User Ratings Research Articles

Mental health disorders before, during and after the COVID-19 pandemic: a nationwide study.

The COVID-19 pandemic and lockdowns prompted a major concern for mental health effects. Comprehensive nationwide studies are lacking on the indirect effect of the COVID-19 pandemic on the population's mental health. We aimed to determine whether the COVID-19 pandemic and lockdowns affected mental health service usage, suicide attempts, and suicides. This comprehensive nationwide register-linked study followed all individuals in Denmark from 1990. The main outcomes were rates of psychiatric admissions, use of psychotropic medication, suicide attempts, suicides, patients in community-based private psychiatry or psychology practices, and referrals to psychiatric hospitals. The impact of the pandemic (March 11, 2020-June 30, 2023) and lockdowns was assessed with log-normal models adjusted for pre-pandemic trends (January 1, 2017-March 10, 2020). We reported Rate Ratios (RR) of the observed and counterfactual rates. We identified the 5 807 714 (50.3% females) individuals living in Denmark on March 1, 2020. The rates of psychiatric admissions (RR: 0.95, 95% Confidence Interval (CI): 0.91 to 0.99, p-value: 0.017) and suicide attempts (RR: 0.85, 95% CI: 0.76 to 0.95, p-value: 0.007) were lower during the pandemic compared to the pre-pandemic trend. The rates of suicides (RR: 0.89, 95% CI: 0.75-1.05, p-value: 0.173), patients in private practices (RR: 1.00, 95% CI: 0.96-1.04, p-value: 0.986), and referrals (RR: 1.06, 95% CI: 0.95-1.18, p-value: 0.307) were not significantly different during the pandemic compared to the pre-pandemic trend. During the first lockdown, rates were lower for psychiatric admissions (RR: 0.85, 95% CI: 0.80 to 0.90, p-value <0.001), suicide attempts (RR: 0.80, 95% CI: 0.69 to 0.94, p-value: 0.007), suicides (RR: 0.67, 95% CI: 0.52 to 0.86, p-value: 0.002), patients in private practices (RR: 0.88, 95% CI: 0.82 to 0.93, p-value <0.001), and referrals (RR: 0.69, 95% CI: 0.60 to 0.81, p-value < 0.001) compared to the pre-pandemic trend. However, during the pandemic, the rate of psychotropic medication users increased by 6% compared to the pre-pandemic trend (RR: 1.06, 95% CI: 1.05 to 1.06, p-value < 0.001). The COVID-19 pandemic and lockdowns did not severely influence pre-pandemic trends of the mental health burden in Denmark's population on a nationwide level.

Multimodal examination of daily stress rhythms in chronic Cannabis users.

Chronic cannabis users frequently report stress relief as their primary reason for use. The endocannabinoid system is involved in the neuroendocrine stress response, and diurnal cortisol rhythms may be disrupted in chronic cannabis users. The objectives were to determine whether cannabis users demonstrate disruptions in diurnal stress rhythms and examine the acute effects of cannabis on stress-related outcomes in cannabis users' natural environments. Eighty-two participants (39 cannabis users, 43 non-users) collected saliva samples to quantify cortisol concentrations and provided subjective stress ratings at 8 time points throughout the day. They wore a medical-grade wearable device for 24h that recorded physiological indicators of stress (heart rate variability, electrodermal activity). Cannabis users collected additional saliva samples before and after cannabis use to examine acute effects of cannabis use. Cannabis users exhibited significant dysregulations in diurnal cortisol rhythms, including a blunted cortisol awakening response, flattened diurnal cortisol slope, and elevated afternoon cortisol concentrations. There were no differences in diurnal heart rate variability or electrodermal activity except for elevated evening heart rate in cannabis users. Finally, there were significant decreases in cortisol, subjective stress, and electrodermal activity following acute cannabis use in cannabis users' natural environment. These results provide evidence of dysregulated diurnal cortisol rhythms in cannabis users that were related to later waking times and acute stress-relieving properties of cannabis use in naturalistic environments. Future research should examine the direction of the relationship between cannabis use and diurnal cortisol rhythms and potential implications for other psychological disorders.

