Query Language Research Articles

Black-Box Adversarial Attacks Against SQL Injection Detection Model

Structured Query Language (SQL) injection attacks represent a substantial threat to the security of web applications, making the development of effective detection techniques crucial. These techniques have evolved from traditional signature-based techniques to more advanced techniques based on machine learning models. Machine learning detection models are often vulnerable to adversarial examples. Adversarial examples are deliberately crafted inputs designed to deceive models into making incorrect predictions by subtly altering the original dataset in ways that are typically imperceptible to humans. To train and test these machine learning models, datasets comprising both malicious and normal data are indispensable. However, the lack of sufficient and balanced datasets presents a significant challenge, particularly for models intended to detect SQL injection attacks. Most network traffic datasets exhibit a substantial class imbalance, with a disproportionate amount of normal traffic compared to malicious traffic, making it difficult to train effective and reliable detection models. This study addressed the shortcomings of current SQL injection detection techniques and proposed a conditional tabular generative adversarial network adversarial attack method. We evaluated the effectiveness of the generated adversarial SQL injection examples using qualitative and quantitative methods, measuring their ability to evade the detection model. A conventional neural network algorithm detection model was built and tested, and the generated adversarial examples successfully bypassed the detection model at a rate of up to 6%. The evidence demonstrated that the conditional tabular generative adversarial network successfully captures the statistical properties of real data and generates synthetic data that accurately represents the real data. This method is also expected to address the problem of insufficient and imbalanced SQL injection datasets, which could aid in training various machine learning models beyond the one used in our study.

Postoperative Ketamine May be Associated with Venous Thromboembolism in Chronic Opioid Users after Spine Surgery

/Background: Venous thromboembolism (VTE) is a serious postoperative adverse event after spine surgery. In patients with pain refractory to typical multimodal analgesia regimens after spine surgery, whom are often chronic opioid users, perioperative ketamine is an alternative analgesic that has grown in popularity. The aim of this study is to assess the risk of venous thromboembolism (VTE) in chronic opioid users undergoing spine surgery. We conducted a structured query language (SQL) search to identify patients with a diagnosis of VTE after spine surgery from 2017-2021. SQL identified patient demographic information and chart review was performed to confirm procedure details, including levels fused and decompressed. Opioid use was identified using the Prescription Drug Monitoring Program (PDMP) for each patient. Chronic opioid users were defined as having >180 days or >9 opioid prescriptions prescribed in the 12 months prior to surgery. 322 total patients were identified with complete PDMP and postoperative ketamine data, including 29 patients with confirmed VTE after spine surgery. Length of stay (p<0.001) and Elixhauser comorbidity index (p=0.008) were significantly greater in the VTE group compared to the no VTE group. Analysis of only chronic opioid users also found a significant association between postoperative ketamine and VTE (p=0.034). Our study found no association between chronic opioid use and VTE occurrence; however, we identified a significant association between ketamine infusion and VTE after spine surgery. Further studies should be conducted to identify the risks associated with ketamine and other newly implemented alternative analgesics.

Enhancing Structured Query Language Injection Detection with Trustworthy Ensemble Learning and Boosting Models Using Local Explanation Techniques

This paper presents a comparative analysis of several decision models for detecting Structured Query Language (SQL) injection attacks, which remain one of the most prevalent and serious security threats to web applications. SQL injection enables attackers to exploit databases, gain unauthorized access, and manipulate data. Traditional detection methods often struggle due to the constantly evolving nature of these attacks, the increasing complexity of modern web applications, and the lack of transparency in the decision-making processes of machine learning models. To address these challenges, we evaluated the performance of various models, including decision tree, random forest, XGBoost, AdaBoost, Gradient Boosting Decision Tree (GBDT), and Histogram Gradient Boosting Decision Tree (HGBDT), using a comprehensive SQL injection dataset. The primary motivation behind our approach is to leverage the strengths of ensemble learning and boosting techniques to enhance detection accuracy and robustness against SQL injection attacks. By systematically comparing these models, we aim to identify the most effective algorithms for SQL injection detection systems. Our experiments show that decision tree, random forest, and AdaBoost achieved the highest performance, with an accuracy of 99.50% and an F1 score of 99.33%. Additionally, we applied SHapley Additive exPlanations (SHAPs) and Local Interpretable Model-agnostic Explanations (LIMEs) for local explainability, illustrating how each model classifies normal and attack cases. This transparency enhances the trustworthiness of our approach to detecting SQL injection attacks. These findings highlight the potential of ensemble methods to provide reliable and efficient solutions for detecting SQL injection attacks, thereby improving the security of web applications.

