Information To Users Research Articles

Algorithms and strategies for fraud prevention on online platforms

The topic of fraud prevention on online platforms is an urgent area in the field of information security and user protection. Modern fraud prevention algorithms and strategies include an integrated approach combining machine learning, analytical tools, and multi-layered protection systems. The key aspects are the detection of anomalies in user behavior, the use of machine learning algorithms to identify suspicious activities, as well as the integration of data verification and verification systems. Behavioral analytical models that help predict and prevent potential threats also play an important role in the fight against fraud. The effectiveness of these methods and strategies depends on their ability to adapt to evolving threats and ensure a balance between security and user convenience.

Decoding patterns: the crucial role of social media in health information consumption and user dynamics among the general population

Decoding patterns: the crucial role of social media in health information consumption and user dynamics among the general population

New technology in libraries work and the problems of education in the field of librarianship and bibliography

The article deals with the problem of the changing demands of education for librarianship and information work in developed capitalist countries as a result of the application of computers and changing technology of library work. There are indications that, under these conditions, education and training must be something rather different from traditional library education. The changing role of information, which becomes one of the natural resources of a country, and the rapid development of computer technology reveal new professional demands for librarians. They are becoming something like gatekeepers of information, mediators between information stored in databases and information users. As a result of this tendency, the teaching of online bibliographic searching is of great importance in the curricula of library schools all over the world. The main trends of alteration in the programs of library schools during the last 10 years are surveyed, and principal tendencies of future development in the education of librarians are revealed.

Information needs of students and the problem of their satisfaction in higher school

The higher school forms reading, social, and professional interests of the students. The investigation of students' information needs enables better service to readers, improves the promotion of books, enhances reference and bibliographic work, and creates optimal methods in information skills training. The article analyzes students' information needs, ways of satisfying these needs, readers' attitudes towards library resources, and the use of information opportunities in universities in Ukraine, Moscow, Vilnius, and other higher education institutions in our country. Based on students' interests, purposes of their activities, and work in scientific and social circles, six types of students were identified. By connecting these types with the students' information culture formation program, it is possible to develop the structure of students' information needs. The article provides suggestions for training information users. On one hand, it helps to meet students' information needs, and on the other, it trains creative information users and creators of new information.

Generic framework for a coherent integration of experience and exposure rating in reinsurance

Abstract This article introduces a comprehensive framework that effectively combines experience rating and exposure rating approaches in reinsurance for both short-tail and long-tail businesses. The generic framework applies to all nonlife lines of business and products emphasizing nonproportional treaty business. The approach is based on three pillars that enable a coherent usage of all available information. The first pillar comprises an exposure-based generative model that emulates the generative process leading to the observed claims experience. The second pillar encompasses a standardized reduction procedure that maps each high-dimensional claim object to a few weakly coupled reduced random variables. The third pillar comprises calibrating the generative model with retrospective Bayesian inference. The derived calibration parameters are fed back into the generative model, and the reinsurance contracts covering future cover periods are rated by projecting the calibrated generative model to the cover period and applying the future contract terms.

System Development Methodology For Accounting Infoemation System

This study discusses the importance of selecting an appropriate information system development method before starting the development process to ensure the creation of a system that meets expectations. Although there are many methodologies or models for software development, confusion often arises when choosing the most suitable one based on specific situations or environments. The article also outlines the basic concepts of systems, accounting information systems, and the stages of system analysis, including proposal preparation, system analysis implementation, and the preparation of system analysis reports. The research methodology used is library research, which involves studying literature and analyzing various written information sources. The findings highlight the importance of written documentation at each stage of system development to effectively communicate plans and findings to information users, as well as the significance of employing effective information-gathering techniques during system analysis. Therefore, selecting the right method and maintaining thorough documentation are crucial to successfully developing an information system that meets user needs and supports organizational goals.

Climate Change Adaptation Information Usability Among Maize Farmers in Ido Local Government Area of Oyo State, Nigeria

The availability of climate change information has not always led to more successful adaptation; even when climate change adaptation information is available, there are still obstacles to its efficient use. Furthermore, the tactics required to communicate climate information to end-users for successful adaptation have received less attention. This study focuses on utilization of available climate change adaptation information among maize farmers in the study area. Simple random sampling technique was used to select 203 respondents for data collection. The data were collected using a questionnaire and analysed through the use of the standard statistical package for social sciences. The communication strategies used include inter-personal, audio, visual and social media. The results revealed that the strategies used in communicating climate change adaptation information to maize farmers in the study area yielded great influence in terms of benefits to the farmers such as the increase in income, prevention of yield loss, reduced soil degradation, maintaining good human health status, and improved weed management. The information that is popularly utilised is mixed cropping (61.6%), water management (58.1%), and early warning (58.1%). A significant relationship existed between communication channels (r=0.8, p<.05) and respondents’ climate change adaptation information utilization. Poor understanding of the message and lack of funds to put the information into practice hinders some respondents from utilising adaptation information. Hence, extension agents need to locate these category farmers and link them with service providers that could provide incentives or credit facilities for sustainable utilisation of available information to combat climate change.

