User Priorities Research Articles

A Heterogeneous Multi-Satellite Dynamic Mission Planning Method Based on Metaheuristic Algorithms

The increasing demand for satellite communication necessitates efficient resource management, especially as next-generation high-throughput satellites (HTSs) face challenges in optimizing user connections. This paper presents a novel method that integrates a discretely improved Particle Swarm Optimization (PSO) algorithm with a dynamic task management framework to enhance satellite resource allocation efficiency. The PSO algorithm is adapted for discrete selection problems to maximize a fitness function based on user priority, user preference, and capacity satisfaction, thus ensuring accurate user–satellite matching. A main loop function iteratively updates user data and connection statuses, thus achieving continuous optimization of satellite connections. Through simulation studies, we validated the effectiveness of this method under dynamically changing user demands, demonstrating that the Discrete PSO algorithm significantly outperforms Simulated Annealing (SA) and the Genetic Algorithm (GA) in user satisfaction, maintaining levels above 0.996 even under high demand. Additionally, it effectively prioritizes high-demand users, ensuring their satisfaction remains above 0.95. Overall, our method enhances the management of daily communication tasks, significantly improving service quality and user satisfaction.

Unveiling the multifaceted public interest in ChatGPT

The present study was aimed at identifying key topics in online discussions about the use of ChatGPT by examining a large dataset extracted from Reddit social media using natural language processing. A corpus of 159,971 posts about ChatGPT were extracted from a custom-made python-coded Reddit content scraper for posts in the r/ChatGPT subreddit discussions. After cleaning the data, the sample was reduced to 119,853 posts which was subjected to cluster analysis using the open-source IRaMuTeQ software to identify main topics based on the cooccurrence of texts. These clusters were named by a panel of social psychology experts (n=3) by reading typical text segments within each cluster. Four thematic clusters emerged, categorized into two main topics: “Society and AI Integration”, focusing on ethical concerns (32.1%), and “Operational Aspects and Applications”, which delves into technical and practical facets (67.9%). The latter includes clusters like “AI Technical Framework”, “Casual AI Interactions”, and “Human-AI Etiquette”. The Reddit discourse provides a comprehensive understanding of ChatGPT, revealing user priorities like system capabilities and ethical considerations. Notably, the “Human-AI Etiquette” cluster is a new topic less covered in existing literature. The findings underscore the importance of effective prompting for meaningful user engagement with ChatGPT.

Multidimensional Indicator for Data Quality Assessment in Wireless Sensor Networks: Challenges and Opportunities

Wireless Sensor Networks (WSN) are equipped with numerous sensors that generate vast quantities of data, essential for operational efficiency and informed decision-making. However, the value of this data is contingent upon its suitability for the specific applications it serves. A significant challenge in WSNs is the selection of appropriate data quality dimensions and metrics necessary to construct robust Data Quality Indicators (DQI) and comprehensively assess data quality in various contexts. This systematic literature review seeks to identify the key data quality dimensions and the corresponding measurement metrics within WSNs, while exploring the use of multi-dimensional data quality criteria in developing DQI. A thorough search of SCOPUS and Web of Science databases yielded 475 potential research articles, from which 64 primary studies were selected for in-depth analysis. The findings highlight four key data quality dimensions in WSN: accuracy, timeliness, completeness, and consistency. However, choosing measurement metrics for each dimension requires an in-depth understanding of the data's context. Various approaches for obtaining DQI in WSN research were identified, including weighted linear average models and application-specific contextual information. Effective DQI incorporates weights to each dimension, reflecting the priorities of specific data users, and leverages contextual information pertinent to the sensors’ data. It is crucial to evaluate whether the data collected by WSNs meets established quality standards, a key aspect of WSN operation. These insights will aid in developing more robust and reliable WSNs, ensuring the provision of high-quality data essential for effective operation and decision-making.

