outperforms benchmarking solutions in terms of EE, average user rate, and network throughput while minimizing energy consumption.

Exploring the relationship between users characteristics and movie recommendations using a KNN-based recommender system

The central aim of this research is to elucidate the degree to which demographic variables, including but not limited to age and gender, bear on the performance of movie recommendation algorithms within film recommendation systems. Further, we endeavor to uncover any existent correlations between these user characteristics and the resultant outputs of such systems. Leveraging the expansive dataset available via MovieLens, we employ a linear regression model to ascertain the four critical variables (age, gender, occupation, and the average user rating for films previously watched) that have the most profound influence on movie recommendation algorithms. Once these salient factors have been determined, we assign their respective weights and incorporate these into a KNN algorithm. We then subject the resultant model to rigorous testing to verify the accuracy of our results and to ascertain whether the integration of these weighted elements enhances the overall precision of the movie recommendation system. While extant literature predominantly focuses on the amalgamation of KNN with other algorithms, our study charts a novel course by using linear regression. This methodology allows us to intuitively illustrate the relationship between user demographics and the movie recommendation system and enables us to evaluate whether emphasizing certain characteristics can augment the system's effectiveness. Our findings suggest that of all the user characteristics examined, the mean of users ratings for movies previously watched exerts the greatest influence on the outputs of the movie recommendation system. Moreover, incorporating weights reflective of the average user ratings across all movie features within the KNN algorithm can significantly bolster the accuracy of the resultant movie recommendations.

Display and Interaction Design of VR Technology in Environmental Design

Abstract VR technology, with its unique interactivity and immersive environment display, has a wide range of application prospects in various industries. In this paper, starting from the display and interaction experience of users for virtual environment design, we propose the optimization experience path of virtual environment user display and interaction and build up the optimization technology framework based on this. AutoCAD modeling software is used to complete three-dimensional modeling, and a three-dimensional display technology based on dual-computer communication is proposed to display the virtual environment in three dimensions. In the interaction design aspect, the virtual hand is optimized for detecting targets and generating grasping gestures. In the application of the optimization technology framework, the hotel apartment of Yuntianlou Intercontinental Plaza in Wenzhou City, Zhejiang Province, China, was selected as the design content for the virtual environment design. In the interactive performance evaluation, the average user rating of the virtual hand interaction method is 0.42 points higher than that of the traditional mouse interaction method, and the average computing time of the interaction is only about 60ms. In the comprehensive evaluation test, the average value of the environment display and interaction experience modules is more than 4 points, which indicates excellent display and interaction experiences.

Toward Optimal Deployment of UAV Relays in UAV-Assisted Internet of Vehicles

The past decade has witnessed an explosive growth of unmanned aerial vehicles (UAVs), which have attracted great attention in assisting Internet of Vehicles (IoV) communication systems thanks to the advantages of cost-effectiveness, high maneuverability, and flexible deployment. Yet several issues, such as UAVs' transmit power and energy resources as well as the high-speed mobility of IoV user equipment (VUE), pose challenges to the development of UAVs in UAV-assisted IoV networks. To provide reliable and high-throughput wireless links for VUEs in a given ground area, this paper considers a scenario in which a ground VUE is facing difficulties in receiving service from a ground roadside unit (RSU) owing to possible sudden RSU malfunction or poor signal coverage. In this case, UAVs are generally deployed in the air, serving as relays to establish communication links between uncovered VUE and the neighboring RSU. To maximize the system capacity, we investigate the deployment problem of UAV relays under single-UAV relaying and multi-UAV relaying cases upon considering VUE mobility, and exploit the optimal deployment strategy of UAV relays under fixed total power consumption. Comprehensive simulation results verify that the proposed algorithm can outperform benchmark methods on significant critical indicators such as the average achievable user rate, and the outage probability. Also, evaluations and analyses have highlighted the effects of key system parameters (e.g., altitude, number, and transmit power of UAV relays) on optimal UAV relay deployment position and system performance.

The quest for quality within an online educational marketplace: Indicators of expert-evaluated quality in U.S. history activities

Online educational marketplaces such as TeachersPayTeachers.com (TPT) have become popular among teachers, but the quality of materials offered is inconsistent. This descriptive study explored quality indicators for site materials. Examining a sample of TPT’s 100 best-selling 11th-grade U.S. history activities, we compared overall quality scores (as evaluated by three experts) with user review metrics and store characteristics. Expert ratings were not correlated with average user ratings, the number of user ratings, qualitative user feedback, the resource’s popularity rank, or marketing appeal. However, price and the seller’s number of followers were positively correlated with expert ratings, albeit weakly. These findings highlight how for-profit educational marketplaces constrain teachers’ ability to access and evaluate curricular materials. Ways forward for teachers, teacher educators, and the for-profit platforms are discussed.