Electric Vehicle Charging Station Recommendations Considering User Charging Preferences Based on Comment Data

User preferences are important for electric vehicle charging station (EVCS) recommendations, but they have not been deeply analyzed. Therefore, in this study, user charging preferences are identified and applied to EVCS recommendations using a hybrid model that integrates LightGBM and singular value decomposition (SVD). In the model, LightGBM is used to predict user ratings according to users’ comments regarding charging orders, and the feature importance reported by each user is output. Then, a co-occurrence matrix between users and charging stations (EVCSs) is constructed and decomposed using SVD. Based on the decomposed results, the final evaluated scores of each user for EVCSs can be calculated. Upon ranking the EVCSs according to the scores, the EVCS recommendation results are obtained, taking into account the users’ charging preferences. The sample data consist of 28,306 orders from 508 users at 241 charging stations in Linyi, Shandong, China. The experimental results show that the proposed hybrid model outperforms the benchmark models in terms of precision, recall, and F1 score, and its F1 score can be increased by 96% compared with that of the traditional item-based collaborative filtering method with charging counts for EVCS recommendations.

Acceptability and psychometric properties of four scales assessing the impact of Type 2 diabetes on quality of life-Results of 'YourSAY: Quality of Life'.

To assess and compare the psychometric properties and acceptability of four diabetes-specific quality of life (QoL) scales among adults with Type 2 diabetes (T2D). Adults (≥18 years) with T2D living in the United Kingdom (n = 1465) or Australia (n = 248) completed a cross-sectional, online survey including the following: ADDQoL, DCP, DIDP and Diabetes QoL-Q (presented in randomised order), followed by rating scales to assess clarity, relevance, ease of completion, length and comprehensiveness of each scale. Demographic, clinical and psychosocial characteristics were collected. Acceptability (scale completeness and user ratings), response patterns, structure (exploratory and confirmatory factor analyses) and validity (convergent, confirmatory, divergent and known-groups) were examined. Data were analysed by country to assess cross-country reproducibility. High completion rates (≥89%) and positive user ratings were observed across scales indicating broad acceptability. The DIDP was the strongest performing scale: highest completion rate (97%), user ratings (≥84% positive) and most satisfactory psychometric properties (highest variance explained, consistent factor loadings >0.5 on all items and most permissible model fit parameters). Scale-level floor effects may suggest domain omissions for the brief DIDP. The current study provides novel insights into the acceptability, validity and reliability of diabetes-specific QoL measures for adults with T2D. Consistent with the published Type 1 diabetes cohort findings, the DIDP is recommended as a brief, acceptable and psychometrically sound measure. However, selection needs to be considered in the context of the specific research or clinical aims and further evidence (e.g. responsiveness) may be required before it can be recommended for use in trials or prospective studies.

Spatial pilot reassignment algorithm for channel estimation stage of cell-free multi-ARS communication systems

In this paper, we investigate the user throughputs of a Cell-Free (CF) system with multiple aerial relay stations (ARSs), where each ARS is defined as an unmanned aerial vehicle (UAV)-mounted relay station. The system operates under a decode-and-forward (DF) protocol and facilitates connectivity between a terrestrial base station (TBS) and terrestrial users. ARSs are equipped with multiple antennas and simultaneously serve users that are outfitted with single antennas and distributed in a specific area. Additionally, a small-cell (SC) system based on the CF structure, where each ARS serves one user with the best channel conditions, is also considered. We analyze system communication in two stages, including user-ARS links and ARS-TBS links, and then we derive expressions for the data rate of users and ARSs. Moreover, we propose the spatial pilot reassignment (SPR) algorithm to optimize pilot assignment, enhancing channel estimation over random pilot assignment methods. The user throughput is evaluated by altering several system parameters, including the with/without data power control, the number of users, the number of ARSs, and the time interval allocated for channel estimation. The results show that the SPR algorithm improves throughput by about 10% compared to the random pilot assignment method at a 90%-likely user throughput, which is equal to a cumulative distribution function value of 0.1.