Using Specify 6, 7 and Web Portal Instruments for the North Siberian Biodiversity Data Platform

The digitization and mobilization of biodiversity data are actively ongoing in the northern part of Western Siberia (Tyumen Oblast, Russia), where the history of biodiversity research is relatively short. For small collections and young universities, it is essential to have a ready-made software product, like Specify*1, for managing the collection and publishing data openly. Our use of Specify tools began with a local installation on a single computer in 2015. Now we have a deployed system consisting of two disciplines and ten collections used by five organizations (Table 1), and we utilize Specify 6, Specify 7, the Attachment Server, and Web Portal tools. The checklist of the Western Siberian Specify installation activities includes: Personal collection of fungi since 2015 Using Specify 6 + Specify 7 + Web Portal since 2017 for university collections management Development of a Northwest Siberia bioportal*2 since 2018 Development of a Mycological Portal of West Siberia*3 since 2024 Organization of educational events Helping with localization in Russian language Developing instructions and help videos in Russian language*4 Developing DNA-derived data schema in Specify 6 since 2023 Personal collection of fungi since 2015 Using Specify 6 + Specify 7 + Web Portal since 2017 for university collections management Development of a Northwest Siberia bioportal*2 since 2018 Development of a Mycological Portal of West Siberia*3 since 2024 Organization of educational events Helping with localization in Russian language Developing instructions and help videos in Russian language*4 Developing DNA-derived data schema in Specify 6 since 2023 The main uses of the installation include storing specimen label information, the physical storage location of specimens, and associated digital information (e.g., scans, live images of objects, or microphotographs). We also utilize Specify's reports for generating customized labels and reports on loans or gifts. Taxonomical, geographical, and storage hierarchical trees are used to organize data. Recently, tables associated with molecular data have been more actively utilized to store DNA sequences, their metadata, and trace files. The Specify database is linked via Structured Query Language (SQL) to the Global Biodiversity Information Facility's (GBIF) Integrated Publishing Toolkit (IPT) for publishing collections data through GBIF. The automatic updates are regularly done once a week. Using separate queries, our collection data is regularly exported and published on the local portals, where data on labels, molecular data, distribution maps (Fig. 1) and photographs are presented (Fig. 2). Currently the Specify installation hosts 10 collections from two disciplines and has about 20 users. It contains about 22,000 specimens with 33,000 images attached, and about 1,500 DNA sequences (Table 1). Two platforms for external access to collections were created using the Specify Web Portal tool, and serve as a hub for region- and discipline-oriented collections: Northwestern Siberia Bioportal*2 for all regional collections (currently hosts 10 collections from 5 organizations); Siberian Mycological Portal*3 specifically for mycology collections in Siberia (5 collections from 3 organizations). Northwestern Siberia Bioportal*2 for all regional collections (currently hosts 10 collections from 5 organizations); Siberian Mycological Portal*3 specifically for mycology collections in Siberia (5 collections from 3 organizations). In recent years, we have been actively barcoding the specimens of the Fungarium of Yugra State University (YSU) collection. As a result, two related tables are exported to IPT: one for the collection specimens and another for DNA-derived data (the resulting dataset Filippova et al. 2024). Several ready-made queries were built to export DNA-derived data to other molecular data portals (GenBank, International Barcode of Life (iBOL), and PlutoF). A special protocol on DNA-derived data storage and export using Specify was published on Protocols.io to standardize the procedures and pass on the skills (Filippova and Zvyagina 2024).

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.

Concurrent Access Performance Comparison Between Relational Databases and Graph NoSQL Databases for Complex Algorithms

Databases are a fundamental element of contemporary software applications. The most widely used and recognized type in practice is the relational database, valued for its ability to store and organize data in tabular structures, its emphasis on data consistency and integrity, and its use of a standardized query language, SQL. However, with the rapid increase in both the volume and complexity of data, relational databases have recently encountered challenges in effectively modeling this expanding information. To address performance challenges, new database systems have emerged, offering alternative approaches to data modeling—these are known as NoSQL databases. In this paper, we present an indoor navigation application designed to operate on both a relational database, Microsoft SQL Server, and a graph-based NoSQL database, Neo4j. We describe the algorithms implemented for testing and the performance metrics analyzed to draw our conclusions. The results revealed Neo4j’s strength in managing data with complex relationships but also exposed its limitations in handling concurrent access, where SQL Server demonstrated significantly greater stability.

