User Ratings Research Articles

The Influence of Mobile App Store Ratings and Reviews on Download Intentions: A Study of Sri Lankan Consumers

Purpose: This study aims to understand the impact of user reviews and ratings on mobile app download intention and the moderating impact of perceived usefulness on the strength of this relationship. Design/methodology/approach: A scenario-based survey method was selected following the positivism research paradigm. For this purpose, 304 responses were collected using different versions of the same scenario. Findings: The statistical results show that 37.5% of Sri Lankan consumers' mobile app download intention is explained by mobile app ratings and reviews, which have been found to be a significant influencing factor in potential users' intentions to download the app. However, perceived usefulness does not significantly moderate this relationship. Originality: This study is the first attempt to understand the impact of user reviews and ratings on mobile app download intention in the Sri Lankan context. Implications: The findings encourage Sri Lankan mobile app developers and owners to closely monitor and respond to user reviews and ratings to better meet user expectations and improve app ratings.

Efficacy and safety of calcineurin inhibitors (CNIs) for septic patients in ICU: a cohort study from MIMIC database.

Sepsis is marked by a dysregulated immune response to infection. Calcineurin inhibitors (CNIs), commonly used as immunosuppressants, have unique properties that may help mitigate the overactive immune response in sepsis, potentially leading to better patient outcomes. This study aims to assess whether CNIs improve prognosis in septic patients and to evaluate any associated adverse reactions. We utilized the Medical Information Mart for Intensive Care IV 2.2 (MIMIC-IV 2.2) database to identify septic patients who were treated with CNIs and those who were not. Propensity score matching (PSM) was employed to balance baseline characteristics between the CNI user group and the non-user group. The primary outcome was 28-day mortality, analyzed using the Kaplan-Meier method and Cox proportional hazard regression models to examine the relationship between CNI use and patient survival. From the MIMIC-IV database, 22,517 septic patients were identified. After propensity score matching, a sample of 874 patients was analyzed. The CNI group exhibited a significantly lower 28-day mortality risk compared to the non-user group (HR: 0.26; 95% CI: 0.17, 0.41) in the univariate Cox hazard analysis. Kaplan-Meier survival curves also demonstrated a significantly higher 28- and 365-day survival rate for CNI users compared to non-users (log-rank test p-value = 0.001). No significant association was found between CNI use and an increased risk of new-onset infection (p = 0.144), but an association with mild hypertension (P < 0.001) and liver injury (P < 0.001) was observed. The use of calcineurin inhibitors was associated with reduced short- and long-term mortality in septic patients without an increased incidence of new-onset infections, hyperkalemia, severe hypertension, or acute kidney injury (AKI). However, CNI use may lead to adverse effects, such as liver injury and mild hypertension.

Analysis and discovery of procrastination patterns in a language learning MOOC

Online learning has been gaining a broad notoriety in society in the last years. The ease with which users from all over the world can learn is one of its main attractions. MOOCs are one of these technologies, which have enabled users to learn almost any subject of their interest. The use of MOOCs generates a massive amount of data that has been used by researchers with different goals: to predict dropout, predict grades and determine learning styles. Users of MOOCs face several challenges, some of which have been extensively studied. However, there are some of them, such as procrastination, whose study as a determining factor in the failure of such courses has not been addressed in sufficient depth. Through this paper, the influence of procrastination on the failure rates of users in a MOOC has been uncovered. Furthermore, by using process mining, this research has revealed the existence of a pattern of procrastination and the type of material used during study sessions by the users who fail the course. Speclifically, diverse forms of procrastination have been identified, resulting in differing effects on the educational outcomes attained by the user. These findings possess considerable implications, as they underscore the potential worth of detecting specific procrastination patterns to ascertain users who necessitate supplementary support during their educational pursuits within MOOCs.

