International Classification of Primary Care (ICPC-2) and search engines: an exploration of three algorithms for information retrieval to aid medical coding

Discriminative meets generative: Automated information retrieval from unstructured corporate documents via (large) language models

Neuropsychological assessments commonly include word list learning tasks to assess verbal memory and learning. The California Verbal Learning Test (CVLT) provides multiple outcome measures and information regarding strategies used to enhance the coding and retrieval of information. Despite its popularity, the CVLT has not yet been formally translated into Hebrew and adapted to the Israeli population. The CVLT-III was adapted to Hebrew (CVLT-IIIHebrew), and normative data of healthy Hebrew-speaking adults living in Israel (age range: 20 - 65, education range: 9 - 20) were collected (N = 235). CVLT-IIIHebrew core scores were influenced by age, education level, and, to a lesser extent, sex. Normative data for the Hebrew-speaking Israeli population were generated using an overlapping interval strategy, and regression models were used to evaluate the necessity of adjusting core scale scores for sociodemographic variables. Internal reliability was very high. Clinicians can employ an easy-to-use calculator for adjusting CVLT-IIIHebrew core scores. The adapted CVLT-IIIHebrew provides a valuable tool for evaluating the verbal memory of Hebrew speakers. Caution, however, is warranted when assessing individuals with lower education levels, as the normative sample was relatively highly educated. This highlights the importance of expanding the normative sample to include a broader spectrum of educational levels and ages. Moreover, the inclusion of Israeli minority groups, currently unrepresented in this normative sample, is of importance.

Excitation-dependent (Ex-De) photoluminescence, characterized by distinct emission color changes under varying excitation wavelengths, has been extensively investigated in doped systems, where luminescence relies on a dopant-host synergy. However, Ex-De behavior achieved through two independent luminescent centers in undoped metal halide lattices has scarcely been reported. Herein, we synthesized two novel zero-dimensional (0D) bimetallic halides (BMHs), [Eu(UREA)7H2O][SbCl6] (Eu-U) and [Tb(UREA)7H2O][SbCl6] (Tb-U), via a cooling crystallization method. Structural analyses reveal that Eu3+/Tb3+ ions coordinate with UREA and water molecules to form cationic complexes [Eu(UREA)7(H2O)]3+/[Tb(UREA)7(H2O)]3+, which isolate [SbCl6]3- anions. Hirshfeld surface analysis further indicates substantial hydrogen bonding between the complex cations and [SbCl6]3- octahedra, leading to notable structural distortion. Spectroscopic studies confirm that both compounds can exhibit simultaneous broadband emission from Sb3+ and narrowband emission from Eu3+/Tb3+, with no energy transfer between the centers. Leveraging this unique Ex-De behavior, we fabricated anticounterfeiting patterns by embedding Eu-U and Tb-U in silicone, demonstrating effective information concealment and retrieval. This work not only enriches the family of BMHs but also realizes synergistic narrowband and broadband emission, offering new insights for designing Ex-De luminescent materials with independent dual emitting centers.

Orbital angular momentum (OAM) multiplexing holography has emerged as a pivotal technology for high-capacity optical communication, encryption and display, but it requires multiple inputs for decoding and its security remain constrained due to the rotational symmetry of topological charge (TC) distribution in conventional OAM modes. Here, we introduce a general paradigm of OAM multiplexing holography that enables multi-channel holographic encoding using a single incident light. Our methodology leverages a discontinuous OAM with a spatially varying TC across the azimuth, which breaks the rotational symmetry and imposes angular selectivity for information retrieval. Notably, by rationally designing the TC distribution, the discontinuous OAM exhibits self-orthogonality at different rotation angles, laying the foundation for multiplexed holography. A modified weighted Gerchberg-Saxton algorithm is developed to calculate the holographic phase profile, which can then be encoded onto a pure geometry-phase metasurface. By further integrating different pairs of discontinuous OAMs, we successfully expand the channel capacity for holographic multiplexing, significantly advancing high-security and high-capacity optical information encryption. Our work establishes discontinuous OAM as a versatile platform for secure optical communications, high-density data storage, and dynamic holographic displays, bridging the gap between structured light manipulation and cryptographic robustness.

Hierarchical knowledge graph based legal information retrieval from multiple documents using intelligent agents

In this article, the possibilities of creating an inclusive environment for people with disabilities in Uzbek media, particularly for information consumers with visual disabilities, as well as the problems of media coverage of human issues of this category, are studied on the basis of the questionnaire-survey method. This will focus on the analysis of problems such as inclusive opportunities in information retrieval and dissemination processes of persons with visual disabilities in Uzbekistan, favorable information retrieval methods, forms, tools, obstacles to this, the level of inclusion of Uzbek media and media, the level of adaptation of digital information resources for people of the same category. In addition, the level of coverage of the problems of individuals with visual disabilities in Uzbek media, methods and formats for presenting the same topic in content, the variety of issues, the involvement of professionals in the processes of analysis, in particular, experts with visual disabilities, analysis of stereotypes that hinder the preparation of content in this direction are also provided. Also, based on the results of empirical research, practical recommendations for improving the field are developed.