The analysis of information needs in agriculture

The article is devoted to the groups of information users in agriculture and to the structure of their information needs. The methods of their analysis are discussed. The data and the results of a questionnaire survey conducted at different levels of the agricultural information system in Lithuania are presented. The aim of this survey was to find out how data from mechanical information carriers are used. Statistical material is analyzed according to the main groups of agricultural specialists: scientists and producers. The received results enable the following statement: agricultural specialists are not satisfied with traditional information methods; however, they do not fully utilize new types of information due to unsatisfactory promotion, a shortage of technical devices, and a lack of knowledge in the information field.

The automatization of press statistics as one of the ways to improve the service of information users

The Department of Press Statistics at the Book Palace of Lithuania is the first in the Soviet Union to apply computers for preparing quick accounts of gathered information. This significantly helps to regulate book production and its thematic planning. The Department of Press Statistics functions independently as a constituent part of the automatization of press statistics in the Book Palace. The rapid and precise handling of statistical tasks, which reflects the basic tendencies of press production activities, enables the improvement of the press production process. The grouping and gathering of the main features of the press and the analysis of statistical feedback help to use computers in the most rational way to increase the capacity for solving statistical tasks.

Reuse of Adverse Effect Reports from the French National Agency of Medicines: A Visual Analytic Tool to Improve Patient Safety.

Adverse drug reaction are defined as "harmful or unpleasant reaction, resulting from an intervention related to the use of a medicinal product". In France, adverse effects due to medicines are reported to the French National Agency of Medicines (ANSM) by the healthcare professionals or consumers. The objective of this study was to implement a tool that facilitates the utilization of ANSM reports by synthesizing information to effectively inform prescribers and users. We focused on 3 psychotropic classes: antidepressants, antipsychotics and anxiolytics. We extracted relevant data from the ANSM website through a webscraping process, based on the names of molecules in these 3 classes: antidepressants, antipsychotics, and anxiolytics. We implemented a web interface with R Shiny that provides three panels: (i) a presentation of the active ingredient with the fewest reports for a selected adverse effect category, (ii) the adverse reactions for a selected active ingredient ranked in descending order, and (iii) a comparison of two active ingredients where, for each adverse effect, the active ingredient with the fewest reported adverse drug events (ADEs) is displayed. Our application allows for synthesizing information to effectively inform prescribers and users. In the ANSM existing interface, molecules can only be viewed one by one, and the ratio needs to be calculated manually, making it difficult to compare molecules. It is important to note that this is not a prescription assistance device but rather for informational purposes. In the future, the application may be expanded to include other categories of molecules. Finally, the indicators provided by our tool could be compared to those from other pharmacovigilance databases.

Research and Design of a Job Search Service Platform Based on Recommendation Algorithm

In recent years, with the rapid development of higher education in China, the number of graduates has continued to increase significantly. The country attaches great importance to the employment of college graduates. The report of the 17th National Congress of the Communist Party of China clearly puts forward the overall requirement of "implementing the strategy of prioritizing education development and actively doing a good job in the employment of college graduates". Increasing the intensity of employment informatization is an effective means to promote employment. This article follows the software development lifecycle method to complete the research and design of the platform. The platform is divided into three major functional modules: personal job search, enterprise recruitment, and backend management. It mainly realizes the registration and login of two different types of user roles, the editing and delivery of resumes by job seekers online. The platform realizes intelligent recommendation and position retrieval based on job seeker search and collection records, as well as recruitment information management, enterprise management, and user review functions. The implementation of the platform meets the current job hunting needs of graduates, overcomes the time and space limitations of traditional offline job fairs, and uses recommendation algorithms to bring more accurate and effective recruitment information to job seekers. It can achieve unobstructed communication and exchange among the three parties and promote graduate employment.

