Database Integration Research Articles

Deep Fuzzy Credibility Surfaces for Integrating External Databases in the Estimation of Operational Value at Risk

Operational risk (OR) is usually caused by losses due to human errors, inadequate or defective internal processes, system failures, or external events that affect an organization. According to the Basel II agreement, OR is defined by seven risk events: internal fraud, external fraud, labor relations, clients, damage to fixed assets, technical failures and failures in the execution and administration of processes. However, the low frequency with which a loss event occurs creates a technological challenge for insurers in estimating the operational value at risk (OpVar) for the protection derived from an organization’s business activities. Following the above, this paper develops and analyzes a Deep Fuzzy Credibility Surface model (DFCS), which allows the integration in a single structure of different loss event databases for the estimation of an operational value at risk (OpVar), overcoming the limitations imposed by the low frequency with which a risk event occurs within an organization (sparse data). For the estimation of OpVar, the DFCS model incorporates a novel activation function based on the generalized log-logistic function to model random variables of frequency and severity that define a loss event (linguistic random variables), as well as a credibility surface to integrate the magnitude and heterogeneity of losses in a single structure as a result of the integration of databases. The stability provided by the DFCS model could be evidenced through the structure exhibited by the aggregate loss distributions (ALDs), which are obtained as a result of the convolution process between frequency and severity random variables for each database and which are expected to achieve similar structures to the probability distributions suggested by Basel II agreements (lean, long tail, positive skewness) against the OR modeling. These features make the DFCS model a reference for estimating the OpVar to protect the risk arising from an organization’s business operations by integrating internal and external loss event databases.

Comparative Analysis of Chatbots Using Large Language Models for Web Development Tasks

In this study, we compare the performance of five chatbots using large language models (LLMs) in handling web development tasks. Three human testers asked each chatbot nine predefined questions related to creating a simple website with a dynamic form and database integration. The questions covered tasks such as generating a web document structure, designing a layout, creating a form, and implementing database queries. The chatbots’ outputs were ranked based on accuracy, completeness, creativity, and security. The experiment reveals that conversational chatbots are adept at managing complex tasks, while programming assistants require more precisely formulated tasks or the ability to generate new responses to address irrelevant outputs. The findings suggest that conversational chatbots are more capable of handling a broader range of web development tasks with minimal supervision, whereas programming assistants need more precise task definitions to achieve comparable results. This study contributes to understanding the strengths and limitations of various LLM-based chatbots in practical coding scenarios, offering insights for their application in web development.

Integration of legal databases in the context of EU accession aspirations

It is determined that the process of cooperation within the framework of the European Union has increased the need for identification and exchange of legal information coming from regional and national authorities at the European level. This need is partially met by digitally accessible legal information and the widespread use of the Internet. However, the exchange of legal information is largely limited by the differences that exist in different national legal systems, as well as the differences in their technical systems used to store and display the law on their respective websites. This hinders interaction between the information systems of national and European institutions, despite the increase in the availability of documents in electronic format. At the same time, the interconnection of national and international legal information from various sources and their presentation in the form of linked data of legal knowledge will undoubtedly improve the process of searching for legal information, expanding the possibilities of inquiries that are not currently possible. Moreover, in an increasingly digital society, where the number of cross-border civil and criminal proceedings is rapidly increasing, and given the complex diversity of different legal systems in the EU Member States, the simplification and facilitation of cross-border judicial cooperation is essential to ensure effective access of citizens and businesses to justice. At the same time, the system of identification and metadata should be well known to citizens and specialists in the field of law. In addition, to increase the chances of finding case law that is supported by an identifier and metadata, these court decisions should be searchable - by identifier and a minimal set of metadata - through a common interface. The exchange of digital evidence in the EU is also an important aspect. Currently, countries cooperate using private international law in cross-border criminal cases and exchange digital evidence based on these tools. The choice of tool depends on the countries involved and the type of information required.

MVP: a modular viromics pipeline to identify, filter, cluster, annotate, and bin viruses from metagenomes.