SABTR: semantic analysis-based tourism recommendation

Online tourism spot recommendations, as a key component of tourism services, aim to present travel options that align with users’ personal preferences. However, current recommendation systems often underperform due to the sparsity of tourism data and the wide variance in user preferences. To address this challenge, we propose a Semantic Analysis-Based Tourism Recommendation framework, abbreviated as SABTR (Semantic Analysis-Based Tourism Recommendation). The framework comprises two stages: Firstly, Latent Dirichlet Allocation (LDA) models are utilized to deeply mine data between users and attractions, constructing two core matrices: the user similarity matrix and the attraction similarity matrix. Secondly, based on the user similarity matrix, similarity calculation methods are applied to predict ratings for tourism spots that users have not yet evaluated. Simultaneously, within the attraction similarity matrix, probability distributions for each attraction across various thematic interests are calculated. When the system identifies a user’s interest in specific types of attractions, SABTR can select a series of related attractions from associated interest tags. Then, these candidate attractions are ranked according to both known and predicted user ratings, ultimately forming personalized attraction packages recommended to users. Extensive experiments have demonstrated that compared to existing tourism recommendation solutions, our method significantly improves the quality of attraction recommendations and enhances user satisfaction.

Retrospective Analysis of the Anti Spitting Laws in Various Geographical Locations and Implementing Sectors in India

Objective: Public spitting attributable to Smokeless Tobacco (SLT) use not only causes a threat to public cleanliness but also to the people exposed around them. An electronic search on various anti-spitting laws and ban in India was done using Google search engine for duration of three months (November 2018 to January 2019). Material and Methods: The original state gazettes were reviewed to extract the data on spit ban and penalty. Renewals of Acts were also thoroughly checked for recent updates of penalty or severity of the act. The law implementing bodies were broadly classified as National, State, Municipal, Village councils (Gram Panchayat), Airports, Metro Railway, and Company Law Tribunal. Results: A total of 42 Anti-spitting laws were identified in this search and 16 states had anti spitting law under different sectors. Discussion: Under these laws, sign boards and warnings are placed commonly at Airports, Railway stations, and metro stations. States with high rates of SLT users like Tripura, Arunachal Pradesh and Mizoram did not have any policy on spitting. Conclusion: Strong enforcement of anti-spitting laws in India will not only reduce spread of communicable diseases but also indirectly will reduce the act of using SLT also.

Optimizing secure multimedia communication in embedded systems a parallel convolutional neural network approach to RIS and D2D resource allocation

With the rapid development of Internet of Things (IoT) services, technologies that leverage multimedia computer communication for information sharing in embedded systems have become a research focus. To address the challenges of low spectral efficiency and poor network flexibility in multimedia computer communications, this paper proposes a resource allocation scheme based on parallel Convolutional Neural Network (CNN). The scheme optimizes the base station beamforming vector and the Reconfigurable Intelligent Surface (RIS) phase shifts to maximize the secure transmission rate for cellular users (CUs), while ensuring normal and secure communication for device-to-device (D2D) users. First, to mitigate interference caused by D2D users reusing CU spectrum resources, the RIS phase shifts and beamforming vectors are optimized to suppress interference and enhance system secrecy rates. Second, to maximize the CU secrecy rate, the paper proposes a parallel CNN-based resource allocation model that considers base station transmission power, RIS reflection coefficients, and D2D communication rate constraints, incorporating multi-scale residual modules in the convolutional layers of the model. Simulation results demonstrate that the proposed CNN-based resource allocation scheme significantly improves the secrecy rate of embedded system communications, ensuring secure multimedia computing, and outperforms traditional methods.

Nutrition-Related Mobile Apps in the Spanish App Stores: Quality and Content Analysis.