Joint Resource Allocation and Trajectory Design for Multi-UAV Systems With Moving Users: Pointer Network and Unfolding

As an important part of the fifth generation (5G) mobile networks, unmanned aerial vehicles (UAVs) have been applied in various communication scenarios due to their high operability and low cost. In this paper, we investigate a multi-UAV communication system with moving users and consider the co-channel interference caused by the transmissions of all other UAVs. To ensure the fairness, we maximize the minimum average user rate during the observed time by jointly optimizing UAVs’ trajectories, transmission power, and user association. Considering that UAVs can cover a large area for communications, UAVs do not need to move as soon as the users move. Therefore, a two-timescale structure is proposed for the considered scenario, where the UAVs’ trajectories are optimized based on the channel state information (CSI) in a long timescale, while the transmission power and the user association are optimized based on the instantaneous CSI in a short timescale. To effectively tackle this challenging non-convex problem with both discrete and continuous variables, we propose a joint neural network (NN) design, where a deep reinforcement learning based Pointer Network named advantage pointer-critic (APC) is applied to optimize discrete variables and a deep-unfolding NN is used to optimize the continuous variables. Specifically, we first formulate a Markov decision process to model the user association, and then employ the APC network trained by the advantage actor-critic algorithm to address it. The APC network consists of a Pointer Network and a Multilayer Perceptron. As for the deep-unfolding NN, we first develop a block coordinate descent based algorithm to optimize the UAVs’ trajectories and transmission power, and then unfold the algorithm into a layer-wise NN with introduced trainable parameters. These two networks are jointly trained in an unsupervised fashion. Simulation results validate that the proposed joint NN significantly outperforms the optimization algorithm with much lower complexity, and achieves good performances on scalability and generalization ability.

In the Arms of a Robot: Designing Autonomous Hugging Robots with Intra-Hug Gestures

Hugs are complex affective interactions that often include gestures like squeezes. We present six new guidelines for designing interactive hugging robots, which we validate through two studies with our custom robot. To achieve autonomy, we investigated robot responses to four human intra-hug gestures: holding, rubbing, patting, and squeezing. A Total of 32 users each exchanged and rated 16 hugs with an experimenter-controlled HuggieBot 2.0. The robot’s inflated torso’s microphone and pressure sensor collected data of the subjects’ demonstrations that were used to develop a perceptual algorithm that classifies user actions with 88% accuracy. Users enjoyed robot squeezes, regardless of their performed action, they valued variety in the robot response, and they appreciated robot-initiated intra-hug gestures. From average user ratings, we created a probabilistic behavior algorithm that chooses robot responses in real time. We implemented improvements to the robot platform to create HuggieBot 3.0 and then validated its gesture perception system and behavior algorithm with 16 users. The robot’s responses and proactive gestures were greatly enjoyed. Users found the robot more natural, enjoyable, and intelligent in the last phase of the experiment than in the first. After the study, they felt more understood by the robot and thought robots were nicer to hug.

Study on optimization of communication network for multi-unmanned aerial vehicles

The unmanned aerial vehicles (UAV) base station is most widely used as the emergency communication in response to emergencies. However, the performance improvement is in great demand at communication network. To improve the performance between the UAVs and the users, a novel method is proposed based on k-means++ algorithm and antenna adjusting. The established simulation platform and models are credible and reliable according to the very excellent approximation capability and the strong correlation between the simulation results and the Shannon’s formula. The transmission success rate for the three conditions (using only k-means++, antenna pointing optimization and antenna downtilt angle optimization) dramatically increases with an increase in k and all reach the maximum when k is equal to 5. The fan throughput of the proposed method is almost two times than that of the other two conditions after the k value exceeds 5. Meanwhile, the edge users’ rate and average user rate of the proposed method are all markedly increased. Overall, a novel method is provided for optimizing the performance between multi-UAVs and users, and the performance communication network for multi-UAVs is optimized by using the proposed method in this paper.