While numerous computational frameworks and workflows are available for recovering prokaryote and eukaryote genomes from metagenome data, only a limited number of pipelines are designed specifically for viromics analysis. With many viromics tools developed in the last few years alone, it can be challenging for scientists with limited bioinformatics experience to easily recover, evaluate quality, annotate genes, dereplicate, assign taxonomy, and calculate relative abundance and coverage of viral genomes using state-of-the-art methods and standards. Here, we describe Modular Viromics Pipeline (MVP) v.1.0, a user-friendly pipeline written in Python and providing a simple framework to perform standard viromics analyses. MVP combines multiple tools to enable viral genome identification, characterization of genome quality, filtering, clustering, taxonomic and functional annotation, genome binning, and comprehensive summaries of results that can be used for downstream ecological analyses. Overall, MVP provides a standardized and reproducible pipeline for both extensive and robust characterization of viruses from large-scale sequencing data including metagenomes, metatranscriptomes, viromes, and isolate genomes. As a typical use case, we show how the entire MVP pipeline can be applied to a set of 20 metagenomes from wetland sediments using only 10 modules executed via command lines, leading to the identification of 11,656 viral contigs and 8,145 viral operational taxonomic units (vOTUs) displaying a clear beta-diversity pattern. Further, acting as a dynamic wrapper, MVP is designed to continuously incorporate updates and integrate new tools, ensuring its ongoing relevance in the rapidly evolving field of viromics. MVP is available at https://gitlab.com/ccoclet/mvp and as versioned packages in PyPi and Conda.IMPORTANCEThe significance of our work lies in the development of Modular Viromics Pipeline (MVP), an integrated and user-friendly pipeline tailored exclusively for viromics analyses. MVP stands out due to its modular design, which ensures easy installation, execution, and integration of new tools and databases. By combining state-of-the-art tools such as geNomad and CheckV, MVP provides high-quality viral genome recovery and taxonomy and host assignment, and functional annotation, addressing the limitations of existing pipelines. MVP's ability to handle diverse sample types, including environmental, human microbiome, and plant-associated samples, makes it a versatile tool for the broader microbiome research community. By standardizing the analysis process and providing easily interpretable results, MVP enables researchers to perform comprehensive studies of viral communities, significantly advancing our understanding of viral ecology and its impact on various ecosystems.

Informatics and Artificial Intelligence-Guided Assessment of the Regulatory and Translational Research Landscape of First-in-Class Oncology Drugs in the United States, 2018-2022.

Cancer drug development remains a critical but challenging process that affects millions of patients and their families. Using biomedical informatics and artificial intelligence (AI) approaches, we assessed the regulatory and translational research landscape defining successful first-in-class drugs for patients with cancer. This is a retrospective observational study of all novel first-in-class drugs approved by the US Food and Drug Administration (FDA) from 2018 to 2022, stratified by cancer versus noncancer drugs. A biomedical informatics pipeline leveraging interoperability standards and ChatGPT performed integration and analysis of public databases provided by the FDA, National Institutes of Health, and WHO. Between 2018 and 2022, the FDA approved a total of 247 novel drugs, of which 107 (43.3%) were first-in-class drugs involving a new biologic target. Of these first-in-class drugs, 30 (28%) treatments were indicated for patients with cancer, including 19 (63.3%) for solid tumors and the remaining 11 (36.7%) for hematologic cancers. A median of 68 publications of basic, clinical, and other relevant translational science preceded successful FDA approval of first-in-class cancer drugs, with oncology-related treatments involving fewer median years of target-based research than therapies not related to cancer (33 v 43 years; P < .05). Overall, 94.4% of first-in-class drugs had at least 25 years of target-related research papers, while 85.5% of first-in-class drugs had at least 10 years of translational research publications. Novel first-in-class cancer treatments are defined by diverse clinical indications, personalized molecular targets, dependence on expedited regulatory pathways, and translational research metrics reflecting this complex landscape. Biomedical informatics and AI provide scalable, data-driven ways to assess and even address important challenges in the drug development pipeline.