Background The increasing use of AI tools for information retrieval (IR) among students raises both ethical concerns and the need to examine their awareness of the potential algorithm bias that characterizes AI-enabled IR systems. Purpose The study investigated AI algorithm bias awareness and ethical concern as predictors of use of AI for information retrieval among LIS students in Nigeria. Research Design The study employed the descriptive type of correlational research design, based on its ability to describe the variables of the study and ascertain the relationship that existed among them. Study Sample A total of 213 respondents were selected for the study using the accidental sampling technique of the nonprobability type. Data Collection The instrument used for data collection was a self-developed structured questionnaire. The questionnaire was converted to an online survey using Google Forms to elicit data from the respondents for a period of four weeks. Data analysis The collected data were analysed using descriptive (frequencies, mean and standard deviation) and inferential (PPMC and multiple regression) statistics and with the aid of the Statistical Package for the Social Sciences (SPSS) version 28. Results Findings revealed a significant relationship among AI algorithm bias awareness, ethical concerns and use of AI for information retrieval among LIS students in Nigeria. The study also showed a high level of LIS students` awareness of the potential bias in AI algorithm in information retrieval. In spite of this, the study recorded that the level of LIS students’ use of AI for information retrieval is high. Conclusions The study concluded that the structure and content of LIS curriculum in Nigeria exposes the students to the rudiments of ICT application to librarianship, putting them at a position of knowledge-advantage.

This study aims to implement an information retrieval system for cryptocurrency data using an attribute-based approach integrated with the Vector Space Model (VSM). The primary objective is to develop a system capable of retrieving the most relevant digital asset information according to specific search attributes, including positive sentiment, price fluctuation, and prediction confidence level. The research adopts a descriptive qualitative method combined with an experimental approach to evaluate the retrieval performance of the cosine similarity algorithm on normalized numerical data. Data preprocessing and attribute weighting were conducted to ensure consistency and improve retrieval accuracy. The experiment demonstrates that the proposed system achieves a Precision@5 value of 1.0, which indicates that all top-five retrieved results are fully relevant to user queries. These findings validate the effectiveness of the attribute-based VSM in analyzing multidimensional cryptocurrency datasets. Overall, this research contributes to the advancement of information retrieval applications in the cryptocurrency domain, particularly for supporting data-driven decision-making and intelligent financial analysis.

Application of Natural Language Processing for Improved Information Retrieval in University Libraries in Kebbi State

Background: Type 1 diabetes mellitus or juvenile/adolescent diabetes has a significant impact on the quality of life among children and parents. Thus, the development of a deep understanding of the disease of diabetes in childhood and the ways to prevent and treat patients allows health care providers to respond to their specific needs. One of the solutions to improve the knowledge of the patients is online education. Therefore, this study designed and evaluated a childhood diabetes education website with the goal of improving the quality of care for children and reducing mortality. Methods: This study involved three phases of content collection, website design, and evaluation. The website content information was collected from reliable library sources and created using the WordPress website design tool. Subsequently, the website content, performance, display capabilities, terminology and information retrieval, and ease of learning were evaluated by physicians and nurses. Results: Comprehensive clinical information about pediatric diabetes was provided on the website. In the evaluation of the website content, the highest average was related to the type 1 diabetes section at a glance and the lowest average was related to the pathology section. In evaluating the website performance, the highest average was related to the overall design and the lowest average was related to the ease of use. Overall, the website performance was of good quality. Conclusion: To increase the effectiveness of care services and reduce the complications of diseases, providing accurate and reliable information in the form of educational websites should be considered.