ANEL: a novel efficient and lightweight authentication scheme for enhancing security in Vehicular Ad Hoc Networks (VANETs) using elliptic curve cryptography

Vehicular Ad-hoc Networks (VANETs) offer immense potential for revolutionizing modern transportation systems through real-time communication and interaction between vehicles and roadside infrastructure. These networks can significantly enhance traffic management, improve safety, and support various vehicular services. However, ensuring secure communication in VANETs remains a critical challenge due to the highly sensitive nature of the data exchanged, such as location and personal information. Current authentication schemes, including the ANEL scheme, exhibit vulnerabilities like data interception and tracking risks. In this paper, we propose a novel, efficient, and lightweight authentication scheme that leverages elliptic curve cryptography (ECC) and message mapping techniques to address these security issues. The proposed solution strengthens encryption, enhances privacy protection, and mitigates risks such as unauthorized access and man-in-the-middle attacks. Furthermore, the scheme offers scalability and distinguishes between regular and priority users, making it adaptable to various VANET applications. This approach significantly improves the overall security and privacy framework of VANET systems while ensuring the efficient use of computational resources in vehicular environments.

Echo chamber formation sharpened by priority users

Echo chamber formation sharpened by priority users

Stroke recovery—what are people talking about on Twitter? A content analysis

Purpose Prioritisation exercises seek out what matters to key stakeholders to inform the planning of research. Social media platforms are potentially useful data sources. The aim was to examine the content of tweets, short messages containing text and pictures, to ascertain the priorities of Twitter users regarding stroke recovery. Materials and methods Content analysis of Twitter was conducted. An electronic search used the identifiers: #strokesurvivor and #strokerecovery. Tweets spanning four weeks from January 2021 were analysed. Results There were 1361 tweets extracted and 486 analysed following exclusion of duplicates and unrelated material. Six themes were uncovered (n = number of tweets): maintaining motivation and positivity (153); sharing of resources (146); raising awareness of stroke (74); symptomatic aspects of recovery (39); experience of rehabilitation (63); and concerns about Covid-19 (17). Conclusions Despite the brevity of tweets, a rich picture arose. A key limitation was lack of biographical data about Twitter users. Recommendations about topics requiring attention from stroke researchers, clinicians and policy makers are: management of psychological problems; public perception of stroke; rehabilitation considerations including treatment burden, person-centred care and equality of care; symptom management including fatigue and aphasia. Findings can be used to supplement and validate other priority-setting exercises.

Exploring Design Opportunities for Improved Self-motivation in Self-tracking and Health Goal Achievement

Self-tracking has grown in popularity over recent years, leading to a rise in users who monitor data for improving their health. However, decreasing self-motivation due to different user circumstances such as priorities, lifestyles, and habits affects how users conduct self-tracking in apps and hamper their progress towards achieving health goals. We investigated factors that contribute to increasing and maintaining self-motivation while self-tracking for health goals, as well as the potential applications of these factors in health apps. We conducted semi-structured interviews with 15 participants to gain insights into their self-tracking habits and health app usage. Our thematic analysis suggests that users value convenient self-tracking methods and seeing notable progress changes toward a health goal. These findings reveal design opportunities for streamlining app features, reframing progress indicators in data visualizations, and accommodating user priorities.

Pre-filters design for weighted sum rate maximization in multiuser time reversal downlink systems

In high-speed multiuser Time Reversal (TR) downlink systems, the transmission rate is degraded due to the presence of severe inter-user and inter-symbol interference. Moreover, maximizing the weighted sum rate in such systems is a critical objective, since the weighting factors represent the priority of different users in different applications. However, it faces significant challenges as it is an NP-hard and non-convex problem. In order to suppress these interferences and maximize the weighted sum rate, in this paper we present a novel approach for the joint design of the pre-filters. The proposed method applies successive convex approximation to transform the original problem into a Second-Order Cone Programming (SOCP) problem. Then, a low-complexity iterative algorithm is provided to effectively solve the resulting SOCP problem. According to the simulation results, the proposed method reaches a local optimum within a few iterations and demonstrates superior performance in terms of weighted sum rate compared to the current algorithm.

