User Ratings Research Articles

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.

Reinforcement learning-based energy efficiency optimization for RIS-Assisted UAV hybrid uplink and downlink system

As Reconfigurable Intelligent Surface (RIS) technology can intelligently reflect the incident signal, it can be deployed to maintain a line-of-sight connection and improve the signal strength between unmanned Aerial Vehicles (UAV) and users. Current researches on RIS-assisted UAV communication typically consider optimizing data rates for single-line communications and do not focus on the energy consumption of the UAV. Furthermore, it is frequently challenging to apply conventional algorithms to RIS-assisted UAV optimization problems in a timely way, and the two-dimensional trajectory design cannot fully utilize the 3D mobility of the UAV. In order to increase the data rates for all ground users while improving the energy efficiency of the system, we consider a RIS-assisted full-duplex UAV communication system, in which a DDQN-based algorithm is proposed to jointly optimize the phase shift of the RIS and the 3D trajectory of the UAV. Simulation results demonstrate that the proposed algorithm can achieve a significant data rate gain and energy efficiency compared to time-division duplex systems and other deep reinforcement learning algorithms.

Reinforcement learning for addressing the cold-user problem in recommender systems

Recommender systems are widely used in webshops because of their ability to provide users with personalized recommendations. However, the cold-user problem (i.e., recommending items to new users) is an important issue many webshops face. With the recent General Data Protection Regulation in Europe, the use of additional user information such as demographics is not possible without the user’s explicit consent. Several techniques have been proposed to solve the cold-user problem. Many of these techniques utilize Active Learning (AL) methods, which let cold users rate items to provide better recommendations for them. In this research, we propose two novel approaches that combine reinforcement learning with AL to elicit the users’ preferences and provide them with personalized recommendations. We compare reinforcement learning approaches that are either AL-based or item-based, where the latter predicts users’ ratings of an item by using their ratings of similar items. Differently than many of the existing approaches, this comparison is made based on implicit user information. Using a large real-world dataset, we show that the item-based strategy is more accurate than the AL-based strategy as well as several existing AL strategies.

Tracking the progress towards adopting LPG as a clean cooking fuel in Rwanda: User's perspective

Many households in Rwanda rely on firewood and charcoal for cooking with inefficient-burning stoves. To mitigate the issue of cooking with traditional fuels, the government of Rwanda has promoted the use of alternative energy such as liquefied petroleum gas (LPG).This study aims to assess the determinants behind the transition from traditional biomass to clean cooking technology in Rwanda and analyzes the consumers' preferences in choosing cooking fuels. A self-administered questionnaire was employed to collect data to people living in urban areas in Rwanda through simple random sampling method. A total of 460 valid responses were then collected, which were subsequently analyzed using statistical analysis with the help of Statistical Package for Social Sciences (SPSS) and Excel.The results showed a progress in adopting LPG fuel for households' cooking, despite biomass fuel still accounting a high rate of household's users. The results also revealed that fuels stacking is a common practice among households where half of the respondents cook with LPG as a primary cooking fuel, with predominance of charcoal as the secondary cooking fuel choice. Meanwhile, the study also identified high cost of stoves and fuels as well as safety risks as major barriers hindering the choice of LPG as primary cooking fuel and finally provided some policy recommendations to increase the adoption of LPG.This study fills the gap in literature and may assist in policy formulation to accelerate the uptake of LPG as a primary cooking fuel in Rwanda.

Review-based analysis of clustering approaches in a recommendation system

&lt;span&gt;Because of the explosion in data, it is now incredibly difficult for a single person to filter through all of the information and extract what they need. As a result, information filtering algorithms are necessary to uncover meaningful information from the massive amount of data already available online. Users can benefit from recommendation systems (RSs) since they simplify the process of identifying relevant information. User ratings are incredibly significant for creating recommendations. Previously, academics relied on historical user ratings to predict future ratings, but today, consumers are paying more attention to user reviews because they contain so much relevant information about the user's decision. The proposed approach uses written testimonials to overcome the issue of doubt in the ratings' pasts. Using two data sets, we performed experimental evaluations of the proposed framework. For prediction, clustering algorithms are used with natural language processing in this strategy. It also evaluates the findings of various methods, such as the K-mean, spectral, and hierarchical clustering algorithms, and offers conclusions on which strategy is optimal for the supplied use cases. In addition, we demonstrate that the proposed technique outperforms alternatives that do not involve clustering.&lt;/span&gt;