Total and Minimum Energy Efficiency Tradeoff in Robust Multigroup Multicast Satellite Communications

Satellite communication is an indispensable part of future wireless communications given its global coverage and long-distance propagation. In satellite communication systems, channel acquisition and energy consumption are two critical issues. To this end, we investigate the tradeoff between the total energy efficiency (TEE) and minimum EE (MEE) for robust multigroup multicast satellite communication systems in this paper. Specifically, under the total power constraint, we investigate the robust beamforming aimed at balancing the TEE-MEE, so as to achieve the balance between the fairness and total performance on the system EE. For this optimization problem, we first model the balancing problem as a nonconvex problem while deriving its approximate closed-form average user rate. Then, the nonconvex problem is handled by solving convex programs sequentially with the help of the semidefinite relaxation and the concave-convex procedure. In addition, depending on the solution rank value, Gaussian randomization and eigenvalue decomposition method are applied to generate the feasible solutions. Finally, simulation results illustrate that the proposed approach can effectively achieve the balance between the TEE and MEE, thus realizing a tradeoff between fairness and system EE performance. It is also indicated that the proposed robust approach outperforms the conventional baselines in terms of EE performance.

The Design and Delivery of an Astrobiology Massive Open Online Class.

MOOCs, or massive open online classes have reached hundreds of millions of people around the world in the past decade with a model of free open access learning. They are an excellent vehicle for delivering science to lifelong learners. Building on experience in designing astronomy MOOCs for Udemy and Coursera, we have created an astrobiology MOOC for Coursera called "Astrobiology: Exploring Other Worlds" that launched in April 2019. As of early 2022, about 9000 people have started the course, and nearly 10% of those have completed it. The average user rating is 4.9 out of 5. The core content is 10 h of video lectures, divided into six modules, which cover the possibilities of life in the Solar System, the search for exoplanets, the concept of habitability, the definition of life and basics of biology, evolution of life on Earth, and the search for extraterrestrial intelligence. Pedagogy was designed around clear learning objectives and includes quizzes, writing assignments, and a final project that draws on all the concepts presented in the course where students design their own mission to characterize exoplanets. In this study, we describe the design, implementation, and subsequent success of our 2019 astrobiology MOOC.

User-centric base station clustering and resource allocation for cell-edge users in 6G ultra-dense networks

Ultra-Dense Networks (UDNs) have been proposed to meet the ultra-high system capacity and ultra-high user experience rate requirements of sixth generation (6G) mobile networks. However, ultra-dense base stations (BSs) deployment poses challenges for cell-edge users such as BS selection, severe interference, resource allocation, and inter-cell handover. To tackle these obstacles, this paper formulates a joint BS clustering and resource allocation problem for cell-edge users in a 6G heterogeneous UDN system. For efficient resolution, this problem is decoupled into two sub-problems to be solved independently. We first propose a user-centric BS clustering algorithm based on many-to-many matching for the BS clustering problem in the heterogeneous UDN system; This algorithm considers the constraint of the system capacity. A many-to-many stable matching between users and small cell BSs is constructed under the optimization objective of maximizing the achievable user rate. Furthermore, we analyze the effectiveness, stability, convergence, and complexity of the BS clustering algorithm. Given established small cell BS clusters, we also propose a user-centric resource allocation algorithm based on network partitioning. This algorithm accounts for the interference of all users in the heterogeneous UDN system and maximizes intra-sub-network interference while minimizing inter-sub-network interference via a spectral clustering-based sub-network partitioning algorithm. Next, orthogonal allocation of resource blocks (RBs) is implemented within each sub-network, and RBs are spatially multiplexed between sub-networks. Numerical results confirm the benefits of the proposed methods: our algorithms outperform benchmark solutions in terms of the sum achievable system rate, average achievable user rate, and user spectral efficiency.

Fair Licensed Spectrum Sharing Between Two MNOs Using Resource Optimization in Multi-Cell Multi-User MIMO Networks

Licensed spectrum sharing has been a promised approach to provide mobile network operators (MNOs) with required spectrum at times of increased traffic, or to improve the mobile user data rate with limited spectral resources. In this paper, we investigate the use of multi-user multiple-input, multiple-output (MIMO) techniques to enable licensed spectrum sharing. Specifically, we present a fair spectrum sharing system between two MNOs in multi-cell multi-user MIMO networks. We impose fairness by ensuring that each MNO receives spectrum in proportion to the amount it contributes. We formulate a constrained optimization problem to determine resource allocation and user scheduling across two MNOs. Since the problem is non-convex, we develop an algorithm to provide an effective solution through fractional programming and block coordinate descent. Our numerical results illustrate that the proposed spectrum sharing scheme can achieve up to 60 % improvement in terms of the average user rate among the two operators while ensuring that neither MNO is exploited for participating in the sharing mechanism. This improvement is in relation to the baseline of each MNO using multi-user MIMO communications on its own. In addition, most users, especially cell-center users close to the BSs, take advantage of our proposed spectrum sharing framework.