An Analysis of User Complaints on Chat Reference during the COVID-19 Pandemic: Insights into User Priorities

This study aims to identify patterns in user complaints within chat transcripts before and during the COVID-19 pandemic. The researchers analyzed 3,339 chat transcripts from an academic chat reference consortium between January 2019 to December 2021. Transcripts were hand-coded for pandemic status, semester, user type, presence of a complaint, complaint type and subtype, and complaint resolution. We tested the significance of relationships between variables using Pearson’s chi-square test of independence and Fisher’s exact test. Over the three-year study period, 17.6% of chats contained at least one complaint, with faculty and graduate students complaining more than expected, and undergraduates complaining less than expected. The most common complaint types concerned e-resources, accounts, and research. There were significant differences in complaints according to user type: students and faculty complained more about e-resources, staff and alumni complained more about accounts, and members of the public complained more about policies. The researchers found a statistically significant increase in complaints during the pandemic, with more complaints about document delivery and borrowing and e-access eligibility than expected, and fewer complaints about library accounts and noise than expected. Most complaints across the study period were resolved, typically via referral.

A User Priorities Based Strategy for Three Phase Intelligent Recommendation and Negotiating Agents for Cloud Services

As the field of information technology expands, there is a huge need for cloud service providers (CSP). CSP’s vast solutions and services support Cloud, IoT, Fog, and Edge computing. In today’s competitive cloud market, customer satisfaction is critical more than ever. CSP and consumer satisfaction with service level agreement (SLA) fulfilment have always been given more attention. As a result of signing SLA and CSP agreements to supply resources in high demand, customers are now experiencing issues with resource delivery. Cloud and heterogeneous environments necessitate an intelligent recommender and negotiation agent model (IRNAM) to handle responsibilities in the current system. The Recommender system recommends CSP as per users’ priorities, which eases the filtration process. The negotiation process provided by IRNAM ensures that users’ choices are prioritized with maximum jobs to CSP. IRNAM keeps track of the most critical metrics and can reach decisions quickly and for the best possible deal. It uses an analytical concession algorithm that analyzes consumer and CSP choices to find a reliable, secure server with the simplest solution. The negotiation process uses user’s and CSP choice metrics, performance factors, evaluation measures, and success factors in the best execution time to decide. IRNAM provides a flexible and valuable way for selecting CSP and negotiating for services on the user’s terms while considering CSP satisfaction.

Attitudes to Short-Term Staffing and Workforce Priorities of Community Users of Remote Aboriginal Community-Controlled Health Services: A Qualitative Study.

In recent years, there has been an increasing trend of short-term staffing in remote health services, including Aboriginal Community-Controlled Health Services (ACCHSs). This paper explores the perceptions of clinic users' experiences at their local clinic and how short-term staffing impacts the quality of service, acceptability, cultural safety, and continuity of care in ACCHSs in remote communities. Using purposeful and convenience sampling, community users (aged 18+) of the eleven partnering ACCHSs were invited to provide feedback about their experiences through an interview or focus group. Between February 2020 and October 2021, 331 participants from the Northern Territory and Western Australia were recruited to participate in the study. Audio recordings were transcribed verbatim, and written notes and transcriptions were analysed deductively. Overall, community users felt that their ACCHS provided comprehensive healthcare that was responsive to their health needs and was delivered by well-trained staff. In general, community users expressed concern over the high turnover of staff. Recognising the challenges of attracting and retaining staff in remote Australia, community users were accepting of rotation and job-sharing arrangements, whereby staff return periodically to the same community, as this facilitated trusting relationships. Increased support for local employment pathways, the use of interpreters to enhance communication with healthcare services, and services for men delivered by men were priorities for clinic users.

Accelerating a consensus-based EV smart charging algorithm by user priority clustering

