Relational Query Research Articles

Oncointerpreter.ai enables interactive, personalized summarization of cancer diagnostics data.

Cancer diagnosis comes as a shock to many patients, and many of them feel unprepared to handle the complexity of the life-changing event, understand technicalities of the diagnostic reports, and fully engage with the clinical team regarding the personalized clinical decision-making. We develop Oncointerpreter.ai an interactive resource to offer personalized summarization of clinical cancer genomic and pathological data, and frame questions or address queries about therapeutic opportunities in near-real time via a graphical interface. It is built on the Mistral-7B and Llama-2 7B large language models trained on a local database trained using a large, curated corpus. We showcase its utility with case studies, where Oncointerpreter.ai extracted key clinical and molecular attributes from deidentified pathology and clinical genomics reports, summarized their contextual significance and answered queries on pertinent treatment options. Oncointerpreter also provided personalized summary of currently active clinical trials that match the patients' disease status, their selection criteria, and geographic locations. Benchmarking and comparative assessment indicated that the model responses were generally consistent, and hallucination, ie, factually incorrect or nonsensical response was rare; treatment- and outcome related queries led to context-aware responses, and response time correlated with verbosity. The choice of model and domain-specific training also affected the response quality. Oncointerpreter.ai can aid the existing clinical care with interactive, individualized summarization of diagnostics data to promote informed dialogs with the patients with new cancer diagnoses. https://github.com/Siris2314/Oncointerpreter.

Understanding Searches for ‘Weight Loss’ Using Google Trends

It is common for individuals to seek information online when facing health and nutritional problems, particularly those with lower education level and nutritional knowledge(1). One way to access such information is through search engines, with Google being the most popular, currently used by 92% of the world’s population, with an average of 85.59 billion monthly visits(2). Google Trends is a tool provided by Google, freely accessible, allowing users to observe patterns in internet searches(3). The aim of this study is to identify patterns to predict trends related to weight loss at specific times.Data were extracted from Google Trends over the past 20 years, from 2004 to 2024, using the keywords “weight loss” and “diet.” In Google Trends, users may input a keyword consisting of words or phrases that are relevant to the chosen issue or cases. The duration of time that users wish to examine can be specified. Furthermore, users can explore geographical regions or search worldwide. Figures generated represent search interest based on the highest points on the graph for specific regions and times. The popularity of search terms is relative and depends on the category: search term or topic. Data is normalised and presented on a scale from 0-100, where each point on the graph is divided by the highest point, or 100. A value of 100 indicates peak popularity, 50 indicates half popularity, and 0 indicates insufficient data for the term.Over the past 20 years, there has been an increasing trend in searches for websites using the keyword “weight loss,” with an average interest score of 31. This differs from the keyword “diet,” which has shown a tendency towards stagnation in popularity, although it briefly peaked with a score of 100 in early 2014 and has an average interest score of 53. Both keywords exhibit similar search patterns, reaching their highest peaks each year in January and lowest points in December.For the keyword “weight loss,” the most searches were from South Africa (100), Romania (96), and Vietnam (91), while the lowest were from Italy (4), Japan (5), and the Netherlands (9). For the keyword “diet,” the most searches were from Poland (100), Kuwait (87), and Greece (85), while the lowest were from Japan (2), Thailand (7), and France (11). Users also searched for other related topics or queries including ozempic and intermitten fasting.Our data indicates a consistent increasing trend in searches for weight loss over the last 20 years via Google Trends. New Year resolutions may be linked to peak searches which occur in January. While this data may prove useful for researchers and policymakers, further validation of its validity and reliability is necessary.

A next-generation holistic building design framework: a focus on integrating sustainable and vernacular design principles

