User Ratings Research Articles

Smart Health Monitoring with Power-Aware IoT Using a Wireless Body Area Network

Smart health monitoring systems have become increasingly popular in recent years due to the growth of the Internet of Things (IoT) technology and its applications in healthcare. This project proposes an IoT-based smart health monitoring system that utilises an ESP32 Microcontroller, MAX30100 Pulse Oximeter Heart Rate Sensor Module, MLX90614 ESF Non-Contact Human Body Infrared Temperature Measurement Module, LCD 16*2, 3.7v 2000mAh Lithium Battery, TP4056 1A Li-Ion Lithium Battery charging Module, and some buttons. The system is designed to monitor the user's heart rate, blood oxygen level, and body temperature in real-time and display the readings on an LCD screen as well as an IoT web page. The project aims to provide an affordable, portable, and user-friendly smart health monitoring solution that can be used at home, in hospitals, or other healthcare settings. The project methodology involved a combination of hardware and software components, including circuit design and programming using the Arduino IDE. The system was tested and evaluated for its performance, accuracy, and usability using a sample population. The results showed that the system was effective in monitoring the user's vital signs and displaying the readings in real-time. The system was found to be accurate and reliable, with a high level of user satisfaction and ease of use. Overall, the IoT-based smart health monitoring system proposed in this project has significant implications for healthcare, particularly in terms of remote monitoring, telemedicine, and personalised healthcare. The system has the potential to improve patient outcomes, reduce healthcare costs, and increase access to healthcare services. The project also highlights the potential of IoT technology in healthcare and the need for further research and development in this area

A Novel Framework on Book-Recommendation System

With so many books available these days, effective book recommendation engines are crucial to pointing readers in the direction of choices that suit their interests. This paper introduces a prediction algorithm that uses user ratings to improve book recommendations. The program attempts to precisely forecast and rank the top 50 books for specific users by examining user-provided book ratings. The research methodology utilized in this study generates tailored recommendations by combining machine learning algorithms with collaborative filtering techniques. In order to provide predictions, collaborative filtering looks for trends in user behavior and preferences and compares users and books. To be more precise, the model finds books that people with similar preferences have liked by using similarity measures and user-item matrices. The effectiveness of the predictive model is evaluated using a sizable dataset of user-rated novels. The model is guaranteed to be resilient and adaptable to a large range of literary interests due to the dataset's broad scope in terms of genres, authors, and publishing years. Recall, accuracy, and precision are among the performance metrics used to assess how well the model can recommend books to readers based on their interests. The predictive model's ability to produce individualized book recommendations based on user ratings is demonstrated by the results. The model's prediction of the top 50 books is highly relevant and aligned with users' likes, which improves readers' browsing and selecting experience. The concept also exhibits scalability and flexibility, making it possible to accommodate growing book catalogs and changing customer preferences. All things considered, this work advances book recommendation systems by introducing a predictive model that uses user ratings to produce tailored recommendations. The approach improves reading enjoyment and encourages readers to become more deeply involved with books by making it easier for them to find interesting and engaging literary content..

Value‐centric analysis of user adoption for sustainable urban micro‐mobility transportation through shared e‐scooter services

AbstractMicro‐mobility services, which are considered a sustainable alternative to traditional transportation modes, have gained substantial popularity due to advancements in mobile technology. As one of those modes of transportation, shared e‐scooter services have encouraged several startups in urban areas, allowing them to reach massive numbers of consumers in a highly competitive environment. This study aims to explore gains and barriers that affect the intention of consumers to use shared e‐scooter services, all within the framework of sustainability‐driven considerations. The Latent Dirichlet Allocation (LDA) Algorithm was used to analyse 24.798 reviews from the Google Play Store, uncovering eight topics. Those topics were used to discover customer value perceptions in the shared e‐scooter context and compare them with the related literature on perceived value. Besides, their impact on user ratings on the mobile application platform was measured using machine learning algorithms. The study's findings are expected to contribute to developing regulations for shared e‐scooter services, which have gained popularity as an eco‐friendly mode of transportation sustainability in urban areas by introducing a novel perspective.

Demographic, socioeconomic, and health structure factors associated with the use of HIV pre-exposure prophylaxis in Brazil: A nationwide ecological study.