The rapid increase in Electric Vehicle (EV) adoption leads to significant challenges for charging station operators due to the unbalance between the growing demand for charging services and their limited power capacity connected to the grid. To tackle the challenge, this paper applies a two-step architecture in which an energy management system (EMS) in the first step determines the available capacity to charge the present fleet of EVs, after which in the second step this capacity is distributed over the individual EVs. To optimally allocate available power and satisfy user requirements, a user priority-based charging management strategy is proposed. Such priority can optimally order the set of EVs to be charged based on user input. However, relying solely on user-estimated input can be prone to incorrect priority due to inaccurate estimations. Therefore, we introduced a clustering of charging sessions based on the user-provided estimated information in the proposed priority function. These clusters relax the effect of inaccurately estimated user input by grouping charging sessions with similar behavior. Our proposed priority can increase user satisfaction by up to 30%, as measured by the percentage of requested energy that is delivered to users, compared to scenarios without priority, 10% more than priority based solely on estimation information, and 8% more than first-come-first-serve. To avoid exposing the personal behavior of the user, we introduce a decentralized approach, applying a consensus-based method that allows local control per EV charging station and only requires minimal communication of a consensus variable with neighboring charging stations. By introducing the priority in the communication weight matrix of the consensus-based algorithm, we accelerate the convergence rate more than 10 times. Simulation results based on real-world data sets in Belgium demonstrate the proposed method scales well with growing fleet sizes.

Model development of the system for avoiding echo chambers in social networks

This article examines the problems that appear in the process of the development of social networks. The focus of the study is on the echo chamber effect in various social networks. It turned out that the echo chamber effect is the result of the work of a deep neural network, which analyzes the interests and priorities of each social network user, checks other similar posts and priorities of other users, and then forms a “circle” of like-minded people for each user. Thus, the main drawback of the standard content generation algorithm is the selection of only publications and comments that support the user’s position. The goal of the study is a software architecture to solve the echo chamber problem in highly loaded social networks in real-time. The main idea was to cluster opinions on controversial topics and include user opinions from different clusters in user content. Data on controversial topics is collected from news, scholarly articles, publications, and comments with hashtags. The collected topics are clustered usi ng the K-means algorithm and the “elbow” method is used to find the optimal number of clusters. The result of the clustering of opinions by topic is provided as input data for the generation of a news feed by a recurrent neural network. The system uses Kafka as a message broker between microservices and AZURE blob storage for storing publications and comments. Both solutions support high scalability.

Identifying app components that promote physical activity: a group concept mapping study.

Digital interventions are a promising avenue to promote physical activity in healthy adults. Current practices recommend to include end-users early on in the development process. This study focuses on the wishes and needs of users regarding an a mobile health (mHealth) application that promotes physical activity in healthy adults, and on the differences between participants who do or do not meet the World Health Organization's recommendation of an equivalent of 150 minutes of moderate intensity physical activity. We used a mixed-method design called Group Concept Mapping. In a first phase, we collected statements completing the prompt "In an app that helps me move more, I would like to see/ do/ learn the following…" during four brainstorming sessions with physically inactive individuals (n=19). The resulting 90 statements were then sorted and rated by a new group of participants (n=46). Sorting data was aggregated, and (dis)similarity matrices were created using multidimensional scaling. Hierarchical clustering was applied using Ward's method. Analyses were carried out for the entire group, a subgroup of active participants and a subgroup of inactive participants. Explorative analyses further investigated ratings of the clusters as a function of activity level, gender, age and education. Six clusters of statements were identified, namely 'Ease-of-use and Self-monitoring', 'Technical Aspects and Advertisement', 'Personalised Information and Support', 'Motivational Aspects', 'Goal setting, goal review and rewards', and 'Social Features'. The cluster 'Ease-of-use and Self-monitoring' was rated highest in the overall group and the active subgroup, whereas the cluster 'Technical Aspects and Advertisement' was scored as most relevant in the inactive subgroup. For all groups, the cluster 'Social Features' was scored the lowest. Explorative analysis revealed minor between-group differences. The present study identified priorities of users for an mHealth application that promotes physical activity. First, the application should be user-friendly and accessible. Second, the application should provide personalized support and information. Third, users should be able to monitor their behaviour and compare their current activity to their past performance. Fourth, users should be provided autonomy within the app, such as over which and how many notifications they would like to receive, and whether or not they want to engage with social features. These priorities can serve as guiding principles for developing mHealth applications to promote physical activity in the general population.