Exploring artificial intelligence (AI) chatbots adoption among research scholars using unified theory of acceptance and use of technology (UTAUT)

The use of artificial intelligence (AI) tools, such as chatbots, has significantly increased in academia and research. The present study seeks to determine the key factors influencing chatbot adoption, as well as attempts to validate the unified theory of acceptance and use of technology (UTAUT) in the context of AI chatbots adoption among research scholars. The data for this study were collected through purposive sampling using a cross-sectional survey. The population of the study comprised research scholars enrolled in three public sector universities in Pakistan. The eight-factor proposed measurement model was estimated using confirmatory factor analysis (CFA) based on 30 valid items. The goodness of fit indices suggest a favourable fit for the model χ2 = 1.710, DF = 381; p = 0.000; IFI = .902; TLI = 0.886, CFI = 0.900, RMSEA = 0.056. Our research affirms that social influence, trust, and facilitating conditions play pivotal roles as primary predictors of behavioural intentions for AI chatbots adoption among scholars. The study suggests that the perceived risks associated with using AI chatbots due to their potential misuse can be minimized by effectively implementing AI user guidelines, and developing AI literacy among scholars. Information professionals and ethical libraries can play an important role in “building the bridge” between cutting-edge technology capabilities and information users’ needs and rights. The proposed eight-factor AI chatbots adoption model holds substantial potential in understanding the influence of performance expectancy, effort expectancy, social influence, trust, perceived risk, and facilitating conditions on behavioural intention to AI chatbots adoption. This study contributes to the limited body of research investigating the factors influencing AI chatbot adoption among research scholars using the UTAUT model with additional constructs for trust and perceived risk.

Connected Pens or Smart Pens: Technology Needs Context.

Subcutaneous insulin administration has come a long way; pens that are connected to smartphones/cloud enable data transfer about insulin dosing. The usage of detailed dosing information in a smart way can support the optimization of insulin therapy in many ways. This review discusses terminology aspects that are relevant to the optimal usage of this novel option for insulin administration. Taking such aspects into account might also be crucial to improving the uptake of these medical products. In contrast to systems for automated insulin delivery, people with diabetes have to administer the insulin dose themselves; the technology can only support them. Combining smart pens with systems for continuous glucose monitoring provides solutions that are close to an automated solution, but are more discrete and associated with lower costs.

Teaching and Learning with Technology: Effectiveness of usage of Information and Communication Technology (ICT) Facilities in Public Primary Schools in Ondo State, Nigeria

The use of Information and Communication Technology (ICT) in schools is a worldwide requirements meant to replace traditional teaching methods with a technology- based teaching and learning tools and facilities which will assist teachers in their day to day activities of teaching, keeping of records among others. This study aims to analyze the importance of ICT usage to support teaching and learning process in the classroom and also to ascertain the Effectiveness of ICT usage for pupils learning. It also finds out the challenges of usage of ICT facilities in public primary Schools. The findings from the study revealed that computer and internet facilities affected the Educational system better than before. Usage of ICT facilities in public primary Schools will improve the teaching and learning process. The following challenges were revealed from the study: lack of technical support/ Skills, problem of electricity, network issues inadequate ICT facilities in schools among others. It was therefore recommended that the government should give utmost priority to training of teachers in ICT usage. ICT specialist should be recruited and posted to each public primary School to provide technical support to other members of staff, Electricity should be improved upon or solar panels to be provided to schools. Teachers and pupils should be provided with ICT facilities such as educational tablet to improve their ICT usage for teaching and learning.

Characterizing the Adoption and Experiences of Users of Artificial Intelligence-Generated Health Information in the United States: Cross-Sectional Questionnaire Study.

OpenAI's ChatGPT is a source of advanced online health information (OHI) that may be integrated into individuals' health information-seeking routines. However, concerns have been raised about its factual accuracy and impact on health outcomes. To forecast implications for medical practice and public health, more information is needed on who uses the tool, how often, and for what. This study aims to characterize the reasons for and types of ChatGPT OHI use and describe the users most likely to engage with the platform. In this cross-sectional survey, patients received invitations to participate via the ResearchMatch platform, a nonprofit affiliate of the National Institutes of Health. A web-based survey measured demographic characteristics, use of ChatGPT and other sources of OHI, experience characterization, and resultant health behaviors. Descriptive statistics were used to summarize the data. Both 2-tailed t tests and Pearson chi-square tests were used to compare users of ChatGPT OHI to nonusers. Of 2406 respondents, 21.5% (n=517) respondents reported using ChatGPT for OHI. ChatGPT users were younger than nonusers (32.8 vs 39.1 years, P<.001) with lower advanced degree attainment (BA or higher; 49.9% vs 67%, P<.001) and greater use of transient health care (ED and urgent care; P<.001). ChatGPT users were more avid consumers of general non-ChatGPT OHI (percentage of weekly or greater OHI seeking frequency in past 6 months, 28.2% vs 22.8%, P<.001). Around 39.3% (n=206) respondents endorsed using the platform for OHI 2-3 times weekly or more, and most sought the tool to determine if a consultation was required (47.4%, n=245) or to explore alternative treatment (46.2%, n=239). Use characterization was favorable as many believed ChatGPT to be just as or more useful than other OHIs (87.7%, n=429) and their doctor (81%, n=407). About one-third of respondents requested a referral (35.6%, n=184) or changed medications (31%, n=160) based on the information received from ChatGPT. As many users reported skepticism regarding the ChatGPT output (67.9%, n=336), most turned to their physicians (67.5%, n=349). This study underscores the significant role of AI-generated OHI in shaping health-seeking behaviors and the potential evolution of patient-provider interactions. Given the proclivity of these users to enact health behavior changes based on AI-generated content, there is an opportunity for physicians to guide ChatGPT OHI users on an informed and examined use of the technology.

