Knowledge Base Research Articles

Multiancestry Genome-Wide Association Study of Early Childhood Caries.

Early childhood caries (ECC) is the most common noncommunicable childhood disease-an important health problem with known environmental and social/behavioral influences lacking consensus genetic risk loci. To address this knowledge gap, we conducted a genome-wide association study of ECC in a multiancestry population of U.S. preschool-age children (N = 6,103) ages 3 to 5 y participating in a community-based epidemiologic study of early childhood oral health. Calibrated examiners used International Caries Detection and Assessment System criteria to measure ECC; the primary trait was the number of primary tooth surfaces with caries experience (i.e., dmfs index). We estimated heritability and concordance rates and conducted genome-wide association analyses to estimate overall genetic effects as well as stratified by sex, household water fluoride, and dietary sugar and leveraged combined gene/gene-environment effects using 2-degree-of-freedom joint tests. Common genetic variants explained 24% of ECC phenotypic variance among unrelated individuals, while concordance rates were 0.64 (95% confidence interval [CI] = 0.42-0.79) among monozygotic twins and 0.44 (95% CI = 0.34-0.53) among first-degree relatives. Across all analyses, we identified 21 novel nonoverlapping genome-wide significant loci (P < 5 × 10-8) and 1 genome-wide significant gene (TAAR6) associated with ECC. The taste receptor activity gene set, with known roles in chemosensing, bacterial recognition, and innate immunity in the oral cavity, was strongly associated with ECC. While no locus remained significant after studywise multiple-testing correction, 3 loci were nominally significant (P < 0.05) and directionally consistent in external cohorts of 285,248 adults (rs1442369, DLGAP1 and rs74606067, RP11-856F16.2) and 18,994 children (rs71327750, SLC41A3). Meanwhile, the strongest marker known to be associated with adult caries (rs1122171, tagging the long noncoding RNA PITX1-AS1) was nominally significant (P = 0.01) and directionally consistent with ECC in our study. Taken together, the results of this study add to the genomics knowledge base for early childhood caries, offer several plausible candidates for future mechanistic studies, and underscore the importance of accounting for sex and pertinent environmental exposures in genetic investigations.

When audit confronts blockchain

PurposePrevious work has explored the ability of auditors to expand successfully into seemingly unrelated fields, referred to as new audit spaces. The present paper focuses on how auditors respond to challenges when entering a new audit field and devising strategies to sensemake and sensegive about those challenges.Design/methodology/approachThis study builds on findings from 32 interviews with auditors and participant observation of interactions between auditors and blockchainers to understand how auditors approached a new audit space.FindingsWe find that when auditors enter a new audit space, they endeavour to impose a logic of auditability. First, they determine an acceptable knowledge basis for this target audit space by developing a codified set of rules to organize knowledge, then develop a codified set of practices to verify conformity to the auditor’s set of rules. Next, auditors engage in three strategic tactics to influence members of the target audit space: appealing to the financial benefits of adopting a logic of auditability; appealing to their credentials from established audit markets; and appealing to bona fides in the target audit space to establish credibility. We posit that these sensemaking and sensegiving strategies do not take hold in the blockchain space because auditors are approaching these activities from a different mental model than blockchain natives. Because auditors are unable to adopt the mental model of the blockchain space, they are unable to devise strategies to compellingly influence blockchain natives and secure a stronghold in this new audit space. We developed a model for sensemaking and sensegiving when auditors enter new audit spaces.Originality/valueThis paper challenges and contrasts prior accounts of the seemingly unending expansion of audit firms into new spaces. The study demonstrates that there are limitations to auditors’ abilities to transplant their verification skills in the blockchain field.

Developing a model of physical education teachers’ health service competence in China: based on the grounded theory technical approach

BackgroundIn recent years, public health emergencies have become increasingly frequent, threatening human health. Accordingly, countries worldwide have attached great importance to optimizing their public health service systems. In China, physical education teachers are responsible for conducting health education in schools and improving students’ health; however, China’s health service supply is unbalanced and insufficiently developed. Therefore, this study constructed a health service competence model of Chinese physical education teachers to assess their contribution to the improvement of China’s national public health system.MethodsThe qualitative data were primarily gathered through semi-structured interviews. Physical education teachers and researchers of physical education teachers from diverse regions of China (N = 48) were selected as participants using purposive sampling techniques. Grounded theory technical approach was employed to analyze the data through NVivo 11.0 software.ResultsThe physical education teachers’ health service competence model contained three dimensions: health service beliefs, basic health knowledge, and health service skills. Health service beliefs contained two categories: health service awareness and health service cognition. Basic health knowledge contained two categories: life health knowledge and sports health knowledge. Health service skills contained seven categories: health education skills, management skills, research skills, advocacy skills, emergency skills, organization skills, and alert skills.ConclusionThis study’s model provides a theoretical foundation for Chinese physical education teachers to transition to physical education and health teachers. Utilizing this model in teacher education can contribute to enhancing China’s national public health service system.