The Weakening of Taxpayer Rights in the Exchange of Information between Tax Authorities

Developments in international legislation and the growing digitalization of tax law support the development of global networks between tax authorities. We are witnessing an integration of databases that will lead to increasingly intense coordination of the fight against tax evasion at a supranational level. As in social networks and digital commerce, databases are increasingly enriched, contain increasingly precise information on the individual taxpayer and use common languages that allow for automated exchanges of information. While waiting for the creation of a global database – not conditioned by the constraints of reciprocity and abstractly usable by all authorized entities who need it – the first risks of limitation of the taxpayer’s rights are emerging. In fact, these phenomena have dark sides that are starting to emerge in use, at a national level and with respect to individual taxpayers, of interpolated databases. Moreover, a growing amount of information flows from heterogeneous and increasingly widespread sources, sometimes not protected by the requirements of professionalism, legality and public trust since data collection and entry can be delegated to economic entities, intermediaries and consultants. The absence of an authority responsible for the unitary management of global databases and for the resolution of their conflicts, the slow and timid affirmation (only in some national systems) of the taxpayer’s right of access to information concerning them, the difficult configuration of the faculty to request the correction of erroneous data and of the specular public power to remove the reported inaccuracies, weaken the system of protections gradually erected to protect the taxpayer’s position. In this way, the coordination of national systems that contemporary tax law creates is strongly unbalanced on the side of the protection of tax interests. International and European tax law should instead provide greater guarantees in favor of the taxpayers, defending their right to fair taxation.

Adoption of Advanced Systems for Digital Resource Management in Academic Institutions

In educational organizations, digital resource management refers to the organization and enhancement of electronic resources, including databases, libraries, and digital instructional materials. Excellent administration ensures optimal utilization, improves accessibility, and advances learning objectives. To achieve better institutional results, this approach entails incorporating digital tools, handling issues like data security and user training, and adjusting to new technological developments. Using an emphasis on the integration of digital libraries, e-learning platforms, and research databases, this study investigates the technologies and approaches used in maintaining digital resources within higher education circumstances. Through the use of a survey research design, this study employed a quantitative research methodology. Data gathered from a sample of 250 undergraduate students was utilized in this investigation. This study looks at the connections between teaching profile (TP), technology competence (TC), digital technology awareness (DTA), utility perception (UP), and usage motivation (UM). SPSS was utilized statistically to obtain the outcomes. Descriptive, correlation, and regression analysis were used to obtain the results utilizing data gathered from a sample of university participants. Significant of both conceptual and actual consequences have resulted from the findings.According to the study's findings, efficient use of digital resources in academic institutions greatly improves accessibility and resource utilization, both of which contribute to better learning outcomes.

Enhancing integration of administrative databases in South Africa's HIV program: Validation of record linkage using non-representative gold standard

IntroductionLinked administrative data are widely used in epidemiology to capture patient data across multiple databases. Linkage error rates, critical to measure linkage performance, are rarely reported due to difficulty in obtaining representative gold standard. We propose a training and validation approach for linkage procedures that yield unbiased performance estimates even with a non-representative gold standard. MethodsWe linked patient records from two non-deduplicated databases for HIV monitoring in South Africa, TIER.Net and NHLS laboratory database, using a network-based probabilistic linkage and deduplication approach. National IDs (gold standard) were available for a non-representative minority of records (10%). We calculated sensitivity (Sen, share of true matches identified by the algorithm) and positive predictive value (PPV, share of algorithm-identified matches that were true matches). We adjusted for bias due to informative missingness in National IDs using inverse probability weights to break the link between missingness and match probability. Results111,755 record pairs were considered. National IDs were not missing completely at random. Match probabilities for National ID record pairs exhibited substantially less uncertainty (mid-range match probabilities), inflating Sen and PPV. Before bias correction, Sen and PPV were estimated at 97.0% and 97.8% respectively. After bias correction for missing National IDs, Sen and PPV were estimated at 95.7% and 96.6%. Failure to address this bias understated the overlinkage rate (100% - PPV) by 35% and the underlinkage rate (100% - Sen) by 30%. ConclusionFailure to adjust for informative missingness in the gold standard may lead to biased validation metrics and over/underconfidence in linked data.