How reliable is the artificial intelligence product large language model ChatGPT in orthodontics?

To evaluate the reliability of information produced by the artificial intelligence-based program ChatGPT in terms of accuracy and relevance, as assessed by orthodontists, dental students, and individuals seeking orthodontic treatment. Frequently asked and curious questions in four basic areas related to orthodontics were prepared and asked in ChatGPT (Version 4.0), and answers were evaluated by three different groups (senior dental students, individuals seeking orthodontic treatment, orthodontists). Questions asked in these basic areas of orthodontics were about: clear aligners (CA), lingual orthodontics (LO), esthetic braces (EB), and temporomandibular disorders (TMD). The answers were evaluated by the Global Quality Scale (GQS) and Quality Criteria for Consumer Health Information (DISCERN) scale. The total mean DISCERN score for answers on CA for students was 51.7 ± 9.38, for patients was 57.2 ± 10.73 and, for orthodontists was 47.4 ± 4.78 (P = .001). Comparison of GQS scores for LO among groups: students (3.53 ± 0.78), patients (4.40 ± 0.72), and orthodontists (3.63 ± 0.72) (P < .001). Intergroup comparison of ChatGPT evaluations about TMD was examined in terms of the DISCERN scale, with the highest value given in the patients group (57.83 ± 11.47) and lowest value in the orthodontist group (45.90 ± 11.84). When information quality evaluation about EB was examined, it GQS scores were >3 in all three groups (students: 3.50 ± 0.78; patients: 4.17 ± 0.87; orthodontists: 3.50 ± 0.82). ChatGPT has significant potential in terms of usability for patient information and education in the field of orthodontics if it is developed and necessary updates are made.

An Evaluation of a Winter Mortality Model for the Mountain Pine Beetle

The winter mortality of mountain pine beetle (Dendroctonus ponderosae Hopkins) larvae caused by temperatures below survivable thresholds and sudden decreases in temperature is among the most influential factors limiting population growth. Due to the importance of winter mortality in the demise of mountain pine beetle infestations, a widely used winter mortality model was developed at the Canadian Forest Service and the United States Forest Service. It predicts lethal temperatures and survival probabilities given temperature time series over the winter season. We present a rare and possibly the first peer-reviewed validation of this winter mortality model, wherein we independently tested the model at a new region in Canada by comparing model predictions to the observed lower lethal temperature thresholds and cold-associated mortality. Model predictions were biologically reasonable but slightly biased. Bias was exacerbated by the inaccurate translation of air temperature data from weather stations to temperatures under the bark where larvae develop. The spatial prediction of relative mortality observed across the study area in Banff National Park was poor—likely because the mountainous terrain presents a difficult prediction challenge when under-bark temperatures are not directly observed. Our results will help inform users of model constraints and how to optimize the accuracy of model predictions.

Barriers to Early Progress in Adult Cochlear Implant Outcomes.