Speaking with a “forked tongue” – misalignment between user ratings and textual emotions in LLMs

Speaking with a “forked tongue” – misalignment between user ratings and textual emotions in LLMs

Blockchain-Based Spectrum Sharing Algorithm for UAV-Assisted Relay System

Unmanned aerial vehicles (UAVs) are promising tools in mobile communication due to their flexibility. However, the rapid development of mobile communications further intensifies the challenge of spectrum scarcity, necessitating spectrum sharing with other systems. We suggest a Spectrum Sharing Algorithm for a UAV-Assisted Relay System. The utility function of secondary users (SUs) is defined by their communication rate, rewards from relay primary users (PUs), and spectrum leasing expenses. The utility function of PUs consists of their communication rate and revenue from spectrum leasing. We propose a joint optimization algorithm for the positioning and power allocation of UAVs, maximizing the frequency spectrum utilization rate of users. Spectrum trading between PUs and SUs is modeled as a Stackelberg game, and the problem is solved by using Lagrange multipliers and KKT conditions. To enhance the security of spectrum trading, a reputation-based spectrum sharing blockchain consensus algorithm is designed. We utilize Shamir’s secret sharing method to reduce computational complexity. Additionally, we design a smart contract to optimize the functionality of transaction transfers. The findings demonstrate that the proposed algorithm enhances the benefits for both participants in spectrum sharing, while safeguarding the security of spectrum transactions.

Distributed beamforming design for UAV‐based integrated communication and jamming systems

AbstractThe integrated communication and jamming system is proposed by exploiting the diversity gain and flexible deployment of unmanned aerial vehicle (UAV)‐based distributed multiple input multiple output technology, which is expected to gain higher spectral efficiency and system scalability. This paper designs transmit beamforming of multiple UAVs so that they can simultaneously impose jamming signal to jamming targets and communication signal to communication users at the same frequency. The aim is to maximize the total jamming effect of all jamming targets while achieving a certain communication rate of communication users by optimizing the transmit beamforming matrices. Two cases are considered in which the UAV swarm works in clock synchronization and clock asynchronization. Specifically, the problem is formulated into a non‐convex optimization in the synchronous case. A near optimal solution is derived by decoupling the beamforming matrix into multi‐user interference suppression matrix and single‐user beamforming matrix. In the asynchronous case, the transmitter–receiver matching relation needs to consider and multiple optimization variables are highly coupled, penalty factors and intermediate variables to convert the optimization problem to convex programming are introduced. The simulation results show that the proposed beamforming design algorithm coordinates the communication and jamming signal rationally and outperforms the conventional beamforming design algorithms.

Joint Optimization of User Scheduling, Rate Allocation, and Beamforming for RSMA Finite Blocklength Transmission

Joint Optimization of User Scheduling, Rate Allocation, and Beamforming for RSMA Finite Blocklength Transmission

Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand

ABSTRACTThis paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA‐driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research.

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.

Serological and molecular survey of rat hepatitis E virus (Rocahepevirus ratti) in drug users

ABSTRACT Rat hepatitis E virus (ratHEV) is an emerging cause of acute hepatitis of zoonotic origin. Since seroprevalence studies are scarce, at-risk groups are almost unknown. Because blood-borne infections frequently occur in people with drug use, who are particularly vulnerable to infection due to lack of housing and homelessness, this population constitutes a priority in which ratHEV infection should be evaluated. Therefore, the aim of this study was to evaluate the ratHEV seroprevalence and RNA detection rate in drug users as a potential at-risk population. We designed a retrospective study involving individuals that attended drug rehabilitation centres. Exposure to ratHEV was assessed by specific antibody detection using ELISA and dot blot (DB) assay and the presence of active infection by ratHEV RNA detection using RT-qPCR. Three-hundred and forty-one individuals were included, the most of them being men (67.7%) with an average age of 45 years. A total of 17 individuals showed specific IgG antibodies against ratHEV (4.6%; 95% CI; 3.1%–7.9%). One case of active ratHEV infection was identified (0.3%; 95% CI: 0.1%–1.8%). This was a 57-year-old homeless woman with limited financial resources, who had active cocaine and heroin use via parenteral route. In conclusion, we identified a potential exposure to ratHEV among drug users. Targeted studies in drug users with proper control groups are necessary to evaluate high-risk populations and transmission routes more accurately.