Reinforcement Learning with Multi-Policy Movement Strategy for Weakly Supervised Temporal Sentence Grounding

Temporal grounding involves identifying the target moment based on the provided sentence in an untrimmed video. In weakly supervised temporal grounding studies, existing temporal sentence grounding methods face challenges in (1) learning semantic alignment between the candidate window and language query and (2) identifying accurate temporal boundaries during the grounding process. In this work, we propose a reinforcement learning (RL)-based multi-policy movement framework (MMF) for weakly supervised temporal sentence grounding. We imitate the behavior of human beings when grounding specified content in a video, starting from a coarse location and then identifying fine-grained temporal boundaries. The RL-based framework initially sets a series of candidate windows and learns to adjust them step-by-step by maximizing the rewards, indicating the semantic alignment between the current window and the query. To better learn the alignment, we propose a Gaussian-based Dual-Alignment Module (GDAM) which combines the strengths of both scoring-based and reconstruction-based alignment methods, addressing the issues of negative sample bias and language bias. We also employ the multi-policy movement strategy (MMS) which grounds the temporal position in a coarse-to-fine manner. Extensive experiments demonstrate that our proposed method outperforms existing weakly supervised algorithms, achieving state-of-the-art performance on the Charades-STA and ActivityNet Captions datasets.

Analysis of the Spatial Constraints of Urban Underpass Channels Based on BIM

The construction of urban underpasses faces various spatial constraints, which will lead to deformation or failure of the structures if not satisfied. The introduction of BIM (Building Information Modeling) and other modern information technology to achieve rapid and accurate spatial constraint analysis of the design scheme is of great significance to ensure the quality and safety of the underpass and adjacent structures. Firstly, through extensive literature collection and survey, the key spatial constraint indicators of underpasses in dense urban spaces and the evaluation rules of each indicator are determined. Then, an SQL (Structured Query Language)-based database of spatial constraint rules for underpasses in dense urban space areas is constructed, and the spatial constraint analysis process for underpasses in dense urban space areas is proposed. Finally, based on Building Information Modeling, programming support, and a development environment provided by C# and Visual Studio, a spatial constraint analysis system for underpasses in dense urban space areas is developed to realize complex spatial constraint analysis functions. The application practice of the system is carried out by taking a city underpass project as an example. The results show that in the design of urban underpass engineering, the spatial constraint analysis system designed in this paper and the calculation method adopted can simplify the model transmission process, save on labor costs, and reduce the error of the calculation results.

AI chatbot: I want help, not the answer!

In recent years, the integration of artificial intelligence (AI) into educational settings has become a topic of great interest. As technology continues to evolve, educators and students are exploring how AI can enhance learning experiences. University of South-Eastern Norway (USN) and Kristiania University College (HK) are developing a web-based educational tool, DbPersist, where IT students can practice tasks within the database subject and receive automated feedback. The latest version now also offers students suggestions on how to utilize AI for learning while they are solving various database related tasks. In this study, we explore the students' experiences with getting automatically generated “prompts” for further use in their preferred AI tool, whenever they request assistance. Do they see the value in gaining experience in using prompts designed for learning? Do they believe it might help them using AI for learning purposes in other courses in their study? To answer these questions, we conducted surveys and interviews. Participants included bachelor students across various disciplines and multiple campuses at USN. The participants were all enrolled in database courses with learning goals involving the database query language SQL and ER modelling (drawing Entity-Relationship Diagrams). Early findings suggest that students appreciate the assistance provided by DbPersist’s AI prompts. They value the emphasis on AI-assisted learning and find the prompts useful for getting assistance in solving difficult tasks as opposed to getting complete answers. They also see the value in gaining experience in using AI chatbots using prompts designed for learning and understanding. As AI continues to shape education, tools like DbPersist offer exciting possibilities. DbPersist exemplifies how AI may be used to empower students, emphasizing learning and understanding alongside correctness.

