Popular Queries Research Articles

Assessing the Quality, Readability, and Acceptability of AI-Generated Information in Plastic and Aesthetic Surgery.

Within plastic surgery, a patient's most commonly used first point of information before consulting a surgeon is the internet. Free-to-use artificial intelligence (AI) websites like ChatGPT (Generative Pre-trained Transformers) are attractive applications for patient information due to their ability to instantaneously answer almost any query. Although relatively new, ChatGPT is now one of the most popular artificial intelligence conversational software tools. The aim of this study was to evaluate the quality and readability of information given by ChatGPT-4 on key areas in plastic and reconstructive surgery. The ten plastic and aesthetic surgery topics with the highest worldwide search volume in the 15 years were identified. These were rephrased into question format to create nine individual questions. These questions were then input into ChatGPT-4. The response quality was assessed using the DISCERN. The readability and grade reading level of the responses were calculated using the Flesch-Kincaid Reading Ease Index and Coleman-Liau Index. Twelve physicians working in a plastic and reconstructive surgery unit were asked to rate the clarity and accuracy of the answers on a scale of 1-10 and state 'yes or no' if they would share the generated response with a patient. All answers were scored as poor or very poor according to the DISCERN tool. The mean DISCERN score for all questions was 34. The responses also scored low in readability and understandability. The mean FKRE index was 33.6, and the CL index was 15.6. Clinicians working in plastics and reconstructive surgery rated the questions well in clarity and accuracy. The mean clarity score was 7.38, and the accuracy score was 7.4. This study found that according to validated quality assessment tools, ChatGPT-4 produced low-quality information when asked about popular queries relating to plastic and aesthetic surgery. Furthermore, the information produced was pitched at a high reading level. However, the responses were still rated well in clarity and accuracy, according to clinicians working in plastic surgery. Although improvements need to be made, this study suggests that language models such as ChatGPT could be a useful starting point when developing written health information. With the expansion of AI, improvements in content quality are anticipated.

Artificial Intelligence-Supported Development of Health Guideline Questions.

Guideline questions are typically proposed by experts. To assess how large language models (LLMs) can support the development of guideline questions, providing insights on approaches and lessons learned. Two approaches for guideline question generation were assessed: 1) identification of questions conveyed by online search queries and 2) direct generation of guideline questions by LLMs. For the former, the researchers retrieved popular queries on allergic rhinitis using Google Trends (GT) and identified those conveying questions using both manual and LLM-based methods. They then manually structured as guideline questions the queries that conveyed relevant questions. For the second approach, they tasked an LLM with proposing guideline questions, assuming the role of either a patient or a clinician. Allergic Rhinitis and its Impact on Asthma (ARIA) 2024 guidelines. None. Frequency of relevant questions generated. The authors retrieved 3975 unique queries using GT. From these, they identified 37 questions, of which 22 had not been previously posed by guideline panel members and 2 were eventually prioritized by the panel. Direct interactions with LLMs resulted in the generation of 22 unique relevant questions (11 not previously suggested by panel members), and 4 were eventually prioritized by the panel. In total, 6 of 39 final questions prioritized for the 2024 ARIA guidelines were not initially thought of by the panel. The researchers provide a set of practical insights on the implementation of their approaches based on the lessons learned. Single case study (ARIA guidelines). Approaches using LLMs can support the development of guideline questions, complementing traditional methods and potentially augmenting questions prioritized by guideline panels. Fraunhofer Cluster of Excellence for Immune-Mediated Diseases.