AutoTherm: A Dataset and Benchmark for Thermal Comfort Estimation Indoors and in Vehicles

Thermal comfort inside buildings is a well-studied field where human judgment for thermal comfort is collected and may be used for automatic thermal comfort estimation. However, indoor scenarios are rather static in terms of thermal state changes and, thus, cannot be applied to dynamic conditions, e.g., inside a vehicle. In this work, we present our findings of a gap between building and in-vehicle scenarios regarding thermal comfort estimation. We provide evidence by comparing deep neural classifiers for thermal comfort estimation for indoor and in-vehicle conditions. Further, we introduce a temporal dataset for indoor predictions incorporating 31 input signals and self-labeled user ratings by 18 subjects in a self-built climatic chamber. For in-vehicle scenarios, we acquired a second dataset featuring human judgments from 20 subjects in a BMW 3 Series. Our experimental results indicate superior performance for estimations from time series data over single vector input. Leveraging modern machine learning architectures enables us to recognize human thermal comfort states and estimate future states automatically. We provide details on training a recurrent network-based classifier and perform an initial performance benchmark of the proposed dataset. Ultimately, we compare our collected dataset to publicly available thermal comfort datasets.

Utilizing Metadata from Heterogeneous Sources within the Framework of the JVMG and GOLEM Projects to Identify Patterns in Anime-based Fandoms on AO3

This paper offers an overview of the first step in the data alignment and joint research efforts between the Japanese Visual Media Graph (JVMG) and the Graphs and Ontologies for Literary Evolution Models (GOLEM) projects. Focusing on fully or in-part anime based fandoms in the Archive of Our Own (AO3) data in the GOLEM knowledge graph we connected 534 fandoms with 1,734 anime works from the JVMG knowledge graph. The fanfiction publication numbers and dates for these fandoms were then analyzed in conjunction with the various metadata available on the corresponding anime works, such as release date, genre, content rating and user rating. Fixed effects modeling was employed to identify a positive effect of anime releases on fanfiction publication numbers. Furthermore, individual graphs of the top four hundred fandoms’ fanfiction publication numbers over time overlayed with the corresponding anime release dates were also analyzed manually to identify a number of distinct patterns, as well as groups of fandoms for which the invigorating effect of anime releases on fanfiction publication numbers could be clearly identified.

Leveraging ensemble clustering for privacy-preserving data fusion: Analysis of big social-media data in tourism

Discovering knowledge from social media becomes a trend in many domains such as tourism, where users' feedback and rating are the basis of recommendation systems. In this context, cluster analysis has been a major tool to disclose user groups by which the process of collaborative filtering can better determine a personalised suggestion. Matching this to the curse of big data is a challenge with previous studies either implementing conventional techniques on a distributed system or making use of data sampling. Specific to ensemble clustering, only a few aim to obtain both scalability and privacy preserving that are significant to handling social data. This paper presents a new bi-level framework of ensemble clustering in which an instance-segment based analysis is adopted to ensure data privacy and reduce the complexity of clustering the whole dataset. Unlike existing studies, instead of drawing a single clustering from each segment, multiple clusterings are selected to better represent instances therein. Based on published tourism datasets and different experimental settings, the new approach usually outperforms its baselines whilst being competitive to related methods found in the literature. Additional case studies on simulated big datasets and noisy variations are reported and discussed in addition to the analysis of algorithmic parameters.

The Individual Participant Data Integrity Tool for assessing the integrity of randomised trials.

Increasing concerns about the trustworthiness of research have prompted calls to scrutinise studies' Individual Participant Data (IPD), but guidance on how to do this was lacking. To address this, we developed the IPD Integrity Tool to screen randomised controlled trials (RCTs) for integrity issues. Development of the tool involved a literature review, consultation with an expert advisory group, piloting on two IPD meta-analyses (including 73 trials with IPD), preliminary validation on 13 datasets with and without known integrity issues, and evaluation to inform iterative refinements. The IPD Integrity Tool comprises 31 items (13 study-level, 18 IPD-specific). IPD-specific items are automated where possible, and are grouped into eight domains, including unusual data patterns, baseline characteristics, correlations, date violations, patterns of allocation, internal and external inconsistencies, and plausibility of data. Users rate each item as having either no issues, some/minor issue(s), or many/major issue(s) according to decision rules, and justification for each rating is recorded. Overall, the tool guides decision-making by determining whether a trial has no concerns, some concerns requiring further information, or major concerns warranting exclusion from evidence synthesis or publication. In our preliminary validation checks, the tool accurately identified all five studies with known integrity issues. The IPD Integrity Tool enables users to assess the integrity of RCTs via examination of IPD. The tool may be applied by evidence synthesists, editors and others to determine whether an RCT should be considered sufficiently trustworthy to contribute to the evidence base that informs policy and practice.