Shining a Light on the Specimen Room: Enhancing Global Research on Bird Eggs through Standardized Digital Archives

Bird egg collections in natural history museums are valuable resources for studies such as how birds’ eggs may evolve under different environmental conditions, providing information that is critical in our changing world and of great interest to scientists. However, the millions of bird egg specimens, many collected over 100 years ago, are often not prioritized in digitized archives. To assess the current status of digitized egg specimens, we examined data available on open-source platforms such as iDigBio, based on the Marini et al. (2020) review of egg collections. Millions of bird egg specimens are preserved in museums worldwide, but only about 21% have been digitally cataloged and made accessible on global platforms, mostly from Western countries. While the technology used for extracting features from digital objects has greatly advanced for studying the size, color and spottiness of eggs (e.g., SpotEgg), the digitized specimens may not be formatted to meet the needs for research, thereby limiting their application. For example, among eight museums with digitized collections, images from six include a scale, but only the Yale Peabody Museum also incorporates a color chart with standardized gray patches for calibration (e.g., Calibrite Color Checker). Furthermore, coordinates obtained through georeferencing are essential for studying environmental influences on egg traits. Yet, fewer than half of the specimens (49%) have been georeferenced. Here we demonstrate how properly digitized specimens—using a dark background, adding a scale, and incorporating a standard color chart in the photo—can be utilized to study the evolution of egg color in birds. We urge the adoption of a standard digitization protocol for bird egg collections, the enhancement of georeferencing for the specimens, and the integration of local databases into global platforms to increase accessibility for the scientific community. These procedures should also be implemented at the early stage of the digitization process to maximize the value of digitized specimens.

Toward integration of glycan chemical databases: an algorithm and software tool for extracting sugars from chemical structures.

Integration of glycan-related databases between different research fields is essential in glycoscience. It requires knowledge across the breadth of science because most glycans exist as glycoconjugates. On the other hand, especially between chemistry and biology, glycan data has not been easy to integrate due to the huge variety of glycan structure representations. We have developed WURCS (Web 3.0 Unique Representation of Carbohydrate Structures) as a notation for representing all glycan structures uniquely for the purpose of integrating data across scientific data resources. While the integration of glycan data in the field of biology has been greatly advanced, in the field of chemistry, progress has been hampered due to the lack of appropriate rules to extract sugars from chemical structures. Thus, we developed a unique algorithm to determine the range of structures allowed to be considered as sugars from the structural formulae of compounds, and we developed software to extract sugars in WURCS format according to this algorithm. In this manuscript, we show that our algorithm can extract sugars from glycoconjugate molecules represented at the molecular level and can distinguish them from other biomolecules, such as amino acids, nucleic acids, and lipids. Available as software, MolWURCS is freely available and downloadable ( https://gitlab.com/glycoinfo/molwurcs ).

Utilizing a Dashboard for Efficient Biodiversity Data Overview and Gap-Filling Effort

Taiwan Biodiversity Information Facility has been leading the effort to integrate and mobilize biodiversity data nationally. We recently accomplished the integration of local databases and successfully developed a data portal that efficiently links to the most comprehensive biodiversity database in Taiwan: the Taiwan Biodiversity Information Alliance Data Portal. However, we identified an emerging challenge following this development. Preliminary analyses have revealed several gaps in the current dataset, impeding robust data use. For instance, land data dominates over sea data, and bird records are more abundant than those of other taxonomic groups. In our endeavour to enhance national biodiversity data coverage, we are actively exploring methodologies to address these existing data gaps. Here, we broadly define data gap as the absence of required data or information for use. Our primary objective is to empower users to identify these gaps, thereby facilitating efficient, informed survey planning and eventually filling these gaps. For this purpose, we are evaluating the effectiveness of a user-friendly dashboard, tailored for biodiversity survey planning in Taiwan. We developed and deployed a R Shiny dashboard*1 that provides users with insightful data visualizations that highlight spatial, temporal, and taxonomical data overviews. It also offers customizable filtering options, such as by major taxon groups and habitat types, for easy viewing. Additionally, the dashboard includes a download function, so users can access spatial and taxon gap information in their survey planning efforts. After deploying the dashboard, we conducted user interviews to collect feedback aimed at refining its usability and effectiveness. In addition to helping survey planning, we anticipate that this tool will also aid data users in promptly assessing data integrity within the national biodiversity data portal, thereby enabling them to identify potential data gaps and biases that may affect subsequent use. Through this presentation, we aim to share our experience in developing an interactive dashboard tailored for public use. Preliminary results had indicated that the dashboard highlighted some visible data gaps and biases, particularly in underrepresented areas such as marine habitats and less-studied taxonomic groups. Additionally, user interviews revealed a demand for a feature that provides single species data overviews, suggesting a potential area for further development to enhance the tool's utility. Some challenges remain, such as the need to enhance data granularity and user interface usability. Future work will focus on incorporating more real-time data integration and expanding the range of customizable filters. We believe these efforts will further empower users in Taiwan to conduct more informed and efficient biodiversity surveys, ultimately supporting research and policy-making to address critical biodiversity data gaps.