Poaceae Pollen as the Leading Aeroallergen

The relevance of aerobiological topics is evidenced by the fact that from July 1 to July 5, 2024, an unusual event was held in Vilnius (Lithuania), including the 8th European Symposium on Aerobiology (8th ESA), the 12th International Congress on Aerobiology (12th ICA) and the 5th International Ragweed Conference (5th IRC). World Aerobiology 2024 is an event of unprecedented scale. It discussed the use of artificial intelligence (AI) for calculating and interpreting aerobiological monitoring data. But so far, AI cannot identify all taxa, so careful counting and identification of plant pollen and fungal spores is only possible for a human researcher. Aerobiological monitoring using volumetric traps remains the gold standard of research. In the world community, there are networks of biological monitoring of air quality with the study of the concentration of plant pollen and fungal spores in the air. Through an application on a mobile phone, you can find out the forecast of periods of maximum risk, during which patients with hay fever are exposed to the main aeroallergens. Popular queries from Internet users relate to the complex problem of climate change, environmental pollution and aeroallergens. There is no National Aerobiological Monitoring system in Kyrgyzstan, but the Meteonova service has launched a pollen forecast application for mobile phones, which is still in the testing stage and does not reflect the real features of the aerobiological spectrum. It is known that in the world there are more than 12,000 species in the Poaceae family, classified into 771 genera belonging to 12 subfamilies. In Kyrgyzstan, 300 anemophilous species of annual perennial plants grow, releasing a huge amount of pollen into the atmosphere in the spring and summer. Poaceae pollen is still one of the top ten global aeroallergens, being the leading cause of hay fever, by predominant size, classified as biological pollutants (PM10). The concentration of cereal pollen was consistently observed in the air of Karakol during all seasons of the study and ranged from 167 to 174 days. Its daily maximum concentrations were detected in June-July; the distribution of pollen in the air is unimodal. The influence of the anthropogenic triad on fluctuations in the concentration of cereal pollen was established: climate change (temperature) and land use systems, as well as environmental pollution, which manifested itself in the degradation of pollen grains and the appearance of damaged and unidentified biological particles. The purpose of this article is to study the concentration of cereal pollen in the air, their integrative contribution to the aerobiological spectrum in the city of Karakol as dominant pollen allergens.

Exploring the online landscape of interventional radiology: a global analysis of search trends

This study aimed to analyze the online presence of interventional radiology (IR), and its popularity over time and location, given the reported under-awareness of the specialty. The study analyzed search volumes, searcher demographics, and query type related to IR and its four most searched procedures using Google Trends, Demographics.io, and Also Asked. Search strategies were stratified by volume and region ("Worldwide" and "United States"), and the quality of current patient materials in the first 10 Google search results was analyzed using the DISCERN instrument and Flesch Kincaid levels. The analyzed search trends demonstrated a slow uptrend in search volume over the past 15 years since 2013, with a CAGR rate of 0.6%. Demographics revealed that 80.9% of searchers were female and over half (51.8%) fell into the age range of 35-54 years old. Geographically, the US had the highest search volume (100) for the term "interventional radiology" and website search results mainly related to patient education about the specialty. The quality of online resources was poor with overall college-level readability, and "What is Interventional Radiology?" was the most popular query. There is a growing interest in IR procedures in recent years, particularly in the US, with middle-aged females being the most active demographic online. However, online resources containing information on specific IR procedures remain of poor quality. Actions should be taken to improve the quality, accessibility, and awareness of IR-related webpages to increase public knowledge of IR care in the US and abroad.

Exploratory and directed search strategies at a social science data archive

Researchers need to be able to find, access, and use data to participate in open science. To understand how users search for research data, we analyzed textual queries issued at a large social science data archive, the Inter-university Consortium for Political and Social Research (ICPSR). We collected unique user queries from 988,475 user search sessions over four years (2012-16). Overall, we found that only 30% of site visitors entered search terms into the ICPSR website. We analyzed search strategies within these sessions by extending existing dataset search taxonomies to classify a subset of the 1,554 most popular queries. We identified five categories of commonly-issued queries: keyword-based (e.g., date, place, topic); name (e.g., study, series); identifier (e.g., study, series); author (e.g., institutional, individual); and type (e.g., file, format). While the dominant search strategy used short keywords to explore topics, directed searches for known items using study and series names were also common. We further distinguished exploratory browsing from directed search queries based on their page views, refinements, search depth, duration, and length. Directed queries were longer (i.e., they had more words), while sessions with exploratory queries had more refinements and associated page views. By comparing search interactions at ICPSR to other natural language interactions in similar web search contexts, we conclude that dataset search at ICPSR is underutilized. We envision how alternative search paradigms, such as those enabled by recommender systems, can enhance dataset search.

Gaussian Switch Sampling: A Second-Order Approach to Active Learning

In active learning, acquisition functions define informativeness directly on the representation position within the model manifold. However, for most machine learning models (in particular neural networks) this representation is not fixed due to the training pool fluctuations in between active learning rounds. Therefore, several popular strategies are sensitive to experiment parameters (e.g. architecture) and do not consider model robustness to out-of-distribution settings. To alleviate this issue, we propose a grounded second-order definition of information content and sample importance within the context of active learning. Specifically, we define importance by how often a neural network “forgets” a sample during training - artifacts of second order representation shifts. We show that our definition produces highly accurate importance scores even when the model representations are constrained by the lack of training data. Motivated by our analysis, we develop Gaussian Switch Sampling ( <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GauSS</monospace> ). We show that <monospace xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">GauSS</monospace> is setup agnostic and robust to anomalous distributions with exhaustive experiments on three in-distribution benchmarks, three out-of-distribution benchmarks, and three different architectures. We report an improvement of up to 5% when compared against four popular query strategies. Our code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/olivesgatech/gauss</uri> .