Enhancing Faculty Grading Productivity Using Robotic Process Automation (RPA): The Development of a Structured Query Language (SQL) Automated Grading Tool

ABSTRACT Using the design science methodology, we develop an automated grading bot using robotic process automation (RPA) software. This grader demonstrates RPA’s capability to enhance the efficiency and accuracy of grading structured query language (SQL) queries. Validation of the RPA grader demonstrates that it can grade assignments and/or exams in 30 to 60 minutes, a task that would take a human grader approximately 50 hours. In contrast to human graders, who misgraded 5 to 8 percent of queries, the RPA grader graded all queries correctly. Notably, it correctly evaluated 188 distinct correct solutions submitted by students to a single question. To facilitate broad adoption, we provide instructions and the necessary code for implementing the RPA grader. We validate that professors and teaching assistants can successfully implement the RPA grader even with no previous bot building experience.

Designing Semantic Tutoring Maps for Enhancing Peer Learning through Lesson Preparation

Advancements in Natural Language Processing technology present significant opportunities to enhance student learning through peer tutoring. While there is substantial research on the effectiveness of peer tutoring in learning science, few studies focus on the lesson preparation stage of peer tutoring. This study utilizes Semantic Web technologies to organize tutoring maps, aiming to develop an innovative learning artifact to address this research gap. Adopting a design-based research methodology, this study first provides a literature review regarding the application of semantic technology in education and peer tutoring preparation. Subsequently, a tutoring map application based on a semantic database is developed, encompassing the organization and storage of tutoring lesson plans using Resource Description Framework (RDF) technology, and the querying and feedback on lesson plan quality based on SPARQL Protocol and RDF Query Language (SPARQL) technology. This paper describes the theoretical basis and design rationale by which student tutors’ learning is improved through structured tutoring preparation. This includes enhancing cognitive and meta-cognitive abilities in designing learning activities, raising questions, anticipating answers, and providing guidance responses to incorrect answers. The paper concludes with recommendations for educational researchers and practitioners. Future research will focus on empirical studies of peer tutoring in scientific fields to verify the validity and adaptability of the design, further facilitating peer learning by tutoring and collective knowledge building.

Bridging Data Models in Health Care With a Novel Intermediate Query Format for Feasibility Queries: Mixed Methods Study.

To advance research with clinical data, it is essential to make access to the available data as fast and easy as possible for researchers, which is especially challenging for data from different source systems within and across institutions. Over the years, many research repositories and data standards have been created. One of these is the Fast Healthcare Interoperability Resources (FHIR) standard, used by the German Medical Informatics Initiative (MII) to harmonize and standardize data across university hospitals in Germany. One of the first steps to make these data available is to allow researchers to create feasibility queries to determine the data availability for a specific research question. Given the heterogeneity of different query languages to access different data across and even within standards such as FHIR (eg, CQL and FHIR Search), creating an intermediate query syntax for feasibility queries reduces the complexity of query translation and improves interoperability across different research repositories and query languages. This study describes the creation and implementation of an intermediate query syntax for feasibility queries and how it integrates into the federated German health research portal (Forschungsdatenportal Gesundheit) and the MII. We analyzed the requirements for feasibility queries and the feasibility tools that are currently available in research repositories. Based on this analysis, we developed an intermediate query syntax that can be easily translated into different research repository-specific query languages. The resulting Clinical Cohort Definition Language (CCDL) for feasibility queries combines inclusion criteria in a conjunctive normal form and exclusion criteria in a disjunctive normal form, allowing for additional filters like time or numerical restrictions. The inclusion and exclusion results are combined via an expression to specify feasibility queries. We defined a JSON schema for the CCDL, generated an ontology, and demonstrated the use and translatability of the CCDL across multiple studies and real-world use cases. We developed and evaluated a structured query syntax for feasibility queries and demonstrated its use in a real-world example as part of a research platform across 39 German university hospitals.

A Runtime System for Interruptible Query Processing: When Incremental Computing Meets Fine-Grained Parallelism

Online data services have stringent performance requirement and must tolerate workload fluctuation. This paper introduces PitStop, a new query language runtime design built on the idea of interruptible query processing: the time-consuming task of data inspection for processing each query or update may be interrupted and resumed later at the boundary of fine-grained data partitions. This counter-intuitive idea enables a novel form of fine-grained concurrency while preserving sequential consistency. We build PitStop through modifying the language runtime of Cypher, the query language of a state-of-the-art graph database, Neo4j. Our evaluation on the Google Cloud shows that PitStop can outperform unmodified Neo4j during workload fluctuation, with reduced latency and increased throughput.