Aims: To assess the cumulative rate of HIV Pre-Exposure Prophylaxis (PrEP) users in Brazil since its 2018 implementation and to analyze the association between PrEP usage and state-level structural factors. Methods: A nationwide ecological study from 2018 to 2022 was conducted, examining the 5-year cumulative rate of PrEP users in relation to demographic, socioeconomic, and healthcare infrastructure variables. Multiple linear regression analysis identified significant predictors of PrEP utilization. Results: Between 2018 and 2022, 124,796 individuals used PrEP, with a cumulative rate of 61.5 per 100,000 population. The highest usage was in Minas Gerais, São Paulo, and Santa Catarina, while the lowest was in Distrito Federal, Maranhão, and Alagoas. Regression analysis showed that higher PrEP usage was associated with lower population density, a younger median age, a lower male to female ratio, and reduced social vulnerability. Additionally, PrEP usage was positively associated with the density of medical doctors and the number of dispensing units. Conclusions: The study reveals significant regional disparities in PrEP usage across Brazil, influenced by socioeconomic and healthcare factors. It highlights the need for targeted public health strategies to enhance PrEP access and uptake, especially in socially vulnerable regions.

Low-Resolution Optimization for an Unmanned Aerial Vehicle Communication Network under a Passive Reconfigurable Intelligent Surface and Active Reconfigurable Intelligent Surface

This paper investigates the optimization of an unmanned aerial vehicle (UAV) network serving multiple downlink users equipped with single antennas. The network is enhanced by the deployment of either a passive reconfigurable intelligent surface (RIS) or an active RIS. The objective is to jointly design the UAV’s trajectory and the low-bit, quantized, RIS-programmable coefficients to maximize the minimum user rate in a multi-user scenario. To address this optimization challenge, an alternating optimization framework is employed, leveraging the successive convex approximation (SCA) method. Specifically, for the UAV trajectory design, the original non-convex optimization problem is reformulated into an equivalent convex problem through the introduction of slack variables and appropriate approximations. On the other hand, for the RIS-programmable coefficient design, an efficient algorithm is developed using a penalty-based approximation approach. To solve the problems with the proposed optimization, high-performance optimization tools such as CVX are utilized, despite their associated high time complexity. To mitigate this complexity, a low-complexity algorithm is specifically tailored for the optimization of passive RIS-programmable reflecting elements. This algorithm relies solely on closed-form expressions to generate improved feasible points, thereby reducing the computational burden while maintaining reasonable performance. Extensive simulations are created to validate the performance of the proposed algorithms. The results demonstrate that the active RIS-based approach outperforms the passive RIS-based approach. Additionally, for the passive RIS-based algorithms, the low-complexity variant achieves a reduced time complexity with a moderate loss in performance.

Optimizing the Timeliness of Hybrid OFDMA-NOMA Sensor Networks with Stability Constraints

In this paper, we analyze the timeliness of a multi-user system in terms of the age of information (AoI) and the corresponding stability region in which the packet rates of users lead to finite queue lengths. Specifically, we consider a hybrid OFDMA-NOMA system where the users are partitioned into several groups. While users in each group share the same resource block using non-orthogonal multiple access (NOMA), different groups access the fading channel using orthogonal frequency division multiple access (OFDMA). For this system, we consider three decoding schemes at the service terminals: interfering decoding, which treats signals from other users as interference; serial interference cancellation, which removes signals from other users once they have been decoded; and the enhanced SIC strategy, where the receiver attempts to decode for another user if decoding for a previous user fails. We present the average AoI for each of the three decoding schemes in closed form. Under the constraint of the stable region, we find the minimum AoI of each decoding scheme efficiently. The numerical results show that by optionally choosing the decoding scheme and transmission rate, the hybrid OFDMA-NOMA outperforms conventional OFDMA in terms of both system timeliness and stability.