Популярність єдиноборств в Україні

Purpose: to analyze the trends in the popularity of Olympic martial arts in Ukraine in recent years. Material and Methods. The following methods were used in the study: analysis of scientific and methodological literature and Internet sources, analysis of data from open information systems, methods of mathematical statistics. The database of the Google Trends web application (trends.google.com) was used to determine the popularity of sports in Ukraine and the world. The data was obtained for the last five years, from February 2018 to February 2023. Only certain types of martial arts were considered, whose athletes were represented at the XXXII Summer Olympic Games held in Tokyo in 2021. The list included the following sports: wrestling (freestyle, women's, Greco-Roman), boxing, judo, karate, and fencing. Results: preliminary analysis showed that boxing is many times more popular than other martial arts. The number of queries related to boxing is 6-8 times higher than the number of queries related to karate, judo or wrestling. In 2020, O. Usyk moved up to the heavyweight division, and his fight with Dereck Chisora in 2021 was one of the most anticipated events in the boxing world. Over the past 5 years, Ukraine has seen a certain popularity of martial arts such as wrestling, fencing, judo, and karate. According to Google Trends, Ukrainians' interest in wrestling has declined slightly. The most popular queries were related to athletes and their achievements, as well as women's wrestling. Important events in the world of wrestling were the Olympic Games, the World and European Championships, where Ukrainian athletes performed well. The period 03.2018-02.2020 compared to the period 07.2020-02.2022 shows a higher level of popularity of all four types of martial arts, with the largest difference in karate - by 83 %. The period 03.2020-06.2020 is the period (the period of quarantine restrictions caused by the Covid-19 pandemic) when there was a decline in the popularity of all four martial arts, and the largest decline was in wrestling - by 50 %. The period 03.2022-02.2023 compared to the entire period (03.2018-02.2023) shows that all types of martial arts are experiencing a decline in popularity, with wrestling and judo experiencing the largest decline in popularity. Conclusions. The results show that the level of popularity of martial arts in Ukraine has fluctuated over the past 5 years. The most popular martial arts in Ukraine are karate and wrestling, while fencing and judo are less popular. It can also be seen that the Covid-19 pandemic has affected the level of popularity of martial arts in Ukraine, reducing their popularity in March-June 2020, and the introduction of martial law in Ukraine has also had a significant negative impact.

COVID-19 Vaccination and Alcohol Consumption: Justification of Risks

Since the beginning of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, pharmaceutical companies and research institutions have been actively working to develop vaccines, and the mass roll-out of vaccinations against COVID-19 began in January 2021. At the same time, during lockdowns, the consumption of alcoholic beverages increased. During the peak of vaccination, consumption remained at high levels around the world, despite the gradual relaxation of quarantine restrictions. Two of the popular queries on search engines were whether it is safe to drink alcohol after vaccination and whether this will affect the effectiveness of vaccines. Over the past two years, many studies have been published suggesting that excessive drinking not only worsens the course of an acute respiratory distress syndrome caused by the SARS-CoV-2 virus but can also exacerbate post-COVID-19 syndrome. Despite all sorts of online speculation, there is no specific scientific data on alcohol-induced complications after vaccination in the literature. Most of the published vaccine clinical trials do not include groups of patients with a history of alcohol-use disorders. This review analyzed the well-known and new mechanisms of action of COVID-19 vaccines on the immune system and the effects of alcohol and its metabolites on these mechanisms.