The proliferation of sustainable design approaches and assessment methods has resulted in a vast array of indicators. However, this abundance often leads to confusion during interpretation and application. Additionally, rapid urbanization and environmental concerns sometimes overshadow social and economic considerations, emphasizing environmental impact reduction. This study addresses these challenges through an integrated approach that combines a Systematic Literature Review (SLR) with a Decision-Making Trial and Evaluation Laboratory (DEMATEL) analysis to provide a holistic model for sustainable building design. The SLR was carried out individually through a relative Structural Query Language (SQL) regarding sustainable building design and vernacular principle. The output of SLR was subjected to DEMATEL model to recognize the holistic indicators interconnection and validate the proposed model. The research identified 23 global indicators for building sustainability worldwide, with five—Energy, Materials & Resources, Sites & Ecology, Indoor Environmental Quality, and Water—emerging as the most prevalent. Additionally, 22 consistently applied indicators in vernacular design practices exhibited significant overlap with those in sustainable design. This model integrated two novel indicators—Vernacular Principles and Social—Culture—with common sustainable building indicators. These primary indicators complement the common and applicable sustainable building indicators, ensuring a balanced approach that considers global contexts. DEMATEL analysis confirmed the validity and interconnection of these indicators, emphasizing the critical role of vernacular principles in achieving true sustainability.

Global Utilization of Online Information for Anxiety Disorders From 2004 to 2022: An Infodemiological Study of Google and Wikipedia

The utilization of online information can be used as a measure of public concern and interest. It can be explored to institute, inform, and innovate mental health promotion and strategies in addressing the ever-increasing prevalence of anxiety disorders across the globe. As such, this infodemiological study described and analyzed the public utilization of online information for anxiety disorders using relative search volumes (RSV), top and rising related queries and topics from Google Trends, and Wikipedia page views. After describing the data, autoregressive integrated mean averaging (ARIMA) models were employed to predict future RSVs and page views. Results show that RSVs for anxiety disorders have increased over the years and will further increase in succeeding years, while the use of Wikipedia information for anxiety disorders is declining. Top and rising search-related queries and topics primarily revolved around anxiety disorder concepts, management, manifestations, types, etiology, diagnostics, social consequences, and related mental disorders. Based on the search volumes, public utilization of online information about anxiety disorders has increased and will further increase in the coming years. They can be explained by the rising rates of anxiety disorder and the higher internet access across the globe. As such, the Internet is a promising medium for mental health promotion, prevention, and intervention strategies. Moreover, trustworthy and reliable online information about anxiety disorders is ever-crucial in this age of information and disinformation.

Internet Health Resources for Diabetic Foot Search: The Content, Quality, and Readability of Information on Web Sites.

As the incidence of diabetes mellitus increases, the incidence of diabetic foot also increases. This situation, which may lead to devastating complications and progress to limb loss for patients, exposes patients and their social environments to a big crisis. Thus, patients may seek secondary opinions from online sources about information they initially obtained from health institutions. We aimed to evaluate the information content related to diabetic foot on the Internet that is probably used by patients for Internet searching. After software optimization and reset, related queries with the keyword diabetic foot were determined on Google Trends. Selected keywords were searched in three search engines, and the results were examined. Web sites were classified into five subcategories (nongovernmental health institution, governmental institution, academic, blog, and university) and evaluated with an information content scale (ICS) based on the literature, Journal of the American Medical Association benchmark criteria, the Flesch-Kincaid readability test, and presence of the Health On the Net Foundation Code of Conduct certificate. The search engines, keywords, and Web site subcategories were investigated with the evaluation criteria. In terms of finding Web sites eligible for assessment, the Google search engine listed more eligible Web sites than did Bing and Yahoo. Concerning the ICS, there was no significant difference between search engines for total scores (P > .05). Concerning ICS diagnosis and evaluation and ICS total score, academic Web sites scored significantly higher than other subcategories. Results that can be obtained with an Internet search for diabetic foot depend on the proper keyword selection, Web site type, and search engine to help patients reach more appropriate content.

Discontinued Maternal Health Care Utilization in Bhojpur District, Nepal

Why all pregnant women did not receive all care at antenatal period; those who did not even utilize any ANC visit may also prefer to utilize facility-based delivery or postnatal care related queries are rarely available in several rounds of NDHS data set. A study conducted in Tarai region of Nepal, analysis of Nepal demographic and health surveys of several rounds, Annual Health Report 2077/78 published by the Department of health services, Ministry of health and population were taken into study. A qualitative study designed in 2014-15 from the women of reproductive health having children of 2-23 months were asked their discontinuation, if any. Maternal health service utilization through national and the district level data were analysed to study about the discontinuation in utilization.Several factors are identified as the cause for discontinuation in the Tarai region of Nepal. Data revealed by department of health services for the fiscal year 2077-78 show discontinued maternal health service utilization (MHSU) in Bhojpur district. The NDHS 2022 report still found some level of discontinuation in national level. Therefore, urgent need of qualitative study is a most for Bhojpur district which has low level of utilization. In the new administrative set-up according to constitution of Nepal, effective functional policy adopted by municipal region may solve several issues of maternal health. Hilly regions could have different response than Tarai region of Nepal. Therefore, local bodies of hilly regions should bring maternal health policy and program to achieve continuum of maternal health care which will ultimately help Nepal to achieve SDG related goal and target.

