User Interest Research Articles

Utilidad de los portales en línea de ciencia ciudadana para estudios florísticos: Orquídeas de Santa María Yucuhiti, Oaxaca, México

Background: The beauty of orchids attracts the interest of users of citizen science portals, turning them into additional sources of information for the study of biodiversity. Currently, iNaturalist records observations of almost 70 % of Mexican orchid species. Here, we evaluate the usefulness of the platform to create a local floristic list of this group of plants. Question: Does the information available on iNaturalist allow for the creation of a local orchid checklist as a conventional floristic study does? Study site and dates: Observations recorded between 2007 and 2024 in Santa María Yucuhiti, Oaxaca, Mexico. Methods: Orchid observations in iNaturalist were reviewed and curated, assigning vegetation type and site elevation for each record. The resulting floristic checklist was commented and compared, using a richness index, with those of other regions of Oaxaca, that were prepared through conventional floristic studies. Results: The study area holds 107 species of orchids, 50 % are endemic to Mexico. The highest richness is found between 1,500-1,999 m elevation and in the oak forest. Acianthera hintonii, Habenaria tetranema and Prosthechea mulasii represent new records for Oaxaca. The study area showed the highest orchid richness index compared to other regions of Oaxaca. Conclusions: Information from iNaturalist allowed the preparation of the orchid checklist for Santa María Yucuhiti, supported by the validated observations on the platform. This, along with the availability of high-resolution photographs, the temporal continuity in documentation, and the exploration of sites with restricted access, enhances the quality of the checklist.

Trending extinctions: online interest in recently extinct animals

AbstractHalf of all species are predicted to face extinction by the end of the century. Despite this, awareness of certain species' extinctions remains surprisingly muted, highlighting the need to improve and quantify public awareness. We explore the connection between biodiversity loss and public awareness by quantifying the changes in online interest on Twitter (now known as X) and Wikipedia both before and after the extinctions of eight species and following their reclassification in the IUCN Red List. Our findings reveal that extinction announcements generally spike online interest for most species, albeit briefly on Twitter (i.e. tweets and retweets about species), while Wikipedia (i.e. article pageviews of species) exhibits a more prolonged interest. IUCN reports were generally not associated with increased interest. Coordinated media coverage, especially when aligned with broader environmental narratives and key events, enhanced the impact of extinction announcements. On Twitter, spatially we observed a shift from local to global interest of users following extinction. We also found a small subset of influential users on Twitter, including content creators and media organisations, who disproportionately shaped conservation discussions. Environmentally oriented individuals and organisations also play a significant role, collectively comprising a third of the top retweeted users. Overall, these results highlight the need for conservation bodies, such as the IUCN, to engage more directly both with media organisations and content creators in order to drive public interest for conservation efforts, especially for less charismatic species. The tragedy of species extinction necessitates such efforts to ensure sustained and meaningful public awareness. Our findings also show that while social media outlets can generate global interest quickly, which may drive public discussions regarding ongoing extinctions and potential future de‐extinctions, this is likely to be short‐lived, underscoring the importance of alternative platforms such as Wikipedia, which can foster longer‐term engagement.

The practice of protecting agricultural land in Jiangsu Province of China

The safeguarding of agricultural land is rooted in national land surveys and remote sensing data, which are enhanced by contemporary information technology. This framework facilitates the monitoring and regulation of unauthorized alterations in cultivated land usage. This paper aims to analyze land policies at the national, provincial, and local levels, investigate the cultivated land protection strategies implemented within the research region, where the policies have gained societal acceptance, and propose recommendations and countermeasures to enhance the development and utilization of land resources. The central issue of this study is to identify the challenges in achieving a balance between human activities and natural ecosystems. To address this issue, the research employs a combination of literature review, semi-structured interviews, text analysis, and content analysis, emphasizing the integration of empirical fieldwork and theoretical frameworks. Key areas of focus include: (a) the current state of the farmland protection system, (b) the legal foundations for local enforcement, (c) the systematic mechanisms for implementing arable land protection, and (d) the coordinated oversight system involving both the Party and government. Notably, the practice of cultivated land protection faces several challenges, primarily stemming from two factors. Firstly, there exists a disconnect between the economic interests of certain illegal land users and the objectives of land management, which hinders effective enforcement. Secondly, environmental repercussions arise from misinterpretations of land policy or non-compliant land development practices aimed at profit, which contradict the goals of ecological sustainability. The study examines two approaches to address the issue: the distribution and effective use of land resources, and the capacity for monitoring and early warning systems. Findings indicate that Dongtai City in Jiangsu Province has rigorously implemented all national land management policies, while also preserving the adaptability of local townships in practical applications, thereby ensuring the consistency of both the quality and quantity of arable land.