Integration of endoscopic professional database development and image annotation platform: Radiation control plan for gastric examination

Gastric examination is a common endoscopic procedure that can be used to assess stomach diseases and lesions. However, because the stomach examination requires the use of radiation imaging technology, patients are exposed to radiation risks. Therefore, the aim of this study is to develop a specialized database of endoscopy and integrate it with an image annotation platform to achieve effective control of gastric examination radiation. An image labeling platform was developed to label gastric lesions and structures, annotate and label endoscopic images, and describe the details of gastric lesions more comprehensively and accurately. A complete radiation imaging control system for gastric examination was formed by integrating professional database and image labeling platform. Through this integrated system, doctors can quickly access the endoscopy images in the database when performing stomach exams, and label lesions on the image labeling platform to more accurately evaluate the lesions and structure of the stomach, while reducing the number of times patients undergo radiation examinations. The results show that the radiation imaging of gastric examination can be effectively controlled through the integration of endoscopy database and image annotation platform. This integrated system helps to improve the accuracy and safety of the examination, and has important clinical application value for both doctors and patients.

The Evolution of Digital Cultural Heritage Research: Identifying Key Trends, Hotspots, and Challenges through Bibliometric Analysis

Heritage preservation fundamentally represents an act of communication, with digitization and virtual access driving cultural heritage research development and dissemination. These tools stimulate the creation of cultural heritage content and inspire public engagement in learning about and preserving heritage. Despite substantial academic focus on digital cultural heritage (DCH) research, systematic assessment methods remain lacking. This paper researches the application of digital technologies in cultural heritage studies using CiteSpace 6.2 R4 to analyze the evolution of DCH, identifying research hotspots and predicting future trends. The key findings include the following: (1) Current DCH research focuses on countries with significant national power, but future studies should emphasize regions rich in cultural heritage and promote transnational and interdisciplinary research. (2) DCH research concentrates on three areas: the use of VR, AR, and other interactive digital technologies to enhance cultural heritage experiences; the development of cultural relic databases and digital archives; and the implementation of interactive exhibitions and multimedia guides. (3) Identified research hotspots include user immersion experiences, language and identity, and virtual reality technologies. Digital technologies enhance cultural heritage communication by driving innovations in preservation, information management, and digitization. (4) Future research should focus on practical DCH development, emphasizing innovative virtual technologies, database integration, and standardized assessment methods. These efforts aim to provide valuable insights for the sustainable global preservation and dissemination of cultural heritage. The use of digital technologies in cultural heritage is becoming increasingly important, but there is still a need to strike a balance between technological development and heritage preservation to ensure the sustainability of cultural heritage.

Bridging Real-World Data Gaps: Connecting Dots Across 10 Asian Countries.