SpannerLib: Embedding Declarative Information Extraction in an Imperative Workflow

Document spanners have been proposed as a formal framework for declarative Information Extraction (IE) from text, following IE products from the industry and academia. Over the past decade, the framework has been studied thoroughly in terms of expressive power, complexity, and the ability to naturally combine text analysis with relational querying. This demonstration presents Spanner-Lib---a library for embedding document spanners in Python code. SpannerLib facilitates the development of IE programs by providing an implementation of Spannerlog (Datalog-based document spanners) that interacts with the Python code in two directions: rules can be embedded inside Python, and they can invoke custom Python code (e.g., calls to ML-based NLP models) via user-defined functions. The demonstration scenarios showcase IE programs, with increasing levels of complexity, within Jupyter Notebook.

Technology-Enabled Database Education: Challenges andOpportunities

Motivation: User-friendly systems and tools are paramount for facilitating learning of database systems. In many universities around the world, database systems courses are supplemented with the use of industrial-strength relational database management systems (RDBMS). Although there exist a few efforts on mini RDBMS for learners to practice implementing a database kernel, e.g., Minibase [6], unfortunately, there is a lack of learner-friendly technological support to facilitate the learning of various core components of a database systems course, such as database storage and indexing, relational query processing and optimization, and transaction management.

Synthesizing nested relational queries from implicit specifications: via model theory and via proof theory

Derived datasets can be defined implicitly or explicitly. An implicit definition (of dataset O in terms of datasets I) is a logical specification involving two distinguished sets of relational symbols. One set of relations is for the "source data" I, and the other is for the "interface data" O. Such a specification is a valid definition of O in terms of I, if any two models of the specification agreeing on I agree on O. In contrast, an explicit definition is a transformation (or "query" below) that produces O from I. Variants of Beth's theorem state that one can convert implicit definitions to explicit ones. Further, this conversion can be done effectively given a proof witnessing implicit definability in a suitable proof system. We prove the analogous implicit-to-explicit result for nested relations: implicit definitions, given in the natural logic for nested relations, can be converted to explicit definitions in the nested relational calculus (NRC). We first provide a model-theoretic argument for this result, which makes some additional connections that may be of independent interest, between NRC queries, interpretations, a standard mechanism for defining structure-to-structure translation in logic, and between interpretations and implicit to definability "up to unique isomorphism". The latter connection uses a variation of a result of Gaifman concerning "relatively categorical" theories. We also provide a proof-theoretic result that provides an effective argument: from a proof witnessing implicit definability, we can efficiently produce an NRC definition. This will involve introducing the appropriate proof system for reasoning with nested sets, along with some auxiliary Beth-type results for this system. As a consequence, we can effectively extract rewritings of NRC queries in terms of NRC views, given a proof witnessing that the query is determined by the views.

Google trends in infodemiology: Methodological steps to avoid irreproducible results and invalid conclusions

BackgroundGoogle Trends is a widely used tool for infodemiological surveys. However, irregularities in the random sampling and aggregation algorithms compromise the reliability of the relative search volume (RSV) and the regional online interest (ROI). ObjectiveThe study aims to unmask methodological criticalities commonly ignored in carrying out infodemiological surveys via Google Trends. A guide to avoiding these shortcomings is also provided. Material and methodsThe Google Topic “Coronavirus disease 2019” has been investigated using different timelapses, categories, and IP addresses. The same samples were manually collected multiple times to evaluate the RSV and ROI stability. Stability was estimated through indicators of variability (e.g., coefficient of percentage variation “CV%” and its 4-surprisal interval “4-I”). The content aggregation capacity of the algorithms relating to topics and categories was evaluated through the quantitative analysis of RSV and ROI and the qualitative examination of the related queries. ResultsThe stability of Google Trends’ RSV and ROI is not linked exclusively to the dataset dimension or the IP address. Subregional datasets can be highly unstable (e.g., CV% = 10, 4-I: [8,13]). Google Trends categories and topics can exclude relevant queries or include unnecessary queries. The statistical scenario is consistent with the following hypotheses: i) datasets containing too few queries are highly unstable, ii) the “interest over time” data format is generally reliable for evaluating trends and correlations, iii) Google Trends improvements have altered the RSV historical trends. ConclusionsGoogle Trends can be an effective and efficient infodemiological tool as long as the reliability of web search indexes is appropriately analyzed and weighted for the scientific goal. The methodological steps discussed in this study are critical to drawing valid and relevant scientific conclusions.