A Representation-Based Query Strategy to Derive Qualitative Features for Improved Churn Prediction

The effectiveness of any Machine Learning process depends on the accuracy of annotated data that is used to train a learner. However, manual annotation is expensive. Hence, researchers adopt a semi-supervised approach called active learning that aims to achieve state-of-the-art performance using minimal number of samples. Although it boosts classifier performance, the underlying query strategies are unable to eliminate redundancy in selected samples. Redundant samples lead to increased cost and sub-optimal performance of learner. Inspired by this challenge, the study proposes a new representation-based query strategy that selects highly informative and representative subsets of samples for manual annotation. Data comprises messages of a set of customers sent to a service provider. Series of experiments are conducted to analyze the effectiveness of the proposed query strategy, called “Entropy-based Min Max Similarity” (E-MMSIM), in the context of topic classification for churn prediction. The foundation of E-MMSIM is an algorithm that is popularly used to sequence proteins in protein databases. The algorithm is modified and utilized to select the most representative and informative samples. The performance is evaluated using F1-score, AUC and accuracy. It is observed that “E-MMSIM” outperforms popular query strategies, and improves performance of topic classifiers for each of the 4 topics of churn prediction. The trained topic classifiers are used to derive qualitative features. These features are further integrated with structured variables for the same group of customers to predict churn. Experiments provide evidence that inclusion of qualitative features derived using E-MMSIM, enhance the performance of churn classifiers by 5%.

An artificially intelligent, natural language processing chatbot designed to promote COVID-19 vaccination: A proof-of-concept pilot study.

Our goal is to establish the feasibility of using an artificially intelligent chatbot in diverse healthcare settings to promote COVID-19 vaccination. We designed an artificially intelligent chatbot deployed via short message services and web-based platforms. Guided by communication theories, we developed persuasive messages to respond to users' COVID-19-related questions and encourage vaccination. We implemented the system in healthcare settings in the U.S. between April 2021 and March 2022 and logged the number of users, topics discussed, and information on system accuracy in matching responses to user intents. We regularly reviewed queries and reclassified responses to better match responses to query intents as COVID-19 events evolved. A total of 2479 users engaged with the system, exchanging 3994 COVID-19 relevant messages. The most popular queries to the system were about boosters and where to get a vaccine. The system's accuracy rate in matching responses to user queries ranged from 54% to 91.1%. Accuracy lagged when new information related to COVID emerged, such as that related to the Delta variant. Accuracy increased when we added new content to the system. It is feasible and potentially valuable to create chatbot systems using AI to facilitate access to current, accurate, complete, and persuasive information on infectious diseases. Such a system can be adapted to use with patients and populations needing detailed information and motivation to act in support of their health.

Design and Implementation of a Web Editing and Publishing System Based on a Semantic Network Generation Algorithm

In order to solve the problem of web editing data mining effectively, a semantic network generation algorithm is proposed. First of all, on the basis of preprocessing the variant short text, the maximum matching distance between short text is calculated by using the dictionary to expand the semantics of the Chinese words, which is used as an index to measure the formal distance between short text. Finally, a weighted method is used to synthesize formal distance and unit semantic distance into text distance, which is applied to the clustering analysis of online comments. The length of the word list is used to punish the distance. Results show that the most popular query topics on the Internet are shopping 10%, entertainment 10%, pornography 12%, computer 9%, research 9%, healthy life 5%, travel 5%, games 5%, family medical 5%, sports 3%, personal economic plan 3%, holiday 1% and others. It is proved that the improved algorithm proposed in this paper is superior to other methods and the clustering performance is significantly improved.

Api test automation of search functionality with artificial intelligence

One of the steps in software development is to test the software product. With the development of technology, the testing process has improved to automated testing, which reduces the impact of the human factor on error and speeds up testing. The main software products for testing are considered to be web applications, web services, mobile applications and performance testing. According to the testing pyramid, when testing web services, you need to develop more test cases than when testing a web application. Because automation involves writing software code for testing, the use of ready-made tools will speed up the software development process. One of the most important test indicators is the coverage of search functionality. The search functionality of a web application or web service requires a large number of cases, as you need to provide many conditions for its operation through the free entry of any information on the web page. There is an approach to data-based testing, which involves working with a test data set through files such as CSV, XLS, JSON, XML and others. However, finding input for testing takes a lot of time when creating test cases and automated test scenarios. It is proposed to use artificial data set generators based on real values and popular queries on the website to form a test data set. In addition, it is possible to take into account the probable techniques of developing test cases. It is proposed to conditionally divide the software for testing into several layers: client, test, work with data, checks and reports. The Java programming language has a number of libraries for working at each of these levels. It is proposed to use Rest Assured as a Restful client, TestNG as a library for writing tests with checks, and Allure report for generating reports. It is noted that the proposed approach uses artificial intelligence for automated selection of test cases when creating a test to diversify test approaches and simulate human input and behavior to maximize the use of cases.