QoS-Aware Resource Allocation with Pilot-Aided Channel Estimation for Heterogeneous Wireless Networks

The deployment of heterogeneous networks (HetNets) is a way to increase the network capacity and release part of the traffic generated by users inside a cell to small-scale wireless networks for service. In this context, the main problem is managing the interference due to the coexistence of small cells and macro cells. In this paper, a QoS-aware Resource Allocation (RA) algorithm jointly working with admission control (AC) over a two-tier HetNet scenario is investigated in the presence of both the pilot-symbols for channel estimation and the channel estimation error. The RA algorithm allows two users, the macro cell user (CU) and small cell user (SU), to simultaneously share the same resource block. Moreover, system performance and fairness are improved by including adaptive power allocation to users over resource blocks. In the framework of RA with proportional rate constraints, a novel algorithm is designed by including the effects of pilot-aided channel estimation. The algorithm is able to distribute the same proportional rate to all CUs and SUs, even in the presence of channel estimation error. Relevant numerical results for the downlink of a two-tier HetNet with pilot-aided channel estimation show that the rate dispersion is driven to zero while the sum-rate is maximized, and the average user rate penalty with respect to a perfect-CSI scenario may rise to 20%.

MHealth Solutions for Mental Health Screening and Diagnosis: A Review of App User Perspectives Using Sentiment and Thematic Analysis.

Mental health screening and diagnostic apps can provide an opportunity to reduce strain on mental health services, improve patient well-being, and increase access for underrepresented groups. Despite promise of their acceptability, many mental health apps on the market suffer from high dropout due to a multitude of issues. Understanding user opinions of currently available mental health apps beyond star ratings can provide knowledge which can inform the development of future mental health apps. This study aimed to conduct a review of current apps which offer screening and/or aid diagnosis of mental health conditions on the Apple app store (iOS), Google Play app store (Android), and using the m-health Index and Navigation Database (MIND). In addition, the study aimed to evaluate user experiences of the apps, identify common app features and determine which features are associated with app use discontinuation. The Apple app store, Google Play app store, and MIND were searched. User reviews and associated metadata were then extracted to perform a sentiment and thematic analysis. The final sample included 92 apps. 45.65% (n = 42) of these apps only screened for or diagnosed a single mental health condition and the most commonly assessed mental health condition was depression (38.04%, n = 35). 73.91% (n = 68) of the apps offered additional in-app features to the mental health assessment (e.g., mood tracking). The average user rating for the included apps was 3.70 (SD = 1.63) and just under two-thirds had a rating of four stars or above (65.09%, n = 442). Sentiment analysis revealed that 65.24%, n = 441 of the reviews had a positive sentiment. Ten themes were identified in the thematic analysis, with the most frequently occurring being performance (41.32%, n = 231) and functionality (39.18%, n = 219). In reviews which commented on app use discontinuation, functionality and accessibility in combination were the most frequent barriers to sustained app use (25.33%, n = 19). Despite the majority of user reviews demonstrating a positive sentiment, there are several areas of improvement to be addressed. User reviews can reveal ways to increase performance and functionality. App user reviews are a valuable resource for the development and future improvements of apps designed for mental health diagnosis and screening.

The intellectual system of movies recommendations based on the collaborative filtering

The investigation deals with designing and developing of intellectual system of movies recommendations based on the collaborative filtering using the Python software environment. In particular, the approaches (Content-based approach, Collaborative filtering, Hybrid models) in recommendatory system construction with the help of neural networks have been analyzed. It has been established that it is difficult to implement and learn the Content-based approach and it strongly depends on the subject area. Collaborative filtering is more simple in implementation, training, it is universal, but it has a flaw in the form of a «cold-start». Accordingly, the collaborative filtering has been chosen for the design and development of the intellectual system of movies recommendations. While designing a system of recommendations based on collaborative filtering, the Naive Recommendations, Recommendations based on average ratings of similar users, Recommendations based on average user ratings and similarity matrix have been described; their algorithm and their implementation using the Python software environment have been demonstrated. As a result the intellectual system of recommendations has been realized and it can offer a movie to the user according to his/her preferences.