HERALD: A domain-specific query language for longitudinal health data analytics

BackgroundLarge-scale health data has significant potential for research and innovation, especially with longitudinal data offering insights into prevention, disease progression, and treatment effects. Yet, analyzing this data type is complex, as data points are repeatedly documented along the timeline. As a consequence, extracting cross-sectional tabular data suitable for statistical analysis and machine learning can be challenging for medical researchers and data scientists alike, with existing tools lacking balance between ease-of-use and comprehensiveness. ObjectiveThis paper introduces HERALD, a novel domain-specific query language designed to support the transformation of longitudinal health data into cross-sectional tables. We describe the basic concepts, the query syntax, a graphical user interface for constructing and executing HERALD queries, as well as an integration into Informatics for Integrating Biology and the Bedside (i2b2). MethodsThe syntax of HERALD mimics natural language and supports different query types for selection, aggregation, analysis of relationships, and searching for data points based on filter expressions and temporal constraints. Using a hierarchical concept model, queries are executed individually for the data of each patient, while constructing tabular output. HERALD is closed, meaning that queries process data points and generate data points. Queries can refer to data points that have been produced by previous queries, providing a simple, but powerful nesting mechanism. ResultsThe open-source implementation consists of a HERALD query parser, an execution engine, as well as a web-based user interface for query construction and statistical analysis. The implementation can be deployed as a standalone component and integrated into self-service data analytics environments like i2b2 as a plugin. HERALD can be valuable tool for data scientists and machine learning experts, as it simplifies the process of transforming longitudinal health data into tables and data matrices. ConclusionThe construction of cross-sectional tables from longitudinal data can be supported through dedicated query languages that strike a reasonable balance between language complexity and transformation capabilities.

8309 Injectable Estradiol Dosing Regimens; A Retrospective Review of Hormone Therapy for Gender-Diverse Adults Assigned Male at Birth

Abstract Disclosure: M. Kanin: None. M. Slack: None. R. Patel: None. K. Chen: None. N. Jackson: None. K. Williams: None. S. Grock: None. Introduction: Many gender incongruent individuals assigned male at birth (AMAB) seek hormone therapy to achieve physical and emotional changes. Hormone therapy typically includes estradiol, with or without an anti-androgen. However, limited data with relatively weak justifications support the currently recommended regimens. In particular, dosing schedules for injectable estradiol can vary considerably. UCSF guidelines recommend initiating estradiol valerate at 5 mg parenterally weekly or estradiol cypionate 2 mg weekly. Endocrine Society Guidelines recommend estradiol valerate or cypionate parenterally 5-30 mg every 2 weeks or 2-10 mg weekly. Our clinical observations led to the concern that current guideline-based dosing of injectable estradiol can result in supratherapeutic estradiol levels. Thus, we designed a retrospective cohort study to evaluate injectable estradiol dosing requirements for gender diverse individuals at our institution. Hypothesis: We hypothesize that low dose estradiol injections can lead to therapeutic estradiol levels (100-200 pg/mL) and suppressed testosterone (&amp;lt;50 ng/dL). Design: We performed a retrospective cohort study of gender diverse individuals AMAB aged 18-89 who were newly prescribed injectable estradiol by a single clinic from January 2017 to March 2023. Exclusion criteria included history of orchiectomy or estradiol use within the previous six months. Patients were identified using structured query language to extract data from our institution’s electronic health record system. A total 33 patients were identified, 29 of whom were eligible for inclusion. Data was collected for 15 months after estradiol initiation. Results: All individuals were prescribed estradiol valerate except for 1 who was prescribed estradiol cypionate. Estradiol dose ranged from 2.5 to 10 mg weekly and an average initial dose of 4.26 mg weekly. Mean initial estradiol level obtained while on treatment for at least 30 days was 349.8 pg/mL, which is supratherapeutic. Mean initial testosterone level was 25.3 ng/dL, which is suppressed. Mean estradiol dose at completion of data collection was 3.68 mg with a mean final estradiol level of 245.2 pg/mL. Testosterone levels reached less than 50 ng/dL for everyone in the sample with a mean final testosterone of 23.3ng/dL despite significant reductions in estradiol dose (change=-0.58 mg per week, p=0.001) Conclusion: Our study demonstrates lower dosages of estradiol can be used to achieve therapeutic estradiol levels in gender incongruent patients seeking estrogen therapy. Using lower dosages can avoid supratherapeutic estradiol levels and the associated risks. Further prospective studies are needed with the aim to reevaluate current guideline dosing recommendations for injectable estradiol. Presentation: 6/2/2024