The economic trend and the health care landscape are rapidly evolving across Asia. Effective real-world data (RWD) for regulatory and clinical decision-making is a crucial milestone associated with this evolution. This necessitates a critical evaluation of RWD generation within distinct nations for the use of various RWD warehouses in the generation of real-world evidence (RWE). In this article, we outline the RWD generation trends for 2 contrasting nation archetypes: "Solo Scholars"-nations with relatively self-sufficient RWD research systems-and "Global Collaborators"-countries largely reliant on international infrastructures for RWD generation. The key trends and patterns in RWD generation, country-specific insights into the predominant databases used in each country to produce RWE, and insights into the broader landscape of RWD database use across these countries are discussed. Conclusively, the data point out the heterogeneous nature of RWD generation practices across 10 different Asian nations and advocate for strategic enhancements in data harmonization. The evidence highlights the imperative for improved database integration and the establishment of standardized protocols and infrastructure for leveraging electronic medical records (EMR) in streamlining RWD acquisition. The clinical data analysis and reporting system of Hong Kong is an excellent example of a successful EMR system that showcases the capacity of integrated robust EMR platforms to consolidate and produce diverse RWE. This, in turn, can potentially reduce the necessity for reliance on numerous condition-specific local and global registries or limited and largely unavailable medical insurance or claims databases in most Asian nations. Linking health technology assessment processes with open data initiatives such as the Observational Medical Outcomes Partnership Common Data Model and the Observational Health Data Sciences and Informatics could enable the leveraging of global data resources to inform local decision-making. Advancing such initiatives is crucial for reinforcing health care frameworks in resource-limited settings and advancing toward cohesive, evidence-driven health care policy and improved patient outcomes in the region.

Integration of an archaeological database in a virtual reality environment: Venta Micena, Orce (Granada, Spain) archaeological site

ABSTRACT Virtual reality and databases are two technological domains employed in many scientific fields. In archeology, several works are being developed along these lines. This paper presents the workflow for the creation of an executable virtual reality: it integrates a virtualization of the archaeological site of Venta Micena (Granada, Spain) and a database of some of the most relevant archaeological remains from that site. The strength and novelty of this project are that it allows any user or researcher to walk through the archaeological site, pick up and put down any of the remains found there, access the database of the archaeological remains from any place and time, and visualize them in stereoscopic mode, which could unveil new information and conclusions. This document moreover discusses the contributions and possible limitations of this type of technology, as well as key concepts and the significance of archaeological excavation.

Archery Analytic Workflow in a Web-Based Application

The integration of sports science and camera sensing technology has recently emerged to be an advanced analytical tool in sportsperson performance enhancement. The use of computing power and a web-based application can provide quick information analysis and data reporting between coaches and athletes. The design of an archery analytic workflow is demonstrated in this paper using the Python Flask framework, video analytic algorithms, a structured video inventory framework, MongoDB database setup and integration of the Keypoint R-CNN machine learning backend. A user-friendly data visualisation interface on the front end is integrated in the software to deliver athletes’ analytical capabilities such as thorough frame-by-frame video analysis, posture consistency estimation and joint kinematic analysis. This web application framework is not limited to archery sports, and can be extended to numerous sports, such as shooting, weightlifting and cycling. The significance of integrating camera sensing technology with the sports science field can offer quantitative and qualitative observations to improve training programs and performance evaluation.

ELEVATING HOSPITALITY THROUGH DIGITAL INNOVATION

Abstract: In response to the burgeoning demand for elevated hospitality experiences in Nagpur’s luxury event venues, Rama Celebration has embarked on a transformative digital journey aimed at redefining guest experiences and operational excellence. This research paper delves into Rama Celebration’s innovative approach to hospitality through the integration of cutting-edge digital technologies. The centerpiece of this endeavor is Rama Celebration’s web-based platform, meticulously crafted with advanced technologies such as Node.js, Tailwind CSS, and JavaScript. This platform represents a quantum leap in event planning and management, offering an intuitive interface that simplifies the complexities of event booking, venue exploration, and access to premium amenities. Through an exhaustive analysis of the platform’s development process, this paper sheds light on its pivotal role in enhancing guest satisfaction, optimizing resource allocation, and fostering business growth. The platform’s user-centric design philosophy prioritizes seamless navigation, aesthetically pleasing layouts, and interactive elements, thereby enhancing user engagement and satisfaction. Leveraging modern web technologies, the platform ensures scalability, responsiveness, and cross-device compatibility, catering to the diverse needs and preferences of users.Moreover, the platform’s robust architecture, fortified by Node.js server-side scripting and MongoDB database integration, ensures reliability, security, and efficient data management. Advanced features such as real-time availability updates, map-based visualization, and personalized recommendations further enrich the user experience, empowering guests to make informed decisions effortlessly. By embracing digital innovations, Rama Celebration aims to set a new benchmark for excellence in the hospitality industry, spearheading a paradigm shift in luxury event venue management.Looking ahead, the platform holds immense potential for future enhancements and innovations. Integration with mobile applications, augmented reality (AR) features for virtual venue tours, and predictive analytics for personalized guest experiences are among the key areas of focus for future development. Additionally, continuous evaluation, user feedback mechanisms, and performance optimization will be instrumental in ensuring the platform’s sustained relevance and competitiveness in the dynamic hospitality landscape. Keywords: Rama Celebration, Node.js, Tailwind CSS, JavaScript, Event Venue, Digital Transformation, Guest Experience, Operational Efficiency.