DECLAREd: A Polytime LTLf Fragment

This paper considers a specification rewriting meachanism for a specific fragment of Linear Temporal Logic for Finite traces, DECLAREd, working through an equational logic and rewriting mechanism under customary practitioner assumptions from the Business Process Management literature. By rewriting the specification into an equivalent formula which might be easier to compute, we aim to streamline current state-of-the-art temporal artificial intelligence algorithms working on temporal logic. As this specification rewriting mechanism is ultimately also able to determine with the provided specification is a tautology (always true formula) or a formula containing a temporal contradiction, by detecting the necessity of a specific activity label to be both present and absent within a log, this implies that the proved mechanism is ultimately a SAT-solver for DECLAREd. We prove for the first time, to the best of our knowledge, that this fragment is a polytime fragment of LTLf, while all the previously-investigated fragments or extensions of such a language were in polyspace. We test these considerations over formal synthesis (Lydia), SAT-Solvers (AALTAF) and formal verification (KnoBAB) algorithms, where formal verification can be also run on top of a relational database and can be therefore expressed in terms of relational query answering. We show that all these benefit from the aforementioned assumptions, as running their tasks over a rewritten equivalent specification will improve their running times, thus motivating the pressing need of this approach for practical temporal artificial intelligence scenarios. We validate such claims by testing such algorithms over a Cybersecurity dataset.

Changes in public interest in sudden cardiac arrest: a big data analysis

Abstract Background Search engine queries (SEQ) are considered a surrogate for public interest, with the most popular global search engine processing ~ 1.2 trillion online queries annually and >1 billion health-related queries daily.1 Big data from SEQ trends have been used for disease surveillance and may be able to offer insights for public health interventions, such as detecting respiratory illness outbreaks and understanding cardiac disease patterns.2-4 Sudden cardiac arrest (SCA) occurs in ~5 million people annually worldwide; October is internationally designated for SCA awareness, with public health campaigns aimed to improve early recognition of and early defibrillation for SCA. Use of big data from SEQ as a public health tool to assess changes in public interest surrounding SCA campaigns has not been explored. Purpose To characterize changes in public interest in SCA via analysis of internet SEQ trends. Methods Google Trends was queried for the terms "cardiac arrest", "cardiopulmonary resuscitation" (CPR), "automated external defibrillation" (AED) and "myocardial infarction" (MI) worldwide for the past 5 years (11/2018 – 11/2023). Relative search volumes (RSV, expressed in scale from 0-100) and related queries were analyzed. Baseline RSV for each term was defined as 2 weeks pre-peak. Numerical percentage and Chi square tests were used for statistical comparison. Results Compared to baseline, RSV for "cardiac arrest" significantly increased in correlation with the SCA of 2 professional athletes: Christian Eriksen (football, Denmark) on 6/12/2021 (+321%, p<0.001), Damar Hamlin (American football, United States) on 1/2/2023 (+653%, p<0.001) and collegiate athlete Bronny James (basketball, United States) on 7/24/2023 (+164%, p<0.001). (Figure) RSV significantly increased for "cardiopulmonary resuscitation" (+61% and +170% respectively, p<0.001 for both) and "Automated external defibrillator" (+141% and +192% p<0.001 and p=0.003, respectively) during the same period for pro-athletes but not for the college athlete (p=1.0 and p= 0.84 respectively). While none of the athletes had MI, RSV for "myocardial infarction" also had correlating increases over baseline for all 3 athletes (+35%, p<0.001 +30%, p<0.001 and +22%, p=0.027, respectively). RSV did not significantly change for any search terms during October SCA awareness months during the study period (all p>0.05). Conclusions Public interest in SCA, CPR and AED surge when SCA occurs in well-known individuals such as athletes, rather than with scheduled public health campaigns; co-surge in MI interest likely indicates on-going public confusion between SCA and MI. Analyses of big data from SEQ could assist cardiovascular societies in planning targeted SCA educational campaigns by building upon periodic natural surges in public interest in SCA, CPR and AED; this should include continued education on the differences in recognition and treatment of SCA and MI.