Background:Aflatoxin serves as a distinct risk factor for hepatic carcinoma, making the investigation into its association with hepatic carcinoma is essential for unraveling the molecular underpinnings of oncogenesis and devising therapeutic strategies for tumors. However, the mechanism by which aflatoxin causes liver cancer is not yet clear. This article aims to analyze the latest research progress and cutting-edge exploration directions for aflatoxin-induced liver cancer.Method:This research relies on the Web of Science core collection for information retrieval, leveraging the broad scope of the SCI-EXPANDED index to guarantee comprehensiveness and high precision of the gathered data. From April 30, 2014 to April 30, 2024, relevant original research literature and reviews on aflatoxin and liver cancer were retrieved. Subsequently, VOSviewer, CiteSpace, and R software were used to visualize and analyze the articles.Results:A total of 597 relevant studies were obtained, with 3228 authors from 1116 organizations in 94 countries. America and China are major contributors to international publications. Groopman, John D has the most publications, and Jessica Zucman-Rossi has the highest number of citations. Guangxi Medical University, China Agricultural University, Johns Hopkins University, etc were the main research institutions. Toxins, Food and Chemical Toxicology are popular journals in this field, and the most cited journal is Nature Reviews Gastroenterology & Hepatology. Research primarily focuses on 4 areas: the correlation between aflatoxin contamination, exposure levels, and the progression of hepatic carcinoma; the underlying processes by which aflatoxin inflicts liver injury that may result in hepatic carcinoma; the combined impact of aflatoxin B1 and the hepatitis B virus (HBV) on the onset of hepatic carcinoma; and strategies for the prevention and management of aflatoxin-induced hepatic malignancies. Specifically, aflatoxins can induce hepatotoxicity, immunotoxicity, alter expression of coding genes and noncoding RNAs, and synergize with hepatitis B virus to promote hepatocarcinogenesis. Physical, chemical, and biological methods have been widely employed to degrade aflatoxins for liver cancer prevention and control, among which biological control have garnered significant attention from researchers.Conclusion:Research on the aflatoxin-hepatic carcinoma link is rapidly advancing. Furthermore, it confirms aflatoxin’s pivotal role in the pathogenesis of liver cancer.

The proliferation of generative AI presents significant productivity opportunities for software companies, but the practice of employees using public AI chatbots poses severe data security risks. This research paper demonstrates the application of a Retrieval-Augmented Generation (RAG) architecture into building a dedicated AI assistant for secure, internal corporate use by reasoning its responses exclusively in a company's private document repository, unlike a general-purpose model. This will ensure that sensitive internal data such as project specifications, internal wikis, project codes, knowledge documents, policy documents etc do not leave the company firewall during the AI operation cycle while also letting the company entity use AI to compare and access the needful resources from huge internal data.Our methodology involves processing and vectorizing internal documents, enabling semantic search for precise information retrieval, and leveraging a large language model (LLM) solely for generating context-aware responses from the retrieved data. We argue that this system provides a practical, secure, and efficient solution for knowledge management and employee assistance, balancing the power of modern AI with the non-negotiable demands of corporate data privacy.

Abstract Narratives are key interpretative devices by which humans make sense of political reality. As the significance of narratives for understanding current societal issues such as polarization and misinformation becomes increasingly evident, there is a growing demand for methods that support their empirical analysis. To this end, we propose a graph-based formalism and machine-guided method for extracting, representing, and analyzing selected narrative signals from digital textual corpora, based on Abstract Meaning Representation (AMR). The formalism and method introduced here specifically cater to the study of political narratives that figure in texts from digital media such as archived political speeches, social media posts, transcripts of parliamentary debates, and political manifestos on party websites. We conceptualize these political narratives as a type of ontological narratives: stories by which actors position themselves as political beings, and which are akin to political worldviews in which actors present their normative vision of the world, or aspects thereof. We approach the study of such political narratives as a problem of information retrieval: starting from a textual corpus, we first extract a graph-like representation of the meaning of each sentence in the corpus using AMR. Drawing on transferable concepts from narratology, we then apply a set of heuristics to filter these graphs for representations of (1) actors and their relationships, (2) the events in which these actors figure, and (3) traces of the perspectivization of these events. We approach these references to actors, events, and instances of perspectivization as core narrative signals that allude to larger political narratives. By systematically analyzing and re-assembling these signals into networks that guide the researcher to the relevant parts of the text, the underlying narratives can be reconstructed through a combination of distant and close reading. A case study of State of the European Union addresses (2010–2023) demonstrates how the formalism can be used to inductively surface signals of political narratives from public discourse.

This paper presents a novel approach to knowledge organization and information retrieval in digital libraries through an adaptive semantic retrieval framework that integrates graph neural networks (GNNs), ontological knowledge structures, and user behavior analysis. Traditional knowledge organization systems often employ static classification methods that inadequately represent multidimensional relationships and fail to adapt to evolving user needs. Our framework addresses these limitations through a unified computational approach that combines formal semantic representations with empirical usage patterns. The system architecture includes ontology-driven knowledge graph construction, multi-relational GNN-based representation learning, comprehensive user behavior modeling, and an adaptive retrieval mechanism that dynamically balances domain semantics with personalized relevance signals. Experimental evaluation across diverse digital library collections demonstrates significant performance improvements, with the integrated framework achieving 81% precision and 85% recall, substantially outperforming conventional retrieval models. The proposed approach enables more intelligent and responsive information discovery while maintaining semantic coherence, offering a promising direction for adaptive knowledge organization that bridges traditional boundaries between formal classification approaches and user-centered design principles.