The Nexus of AI and Vector Databases: Revolutionizing NLP with LLMs

Vector databases play a critical role in the efficiency and functionality of large language models (LLMs), providing scalable and efficient storage and retrieval of high-dimensional vectors. This paper explores the significance of vector databases in the context of LLMs, highlighting their role in information retrieval, similarity search, training, and adaptation processes. Despite the challenges posed by high-dimensional data, vector databases offer invaluable benefits in enhancing the capabilities of LLMs and driving advancements in natural language processing (NLP). Future research and development in this area promise to further optimize the integration and performance of vector databases, fueling continued innovation in LLM applications. Keywords: Vector databases, Large language models (LLMs), Natural language processing (NLP), Information retrieval, Similarity search, Training, Adaptation, Scalability, Efficiency.

Intelligent alert system for predicting invasive mechanical ventilation needs via noninvasive parameters: employing an integrated machine learning method with integration of multicenter databases.

The use of invasive mechanical ventilation (IMV) is crucial in rescuing patients with respiratory dysfunction. Accurately predicting the demand for IMV is vital for clinical decision-making. However, current techniques are invasive and challenging to implement in pre-hospital and emergency rescue settings. To address this issue, a real-time prediction method utilizing only non-invasive parameters was developed to forecast IMV demand in this study. The model introduced the concept of real-time warning and leveraged the advantages of machine learning and integrated methods, achieving an AUC value of 0.935 (95% CI 0.933-0.937). The AUC value for the multi-center validation using the AmsterdamUMCdb database was 0.727, surpassing the performance of traditional risk adjustment algorithms (OSI(oxygenation saturation index): 0.608, P/F(oxygenation index): 0.558). Feature weight analysis demonstrated that BMI, Gcsverbal, and age significantly contributed to the model's decision-making. These findings highlight the substantial potential of a machine learning real-time dynamic warning model that solely relies on non-invasive parameters to predict IMV demand. Such a model can provide technical support for predicting the need for IMV in pre-hospital and disaster scenarios.

High‐degree penalty based global statistical network embedding for name disambiguation in anonymized graph

SummaryIn person‐centric applications, the prevalence of shared names significantly hampers document retrieval, web search, and database integration, highlighting the critical need for name disambiguation. Network embedding based unsupervised name disambiguation methods have received much attention due to their wide applicability and superior disambiguation performance. However, existing methods require complex feature engineering, such as extracting biographical and source content features or constructing supplementary features from external knowledge bases, which are unavailable in privacy‐preserving scenarios. Moreover, they may face challenges such as imbalanced node training or overlooking global statistical information during node embedding learning. In this article, we propose a method to tackle the name disambiguation problem based on high‐degree penalty and global statistical information using only relational data. First, we construct a weighted source similarity network based on multi‐hop collaborator relationships. Second, we employ a high‐degree penalty based global statistical network embedding model to learn low‐dimensional node embeddings and preserve the structural features of the network. Finally, we cluster the same sources using a joint clustering algorithm that does not require prior knowledge of the number of clusters. The experiment validates the effectiveness of the proposed method on two real‐life datasets.