Evaluating Evidence-Based Content, Features of Exercise Instruction, and Expert Involvement in Physical Activity Apps for Pregnant Women: Systematic Search and Content Analysis.

BackgroundGuidelines for physical activity and exercise during pregnancy recommend that all women without contraindications engage in regular physical activity to improve both their own health and the health of their baby. Many women are uncertain how to safely engage in physical activity and exercise during this life stage and are increasingly using mobile apps to access health-related information. However, the extent to which apps that provide physical activity and exercise advice align with current evidence-based pregnancy recommendations is unclear.ObjectiveThis study aims to conduct a systematic search and content analysis of apps that promote physical activity and exercise in pregnancy to examine the alignment of the content with current evidence-based recommendations; delivery, format, and features of physical activity and exercise instruction; and credentials of the app developers.MethodsSystematic searches were conducted in the Australian App Store and Google Play Store in October 2020. Apps were identified using combinations of search terms relevant to pregnancy and exercise or physical activity and screened for inclusion (with a primary focus on physical activity and exercise during pregnancy, free to download or did not require immediate paid subscription, and an average user rating of ≥4 out of 5). Apps were then independently reviewed using an author-designed extraction tool.ResultsOverall, 27 apps were included in this review (Google Play Store: 16/27, 59%, and App Store: 11/27, 41%). Two-thirds of the apps provided some information relating to the frequency, intensity, time, and type principles of exercise; only 11% (3/27) provided this information in line with current evidence-based guidelines. Approximately one-third of the apps provided information about contraindications to exercise during pregnancy and referenced the supporting evidence. None of the apps actively engaged in screening for potential contraindications. Only 15% (4/27) of the apps collected information about the user’s current exercise behaviors, 11% (3/27) allowed users to personalize features relating to their exercise preferences, and a little more than one-third provided information about developer credentials.ConclusionsFew exercise apps designed for pregnancy aligned with current evidence-based physical activity guidelines. None of the apps screened users for contraindications to physical activity and exercise during pregnancy, and most lacked appropriate personalization features to account for an individual’s characteristics. Few involved qualified experts during the development of the app. There is a need to improve the quality of apps that promote exercise in pregnancy to ensure that women are appropriately supported to engage in exercise and the potential risk of injury, complications, and adverse pregnancy outcomes for both mother and child is minimized. This could be done by providing expert guidance that aligns with current recommendations, introducing screening measures and features that enable personalization and tailoring to individual users, or by developing a recognized system for regulating apps.

Maximizing lower bound of energy efficiency in multi-tier heterogeneous cellular network via stochastic geometry

In this paper, an optimization method is proposed to maximize the lower bound of energy efficiency (EE) in the multi-tier heterogeneous cellular network (HCN) and resolved by adjusting Base Station (BS) density and BS transmit power. Firstly, we derive the mathematical formulation of network coverage probability and the average user rate. We prove that the network coverage probability and the average user rate monotonically increase with the cumulative sum of the product of BS density and BS transmit power. Then, the network coverage probability and the average user rate requirements are converted to the BS density and the BS transmit power constraints. An EE maximization problem is formulated. Nevertheless, the intractable problem is reformulated by maximizing the lower bound of EE since the coupled parameters are tight. The simplified optimization problem can be converted to a standard form of geometric programming (GP) by a convex approximation method. The problem in GP form can be further transformed into the traditional convex problem and can be fully solved using the interior point method. Numerical simulations show that the optimal solutions can be determined with fast convergence, while the network EE is significantly improved. The gap between the approximate and the original solutions is analyzed through simulations.

Average user ratings prompt disparate decision strategies in online retail shopping

AbstractOnline shoppers have a vast array of information available to aid their purchasing decisions (e.g., average user ratings, histograms of ratings). Past research is ambiguous regarding the degree to which shoppers try to simplify this information integration process. Some results suggest that customers will invest a substantial amount of time and effort into the decision process, while others suggest that customers prefer to keep the process as simple as possible and avoid extra effort. In the present study, we sought to examine the conditions under which people will use heuristic strategies versus more analytic ones. We found that the average user rating plays a large role in determining which strategy customers use to integrate product information into a purchasing decision. We show that people use a “binary bias”—assessing the relative number of “high” and “low” ratings and choosing items with the more favorable ratio—when products have a lackluster (~three‐star) average user rating. However, when average ratings are higher, customers instead use even simpler cues to make decisions. We also found evidence that individual differences in analytical thinking dispositions are associated with using more effortful information integration strategies. Thus, the present research suggests different strategies for displaying product information to customers depending on the average user ratings of the product and characteristics of the shoppers themselves.