Adult cochlear implant (CI) recipients obtain varying levels of speech perception from their device. Adult CI users adapt quickly to their CI if they have no peripheral "bottom-up" or neurocognitive "top-down" limiting factors. Our objective here was to understand the influence of limiting factors on the progression of sentence understanding in quiet and in noise, initially and over time. We hypothesized that the presence of limiting factors, detected using a short test battery, would predictably influence sentence recognition with practical consequences. We aimed to validate the test battery by comparing the presence of limiting factors and the success criteria of >90% sentence understanding in quiet 1 month after activation. The study was a single-clinic, cross-sectional, retrospective design incorporating 32 adult unilateral Nucleus CI users aged 27 to 90 years (mean = 70, SD = 13.5). Postoperative outcome was assessed through sentence recognition scores in quiet and in varying signal to noise ratios at 1 day, 1 to 2 months, and up to 2 years. Our clinic's standard test battery comprises physiological and neurocognitive measures. Physiological measures included electrically evoked compound action potentials for recovery function, spread of excitation, and polarity effect. To evaluate general cognitive function, inhibition, and phonological awareness, the Montreal Cognitive Assessment screening test, the Stroop Color-Word Test, and tests 3 and 4 of the French Assessment of Reading Skills in Adults over 16 years of age, respectively were performed. Physiological scores were considered abnormal, and therefore limiting, when total neural recovery periods and polarity effects, for both apical and basal electrode positions, were >1.65 SDs from the population mean. A spread of excitation of >6 electrode units was also considered limiting. For the neurocognitive tests, scores poorer than 1.65 SDs from published normal population means were considered limiting. At 1 month, 13 out of 32 CI users scored ≥90% sentence recognition in quiet with no significant dependence on age. Subjects with no limiting peripheral or neurocognitive factors were 8.5 times more likely to achieve ≥90% score in quiet at 1 month after CI switch-on (p = 0.010). In our sample, we detected 4 out of 32 cases with peripheral limiting factors that related to neural health or poor electrode-neural interface at both apical and basal positions. In contrast, neurocognitive limiting factors were identified in 14 out of 32 subjects. Early sentence recognition scores were predictive of long-term sentence recognition thresholds in noise such that limiting factors appeared to be of continuous influence. Both peripheral and neurocognitive processing factors affect early sentence recognition after CI activation. Peripheral limiting factors may have been detected less often than neurocognitive limiting factors because they were defined using sample-based criteria versus normal population-based criteria. Early performance was generally predictive of long-term performance. Understanding the measurable covariables that limit CI performance may inform follow-up and improve counseling. A score of ≥90% for sentence recognition in quiet at 1 month may be used to define successful progress; whereas, lower scores indicate the need for diagnostic testing and ongoing rehabilitation. Our findings suggest that sentence test scores as early as 1 day after activation can provide vital information for the new CI user and indicate the need for rehabilitation follow-up.

(Invited) Hypersectral Sensing of Biological Markers at the Point of Need

There is an urgent need for truly reagent free and agnostic sensing at the point of need for biological and chemical targets. Indeed, the COVID-19 pandemic has re-emphasized the significant need for truly agnostic diagnostics at the point of need. Whereas highly targeted and tailored diagnostics can help us address a known and anticipated threat, they do not prepare us against the next emerging outbreak. To address the significant challenge of developing truly agnostic diagnostics that can improve situational awareness and guide decision making, our team has looked to spectroscopy as an inspiration for the sensing modality; and innate immunity as an inspiration for the biological process and signatures. All biological molecules emanate spectral signatures along the electromagnetic spectrum, integral information from which can provide insights into processes or signatures more effectively. Therefore, we propose the use of hyperspectral sensing as a modality for accurate diagnosis and detection of signatures. With regards to the biology - innate immunity is a broad, agnostic pathogen sensing strategy that allows for the early recognition of all pathogens – known and unknown – with an associated response. Our team has developed strategies to mimic this response in the laboratory, culminating with the (ongoing) development of a data-science and machine learning enabled effort to unravel the complexity of the immune recognition (host cytokine and chemokine response), allowing for their ability to inform on categories of pathogens/disease. It is important to integrate this knowledge with a point of contact diagnostic approach in order to be able to translate the data into usable diagnostic information. To this end, we are working on developing a hyperspectral approach that measures these signatures without reagents from saliva samples in a few seconds. Charged by a back-end machine learning/artificial intelligence algorithm, our approach uses Pattern’s ProSpectral Sensor that measures signatures across various realms of the electromagnetic spectrum. Data on developing this sensing modality as a diagnostic in real time – challenges and advantages – will be presented.

Accounting and innovation: Paths forward for research

Glaeser and Lang (2024; GL) reviews the accounting literature on innovation, which has increased substantially in recent years. GL makes an important contribution to accounting research by bringing into the literature the implications of Romer's Nobel Prize winning endogenous growth theory and by explaining how accounting research addresses questions related to innovation. We contribute to accounting research by building on GL's foundation to suggest three main paths forward for future innovation research. First, focus on innovation's three defining attributes: novelty, nonrivalry, and partial excludability. Second, determine the needs of various users of information about a firm's innovation activities and how to meet those needs; we focus on the needs of investors. Third, address questions our discussion highlights as potentially important for future research on financial reporting and innovation, including the crucial question of an innovation's identifiability.