Mobile apps represent accessible and cost-effective tools to improve nutrition and prevent chronic diseases. However, most of these apps have been characterized as having limited functionality, raising concerns about their effectiveness, acceptability, and efficacy. The aims of the study were to assess the quality of popular nutrition-related app platforms in Spain and to describe their characteristics and functionalities. We screened apps providing information on dietary advice, food advice, and nutritional content in the Apple App Store and Google Play Store in Spain from March 2 to March 16, 2024. Apps with a star rating of ≥4 (of 5 stars), those available in Spanish, those that were free of charge, those last updated after January 2022, those with >500 reviews, and those with >500,000 downloads were included. The quality of apps was assessed using the user version of the Mobile App Rating Scale (uMARS). General characteristics and nutritional, health, and market-related functionalities of the nutrition-related apps were described. Correlations among total and uMARS sections, star ratings, and number of reviews and downloads were evaluated. Among the 1460 apps identified in the search, 42 apps met the criteria. The majority of these (n=20, 48%) aimed at recording and analyzing food intake, followed by those providing nutritional plans or diets (n=9, 21%), advising on healthy habits (n=7, 17%), and offering recipes (n=6, 14%). The most prevalent nutritional functionalities offered were recording and monitoring body measurements (n=30, 71%), food tracking (n=26, 62%), and dietary analysis (n=25, 60%), whereas nutrition education was less common (n=16, 38%). Among market-related functionalities, advertisements were the most common among the study apps (n=30, 71%), followed by the option of sharing on social media (n=29, 69%) and customizable reminders (n=26, 62%). Sharing the recorded information in the app with health professionals was infrequent (n=1, 2%). The mean (SD) total uMARS score (maximum 5 points) was 3.78 (0.35), while the mean (SD) uMARS scores for functionality, aesthetics, engagement, and information were 4.21 (0.38), 3.94 (0.54), 3.51 (0.46), and 3.48 (0.44), respectively. Lower mean scores were observed for the subjective quality (mean 2.65, SD 0.56) and perceived impact (mean 3.06, SD 0.67). Moderate to strong positive significant correlations were mostly observed between total uMARS and section-specific uMARS scores, while the correlations between the uMARS section scores were mostly moderate positive. Total uMARS scores were very weakly correlated with user rating, number of reviews, and number of downloads. The quality of popular nutrition-related app platforms in Spain was acceptable, with observed remarkable differences between sections. The majority of the apps were appealing due to their user-friendly interfaces. Only a few apps, however, provided dietary structure analysis or nutritional education. Further research is needed to assess the long-term impact of these apps on users.

Smartphone apps pertaining to aquaculture sector in Bangladesh: Current status and future potentials

In recent years, the aquaculture sector in Bangladesh has experienced significant technological advancements, notably the adoption of smartphone applications. This empirical study evaluates the current status of smartphone apps available in the aquaculture industry of the country using data obtained from the internet and field surveys. The study identified 41 smartphone apps currently used in the sector, where 58.53 % are related to pond-based aquaculture, 14.63 % are designed for biofloc system, 4.87 % pertain to cage aquaculture and 4.87 % marketing-aided corporate-related apps, 9.75 % focus on disease and treatment-related, and 7.31 % fall into miscellaneous categories. The “Poultry Animals and Aqua Index (PAAI)" app, which addresses aquaculture pharmaceutical inputs and fish disease treatment guidelines, emerged as the most downloaded app in this domain. Pearson correlation analysis indicated that app size or user ratings did not influence app downloads. The study also found that approximately 57.66 % of fish farmers are aware of these smartphone apps. Private fisheries professionals were the main driving forces behind the use of these apps, accounting for 52 % of the information spread. Despite mixed levels of satisfaction among farmers, about 25 % expressed a positive experience. Most of the farmers (57.66 %) acknowledged the awareness and beneficial impacts of using these apps in aquaculture. This study enhances the understanding of smartphone apps usage in Bangladesh to promote the aquaculture industry, highlighting its potential to promote industry growth, alleviate poverty, and contribute to economic development.

If it ain’t broke, should you still fix it? Effects of incorporating user feedback in product development on mobile application ratings

How can firms utilize collective user feedback in reviews to better tailor their products to improve customer satisfaction? Based on an automated text analysis of 1,075,704 reviews and a content analysis of 3255 mobile application updates, observed over 460 apps’ first year on the market, this paper investigates the role of collective user feedback on user ratings during the process of developing successive mobile app generations. The results reveal that the rewards associated with responding to user feedback and the penalties due to ignoring this feedback can be substantial. The impact of the match/mismatch between user feedback and product development decisions depends on the topic of the feedback and the timing of the update. The findings provide app developers with guidance on the challenge of prioritizing possible development paths: (1) improve ratings by promptly matching user feedback that require new content or smooth functioning of the app; (2) avoid rating penalties by continuously improving existing content or ensuring compatibility with most recent operating systems and devices. The implications shed light on the fundamental question of whether and when to pro-act on or re-act to the voice of the customer.