Why this app? How user ratings and app store rankings impact educators' selection of educational apps

Research indicates that educators value certain benchmarks of educational quality when choosing educational apps from app stores (i.e., curriculum, feedback, scaffolding, learning theory, and development team). However, it is unclear how other users' ratings of the app, or the app's ranking on the app store's list of “top” educational apps, might impact educators' evaluations. The present study examines how educational benchmarks, ratings, and rankings influence educators' selection of educational apps. One-hundred and fifty elementary educators viewed 18 researcher-created educational app pages and indicated their willingness to download, pay for, and rate each app. Results from a repeated-measures MANOVA and non-parametric tests revealed that educators preferred benchmark apps to buzzword apps, with a medium sized effect. However, they also had a strong preference for apps with positive user ratings, with a large effect, and preferred apps with a bottom ranking, with a medium effect. To improve app selection, educators should rely on their own knowledge to choose apps rather than relying on user ratings or company rankings. Companies running app stores should improve their user ratings and rankings systems to facilitate selection of apps that include evidence-backed benchmarks of app quality.

Resource allocation of cognitive relay networks under continuous transmission mechanism

This paper considers the joint power allocation, path selection and subcarrier pairing design of cognitive radio cooperative networks, where the transmission power of secondary users is limited by its own power budget and the tolerable interference threshold of primary users. In particular, a continuous transmission mechanism rather than intermittent emission, leading to severe signal interference between primary and secondary users, as well as mutual interference between secondary users, is taken into account. This cooperative cognitive radio network is seen as an improvement in the sense that the source transmitter can continuously transmit symbols without silence. Although traditional cooperative network technology has long been applied to the cognitive radio field and it has been proposed to allow continuous transmission signals by source transmitter, the deployment of such improved cooperative behavior in cognitive radio networks has not been reported yet. This improvement assumption may render serious mutual interference between the relay station and the destination receiver, and the signal interference borne by the primary user will also exceed the threshold value, if there is no elaborate signal design and resource scheduling. In order to make full use of the available resources, this paper presents the closed-form solution of the power allocation, the decision criteria of the path selection and the subcarrier pairing technique for the problem of maximization of the sum rate of secondary users in such networks under the dual constraints of the power budget and the interference threshold. Simulation results show that the continuous transmission behavior at the source significantly outperforms the traditional switching back and forth between on and off.

Internet Addiction and COVID-19 Misbeliefs Among Hungarian Online Gamers: A Cross-Sectional Study.

The internet has become a part of everyday life, and during the COVID-19 pandemic the rate of internet use has raised even higher, which increases the possibility of compulsive and problematic use leading to the acceptance of online misbeliefs and conspiration theories. This cross-sectional study aimed to explore the relationship between COVID-19-related misconceptions and internet addiction among adult recreational online gamers. A sample of 1671 recreational video game users completed the online survey (male: n = 1522 (91.08%), mean age = 21.83, SD = 4.18; female: n = 149 (8.91%), mean age = 24.33, SD = 8.38). Demographic questions, risk factors and health-related questions, internet use and addiction were measured alongside a short questionnaire about common COVID-19-related topics, such as its origin and risk of infection. Out of all participants 248 (14.8%) answered all the COVID-19-related questions properly, thus having no misconceptions, while 545 (32.6%) had one wrong answer, 532 (31.8%) had 2 wrong answers, 251 (15.0%) had 3 wrong answers, 78 (4.7%) had 4 wrong answers and 17 (1.0%) had 5 wrong answers. Significant factors to a higher number of COVID-misconceptions were time spent studying (χ2 (35,1671) = 63.86, p = 0.002), marital status (χ2 (15,1671) = 30.65 p = 0.01) and secondary employment (χ2 (51,671) = 14.88, p = 0.01). Although 17.1% of the participants reached the threshold score for internet addiction, the predictors of COVID-19 misconceptions were marital status (β = -0.06, p = 0.01) and time spent studying (β = 0.05, p = 0.03), while neither daily internet use, internet addiction scores or risk factors predicted these misconceptions in a linear regression model. Our study concludes that Internet addiction did not directly influence misconceptions about the COVID-19 pandemic in this population despite the surprisingly high rate of problematic users.