Multi-granularity label-aware user interest modeling for news recommendation

Multi-granularity label-aware user interest modeling for news recommendation

Analysis of Google users' interests in information about pacifiers: an ecological digital study.

To determine the interests of Google users from different countries about pacifier-related information over time, through a quantitative and qualitative analysis. This infodemiology longitudinal retrospective ecological study investigated pacifier-related computational metadata from 22 countries using Google Trends platform. Initially, Relative Search Volume (RSV) time series data and their respective main queries were collected employing the topic "pacifier - subject" as the search strategy from January 2004 to June 2024, considering all categories. Data were then analyzed through seasonal decomposition to determine the trend, seasonal, and residual components of the collected time series using Python 3 programming libraries in a Google Collaboratory interface. Additionally, Mann-Kendall tests were performed to access the significance level of trends observed from the seasonal decomposition. Finally, the queries were qualitatively evaluated to identify the main subjects related to users' interests. Statistical significance was determined with p values < 0.05. In general, RSV time series curves exhibited upward trends in all analyzed countries over the years, with significance levels demonstrated by the Mann-Kendall tests. Moreover, queries often centered around types of pacifiers, encompassing searches specifically for certain commercial brands. Some searches concerning newborn and baby care were also observed, particularly highlighting breastfeeding practices. Google users have shown an increasing interest in pacifier-related topics. These data can help understand the needs and concerns of parents and caregivers regarding pacifier use, ultimately supporting the development of effective educational strategies.

The Short Video Platform Recommendation Mechanism Based on the Improved Neural Network Algorithm to the Mainstream Media

Currently, the capacity of short videos continues to increase. Video manufacturers hope to enhance user experience and stickiness through recommendation algorithms, while users seek personalized videos to save time and money. Therefore, in order to address the data sparsity and high-dimensional feature extraction, this study proposes a novel short video platform recommendation model. The proposed method utilizes the term frequency inverse document frequency algorithm for text mining, and combines error back propagation neural network for learning to explore the potential connection between users and videos. This method combines natural language processing and image analysis in deep learning to construct accurate user and video models, deeply explore user interests, and improve the accuracy and effectiveness of recommendation systems for user preferences. The research results showed that the recommendation accuracy of this method was 66% and 70% respectively, and the prediction accuracy was 73.50% and 88% respectively. When Num=128, 200 data points were recommended within 0.3678s. The proposed algorithm outperforms the other three methods in terms of recommendation accuracy compared with the ItemCF and UserCF algorithms. This is because the method uses an approach based on image and user vector, combined with relevant features of the video and user. The proposed method can deeply explore the relevant features of videos and users, overcoming the data scarcity in previous collaborative screening, and guiding video recommendation on practical media platforms.

Extracting Political Interest Model from Interaction Data Based on Novel Word-level Bias Assignment

In democratic countries, political interest is deeply involved in people's daily lives. Research in political consumerism shows that product purchase decision is also influenced by the political orientation of the consumer. In traditional recommendation system design, user interest in an item is provided by a unified model. Recently, interest disentanglement methods have been introduced. It is shown that by disentangling interest factors such as conformity and private interest, recommendation performance can be significantly improved. However, few studies attempt to disentangle political interest in purchase behavior, which is bipolar. In this paper, we propose a method to extract political interest model from e-commerce interaction data, which is supported by a novel word-level political bias assignment. For the bias assignment part, we improved a political bias distilling method. For the political interest model extraction part, we extend a one-side bias method to make it support bipolar bias. We compare our method with state-of-the-art baseline methods in several evaluation settings, and the experimental results show that our method can achieve superior performance. Further investigation shows that our method is consistent with theories of political consumerism.