Enhancing Book Recommendation Accuracy through User Rating Analysis and Collaborative Filtering Techniques

Since online electronic books have become popular, book recommendation systems have been invented and challenged to handle the high demand from users in the digital era. This study aimed to develop and evaluate a book recommendation model using data mining techniques through RapidMiner Studio. The datasets used were comprised of 981,756 user ratings. Before conducting the data analytics, the data was pre-processed to eliminate duplicates and retain only the highest ratings. Collaborative Filtering (CF) techniques, particularly k-Nearest Neighbours (k-NN) and Matrix Factorization (KF), were employed to elicit insightful information for development and to highlight their capabilities in handling enormous datasets. Furthermore, statistical analysis, visualization, elementary modelling, and model combinations were investigated to compare their performance. To reinforce creditability, modelling techniques and parameter adjustments were integrated to optimize the performance of the algorithms, since the results indicated that different model settings and data partitions impacted the effectiveness of the recommendation system. Additionally, these results demonstrated the potential of hybrid models in improving the accuracy and efficiency of recommendation systems and highlighted the trade-off between algorithmic approaches and dataset characteristics that interplay in optimizing the performance of recommendation systems.

User expectations for an automated motorway access assistant system

IntroductionTo avoid the uncertainty present in a cooperation between manual vehicles (MVs) and automated vehicles (AVs), cooperative connected automated mobility (CCAM) is frequently proposed. In this contribution, we consider the situation of a motorway slip road where an AV (SAE level 3 or higher) on the motorway cooperates by opening a suitable gap for an entering AV. To ensure a meeting of the cooperation partners, speed adjustment by the system might often be necessary.MethodsTo investigate the acceptance of an automated cooperative motorway access assistant a driving simulator study (N = 41) was conducted. Participants drove six laps on a virtual test track in a level 3 AV. Three levels of speed adjustment and two levels of an HMI (with or without HMI) were tested. They experienced the automated cooperative motorway access assistant in two situations. Firstly, when they entered the motorway themselves and benefited from the system, and secondly, when the system reduced their vehicle's speed to allow for cooperation with another vehicle on a motorway slip road ahead.ResultsThe results show that the acceptance of the assistant depends significantly on the magnitude of speed reduction: the greater the speed adjustment, the worse the user rating, and the more often the cooperation was terminated. Obviously, the acceptance depends on the perspective: If the users themselves benefit from the speed adjustment of others, they rate the system significantly better than if they themselves must accept the restriction. Only in the case of a small speed reduction, the assistant is rated as well as if the user benefits from the system. The availability of a human-machine interface (HMI) providing information about the system status influenced acceptance only marginally but had a significant influence on the trust in the automation.DiscussionThe results suggest that people are willing to accept a personal disadvantage in order to give others an advantage in the context of CCAM. However, acceptance depends on the magnitude of the disadvantage. The overall agreement scores of the system with HMI did not differ from those without HMI, although the HMI was found to slightly reduce the termination of cooperation. Three possible reasons for this result, which differs from previous research, are discussed.

Usability Optimization for Mobile Menu Design: An Empirical Study of Hand Grips and User Preferences

This paper investigates the relationship between menu design and hand positions in relation to the assessment of end users with main focus on usability, user preference, and potential adaptions to different hand positions. Sixteen (N=16) participants first participated in a co-design workshop, in which they proposed menu designs for different hand grips. Based on the design proposals, a selection of menu designs were derived and implemented in a mobile app prototype, on which a menu selection study was conducted to investigate performance and perceived usability of the menus in one-handed and two-handed interaction. The results include user ratings and performance, which highlight the need for mobile menus to be adapted for different hand positions. Based on that, we derive design recommendations for more adaptive, user-centric and ergonomic mobile menu designs to match the natural interactions of users.

Research on recommended model of personalized interior environment

Personalized recommendation systems are a common feature of e-commerce, streaming services, and other fields. However, they are not common in the field of interior environment design. Existing international research tends to prioritize deep learning-based recommendation algorithms. These methods rely heavily on large-scale data and complex models to improve the accuracy of recommendations. However, they often face significant limitations. Therefore, this paper proposes a personalized recommendation model for interior environment design based on clustering collaborative filtering algorithm and user portrait. The model improves the traditional collaborative filtering recommendation algorithm. Based on the established UPICF recommendation algorithm, the constructed model is verified through performance analysis and practical application test. The experimental results show that when the number of neighbors is 20, the MAE of the model is 0.7035, which has better personalized recommendation performance. In addition, in the personalized recommendation application of interior environment design, the click-through rate of the model is increased by 0.115, and the purchase conversion rate of users is increased by about 0.270, which further proves the effectiveness of the model. The results show that this model can provide more accurate, diversified and personalized indoor environment design solutions to meet the individual needs of users.

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.