Measuring Vital Signs such as Temperature, BPM, Humidity using different IOT Sensors and Designing a Mobile Application using Firebase

Abstract: This research describes the creation of an Internet of Things-based smart health surveillance system that allows for the real-time data tracking of a person's vital signs. An ESP8266 Node MCU microcontroller is integrated with temperature, humidity, and heart rate sensors in the system to gather information on the user's body temperature, ambient humidity, and heart rate. A specially created Android mobile application shows the gathered data, which is wirelessly sent to a Firebase cloud database. With the use of the mobile app, users can easily create individualized profiles, view vital sign data, and get warnings. Early health issue diagnosis and proactive illness management are aided by successful implementation, which shows that lowcost IoT devices, cloud computing, and mobile technologies may all be used for effective remote health monitoring

Hybrid Recommendation Algorithm based on Product Popularity and User Preference

In today's society, recommendation system is widely used to recommend users' preferred products and content. However, with the rapid development of the Internet, there are more and more contents on the Internet, and the algorithm complexity of recommending products to users through the recommendation system is getting higher and higher, and the recommendation algorithm is also more and more inclined to recommend popular products to users, which also leads to the decline in the novelty and diversity of the recommendation algorithm. In order to solve this problem, the study first adopts the method of inhibiting the recommendation ability of current popular products, and on this basis, introduces the influence of user rating on the recommendation ability of different products. At the same time, the product is classified, take the movie for example, can be classified as action film, comedy, children's film and so on. There is a very high probability that an adult will not like children's films, and a very high probability that a child will not like horror films. Impose a penalty on a product category that the user may not like, reducing the likelihood that the product will be recommended to the user. The proposed algorithm is compared with other excellent algorithms in this field on MovieLens movie score dataset, and the results of the experiments showed that the algorithm improved in accuracy, diversity, and novelty.

Virtual reality experiences for breathing and relaxation training: The effects of real vs. placebo biofeedback

Virtual reality biofeedback systems for relaxation training can be an effective tool for reducing stress and anxiety levels, but most of them offer a limited user experience associated to the execution of a single task and a biofeedback mechanism that reflects a single physiological measurement. Furthermore, user evaluations of such systems do not typically include a placebo condition, making it difficult to determine the actual contribution of biofeedback. This paper proposes a VR system for breathing and relaxation training that: (i) uses biofeedback mechanisms based on multiple physiological measurements, (ii) provides a richer user experience through a narrative that unfolds in phases where the user is the main character and controls different elements of the virtual environment through biofeedback. To evaluate the system and to assess the actual contribution of biofeedback, we compared two conditions involving 35 participants: a biofeedback condition that exploited real-time measurements of user's breathing, skin conductance, and heart rate; and a placebo control condition, in which changes in the virtual environment followed physiological values recorded from a session with another user. The results showed that the proposed virtual experience helped users relax in both conditions, but real biofeedback produced results that were superior to placebo biofeedback, in terms of both relaxation and sense of presence. These outcomes highlight the important role that biofeedback can play in virtual reality systems for relaxation training, as well as the need for researchers to consider placebo conditions in evaluating this kind of systems.

Numtrical Evaluation of Optimal Precoding Algorithm and Zero Forcing Precoding in MU-MISO Channel with Channel Aging

In paper the numerical investigation of the impact of delayed channel state information (CSI) due to user movement and caused channel aging on the performance of multiuser precoder in downlink MISO system. The time variant CSI is obtained by the least squares channel estimation. We consider Zero Forcing algorithm and numerical optimization based solution of calculating precoder vectors maximizimg sum rate of multiuser system. For numerical simulation the QuaDRiGa channel model reflecting the real propagation conditions for moving users is used. The obtained performance of multiuser Zero Forcing and optimization based beamforming in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users.