Deciphering Factors Contributing to Cost-Effective Medicine Using Machine Learning.

This study uses machine learning to identify critical factors influencing the cost-effectiveness of over-the-counter (OTC) medications. By developing a novel cost-effectiveness rating (CER) based on user ratings and prices, we analyzed data from Amazon. The findings indicate that Flexible Spending Account (FSA)/Health Savings Account (HSA) eligibility, symptom treatment range, safety warnings, special effects, active ingredients, and packaging size significantly impact cost-effectiveness across cold, allergy, digestion, and pain relief medications. Medications eligible for FSA or HSA funds, treating a broader range of symptoms, and having smaller packaging are perceived as more cost-effective. Cold medicines with safety warnings were cost-effective due to their lower average price and effective ingredients like phenylephrine and acetaminophen. Allergy medications with kid-friendly features showed higher cost-effectiveness, and ingredients like calcium, famotidine, and magnesium boosted the cost-effectiveness of digestion medicines. These insights help consumers make informed purchasing decisions and assist manufacturers and retailers in enhancing product competitiveness. Overall, this research supports better decision-making in the pharmaceutical industry by highlighting factors that drive cost-effective medication purchases.

Smart watch for early heart attack detection and emergency assistance using IoT

This research introduces a Smart Watch equipped with advanced physiological monitoring capabilities for the early detection of heart attacks and automatic initiation of emergency assistance. Cardiovascular diseases, particularly heart attacks, are a leading cause of global mortality. Rapid response during a heart attack significantly improves patient outcomes, emphasizing the need for innovative solutions. The proposed Smart Watch integrates a combination of sensors, including ECG (Electrocardiogram) and PPG (Photoplethysmography), to continuously monitor the user's heart rate, rhythm, and other relevant physiological parameters. Machine learning (Time Series Analysis algorithm) is employed to analyse the collected data in real-time, identifying patterns indicative of a potential heart attack.Upon detecting abnormal cardiac activity, the Smart Watch triggers an immediate response by connecting to a dedicated mobile application. The application utilizes built-in communication features to establish a connection with emergency services, providing vital information about the user's condition, location, and medical history. Simultaneously, the Smart Watch alerts predefined emergency contacts, ensuring a swift response from friends or family members.

Large-Scale Satisfaction Rating-Driven Selection of New Energy Vehicles: A Basic Uncertain Linguistic Information Bonferroni Mean-Based MCGDM Approach Considering Criteria Interaction

The continuous revolution of new energy technologies and the introduction of subsidy policies have promoted green consumers’ willingness to purchase new energy vehicles and automotive online service platforms have disclosed vehicle reputation and consumer satisfaction ratings information. However, due to issues such as uncertain data quality, large data volumes, and the emergence of positive reviews, the cost for potential car buyers to acquire useful decision-making knowledge has increased. Therefore, it is necessary to develop a scientific decision-making method that leverages the advantages of large-scale consumer satisfaction ratings to support potential car buyers in efficiently acquiring credible decision-making knowledge. In this context, the Bonferroni mean (BM) is a prominent operator for aggregating associated attribute information, while basic uncertain linguistic information (BULI) represents both information and its credibility in an integrated manner. This study proposes an embedded-criteria association learning BM operator tailored to large-scale consumer satisfaction ratings-driven scenarios and extends it to the BULI environment to address online ratings aggregation problems. Firstly, to overcome the limitations of BM with weighted interaction (WIBM) when dealing with independent criteria, we introduce an adjusted WIBM operator and extend it to the BULI environment as the BULIWIBM operator. We discuss fundamental properties such as idempotence, monotonicity, boundedness, and degeneracy. Secondly, addressing the constraints on interaction coefficients in BM due to subjective settings, we leverage expert knowledge to explore potential temporal characteristics hidden within large-scale consumer satisfaction ratings and develop a method for learning criteria and interaction coefficients. Finally, we propose a conversion method between user credibility-based ratings and BULI. By combining this method with the proposed adjusted BM operator, we construct a multi-criteria group decision-making (MCGDM) approach for product ranking driven by large-scale consumer satisfaction ratings. The effectiveness and scientific rigor of our proposed methods are demonstrated through solving a new energy vehicle selection problem on an online service platform and conducting comparative analysis. The case analysis and comparative analysis results demonstrate that the interaction coefficients, derived from expert knowledge and 42,520 user ratings, respectively, fell within the ranges of [0.2391, 0.7857] and [0.6546, 1.0]. The comprehensive interaction coefficient lay within the range of [0.4674, 0.7965], effectively mitigating any potential biases caused by subjective or objective factors. In comparison to online service platforms, our approach excels in distinguishing between alternative vehicles and significantly impacts their ranking based on credibility considerations.