TRGST: An enhanced generalized suffix tree for topological relations between paths

This paper introduces the TRGST data structure, which is designed to handle queries related to topological relations between paths represented as sequences of stops in a network. As an example, these paths could correspond to stops on a public transport network, and a query of interest is to retrieve paths that share at least k consecutive stops. While topological relations among spatial objects have received extensive attention, the efficient processing of these relations in the context of trajectory paths, considering both time and space efficiency, remains a relatively less explored domain. Taking inspiration from pattern matching implementations, the TRGST data structure is constructed on the foundation of the Generalized Suffix Tree. Its purpose is to provide a compact representation of a set of paths and to efficiently handle topological relation queries by leveraging the pattern search capabilities inherent in this structure. The paper provides a detailed account of the structure and algorithms of TRGST, followed by a performance analysis utilizing both real and synthetic data. The results underscore the remarkable scalability of the TRGST in terms of both query time and space utilization.

Polymorphic dynamic programming by algebraic shortcut fusion

Dynamic programming (DP) is a broadly applicable algorithmic design paradigm for the efficient, exact solution of otherwise intractable, combinatorial problems. However, the design of such algorithms is often presented informally in an ad-hoc manner. It is sometimes difficult to justify the correctness of these DP algorithms. To address this issue, this paper presents a rigorous algebraic formalism for systematically deriving DP algorithms, based on semiring polymorphism. We start with a specification, construct a (brute-force) algorithm to compute the required solution which is self-evidently correct because it exhaustively generates and evaluates all possible solutions meeting the specification. We then derive, primarily through the use of shortcut fusion, an implementation of this algorithm which is both efficient and correct. We also demonstrate how, with the use of semiring lifting, the specification can be augmented with combinatorial constraints and through semiring lifting, show how these constraints can also be fused with the derived algorithm. This paper furthermore demonstrates how existing DP algorithms for a given combinatorial problem can be abstracted from their original context and re-purposed to solve other combinatorial problems. This approach can be applied to the full scope of combinatorial problems expressible in terms of semirings. This includes, for example: optimization, optimal probability and Viterbi decoding, probabilistic marginalization, logical inference, fuzzy sets, differentiable softmax, and relational and provenance queries. The approach, building on many ideas from the existing literature on constructive algorithmics, exploits generic properties of (semiring) polymorphic functions, tupling and formal sums (lifting), and algebraic simplifications arising from constraint algebras. We demonstrate the effectiveness of this formalism for some example applications arising in signal processing, bioinformatics and reliability engineering. Python software implementing these algorithms can be downloaded from: http://www.maxlittle.net/software/dppolyalg.zip.

Emerging Trends in Information-Seeking Behavior for Alpha-Gal Syndrome: Infodemiology Study Using Time Series and Content Analysis.