A History of the People's Action Party: 1985–2021 by Shashi Jayakumar

Reviewed by: A History of the People's Action Party: 1985–2021 by Shashi Jayakumar Michael D. Barr A History of the People's Action Party: 1985–2021. By Shashi Jayakumar. Singapore: NUS Press, 2021, 782 pp. ISBN: 978-981-325-128-1 Commissioned institutional histories are generally gentle in their interrogation of their subject matter, but they sometimes offer new and hitherto confidential data that compensate for analytical shyness. Shashi Jayakumar's commissioned history of Singapore's People's Action Party (PAP) definitely qualifies for such praise. Let us begin with the story of the selection of Goh Chok Tong to replace Lee Kuan Yew as prime minister. Page 12 tells us that the choice of Goh was settled at a meeting of 'younger ministers' on 30 December 1984, and the results were conveyed to PM Lee the next day, but two paragraphs down Jayakumar reveals that the results of the meeting had already been written up and submitted to Lee the day before the meeting, on 29 December. So, who really chose Goh as PM-in-waiting, and when? And why does Jayakumar repeat the standard (and false) mythology on pages 94 and 385 (n. 137) as if he had never written page 12? Interestingly, the entry of Lee Hsien Loong into politics in 1984 is also revealed to have been less clear cut than is usually depicted. The official narrative [End Page 130] emphasises that it was Goh Chok Tong (not Lee Kuan Yew) who approached the younger Lee to join politics, with acknowledgement that there were divisions in the Old Guard over his entry. Jayakumar's new sources reveal that Lee Kuan Yew was more involved than is usually thought, especially after opposition began surfacing in Cabinet (pp. 38–43). One of the recurring highlights of the book is revelations of how often Lee Kuan Yew has been given insufficient credit for his achievements. Jayakumar reveals that Lee was the prime mover in the introduction of multi-member Group Representation Constituencies (GRCs) (p. 75), Non-Constituency MPs (NCMPs) (p. 71), the elected presidency (p. 72), Nominated MPs (NMPs) (p. 79), Town Councils (p. 87), and Speakers' Corner (p. 277), even though credit for most of these innovations is usually given to Goh. And when Catherine Lim criticised PM Goh for failing to shake himself free of Lee's dominance in 1994, it was Lee who secretly drove and in part dictated (literally, as it turns out) Goh's counter-attacks (pp. 263–67). Hence, Jayakumar has belatedly confirmed Lim's critique. This does not mean that Goh has no achievements to his name. It was Goh's idea to use HDB (Housing and Development Board) upgrades (and also the supply of kindergarten places!) as incentives for voting PAP and punishments for wards that vote for the opposition (p. 201). Goh also led the charge for reducing government control and agency in Singapore's economy, making it more market- and business-friendly (p. 172). Significantly, however, Jayakumar traces the PAP's subsequent electoral problems back to this ideological U-turn, since it left many Singaporeans wondering if the government really cared much about their welfare any more (p. 189). This popular query was validated when, in 2001, Deputy Prime Minister Lee Hsien Loong dismissed concerns about the social consequences of increasing the numbers of foreign workers (to help business) with the words, 'some other part of Government' will be looking at that (p. 261). Except it seems they didn't. In a masterpiece of understatement and disconnection, Jayakumar reports two chapters later that the housing and transport crises generated by sudden population growth during the 2000s came about because 'there was a reluctance to build ahead of demand' (p. 417). The shift towards openness later spread to the media, arts, culture and the internet (pp. 260–63), but not so much to politics. One of the most remarkable features of the book is the thoroughness and candour with which the author documents the corruption of democracy by the deployment of government resources and state power on behalf of the ruling party (pp. 22–23, 68–70, 127, 177, 188, 194–95, 198, 202, 228, 347, 542–44...