Integrating Artificial Intelligence and Big Data in Spanish Journalism Education: A Curricular Analysis

Artificial intelligence (AI) and big data have impacted different professional sectors in our society. Communication and journalism are clearly among them. From the automatic generation of content to the identification of topics of interest or monitoring of users’ usage habits, AI introduces important training challenges for professionals in the field of communication. Meanwhile, big data analytics enables data journalists to handle large amounts of information in an automated manner, allowing them to perform in-depth analysis of disorganized data. This study analyzes the integration of artificial intelligence (AI) and big data in the curricula of journalism degrees offered by Spanish universities. The research employs quantitative and qualitative methods to examine the typology, syllabus, and distribution of subjects directly or indirectly addressing AI and big data topics, based on indicators such as structure, credit system, objectives, competencies, and professional profiles. The results reveal a scarce integration of AI and Big Data subjects in journalism curricula in Spain. Among the analyzed courses, only seven addressed data journalism as a complete course, while 19 introduced AI and Big Data as part of more general content. The study highlights the need for journalism education to adapt to the disruptive impact of AI and big data on the profession. It discusses the debate between focusing on teaching technological skills versus providing critical and ethical values. The research aims to contribute to the discussion on the readiness of journalism curricula to cope with technological advancements by analyzing the Spanish case.

Design and Implementation of a Personalized Text-Based Recommendation System

Abstract: We all know that in the competitive &amp; ever evolving Age of content -less is more and relevancy increases engagementwhich should be focal on UX. In this paper, we describe the designing and implementation of a text-based recommendation system that specializes in offering automatic personalized recommendations for textual content-dependent on user preferences, and behavior. Analyze text data and receive tailored, contextual recommendations using Natural Language Processing (NLP) and machine learning methods. This system combines collaborative, content-based and hybrid filtering techniques along with adaptive learning to improve the accuracy of recommendations over time as user interests change. Analysis of real-world datasets indicates that the system dramatically enhances recommendation precision and user satisfaction. The results highlight the promise of tailored recommendations in areas like commerce and digital media to online education, primarily filtering great volumes of information for a more customized experience

Designing an Effective Job Recommender System based on Embedded Machine Learning Models

The need to automate employment offers to qualify job searchers has gain attentions. For an automated recommendation systems to be used more frequently, a better user-friendly filtering techniques are required. This paper designs an automated process, referred to as “the job recommender”, which focuses on user-centric design and personalization for recommending and matching applicants with appropriate jobs. We use the bottom-up approach that uses dataset based on filtering algorithms to predict and make recommendations for job seekers. The algorithm helps the recruiters to produce the list of the résumé that best meets the job descriptions. In this context, the random forest (RF) and support vector machines (SVM) are adopted to train the data. They are supplied personalized information (qualifications, result of aptitude test, age, and work experience) reported on the résumés of individual candidates from the pool of submissions, and the system train data to learn the evolution of job selection by candidates based on these machine learning tools. The algorithm used would help the recruiters to produce the list of the résumé that best meets the job descriptions. The algorithms are designed to recommend personalized items tailored to each user's interests. Under the minimum hardware and software requirements, the job recommender system was implemented in streamlit - a python template, for designing the frontend.

Technical note: A guide to using three open-source quality control algorithms for rainfall data from personal weather stations

Abstract. The number of rainfall observations from personal weather stations (PWSs) has increased significantly over the past years; however, there are persistent questions about data quality. In this paper, we reflect on three quality control algorithms (PWSQC, PWS-pyQC, and GSDR-QC) designed for the quality control (QC) of rainfall data. Technical and operational guidelines are provided to help interested users in finding the most appropriate QC to apply for their use case. All three algorithms can be accessed within the OpenSense sandbox where users can run the code. The results show that all three algorithms improve PWS data quality when cross-referenced against a rain radar data product. The considered algorithms have different strengths and weaknesses depending on the PWS and official data availability, making it inadvisable to recommend one over another without carefully considering the specific setting. The authors highlight a need for further objective quantitative benchmarking of QC algorithms. This requires freely available test datasets representing a range of environments, gauge densities, and weather patterns.

AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for?