Cost-efficient RAN slicing for service provisioning in 5G/B5G

Network slicing represents a substantial technological advance in 5G mobile network, greatly expanding the variety and manifoldness of network services to be supported. Additionally, 3GPP 5G New Radio (NR) has introduced novel features such as mixed numerology and mini-slots, which can be harnessed by network slicing to cater to the diverse requirements of 5G services. While however the co-existence of multiple network slices leads to a challenging resource allocation problem, these new features also severely complicate the management of radio resources. As a further point of attention, the virtualization of radio functions may exact a significant toll from the, already limited, computing resources at the network edge. It follows that a cost-efficient resource allocation across all the slices becomes crucial. In this paper, we address the above-mentioned issues by modeling a cost-efficient radio resource management in 5G NR featuring network slicing, named CERS, through a Mixed Integer Quadratically constrained Program (MIQCP). We maximize the profit of all slices simultaneously guaranteeing the target data rate and delay specified in the service level agreements (SLAs) fo the different traffic flows. To reduce the complexity of the MIQCP problem, we decompose it into two sub-problems, namely, the scheduling problem of enhanced Mobile Broadband (eMBB) user equipments (UEs) on a time-slot basis and of Ultra-Reliable Low Latency Communications (uRLLC) UEs on a mini-slot basis, while keeping the objective unchanged. To address the scheduling issue of eMBB UEs, we employ a heuristic technique, and, by leveraging the outcome of this heuristic, we derive an optimal solution for the problem of uRLLC UEs. The significance of the proposed approach over a baseline approach is evaluated through extensive numerical simulations in terms of the number of allocated uRLLC resource blocks (RBs) per mini-slot. We also assess our approach by measuring the impact of the uRLLC slice changes on the eMBB slice, and vice versa, including delay for uRLLC users and data rates for eMBB users.

An analysis of apartment listings in best destinations on an OTA: Thailand versus Japan

PurposeThe sharing economy enables apartment owners to generate income from their assets. “Agoda Homes” is an online travel agent (OTA) that directly competes with Airbnb. A destination has to discover its competitiveness, but few studies have provided an overview of accommodation attributes in each destination, which are crucial to shaping its brand image. This paper aims to illustrate firm-generated content or attributes that apartment owners list about their properties on an OTA platform to comprehend factual information about apartments in each destination with various star ratings and user ratings and to formulate a research model for future studies.Design/methodology/approachInformational content and accommodation attributes for apartments are automatically collected using a Web scraping tool (the Data Miner). Descriptive statistics and text analysis (word cloud and word frequency) are used to analyze data.FindingsFindings reveal the primary location, facilities, cleanliness and safety attributes for all apartments in each destination, along with star ratings and user ratings. A research framework for scholars is also suggested. Guidelines for stakeholders in the tourism industry are additionally furnished.Originality/valueThis work concentrates on apartments, which have received less attention in the tourism literature. The study gathers factual data from a website to mitigate respondent bias issues inherent in the traditional survey methods.

The Effects of Corticosteroid and Nonsteroid Anti-Inflammatory Therapies on Permanent Hypothyroidism Occurring After the Subacute Thyroiditis

ABSTRACT Objective Subacute thyroiditis(SAT) is an acute inflammatory, self-limited, and destructive disease of the thyroid gland. Although it is a temporary disease, it has permanent consequences. We aim to investigate the influences of the treatment choice on permanent hypothyroidism occurring after SAT and whether there are predictive factors for the development of permanent hypothyroidism. Methods We retrospectively investigated 57 SAT patients admitted to our tertiary hospital between 2017 and 2019. After excluding 6 patients, demographic, clinical, laboratory, and imaging findings of 36 patients treated with NSAIDs and 15 patients treated with corticosteroids were compared. The median duration of follow-up was 4 (3.5-5.5) years. Results Permanent hypothyroidism occurred in 16 patients (31.4%) of 51 patients. It developed at a significantly higher rate in NSAID users (p=0.019). There was no significant difference in the occurrence of transient hypothyroidism and recurrence (p=0.472, p=0.082). The early maximum TSH values were strongly associated with permanent hypothyroidism. The Odds Ratio (OR) value was 2.59 (95% CI = 1.26 – 5.33, p=0.009), Nagelkerke R2 = 0.821. The early maximum TSH level had a predictive value, with an AUC of 0.966 for post-SAT permanent hypothyroidism (p<0.001). The cutoff values for the early maximum TSH were 9.07uIU/ml (81.3% sensitivity, 100% specificity), and 7.05 uIU/ml (87.5% sensitivity, 94.3% specificity). Conclusion Corticosteroid therapy is significantly effective in preventing permanent hypothyroidism from developing after SAT. The early maximum TSH values are an indicator for the prediction of the development of permanent hypothyroidism.