Mathematical programming models to assist the evaluation of shipbrokers

Shipbrokers play a key role in maritime industry by acting as intermediates between shipping companies and the market. They undertake various chartering, buying or selling operations. In this paper, we propose a mathematical programming approach for the evaluation and selection of shipbrokers. Specifically, the score of each ship broker is a composite measure that is derived by aggregating a set of performance criteria, e.g., reputation, etc. The developed mathematical programming models enable the aggregation and weighting of the criteria. We employ three optimization models to explore the effect of different weighting schemes on the scores and ranking of the shipbrokers. The models that provide a common set of weights for all the shipbrokers establish the appropriate ground for comparisons among them. Also, our models facilitate the incorporation of user priorities over the criteria in the form of weight restrictions. The proposed approach is illustrated by assessing seven shipbroker offers for selling a dry-bulk ship using four criteria, namely revenue, brokerage fee, brokerage time and terms & conditions.

Maximum Entropy Solution for M^X/G/1 Priority Reiterate G-queue Under Working Breakdown and Working Vacation

The maximum entropy principle has grown progressively more pertinent to queueing systems. The principle of maximum entropy (PME) presents an impartial framework as a promising method to examine complex queuing processes. This principle can be employed to assess the most appropriate probability distributions for queueing scenarios in a variety of widespread industrial issues. The aspects of general service bulk arrival retrial G-queue including working vacation, state-dependent arrival, priority users, and working breakdown are all explored in this article. Real-world applications for this kind of waiting line include computer systems, industrial companies, packet-switching networks, and communication facilities, etc. The adverse users (or negative arrivals) can make an appearance when the server (operator) is preoccupied with a positive user. Consumer’s arrival patterns follow the Poisson distribution. Priority consumers and regular (ordinary) consumers are the two groups of consumers that are considered in this investigation. Priority consumers do not have to wait in line and are granted a special right of prevention that allows them to receive services before ordinary consumers. Initially, we have estimated performance metrics including orbit size and long-run probabilities in this research work. The maximum entropy approach is then used to give a comparative perusal between the system’s exact and estimated waiting times. Apart from that a bi-objective optimization model is developed to diminish both consumers waiting times and estimated costs simultaneously. It is manageable to establish an effective balance between the standard of service and operating expenses using the analytical strategy that has been provided.

The League: A person-centred approach to the development of social robotics for paediatric anxiety.

Social robots are promising tools to improve the quality of life of children and youth living with anxiety and should be developed based on the priorities of end users. However, pathways to include young people in patient-oriented research, particularly in the overlap between technology and mental health, have been historically limited. In this work, we describe engagement with experts with lived experiences of paediatric anxiety in a social robotics research programme. We report the experiences of patient advisors in a co-creation process and identify considerations for other research groups looking to involve end users in technology development in the field of youth mental health. We engaged individuals with a lived experience of paediatric anxiety (current, recent past, or from a parent perspective) using three different models over the course of three years. Two initial patient partners were involved during project development, eight were engaged as part of an advisory panel ('the League') during study development and data analysis and four contributed as ongoing collaborators in an advisory role. League members completed a preparticipation expectationsurvey and a postparticipation experience survey. Eight individuals from a range of anxiety-related diagnostic groups participated in the League as patient partners. Members were teenagers (n = 3), young adults aged 22-26 years who had connected with a youth mental health service as children within the past eight years (n = 3) or parents of children presently living with anxiety (n = 2). Preferred methods of communication, expectations and reasons for participating were collected. The League provided specific and actionable feedback on the design of workshops on the topic of social robotics, which was implemented. They reported that their experiences were positive and fairly compensated, but communication and sustained engagement over time were challenges. Issues of ethics and language related to patient-centred brain health technology research are discussed. There is an ethical imperative to meaningfully incorporate the voices of youth and young adults with psychiatric conditions in the development of devices intended to support their mental health and quality of life. Six young people and two parents with lived experiences of paediatric anxiety participated in all stages of developing a research programme on social robotics to support paediatric mental health in a community context. They also provided input during the preparation of this manuscript.