8309 Injectable Estradiol Dosing Regimens; A Retrospective Review of Hormone Therapy for Gender-Diverse Adults Assigned Male at Birth

Abstract Disclosure: M. Kanin: None. M. Slack: None. R. Patel: None. K. Chen: None. N. Jackson: None. K. Williams: None. S. Grock: None. Introduction: Many gender incongruent individuals assigned male at birth (AMAB) seek hormone therapy to achieve physical and emotional changes. Hormone therapy typically includes estradiol, with or without an anti-androgen. However, limited data with relatively weak justifications support the currently recommended regimens. In particular, dosing schedules for injectable estradiol can vary considerably. UCSF guidelines recommend initiating estradiol valerate at 5 mg parenterally weekly or estradiol cypionate 2 mg weekly. Endocrine Society Guidelines recommend estradiol valerate or cypionate parenterally 5-30 mg every 2 weeks or 2-10 mg weekly. Our clinical observations led to the concern that current guideline-based dosing of injectable estradiol can result in supratherapeutic estradiol levels. Thus, we designed a retrospective cohort study to evaluate injectable estradiol dosing requirements for gender diverse individuals at our institution. Hypothesis: We hypothesize that low dose estradiol injections can lead to therapeutic estradiol levels (100-200 pg/mL) and suppressed testosterone (&amp;lt;50 ng/dL). Design: We performed a retrospective cohort study of gender diverse individuals AMAB aged 18-89 who were newly prescribed injectable estradiol by a single clinic from January 2017 to March 2023. Exclusion criteria included history of orchiectomy or estradiol use within the previous six months. Patients were identified using structured query language to extract data from our institution’s electronic health record system. A total 33 patients were identified, 29 of whom were eligible for inclusion. Data was collected for 15 months after estradiol initiation. Results: All individuals were prescribed estradiol valerate except for 1 who was prescribed estradiol cypionate. Estradiol dose ranged from 2.5 to 10 mg weekly and an average initial dose of 4.26 mg weekly. Mean initial estradiol level obtained while on treatment for at least 30 days was 349.8 pg/mL, which is supratherapeutic. Mean initial testosterone level was 25.3 ng/dL, which is suppressed. Mean estradiol dose at completion of data collection was 3.68 mg with a mean final estradiol level of 245.2 pg/mL. Testosterone levels reached less than 50 ng/dL for everyone in the sample with a mean final testosterone of 23.3ng/dL despite significant reductions in estradiol dose (change=-0.58 mg per week, p=0.001) Conclusion: Our study demonstrates lower dosages of estradiol can be used to achieve therapeutic estradiol levels in gender incongruent patients seeking estrogen therapy. Using lower dosages can avoid supratherapeutic estradiol levels and the associated risks. Further prospective studies are needed with the aim to reevaluate current guideline dosing recommendations for injectable estradiol. Presentation: 6/2/2024

Developing a Geo-Spatial Database and Standard Queries for Election Management by Stakeholders within Jos South, Nigeria

One of the major set-backs of the 2023 general elections in Nigeria as highlighted by YIAGA Africa (2024) was the failure of the body saddled with election management which is Independent Electoral Commission (INEC), to deploy Geo-spatial Database (GSDB) as a repository in the efficient management processes which enable all election stakeholder participate seamlessly and interactively. The modus of oparandy for the geo-spatial database was hierarchical by dis-aggregating GSDB to the lowest geographical unit. The recent court verdict on re-delineation of electoral wards in Rivers State did not go down well with the political pundits prior to the forth coming local government election in the state. It surffices to say that, INEC from the on-set never had a geo-spatial database from which delineation and update of electoral wards, polling units can be carried out This will further build confidence and integrity, then reducing litigations that characterized the 2023 elections. The GSDB used was predicated on file geo-database of ArcGIS, where both spatial and attribute data of the study were stored; and standard query languages that are pertinent to elections were issued at prompt which generated maps and texts for decision making by all stakeholders.