An introduction to bee and pesticide research

Abstract Bees and pesticides is a topic which attracts a considerable amount of research and media attention. It is also an applied topic, with experimental results informing policy. Policy decisions have‐real world consequences for food production and need to be made using the best available evidence. This article aims to be an entry point for the increasing number of researchers now studying the topic, who may not have worked with pesticides beforehand. We have assumed a general knowledge of bee ecology, but no knowledge of pesticides. We build up the knowledge base throughout the article, introducing concepts one‐by‐one and building on those concepts to tackle the more complex ideas later in the article. Synthesis and applications. Our article is a starting point for those entering bee and pesticide research, and can help introduce the complex concepts and terminology of the field. We point to some further reading and resources throughout and offer some advice on best practice within the field. We hope this will ultimately help new researchers to quickly design high‐quality experiments, which can better inform pesticide policy and usage.

Il progetto MEDITA La documentazione non letteraria mediolatina e la lessicografia storico-etimologica italo-romanza

This paper focuses on the PRIN 2022 project MEDITA, resulting from the collaboration of the University of Naples Federico II, the University Ca’ Foscari Venice and the Opera del Vocabolario Italiano (CNR, Florence). The project aims to build a textual knowledge base of non-literary Medieval Latin texts of the Italian area. In the first paragraph we describe the fundamental characteristics and main purposes of the project, then we dwell on the digital platform’s structure (§ 2) and on the articulation of the project and its units (§ 3). The conclusion (§ 4) focuses on the twofold purpose of the knowledge base integrated within the MED.ITA digital platform, showing both the relevance of Middle Latin practical texts for Romance lexicography and their linguistic value as an object of analysis in itself.

Completing the web: identifying sampling bias and knowledge gaps within South African spider surveys (Arachnida, Araneae)

Species distribution datasets are fundamental for macroecological studies, although there is an overarching need to ensure that these datasets are representative of the entire community. Shortfalls, or knowledge gaps, within biodiversity datasets originate for a range of reasons, and can lead to incorrect conclusions or recommendations being drawn. Spatial scale influences the interpretations of diversity patterns and thus is an important aspect to consider. South Africa has a rich history of spider sampling and as such, it is possible to investigate the influence that scale, both spatial and taxonomic, has on the overall interpretations of how complete the spider knowledge base is in the country. To do this, we draw on curated natural history spider collections and determine how complete the spider assemblages are across twelve unique combinations of taxonomic and spatial scales. Overall, we received 121 605 usable records from seven collections, with spider records and diversity, being concentrated along the eastern and coastal regions of South Africa. We show that assemblage completeness increases with both increasing taxonomic and spatial scales, and as such, knowledge of the distribution of spider families at the biome level is largely complete. Moreover, we show that our fine-scale knowledge of spider assemblages in South Africa is relatively poor, yet we do identify, even at fine scales, assemblages in South Africa that can be considered complete. We identify under-sampled regions of the country, which in turn are congruent with the distribution of under-sampled regions found in other South African invertebrate groups. We show that the scaling of completeness can only be interpreted in one direction: as scale increases so does completeness. These findings will have important implications for spider research and conservation in South Africa, given that regions where completeness is highest correspond strongly to areas in South Africa with the highest threats to biodiversity.

Exploring Customer Experience Drivers in Night Markets: Examining the Roles of Product Preference, Service Quality, and Facility Accessibility

This study investigates the critical factors shaping customer experiences in night markets, emphasizing product preferences, quality of services provided, and accessibility of facilities. As integral hubs of cultural exchange, community engagement, and tourism, night markets contribute significantly to local economies and cultural heritage preservation. However, research on customer experience in these dynamic environments remains limited. Using a structured questionnaire, data were collected from 201 night market visitors through a purposive sampling. The study utilized structural equation modeling (SEM) to assess the interconnections between the primary variables. The findings reveal that product preference has a substantial positive impact on customer experience (β = 0.465, p &lt; 0.001) along with service quality (β = 0.209, p &lt; 0.01) and facility accessibility (β = 0.585, p &lt; 0.001). Both service quality and facilities play pivotal roles in shaping customer satisfaction and experiences, either directly or indirectly. The results underscore the importance of tailored product offerings, consistent service quality, and well-maintained facilities for fostering satisfaction and loyalty. This study provides practical insights for vendors by emphasizing the value of understanding customer preferences, ensuring operational efficiency, and enhancing facility provisions. These findings contribute to theoretical frameworks on customer experience, enrich the knowledge base for night market operations, and offer actionable strategies for sustainable market development and customer engagement.