A randomised control trial of TeleClinical Care – a smartphone-app based model of care for heart failure and acute coronary syndromes

Abstract Background Acute coronary syndrome (ACS) and heart failure (HF) are frequent causes of hospitalisation and readmissions. A novel smartphone app-based model of care (TeleClinical Care – TCC) was developed to support patients after ACS or HF admission. Purpose This randomised control trial aimed to characterise both the intervention and clinical outcomes. The primary endpoint was the incidence of 30-day readmissions. Secondary endpoints included six-month cardiac and all-cause readmissions, mortality, major adverse cardiovascular events (MACE), cardiac rehabilitation (CR) completion, medication adherence, serum low-density lipoprotein (LDL-C), quality of life, blood pressure, body mass index, waist circumference and six-minute walk distance. Additionally, cost-effectiveness and user satisfaction were evaluated. Methods Patients were randomised 1:1 to either TCC plus usual care or usual care alone and were followed-up at six months. Intervention arm participants received the TCC app and were asked to use Bluetooth-enabled devices for measuring weight, heart rate, blood pressure and physical activity daily. Readings were automatically transmitted to the patient's smartphone and a secure web-server (KIOLA). Customisable thresholds for each parameter were defined at discharge. Abnormal readings were flagged by email to a monitoring team, who discussed management with the patient's usual healthcare providers. The app also provided educational push notifications. Results 164 patients from two hospitals in Sydney, Australia were enrolled between February 2019 and March 2020 (TCC n=81, control n=83). Recruitment ceased during the COVID-19 pandemic. The mean age was 61.5 years. 79% of patients were male. The per-patient mean percentage of days with data transmission was 64.2±27.5%. 565 alerts were received, 16% of which resulted in additional investigations, healthcare consultation or a change in management. There was no difference in 30-day readmission rate (11 readmissions in each arm). There was a significant difference in six-month readmissions, favouring the intervention (21 vs. 41 readmissions, HR=0.40, 95% CI 0.16–0.95, P=0.03), driven by a reduction in cardiac readmissions (11 vs. 25, HR=0.51, 95% CI 0.27–0.94, P=0.03). Use of TCC was associated with improved CR completion (39% vs. 18%, P=0.025) and medication adherence (75% vs. 50%, P=0.002). There was no significant difference in mortality, MACE, LDL-C, quality of life or any of the physical parameters. The average user rating was 4.56 out of 5. The study cost EUR 4015 per readmission saved. Upon modelling, it was calculated that if the number of enrolled patients exceeds 243, total expenditure will be overcome by cost savings from reducing readmissions. Conclusion The TCC model of care was feasible and safe. In this study, clinical benefits were demonstrated including a reduction in six-month readmissions, improved CR completion and improved medication adherence. Funding Acknowledgement Type of funding sources: Public hospital(s). Main funding source(s): Department of Cardiology, Prince of Wales HospitalPrince of Wales Hospital Foundation Figure 1. TCC interfaceFigure 2. Cumulative readmissions over the course of the trial

Analysing User Experience of Mobile Banking Applications in Nigeria: A Text Mining Approach

This paper analyses textual data mined from 37,460 reviews written by mobile banking application users in Nigeria over the period November 2012 – July 2020. On a scale of 1 to 5 (5 being the best), the average user rating for the twenty-two apps included in our sample is 3.5; with the apps deployed by non-interest banks having the highest average rating of 4.0 and those by commercial banks with national authorisation having the least rating of 3.4. Results from the sentiment analysis reveal that the share of positive sentiment words (17.8%) in the corpus more than double that of negative sentiment words (7.7%). Furthermore, we find that about 66 per cent of the emotions expressed by the users are associated with ‘trust’, ‘anticipation’, and ‘joy’ while the remaining 34 per cent relate to ‘surprise’, ‘fear’, ‘anger’, and ‘disgust’. These results imply that majority of the users are satisfied with their mobile banking experience. Finally, we find that the main topics contained in the user reviews pertain to (i) feedback on banks’ responsiveness to user complaints (ii) user experience regarding app functionalities and updates, and (iii) operational failures associated with the use of the apps. These results highlight the need for banks to continue to promote awareness of existing functionalities on their apps, educate users on how those solutions could be accessed, and respond to user feedback in a timely and effective manner.