Exploring the landscape of health technology assessment in Iran: perspectives from stakeholders on needs, demand and supply

BackgroundThe evaluation of health technologies plays a crucial role in the allocation of resources and the promotion of equitable healthcare access, known as health technology assessment (HTA). This study focuses on Iran’s efforts to integrate HTA and aims to gain insights into stakeholder perspectives regarding capacity needs, demand and implementation.MethodsIn this study, we employed the HTA introduction status analysis questionnaire developed by the International Decision Support Initiative (iDSI), which has been utilized in various countries. The questionnaire consisted of 12 questions divided into three sections: HTA need, demand and supply. To identify key informants, we conducted a literature review and consulted with the Ministry of Health and Medical Education (MOHME), as well we experts in policy-making, health service provision and HTA. We selected stakeholders who held decision-making positions in the healthcare domain. A modified Persian version of the questionnaire was administered online from September 2022 to January 2023 and was pretested for clarity. The analysis of the collected data involved quantitative methods for descriptive analysis and qualitative methods for thematic analysis.ResultsIn this study, a total of 103 questionnaires were distributed, resulting in a favourable response rate of 61% from 63 participants, of whom 68% identified as male. The participants, when assessing the needs of HTA, rated allocative efficiency as the highest priority, with a mean rating of 8.53, thereby highlighting its crucial role in optimizing resource allocation. Furthermore, healthcare quality, with a mean rating of 8.17, and transparent decision-making, with a mean rating of 7.92, were highly valued for their impact on treatment outcomes and accountability. The importance of budget control (mean rating 7.58) and equity (mean rating 7.25) were also acknowledged, as they contribute to maintaining sustainability and promoting social justice. In terms of HTA demand, safety concerns were identified as the top priority, closely followed by effectiveness and cost-effectiveness, with an expanded perspective on the economy. However, limited access to local data was reported, which arose from various factors including data collection practices, system fragmentation and privacy concerns. The priorities of HTA users encompassed coverage, payment reform, benefits design, guidelines, service delivery and technology registration. Evidence generation involved the participation of medical universities, research centres and government bodies, albeit with ongoing challenges in research quality, data access and funding. The study highlights government support and medical education as notable strengths in this context.ConclusionsThis study provides a comprehensive evaluation of Iran’s HTA landscape, considering its capacity, demand and implementation aspects. It underlines the vital role of HTA in optimizing resources, improving healthcare quality and promoting equity. The study also sheds light on the strengths of evidence generation in the country, while simultaneously identifying challenges related to data access and system fragmentation. In terms of policy priorities, evidence-based decision-making emerges as crucial for enhancing healthcare access and integrating technology. The study stresses the need for evidence-based practices, a robust HTA infrastructure and collaboration among stakeholders to achieve better healthcare outcomes in Iran.

Allocation of transportation capacity for complex natural gas pipeline network under fair opening

China has formed a national natural gas pipeline network, and reasonable and accurate pipeline capacity allocation scheme has important significance for improving the utilization rate of pipeline network, improving the income of pipeline network operators and ensuring the gas consumption of downstream users, which poses greater challenges to the formulation of pipeline capacity allocation rules and mechanisms. This paper takes the natural gas pipeline network system as the research object, and studies the rules and mechanisms of pipeline capacity allocation. A capacity allocation model of natural gas pipeline network is proposed with the net present value of pipeline network operator's income as the goal, considering the gas consumption characteristics of downstream users, the dynamic change of gas source node capacity, pipeline hydraulic characteristics, pipeline flow direction, compressor and other constraints. Based on the simulation of the actual pipeline network by the proposed model, seven allocation rules and one mechanism are proposed. Specifically, the user priority of non-interruptible type, the pipeline path adjustable type and the gas source node adjustable type is higher than the corresponding comparison type. In the pipeline capacity allocation mechanism, the amount compensation mechanism for pipeline capacity transactions is as follows: When the pipeline capacity transaction occurs between users, the transaction information is submitted to the pipeline network operator. The pipeline network operator proposes the corresponding amount compensation according to the transaction information, and the transaction parties negotiate the proportion of the amount compensation and submit it to the pipeline network operator. In addition, it is recommended that pipeline network operators vigorously develop LNG receiving stations and downstream interruptible and compressible users in the future.

带优先权用户和半休眠机制的云系统节能分析

带优先权用户和半休眠机制的云系统节能分析