DIFFERENCES IN FLOW RATE AND SALIVA PH IN USERS AND NON-USERS OF FIXED ORTHODONTIC APPLIANCES

Introduction: Fixed orthodontic treatment is a type of dental treatment that is very popular throughout the world, not only to improve function but also lifestyle. However, it has an impact on the condition of the oral cavity, namely the flow rate and pH of salivary, which can affect dental and oral health. This study aims to determine the differences in flow rate and saliva pH between users and non-users of fixed orthodontic devices. Materials and Methods: This type of research is analytical observational, with a cross sectional approach. The population in this study were students class 2020, 2021, and 2022, the sample in this study was 116 students. Data collection by observation, interviews and examination. The method for measuring saliva flow rate with drainase and saliva pH using the Hanna HI98107 pH meter. Univariate and bivariate data analysis (independent sample t test). Results and Discussions: The results of the study showed that the there was a difference in saliva flow rate in users and non-users of fixed orthodontic appliances (p-value 0.001), there is a difference in saliva pH between users and non-users (p-value 0.000). Conclusion: The use of fixed orthodontic can affect saliva flow rate and salivary pH.

Social Media Data Analytics in the Automotive Industry: A Study of the Interactive Impact of Marketing Strategies and User Ratings

Social Media Data Analytics in the Automotive Industry: A Study of the Interactive Impact of Marketing Strategies and User Ratings

Suboptimal peak inspiratory flow rate in dry-powder inhaler users for chronic obstructive pulmonary disease in Korea

BackgroundA suboptimal peak inspiratory flow rate (PIFR) in dry-powder inhaler (DPI) users can lead to insufficient therapeutic effects in the treatment of chronic obstructive pulmonary disease (COPD). However, few data on the prevalence of and factors associated with suboptimal PIFR in Korean patients with COPD are available. MethodsWe conducted a cross-sectional study of patients with COPD who had been using DPIs for more than three months. PIFR was measured using an In-Check DIAL G16 device. Suboptimal PIFR was defined as below the resistance-matched threshold. Multivariable logistic regression analysis was used to determine factors associated with suboptimal PIFR. ResultsOf 444 DPI users with COPD, the rate of suboptimal PIFR was 22.0 % (98/444). In a multivariable analysis, significant factors associated with suboptimal PIFR were age (adjusted odds ratio [aOR] = 1.06 by 1-year increase; 95 % confidence interval [CI] = 1.02–1.09), male sex (aOR = 0.28; 95 % CI = 0.11–0.73), body mass index (BMI) (aOR = 0.91 by 1 kg/m2 increase; 95 % CI = 0.85–0.99), post-bronchodilator forced vital capacity (FVC) %pred (aOR = 0.97 by 1%pred increase; 95 % CI = 0.95–0.99), and In-Check DIAL R2-type inhaler [medium-low resistance] use (aOR = 3.70 compared with R1-type inhalers [low resistance]; 95 % CI = 2.03–7.03). ConclusionsIn Korea, more than one-fifth of DPI users with COPD had a suboptimal PIFR. The factors associated with suboptimal PIFR were age, female gender, low BMI, low FVC, and R2-type inhaler use. Therefore, clinicians should carefully evaluate the possibility of suboptimal PIFR when prescribing DPIs.

Wisdom of the Masses? Users and Educators Contrasting Perspectives on the Data Privacy and Safety of Early Years’ Apps

A key focus is assessing children's apps, emphasising informational clarity, safety, and data ethics. The present study employs a structured database for app evaluation, by categorising 30 apps into edutainment, education, instruction, and training based on various criteria, including educational value and user ratings. Moreover, a comparative evaluation between the public ratings and the educator's evaluation was carried out. The findings underscore the challenges in app evaluation due to the proliferation of unregulated and untested applications. It highlights the discrepancies between public app evaluations and those conducted by experienced educators, pointing out the difficulties in obtaining relevant information for accurate app assessment. The study also addresses the aesthetic appeal of apps and how they influence parental choices, often overshadowing concerns about user security and privacy. In conclusion, the study calls for heightened parental awareness in selecting apps for their children. It stresses the importance of considering learning value, educational value, information clarity, security, and ethical data use.