Popularity-based and Collaborative Filtering Based Restaurant Recommender System

The creation of a Popularity-based and Collaborative Filtering Based Restaurant Recommendation System is the main focus of this project, which is an important application in the fields of recommendation systems and machine learning. The system uses the different python libraries like scikit-learn, pandas, NumPy, Linear Regression, Hashlib and Matplotlib as well as technologies like Python and the Flask Framework, to recommend the restaurants to the users based on the ratings out of 5 and their tastes. With features including user registration, login, restaurant browsing, and recommendation, the suggested system is a user-centric program. The system offers a smooth user experience by combining backend Python frameworks with frontend technologies like HTML, CSS, and Bootstrap. A thorough analysis of the literature made it clear that recommendation systems are essential for improving user pleasure and experience across a range of industries. But my cutting-edge restaurant suggestion system offers a fresh approach. My solution combines cutting-edge machine learning algorithms with intelligent analysis of customer preferences to deliver customized restaurant recommendations. My system uses collaborative and popularity-based filtering algorithms to make sure that users receive recommendations based on their interests and preferences. Through the integration of data from many sources, such as restaurant attributes and user ratings, My system generates precise and pertinent recommendations that boost user engagement and pleasure. Furthermore, My platform offers a user-friendly experience with little additional infrastructure requirements by seamlessly integrating with current websites and applications. My goal is to revolutionize the restaurant discovery process and improve the eating experience for people globally by promoting the widespread adoption of My technology through smart relationships with industry players.

Semaglutide (Ozempic®) Use in Denmark 2018 Through 2023 ‒ User Trends and off-Label Prescribing for Weight Loss.

A surge in the use of semaglutide injection (Ozempic®) approved to treat type 2 diabetes (T2D) has led to a global supply shortage. We investigated contemporary user rates and clinical characteristics of semaglutide (Ozempic®) users in Denmark, and the extent of "off-label" prescribing for weight loss. Nationwide population-based cross-sectional study based on linked health registries January 2018 through December 2023. All adults who received a first prescription of semaglutide once weekly (Ozempic®) were included. We examined quarterly rates of new users and total user prevalences, using other glucagon-like peptide-1 receptor agonists and weight loss medications as comparison. We also investigated user characteristics including T2D, glucose control, comedications, and cardiorenal disease. The new user rate of semaglutide (Ozempic®) remained stable at approximately 4 per 1000 adult person-years between 2019 and 2021 and then accelerated, peaking at 10 per 1000 in the first quarter of 2023 after which it declined sharply. User prevalence increased to 91,626 users in Denmark in 2023. The proportion of semaglutide (Ozempic®) new users who had a record of T2D declined from 99% in 2018 to only 67% in 2022, increasing again to 87% in 2023. Among people with T2D who initiated semaglutide (Ozempic®) in 2023, 52% received antidiabetic polytherapy before initiation, 39% monotherapy, and 8% no antidiabetic therapy. Most T2D initiators had suboptimal glucose control, with 83% having an HbA1c ≥48 mmol/mol and 68% ≥53 mmol/mol despite use of antidiabetic medication, and 29% had established atherosclerotic cardiovascular disease or kidney disease. The use of semaglutide (Ozempic®) in Denmark has increased dramatically. Although not approved for weight loss without T2D, one-third of new users in 2022 did not have T2D. Conversely, most initiators with T2D had a clear medical indication for treatment intensification, and "off-label" use can only explain a minor part of the supply shortage.

Study protocol for a type-II hybrid effectiveness-implementation trial to reach teenagers using mobile money shops to reduce unintended pregnancies in Uganda

IntroductionUnintended teenage pregnancies have become a global public health challenge, particularly in sub-Saharan Africa. There is a notably high prevalence of unintended pregnancies among unmarried teenagers in Uganda. This study...