Who are the plotters behind the pandemic? Comparing Covid-19 conspiracy theories in Google search results across five key target countries of Russia’s foreign communication

ABSTRACT This article advances extant research that has audited search algorithms for misinformation in four respects. Firstly, this is the first misinformation audit not to implement a national but a cross-national research design. Secondly, it retrieves results not in response to the most popular query terms. Instead, it theorizes two semantic dimensions of search terms and illustrates how they impact the number of misinformative results returned. Furthermore, the analysis not only captures the mere presence of misinformative content but in addition whether the source websites are affiliated with a key misinformation actor (Russia’s ruling elites) and whom the conspiracy narratives cast as the malicious plotters. Empirically, the audit compares Covid-19 conspiracy theories in Google search results across 5 key target countries of Russia’s foreign communication (Belarus, Estonia, Germany, Ukraine, and the US) and Russia as of November 2020 (N = 5280 search results). It finds that, across all countries, primarily content published by mass media organizations rendered conspiracy theories visible in search results. Conspiratorial content published on websites affiliated with Russia’s ruling elites was retrieved in the Belarusian, German and Russian contexts. Across all countries, the majority of conspiracy narratives suspected plotters from China. Malicious actors from the US were insinuated exclusively by sources affiliated with Russia’s elites. Overall, conspiracy narratives did not primarily deepen divides within but between national communities, since – across all countries – only plotters from beyond the national borders were blamed. To conclude, the article discusses methodological advice and promising paths of research for future cross-national search engine audits.

The efficiency and problems of development of the official site of the Kharkiv State Academy of Culture

The purpose of this article is to provide analysis of some current indexes of effectiveness of Kharkiv State Academy of Culture official website for its use and identification of the problems.&#x0D; The methodology. The author used systematic and comparative approaches to study the performance of the website that influences its competitiveness.&#x0D; The results. The main source of the reliable information for some applicants and some applicants for university education is an official website of the university and its official pages on social media. The analysis of the statistical data of the Google Search Console service allowed to obtain the complete information for the last year about the data that influences the effectiveness and popularity of KSAC official website on the Internet. This article includes history of KSAC official website creation and development for the past 10 years. The role of the official website of the academy is considered and the website’s update plan is suggested. Google statistics were analyzed and some metrics that affect attendance figure were found. The obtained data allowed us to conclude that it is necessary to check and update the contact information, to make it more convenient to see. The most popular queries for searching KSAC website have been identified. KSAC website results of the performance indicators were studied and compared. The most popular devices using by the website visitors and their regional affiliation were defined. This paper also describes the problems and the prospects of KSAC official website development. The study results can formulate the requirements for further development of the website and the support tools.&#x0D; The scientific novelty. The author of this paper suggests us analyzing of the various Google indicators, which may influence the official website effectiveness.&#x0D; The practical significance. The study results are important to provide a systematic approach to the development and improvement of the official website. In particular, the performance indicators considered in this paper can be used as a basis for further development of recommendations for increasing the popularity and clickability of the website.

Application of Harmony Search Algorithm to Optimize SPARQL Protocol and Resource Description Framework Query Language Queries in Healthcare Data

The rapid developing international of internet, Semantic Web has become a platform for intelligent agents mainly in the healthcare sector. Inside the beyond few years there is a widening in the Semantic web data field in the healthcare industry. With a growth in the quantity of Semantic web data field in health industry, there exist some challenges to be resolved. One such challenge is to provide an efficient querying mechanism that can handle large number of Semantic web data. Consider many query languages; especially SPARQL (SPARQL Protocol and RDF Query Language) is the most popular query language. Each of these query languages has their own design strategy and it was identified in research that it is difficult to handle and query large quantity of RDF data efficiently using these languages. In the proposed process, Harmony search identify met heuristic algorithm to optimize the SPARQL queries in the healthcare data in the applicable manner. The application of Harmony search algorithm is evaluated with large Resource Description Framework (RDF) datasets and SPARQL queries. To assess performance, the algorithm’s implementation is compared to existing nature-inspired algorithms. The performance analysis shows that the proposed application performs well for large RDF datasets.

‘We watch porn for the fucking, not for romantic tiptoeing’: extremity, fantasy and women’s porn use

ABSTRACT This article examines the appeal of extreme imageries through a 2017 journalistic survey of 2438 participants on Finnish women’s approaches to, opinions on and preferences in porn, with a specific emphasis on responses addressing preferences deemed extreme. The respondents regularly positioned these pornographic fantasies in relation to the assumed tastes of other women while also addressing the complex and ambivalent roles that porn played in their ways of making sense of their sexual selves. By focusing on disconnections articulated both towards the category of women and within one’s sexual self when accounting for the attractions of extremity, this article also questions the ‘will to knowledge’ underpinning popular queries into women’s pornographic likes, asking how such data can be productively explored without reproducing the binary gender logic that structures it.