Semantic language dysfunction is a hallmark of early psychosis, yet the underlying brain structural correlates are largely unexplored. In particular, it is unclear whether core deficits arise from disruptions to semantic representation, which refers to the stored knowledge of word meanings, or to semantic control, which entails top-down mechanisms that guide the retrieval and selection of context-appropriate semantic information. By dissociating semantic representation-related from semantic control-related performance, we aimed to identify the preferential impairment in early psychosis and its structural correlates in the ventral and dorsal language streams. We investigated N = 120 individuals across the psychosis spectrum: N = 40 individuals with early psychosis, N = 40 individuals with high schizotypy, and N = 40 individuals with low schizotypy. Participants with high and low schizotypy constituted the non-clinical comparison group. All participants completed tasks designed to isolate semantic representation-related and semantic control-related processes. Given the importance of accurate delineation, this study employed meticulous manual fiber tractography of diffusion tensor imaging (DTI) data to ensure reliable evaluation of ventral and dorsal pathway microstructure. Compared to individuals with high and low schizotypy, individuals with early psychosis showed pronounced deficits in semantic control-related performance, while the semantic representation-related measure remained largely intact. Mean diffusivity in the left inferior fronto-occipital fasciculus and left uncinate fasciculus was lower in the early psychosis group than in individuals with schizotypy. In the early psychosis group, fractional anisotropy in the left arcuate fasciculus was negatively correlated with semantic control-related performance, but no DTI measure was associated with the semantic representation-related measure. These results underscore semantic control-related performance as a core deficit in early psychosis and extend the conventional view that semantic processing is subserved primarily by ventral pathways. The arcuate fasciculus appears implicated in semantic control-related processes, indicating a more integrated interplay of dorsal and ventral streams in semantic language processing.

Spelling variations in personal names pose significant challenges for information retrieval and record linkage, particularly in low-resource languages such as Hausa. This paper presents a phonetic encoding algorithm, Sautex, specifically adapted to the phonological structure of Hausa, derived from the English Soundex system. Sautex was evaluated using a dataset of 17,591 Hausa name spelling attempts with edit distances ranging from 0 to 4. The system achieves a phonetic match accuracy of 77.20% and 66.86% recall for the positive class for the H* variant and 83.02% and 75.32% correspondingly for the H** variant, outperforming the baseline English Soundex by up to 11.53 and 16.76 percentage points in accuracy and recall for the positive class. These results demonstrate the viability of phonology-aware, language-specific encoding systems for African languages. Further studies might be undertaken to evaluate the performance of this algorithm on English names and its generalisation for other Nigerian names. The research aligns with two United Nations Sustainable Development Goals (SDGs), notably SDG 10 (Reduced Inequalities) by ensuring equitable digital representation of Hausa names, and SDG 9 (Industry, Innovation, and Infrastructure) by advancing localized NLP innovations.Note: i. Sautex is the contraction of the word Sauti, which means Sound in Hausa, and Soundex.ii. H* indicates values for the Sautex code with the first character includediii. H** indicates values for the Sautex code with the first character excludediv. English is abbreviated as Eng.

In today's digital era, email remains a vital communication medium within academic and organizational settings. Nonetheless, the rapid increase in email volume and information overload poses significant difficulties in effectively managing and addressing relevant content. This paper investigates AI-enhanced email automation systems that incorporate contextual knowledge retrieval. These advanced systems utilize natural language processing (NLP), deep learning, and information retrieval techniques to comprehend, prioritize, and respond to email messages automatically, while concurrently extracting pertinent information from knowledge bases. By integrating contextual search features, the automated responses produced are precise, tailored, and based on specific institutional or domain knowledge. The study further examines the system architecture, employed algorithms, practical applications in academia and industry, and discusses ethical considerations along with prospective developments.

Usability of Koha online public access catalogue during information retrieval with regard to interface design

ABSTRACT To address the issues of high computational overhead, poor real‐time performance, and excessive focus on location privacy while neglecting query privacy in existing Internet of Vehicles (IoV) Location‐Based Services (LBS) privacy protection schemes. This research proposes a joint protection scheme for location and query privacy in IoV LBS based on a subspace‐optimized PIR algorithm. This scheme integrates keyword‐based Private Information Retrieval (PIR) protocols with the BFV fully homomorphic encryption algorithm to protect the privacy of user query content and access patterns, preventing the server and attackers from obtaining specific queries or feedback results. Furthermore, the scheme introduces R‐tree spatial indexing and Order‐Preserving Encryption (OPE) algorithms, optimizing global search into subspace search, thereby reducing the computational complexity of PIR from O(N) to O(logN) and improving efficiency. Security analysis demonstrates excellent performance in query privacy protection and resistance to malicious attacks. Performance tests reveal that the overall computational overhead is reduced by approximately 80% compared to existing typical solutions, significantly enhancing system efficiency under strong privacy protection, making it suitable for resource‐constrained and high real‐time‐demand environments in IoV LBS applications.