Alpha-gal syndrome is an emerging allergy characterized by an immune reaction to the carbohydrate molecule alpha-gal found in red meat. This unique food allergy is likely triggered by a tick bite. Cases of the allergy are on the rise, but prevalence estimates do not currently exist. Furthermore, varying symptoms and limited awareness of the allergy among health care providers contribute to delayed diagnosis, leading individuals to seek out their own information and potentially self-diagnose. The study aimed to (1) describe the volume and patterns of information-seeking related to alpha-gal, (2) explore correlations between alpha-gal and lone star ticks, and (3) identify specific areas of interest that individuals are searching for in relation to alpha-gal. Google Trends Supercharged-Glimpse, a new extension of Google Trends, provides estimates of the absolute volume of searches and related search queries. This extension was used to assess trends in searches for alpha-gal and lone star ticks (lone star tick, alpha gal, and meat allergy, as well as food allergy for comparison) in the United States. Time series analyses were used to examine search volume trends over time, and Spearman correlation matrices and choropleth maps were used to explore geographic and temporal correlations between alpha-gal and lone star tick searches. Content analysis was performed on related search queries to identify themes and subcategories that are of interest to information seekers. Time series analysis revealed a rapidly increasing trend in search volumes for alpha-gal beginning in 2015. After adjusting for long-term trends, seasonal trends, and media coverage, from 2015 to 2022, the predicted adjusted average annual percent change in search volume for alpha-gal was 33.78%. The estimated overall change in average search volume was 627%. In comparison, the average annual percent change was 9.23% for lone star tick, 7.34% for meat allergy, and 2.45% for food allergy during this time. Geographic analysis showed strong significant correlations between alpha-gal and lone star tick searches especially in recent years (ρ=0.80; P<.001), with primary overlap and highest search rates found in the southeastern region of the United States. Content analysis identified 10 themes of primary interest: diet, diagnosis or testing, treatment, medications or contraindications of medications, symptoms, tick related, specific sources of information and locations, general education information, alternative words for alpha-gal, and unrelated or other. The study provides insights into the changing information-seeking patterns for alpha-gal, indicating growing awareness and interest. Alpha-gal search volume is increasing at a rapid rate. Understanding specific questions and concerns can help health care providers and public health educators to tailor communication strategies. The Google Trends Supercharged-Glimpse tool offers enhanced features for analyzing information-seeking behavior and can be valuable for infodemiology research. Further research is needed to explore the evolving prevalence and impact of alpha-gal syndrome.

COLLEGE ENQUIRY CHAT-BOT SYSTEM

Nowadays, many people are using smartphone with many new applications i.e. technology is growing day by day. Today Artificial Intelligence is playing a major role in a variety of fields ranging from industries in product manufacturing, to customer care in public relations. As there are many online Artificial Intelligence (AI) systems or chat bots which are in existence that help people solve their problems. So, we are going to implement a virtual assistant based on AI that can solve any college related query. This will work as a College Oriented Intelligence machine. This virtual machine will respond the queries of students on college related issues. A chat bot has information stored in its database to identify the sentences and making a decision itself as response to answer a given question. The college enquiry chat bot will be built using algorithm that analyses queries and understand user’s message Chatbots are conversational systems that can do chat interactions with human automatically. It is developed to be virtual assistant, making entertainment for people, helping for answering the questions, getting driving directions, serving as human partner in smart homes etc. Most of the chatbots utilize the algorithms of artificial intelligence (AI) in order to get the required responses. In this paper, we provide the design of a University Chatbot that provides an efficient and accurate answer for any user questions about university information. This is the first University Chatbot for inquiring about school information in Myanmar Language based on Artificial Intelligence Markup Language and uses Pandora bots as the interpreter. This paper introduces approach made for improving the efficiency of the chatbot or artificial conversational entity used in various commercial and banking sector. Humanizing is to improving the response generation ability of the chatbot. Key Words: Artificial Intelligence, Database. Intelligence Machine.

ВІДПОВІДАЛЬНЕ ТА ЕТИЧНЕ ВИКОРИСТАННЯ ШТУЧНОГО ІНТЕЛЕКТУ В ДОСЛІДНИЦЬКІЙ І ПУБЛІКАЦІЙНІЙ ДІЯЛЬНОСТІ

Artificial intelligence (AI) is gradually gaining a stable place in our lives, and its use is spreading too quickly in all areas of activity, changing approaches and simplifying the solution of some tasks. AI can help at certain stages of scientific research, but it also poses risks of plagiarism, lack of academic integrity and high ethical standards. The problem of plagiarism and its prevention in the research and publication process has reached a high degree of relevance, especially with the use of AI programs and services. The article presents a comprehensive analysis of scientific sources covering the prevention of plagiarism in scientific publications, the development of AI in research culture, the observance of academic integrity in the university environment, and changes in the assessment of students' academic achievements..Using the GoogleTrends service, based on the search engine's statistical data, it was determined that recently, scientific topics related to ChatGPT have been dominating in the queries of Internet users, while the discourse related to plagiarism, academic integrity, research ethics, and responsible use of AI is not noticeably relevant, and there is an increase in related queries between "plagiarism" and AI tools. The authors substantiate the impact of the use of AI on the responsible attitude to the presentation of research results. After processing the downloaded data in Excel, a hypothesis is formulated regarding the convergence of interests and joint consideration of the problems of ethics in research and responsible use of AI. The analysed data demonstrate that the relevance of ethical and responsible use of artificial intelligence tools in writing scientific manuscripts is extremely high. The article demonstrates how digital tools such as Connected Papers, Inciteful, LitMaps, Scite.Ai, ResearchRabbit, and Semantic Scholar can conveniently visualise documents related to search terms. The article offers recommendations for researchers and editorial board members of scientific journals to promote responsible presentation of research findings in the context of the growing availability and use of AI.