Error messages in relational database management systems: A comparison of effectiveness, usefulness, and user confidence

The database and the database management system (DBMS) are two of the main components of any information system. Structured Query Language (SQL) is the most popular query language for retrieving data from the database, as well as for many other data management tasks. During system development and maintenance, software developers use a considerable amount of time to interpret compiler error messages. The quality of these error messages has been demonstrated to affect software development effectiveness, and correctly formulating queries and fixing them when needed is an important task for many software developers. In this study, we set out to investigate how participants (N=152) experienced the qualities of error messages of four popular DBMSs in terms of error message effectiveness, perceived usefulness for finding and fixing errors, and error recovery confidence. Our results show differences between the DBMSs by three of the four metrics, and indicate a discrepancy between objective effectiveness and subjective usefulness. The results suggest that although error messages have perceived differences in terms of usefulness for finding and fixing errors, these differences may not necessarily result in differences in query fixing success rates.

Unique Internet Search Strategies of Individuals With Self-Stated Autism: Quantitative Analysis of Search Engine Users' Investigative Behaviors.

BackgroundAlthough autism is often characterized in literature by the presence of repetitive behavior, in structured decision tasks, individuals with autism spectrum disorder (ASD) have been found to examine more options in a given time period than controls.ObjectiveWe aimed to examine whether this investigative tendency emerges in information searches conducted via the internet.MethodsIn total, 1746 search engine users stated that they had ASD in 2019. This group’s naturally occurring responses following 1491 unique general queries and 78 image queries were compared to those of all other users of the search engine. The main dependent measure was scrolled distance, which denoted the extent to which additional results were scanned beyond the initial results presented on-screen. Additionally, we examined the number of clicks on search results as an indicator of the degree of search outcome exploitation and assessed whether there was a trade-off between increased search range and the time invested in viewing initial search results.ResultsAfter issuing general queries, individuals with self-stated ASD scanned more results than controls. The scrolled distance in the results page of general queries was 45% larger for the group of individuals with ASD (P<.001; d=0.45). The group of individuals with ASD also made the first scroll faster than the controls (P<.001; d=0.51). The differences in scrolled distance were larger for popular queries. No group differences in scrolled distance emerged for image queries, suggesting that visual load impeded the investigative behavior of individuals with ASD. No differences emerged in the number of clicks on search results.ConclusionsIndividuals who self-stated that they had ASD scrutinized more general search results and fewer image search results than the controls. Thus, our results at least partially support the notion that individuals with ASD exhibit investigative behaviors and suggest that textual searches are an important context for expressing such tendencies.

ANALYSIS OF PUBLIC AWARENESS OF DISEASES CAUSING CHRONIC ARTERIAL INSUFFICIENCY BASED ON THE ASSESSMENT OF RELEVANT GOOGLE QUERIES

Objective : to evaluate the awareness of diseases causing to chronic arterial insufficiency by analyzing web search queries. Material and methods . In this study, we used the online keyword selection tool ‘Google Trends.’ The analysis was conducted for different countries and separately for CIS countries. Results . We found that the search query ‘obliterating endarteritis’ was most common in Kazakhstan and Pakistan, while the query ‘obliterating atherosclerosis’ was most common in Belarus, Trinidad, and Tobago. The query ‘leg cramps’ was most frequent among residents of Ukraine and the Republic of South Africa, whereas people residing in the Republic of Kazakhstan and Philippine Islands usually searched for ‘leg numbness.’ As for diagnostic methods, ‘angiography’ was the most frequent query in countries such as Bulgaria and Pakistan, while ‘dopplerography’ was the most popular query in the Republic of Kazakhstan. The most common queries related to treatment of chronic arterial insufficiency were ‘balloon angioplasty’ (most popular in Russia) and bypass surgery (most popular in Zambia, Ethiopia, Myanmar, and Ghana). The most frequent queries associated with pharmacotherapy were ‘acetylsalicylic acid’ and ‘pentoxifylline’ in the Russian Federation and Trinidad, Tobago, Jamaica, and Puerto Rico. Conclusion. The assessment of web search queries related to the diagnosis and treatment of chronic arterial insufficiency may help to understand the health status of people residing in different countries, which will enable effective disease prevention by reducing the risks and will ensure sufficient attention to timely diagnosis and effective treatment of chronic arterial insufficiency. It is also very important to inform people about obliteration of lower extremity and explain that people with such symptoms should consult their phusician rather than search for information online.