Issues linked to the increasing presence of AI-generated content in people’s lives, and the importance of being able to effectively navigate and distinguish such content, are inherently linked to transparency, a notion that our study focuses on by evaluating Art. 50 of the AI Act. This article is a call for action to take the interests of end users into account when specifying AI Act's transparency requirements. It focuses on a specific use case – media organisations producing text with the help of generative AI. We argue that in its current form, Art. 50 leaves many uncertainties and risks doing too little to protect natural persons from manipulation or to empower them to take protective actions. The article combines documental and survey data analysis (based on a sample representative of the Dutch population) to propose concrete policy and regulatory recommendations on the operationalisation of the AI Act’s transparency obligations. Its main objective is to respond to the following question: how to reconcile AI Act’s transparency provisions applicable to digital news articles generated by AI with news readers’ perceptions of manipulation and empowerment?

Public non‐financial reporting: Reflection of environmental aspects of development and activities of economic entities

The article considers economically significant information reflected in public non‐financial reporting, enabling reflection on the environmental aspects of an organization's development. The analysis aims to articulate problems in the disclosure economically significant information in public non‐financial reporting due to a significant difference between national and international requirements in terms of the environmental aspects of development.In the process of conducting this study, a monograph (descriptive) research method, a system analysis method and a comparative analysis method were used.The results of the study confirmed the need to improve environmental reporting as part of the development of the concept of non‐public financial reporting, while authors emphasising the need to develop the composition and content of environmental reporting, taking into account the integration of financial and non‐financial reporting. The application of the approaches outlined in the article allowed the generation of more transparent information for all interested users regarding the environmental activities of companies and will bring national and international standards closer to application.The environmental reporting of companies and its components are proposed as part of the development of the concept of public non‐ financial reporting.

Retraction notice: Prediction of user interest fluctuation using fuzzy neural networks in web search

Retraction notice: Prediction of user interest fluctuation using fuzzy neural networks in web search

A Study on the Development of Information and Communication Technology-Based Oral Functional Rehabilitation Exercise Program Content for Elderly People.

Background/Objectives: This study was conducted to develop information and communication technology (ICT)-based oral functional rehabilitation exercise (OFRE) program content to effectively improve the oral function of the elderly people. Methods: After selecting evidence-based effective OFRE items through systematic review, the final items were constructed through the validity evaluation of detailed items through an expert Delphi survey. The items were composed in a simple content form that can be performed directly and applied to ICT-based mobile applications. Results: The final content items consisted of an oral functional motor-ability measurement, oral Pilates videos, and games. The first is to measure the maximum opening amount before and after exercise, and the opening amount was designed to be measured by eating the fruit displayed on the screen by opening and closing the lips. The second one consisted of eight exercises in the video, and each exercise was to be performed at least three times a day, with a total of two sets. The third is a salivary secretion function exercise that stimulates the salivary glands to stimulate the user's interest and enable them to perform oral movements on their own. It consists of a lip and respiratory muscle exercise that inflates the cheeks and bursts a balloon, and the image disappears when the word in the image presented on the screen is pronounced correctly. It consists of pronunciation exercises. Conclusions: This content development attempt can be expanded into new convergence research linked to ICT and can be used as basic data when developing related content as part of digital care for the elderly in the future.

Online Writeup Categorization using a Machine Learning Strategy

A variety of online sources produces a huge amount of daily news; thus, it is important to categorize the news items to make the information accessible to consumers easily and quickly scraping is used to gather existing news items from news websites and then categorize them automatically using a variety of classification algorithms. As a result, news categorization is a technique for discovering untracked news themes and providing specific recommendations based on the user's historical interests. The BBC News dataset, which includes articles from five different categories including Business, Entertainment, Politics, Sport, and Technology, is used in this task to discuss various steps in news classification and implement a few algorithmic approaches such as Naive Bayes, Binary Classifier, SVM, Perceptron, and SGD. In the study, results from several classification algorithms are examined, and their accuracy is measured.