ChatGPT as an educational resource for civil engineering students

AbstractThe purpose of the investigation was to evaluate the utility of ChatGPT, a state‐of‐the‐art language model developed by Open AI, in the context of civil engineering education. To achieve this objective, 44 civil engineering students from a large state university in the United States were recruited and asked to respond to three questions pertaining to Construction Surveying and Geomatics. The students were then introduced to ChatGPT and provided with guidelines on how the platform could be utilized for educational purposes, including obtaining answers to questions like those presented to them earlier. The students were then given the opportunity to leverage ChatGPT to obtain responses to questions they had been presented with earlier and any other related queries of interest. Subsequently, the students were asked to provide written responses to the same questions they were presented with earlier, but without access to ChatGPT or its response, to assess any knowledge gains. The investigation also captured the student's acceptance and perceived usability based on their experience of using ChatGPT. The findings indicated that the student's written responses were more thorough, detailed, and informative after utilizing ChatGPT. The results also revealed that ChatGPT was largely well‐received by the students and they held a positive perception of the AI platform. The findings can inform efforts targeted at successfully integrating AI tools for civil engineering education.

Is the prediction of precious metal market volatility influenced by internet searches regarding uncertainty?

There are countless articles on the relationship between economic uncertainty and the precious metals market, but studies examining whether economic uncertainty related queries (EURQ) can provide valuable information for the prediction of precious metal price volatility are scarce. Therefore, this paper examines the predictive power of EURQ for price volatility in the precious metals market with the GARCH-MIDAS model structure. The parametric results indicate a significant positive effect of the EURQ indicator on precious metal market volatility and mixed results for its asymmetric variation. Additionally, we investigate the performance of forecasting models that incorporate long- and short-term asymmetric effects from an out-of-sample perspective. We discover that using information from negative changes in returns and positive and negative shocks to the EURQ is more beneficial for forecasting precious metal futures price volatility than forecasting models that consider only a single indicator. Our findings emphasize the importance of queries on uncertainty-related topics in forecasting gold, silver, and copper price volatility and provide valuable guidance for rationally planning investment ratios.

Googling “Twitter Alter” and “HIV/AIDS” in the Philippines: An infodemiological study from 2011 to 2021

Introduction: Online “Twitter Alter” accounts have emerged in the Philippines, particularly among its men who have sex with men (MSM) community, including gay and bisexual men. It enabled them to mask their identities, avoiding discrimination based on their sexual preferences, orientations, and behaviors. Given these and the rising use of the internet, Google Trends can be used as a surrogate measure to determine changes in public interest concerning HIV and Twitter Alter. Methods: This study aimed to elucidate search pattern changes and related interest of the online public regarding “Twitter Alter” and “HIV/AIDS” over ten years from July 2011 to June 2021. An analysis of Google Trends for keywords “Twitter Alter” and “HIV/AIDS (Illness)” from July 2011 to June 2021 was done as a surrogate measure of public interest and awareness. Search volumes, related queries, and topics were described and compared. Results: It was found that the search volume for “Twitter Alter” has increased by about 10000% while the “HIV/AIDS (Illness)” search volume decreased to 67%. Conclusion: Online public interest, as an alternative measure to public awareness, has increased for “Twitter Alter” while decreasing for HIV. Given these, “Twitter Alter” may put its users, mostly young MSM, at a more conducive venue for high-risk sexual behaviors due to the higher behavioral control while avoiding public stigma. Nonetheless, the online “Twitter Alter” community may also be mustered in information campaigns for HIV prevention.