National Experience of Technology-based Eye Care Services (TECS): A Comprehensive Ophthalmology Telemedicine Initiative

Technology-Based Eye Care Services (TECS) is a tele-ophthalmology program operating in the Veterans' Administration since 2015. This study explores characteristics of the national TECS population, evaluates implementation and sustainability of TECS, and analyzes possible associations and effects of demographic characteristics and social determinants of health (SDOH) on being diagnosed with a vision threatening (VT) disease. Implementation and sustainability of TECS from 2015-2022 was examined along with the sociodemographic characteristics of Veterans served through TECS in 2021. Veteran patients seen in TECS nationwide. Characteristics, disease prevalence and diagnoses, implementation success rate, sustainability rate, preliminary analysis of outcomes and disparity. Per quarter from 2015-2022, TECS sites were classified as implementing, active, or sustained. Standard Query Language (SQL) was used to determine sociodemographic data and logistic regression models were used to identify risk factors associated with VT eye diagnosis. 21,712 Veterans, 52.1% rural or highly rural, were served by TECS in 2021. The average age was 64.7 years, with females comprising 10.9% of the population served. From 2015-2022, of the 67 TECS sites initiated, 6 were implementing with 51 of 61 initiated sites still operational in the first quarter (Q1) of 2022 (83.6% success rate).Macular degeneration (AMD) and cataracts were more prevalent in rural and highly rural populations (7.6% and 11.3%, and 48.8% and 55.0%, respectively) vs. urban populations; glaucoma and diabetic retinopathy (DR) had the opposite association. The prevalence of any type of VT eye disease was lowest in the Mountains/Central region (0.54%) and highest in the Southeast region (3.2%) of the US. Rural and highly rural residents were 1.3 and 2.5 times as likely, respectively, to be diagnosed with a VT eye disease than urban residents. Implementation and sustainability of TECS has been promising. The data provides key information that can be utilized to improve the deployment of TECS and similar programs, along with the possible future direction of TECS services. Moreover, experience from one national ocular telehealth program clearly illustrates that telemedicine can address eye care disparities in the Veteran population and may be utilized for other vulnerable groups as well.

Comparative Analysis of Single and Multiagent Large Language Model Architectures for Domain-Specific Tasks in Well Construction

Summary This paper explores the application of large language models (LLMs) in the oil and gas (O&amp;G) sector, specifically within well construction and maintenance tasks. The study evaluates the performances of a single agent and a multiagent LLM-based architecture in processing different tasks, offering a comparative perspective on their accuracy and the cost implications of their implementation. The results indicate that multiagent systems offer improved performance in question and answer (Q&amp;A) tasks, with a truthfulness measure 28% higher than single-agent systems but at a higher financial cost. Specifically, the multiagent architecture incurs costs that are, on average, 3.7 times higher than those of the single-agent setup due to the increased number of tokens processed. Conversely, single-agent systems excel in Text-to-SQL (structured query language) tasks, particularly when using the Generative Pre-Trained Transformer 4 (GPT-4), achieving a 15% higher score compared to multiagent configurations, suggesting that simpler architectures can sometimes outpace complexity. The novelty of this work lies in its original examination of the specific challenges presented by the complex, technical, unstructured data inherent in well construction operations, contributing to strategic planning for adopting generative artificial intelligence (AI) (Gen-AI) applications and providing a basis for optimizing solutions against economic and technological parameters.

Application of a Question Answering System in the Ontology-Based Health Domain and Question Templates to Improve the Quality of Answers

Developing an effective Question Answering System (QAS) to address a wide range of health-related inquiries presents a significant challenge. This study introduces an innovative approach using an ontology-based QAS within the health domain, leveraging the Resource Description Framework (RDF) and SPARQL query language. By utilizing ontology, the system can more accurately map health concepts, improving the relevance and precision of its responses. Additionally, the incorporation of structured Question Templates enhances the system’s ability to understand and respond to diverse user queries. The system's performance was evaluated using Black Box Testing, which demonstrated that it consistently provides accurate and relevant answers. The system achieved a Mean Average Precision (MAP) of 79.6%, indicating its potential to effectively address a broad spectrum of health issues.