Health information management students' work-integrated learning (professional practice placements): Where do they go and what do they do?

Background: Work-integrated learning (WIL) is integral to most health disciplines' profession-qualifying degree programs. Objectives: To analyse the categories, locales and foci of final-year (capstone), health information management professional practice (WIL) placements, 2012-2021, at La Trobe University, Australia. Method: A documentary analysis of 614 placement agency proposals, 2012-2021, interrogated multiple characteristics: agency type, placement (sub-) category (WIL model), project type, agency-required student capabilities, intended learning outcomes. Results: Public hospitals offered 50% of all placements. Medical research/health or disease screening/clinical registries offered 17.8%, incorporating 86.7% of "research-based" placements. Government department offerings were consistently stable; private hospital, primary care and community healthcare offerings declined. The majority (64.8%) of offerings were "project-based," followed by "internship" (28.7%: Health Information Service (14%) and "other" (14.7%)), research-based (4.9%) and other (1.6%). Ninety-nine (16.1%) proposals specified additional, pre-placement skills and capabilities: technical (information technologies, software applications; 58.6% of 99 proposals); working independently (49.5%); communications (written, verbal; 45.5%); targeted interest (38.4%) in "informatics and data quality," "quality and safety," "software development," "coding"; organisational and/or time management skills (29.9%); teamwork skills (20.2%); data analysis skills (18.2%); enthusiasm and/or self-motivation (15.2%). Conclusion: The project-based model for the capstone placement is ideal for preparing health information management students for complex, graduate professional work. Agencies' pre-placement expectations of students (knowledge, technical skills, soft skills) are consistent with findings from the WIL literature and align with course curricula and Australia's Health Information Manager (HIM) Profession-entry Competency Standards. Implications: The findings will strengthen the health information management profession's knowledge base of WIL and inform educators, students and agency supervisors.

An Automatic and End-to-End System for Rare Disease Knowledge Graph Construction Based on Ontology-Enhanced Large Language Models: Development Study.

Rare diseases affect millions worldwide but sometimes face limited research focus individually due to low prevalence. Many rare diseases do not have specific International Classification of Diseases, Ninth Edition (ICD-9) and Tenth Edition (ICD-10), codes and therefore cannot be reliably extracted from granular fields like "Diagnosis" and "Problem List" entries, which complicates tasks that require identification of patients with these conditions, including clinical trial recruitment and research efforts. Recent advancements in large language models (LLMs) have shown promise in automating the extraction of medical information, offering the potential to improve medical research, diagnosis, and management. However, most LLMs lack professional medical knowledge, especially concerning specific rare diseases, and cannot effectively manage rare disease data in its various ontological forms, making it unsuitable for these tasks. Our aim is to create an end-to-end system called automated rare disease mining (AutoRD), which automates the extraction of rare disease-related information from medical text, focusing on entities and their relations to other medical concepts, such as signs and symptoms. AutoRD integrates up-to-date ontologies with other structured knowledge and demonstrates superior performance in rare disease extraction tasks. We conducted various experiments to evaluate AutoRD's performance, aiming to surpass common LLMs and traditional methods. AutoRD is a pipeline system that involves data preprocessing, entity extraction, relation extraction, entity calibration, and knowledge graph construction. We implemented this system using GPT-4 and medical knowledge graphs developed from the open-source Human Phenotype and Orphanet ontologies, using techniques such as chain-of-thought reasoning and prompt engineering. We quantitatively evaluated our system's performance in entity extraction, relation extraction, and knowledge graph construction. The experiment used the well-curated dataset RareDis2023, which contains medical literature focused on rare disease entities and their relations, making it an ideal dataset for training and testing our methodology. On the RareDis2023 dataset, AutoRD achieved an overall entity extraction F1-score of 56.1% and a relation extraction F1-score of 38.6%, marking a 14.4% improvement over the baseline LLM. Notably, the F1-score for rare disease entity extraction reached 83.5%, indicating high precision and recall in identifying rare disease mentions. These results demonstrate the effectiveness of integrating LLMs with medical ontologies in extracting complex rare disease information. AutoRD is an automated end-to-end system for extracting rare disease information from text to build knowledge graphs, addressing critical limitations of existing LLMs by improving identification of these diseases and connecting them to related clinical features. This work underscores the significant potential of LLMs in transforming health care, particularly in the rare disease domain. By leveraging ontology-enhanced LLMs, AutoRD constructs a robust medical knowledge base that incorporates up-to-date rare disease information, facilitating improved identification of patients and resulting in more inclusive research and trial candidacy efforts.