Why youth continue watching reality shows on social media? A case study in Vietnam

This research investigates the motivations of Vietnamese college students to use social media to watch reality shows. Understanding students’ motivations for using social media to watch reality shows is vital to developing relevant and compelling content, creating content that meets the insight of target audiences, and promoting the growth of the media industry and the interests of users and businesses. This research uniquely combines Technology Acceptance Model and Use and Gratification Theory theoretical frameworks to develop a conceptual model to investigate the motivations that motivate Vietnamese students to watch reality shows through social media platforms. Findings show that college students know the ease of use and usefulness of media technology and the basic cognitive, social, and emotional satisfaction they derive from media use to watch reality shows. The results show that this study was successful in exploring Vietnamese students’ motivation for utilizing social media to view reality shows. This research contributes to individuals’ understanding and motivation for using social media to meet their needs. These findings help improve the content creation capabilities of reality show producers through technological innovation and enhanced experiences based on the needs of viewers, especially Vietnamese students.

Design and Development of a Smart Fidget Toy Using Blockchain Technology to Improve Health Data Control.

This study explores the integration of blockchain technology in wearable health devices through the design and development of a Smart Fidget Toy. We aimed to investigate design challenges and opportunities of blockchain-based health devices, examine the impact of blockchain integration user experience, and assess its potential to improve data control and user trust. Using an iterative user-centered design approach, we developed a mid-fidelity prototype of a physical fidget device with a blockchain-based web application. Our key contributions include the design of a fidget toy using blockchain for secure health data management, an iterative development process balancing user needs with blockchain integration challenges, and insights into user perceptions of blockchain wearables for health. We conducted user studies, including a survey (n = 28), focus group (n = 6), interactive wireframe testing (n = 7), and prototype testing (n = 10). Our study revealed high user interest (70%) in blockchain-based data control and sharing features and improved perceived security of data (90% of users) with blockchain integration. However, we also identified challenges in user understanding of blockchain concepts, necessitating additional support. Our smart contract, deployed on the Polygon zkEVM testnet, efficiently manages data storage and retrieval while maintaining user privacy. This research advances the understanding of blockchain applications in health wearables, offering valuable insights for the future development of this field.

Exploration of UCD Practice Limitations

Abstract The integration of user-centred design (UCD) practices in software development is fundamental for enhancing the overall experiences of software users. In this paper, we summarize the results of various research studies on what the stated limitations of UCD practices are. A process called the UCD Sprint was introduced recently with the goal of addressing some of the existing UCD practice limitations for integrating UCD practices in software development. The guided usage of the step-by-step process has been studied with students and UCD practitioners in courses showing positive results, where participants describe benefits while using the process, such as the speed of the process, its clear structure, guidance. In this paper, we describe results from a synthesis study summarizing results on how guided usage of the UCD Sprint can address UCD practice limitations. Furthermore, we describe results from a series of interviews with UCD practitioners on how they currently practice UCD, the challenges and limitations they experience and their views on how unguided usage of the UCD Sprint would potentially fit their UCD practices. While the interviewees recognize the importance of user involvement, they highlight challenges in aligning user interests with other stakeholders' interests. All participants had used processes like design sprints for structuring their UCD practices. Interviewees have generally tailored the traditional approach of design sprints, showcasing a nuanced understanding of UCD practices. The results show that the UCD Sprint process is considered by UCD practitioners as a promising approach for practicing UCD. The process is viewed with enthusiasm, but the need for clear objectives and adaptability to the work contexts of the UCD practitioners remains essential. UCD practitioners’ adaptive strategies of their UCD practices emphasize the need for flexibility and adaptation of UCD practices and processes.

SYRMEP beamline: state of the art, upgrades and future prospects

SYRMEP is the hard X-ray imaging beamline of Elettra synchrotron offering X-ray full-field techniques, micro-computed tomography (microCT) and phase-contrast modality in the energy range 10–40 keV. The beamline operates in a multidisciplinary research context spanning from biomedical applications to botany, from zoology to food technology and cultural heritage, from materials engineering to geology and earth science. Thanks to the flexibility of SYRMEP setup, in situ experiments can be performed as well, novel imaging methods can be developed and implemented in a synergical manner with interested users and collaborators. SYRMEP peculiar wide beam together with the long sample-to-detector distance enables multiscale phase-contrast studies with optimized contrast and spatial resolution on rather large specimens, such as human lung phantoms. This is particularly relevant in view of future clinical lung imaging foreseen in the framework of Elettra 2.0 program. Here, the current beamline features and recent upgrades are illustrated, an overview of the imaging methods routinely offered to SYRMEP users’ community is presented, and the outlook for the new beamline SYRMEP-Life Science (SYRMEP-LS) is reported.