Effect of Social Media on Consumer Buying Behavior in the Electronics Market

This research examines the effect of social media on consumer buying behavior in the electronics market in Kathmandu, Nepal. A survey was conducted on 151 social media users to analyze the influence of psychographic factors, advertising strategies, social media engagement metrics, and influence of social media influencers and celebrities. Descriptive statistics, correlation, ANOVA, and linear regression were employed for data analysis. In line with prior studies, it has indicated that consumer buying behavior can be influenced by focusing on psychographic factors, advertising strategies, social media engagement metrics, and the influence of social media influencers and celebrities. In addition to these results suggest that social media plays a crucial role in shaping consumer electronics purchasing decisions, with user reviews, ratings, and influencer recommendations holding more weight than traditional marketing methods.

Effects of exposure on adoption of agricultural smartphone apps among smallholder farmers in Southwest, Nigeria: implications on farm-level-efficiency

BackgroundWhen considering new technologies that are not widely known such as agricultural smartphone apps, exposure plays a significant role in facilitating farmers’ decision to use the agricultural smartphone apps. In this study, we examine the role of exposure to agricultural smartphone apps on adoption of agricultural smartphone apps among smallholder farmers in Southwest, Nigeria and also evaluates the effect of use of agricultural smartphone apps on total factor productivity and technical efficiency of farmers. Through counterfactual framework evaluation approach, we used a multistage sampling procedure to select 380 farmers in Southwest, Nigeria through well-structure questionnaire.ResultsWe found that the agricultural smartphone apps user rate in 2022 for the sub-sample of the exposed farmers was higher in both Oyo and Osun State, indicating that exposure to agricultural smartphone apps played an important and significant role in increasing the adoption and use of agricultural smartphone apps in Nigeria. We also found that the mean TFPI and TE of the treated (users of agricultural smartphone apps) is higher than the non-treated and control group (non-users) implying that the users of agricultural smartphone apps have higher productivity margin than the non-users.ConclusionBased on the result of the study, it was concluded that exposure to the technology has a higher chances of increasing the use of agricultural smartphone apps across farmers populations in Southwest, Nigeria.

Optimizing User Satisfaction in Movie Recommendations Using Variable Learning Rates and Dynamic Neighborhood Functions in SOMs

Customized movie recommendations are crucial in elevating user satisfaction and engagement in the era of vast online entertainment options. This study presents an innovative approach utilizing Enhanced Self-Organizing Maps (SOMs) for movie categorization. SOMs, as unsupervised neural networks, are highly effective in recommendation systems due to their ability to identify intricate data patterns accurately. The proposed method involves collecting user-movie interaction data, such as user ratings and movie attributes. Data standardization is performed to ensure consistency before training the refined SOM. By integrating variable learning rates and dynamic Neighborhood functions, the advanced SOM can uncover complex patterns within datasets, thus enhancing the accuracy of personalized movie recommendations by identifying meaningful connections between users and films. To further improve recommendation quality, hybrid filtering techniques are employed, combining content-based filtering, which considers movie characteristics like genre and description, with collaborative filtering algorithms that analyze user-item interactions to expand the range of recommended films. This integrated approach allows for the generation of user-movie matrices by employing SVD collaborative filtering to give precedence to movie recommendations. The hybrid technique demonstrates superior performance compared to earlier models, attaining an RMSE of 0.410, MAE of 0.211, precision of 92.09%, recall of 93.12%, and an F1-score of 92.15%, consequently offering very accurate movie recommendations. Subsequent studies could concentrate on improving personalised recommendations by integrating supplementary contextual data.