Transition to circular economy of urban areas and communities with special attention to lifestyles

Cities are important hubs of consumption and public services. But also, for exchange of goods and ideas among people, institutions and businesses. As current literature suggests regarding the enforcement of SDG12, Sustainable Consumption and Production in cities is supported by the promotion of circular economy (CE). There is a growing number of circular businesses, but this does not guarantee the spread of circular lifestyles in the whole society. In addition, the effects of awareness raising activities and any actions aiming to change the behavioural patterns of consumers can be limited if people cannot find sufficient offers for sustainable consumption. Therefore, in our research we aim to answer the following questions: 1. What kind of circular offers and initiatives are available which enable consumers to implement circular lifestyles in cities? 2. What kind of methodologies and forms can support engaging consumers to circular lifestyles in urban areas? In this study, we aim to enrich the knowledge base connected to encouraging CE citizenship in urban areas by providing an overview of circular offer types based on the analysis carried out in three examined cities (Bologna, Italy; Budapest, Hungary and Würzburg, Germany). Circular offers and initiatives have been examined by desk research to foster urban portfolios. 101 good examples have been collected and analysed with self-developed typology for circular lifestyle enablers. Results indicate that in the examined cities special attention is devoted on forming behavioural patterns of inhabitants. In this context, the Living Lab approach as a specific form for community engagement and awareness raising has high importance for the facilitation of circular lifestyles. Moreover, care should be taken to ensure that the important activities for a circular lifestyle and sustainable consumption, the Big Points, are considered and included within the different initiatives and offers.

A novel approach to sustainable behavior enhancement through AI-driven carbon footprint assessment and real-time analytics

This research introduces an Artificial Intelligence-driven mobile application designed to help users calculate and reduce their Carbon Footprint (CFP). The proposed system employs an Intelligent Sustainable Behavior Tracking and Recommendation System, analyzing users' carbon emissions from daily activities and suggesting eco-friendly alternatives. It facilitates sustainability discussions through its chat community and educates users on sustainable practices via an intelligent chatbot powered by a sustainability knowledge base. To promote social engagement around sustainability, the application incorporates a competition and reward system. Additionally, it aggregates behavioral data to inform government sustainability policies and address challenges. Emphasizing individual responsibility, the proposed system stands out from other systems by offering a comprehensive solution that integrates recommendation, education, monitoring, and community engagement, contributing to the cultivation of sustainable communities. The results of a user study (n = 10) employing paired sample t-tests across the three dimensions of the Theory of Reasoned Action (TRA) revealed varying effects of using the application on attitudes, subjective norms, and behavioral intentions related to promoting sustainable human behavior. While the application did not yield significant changes in attitudes (t (9) = 1.7, p = 0.123), or behavioral intentions (t (9) = 0.6, p = 0.541), it did produce a significant increase in subjective norms (t (9) = 4.2, p = 0.002). This suggests that while attitudes towards using this application for sustainability and behavioral intentions remained relatively stable, there was a notable impact on the perception of social influence to engage in sustainable behavior through the use of the application attributed to the sustainability reward system.

Illuminating the Progress of Research into Inferentialism: A Systematic Analysis of CiteSpace

The goal of this article is to use the scientometrics software CiteSpace as the analysis tool, studying 317 academic documents about inferentialism from 1999 to 2021 collected in the database Web of Science systematically and intuitevely present the development trends and hot topics of inferentialism, hoping to explore new ideas and directions. The procedure includes visualizing and analizing the co-citation networks generated by the 6.1.R.3 version of CiteSpace. The knowledge bases and the research front about inferentialism are identified by analyzing the cited references in the major clusers and the citing articles to the major clusters in citation networks. The landscape view and timeline view of CiteSpace facilitate the comprehension of the interrelationships among clusters and their temporal evolution, thereby offering dependable histographic survey of inferentialism research spanning from theory to practice. Through the utilization of co-keyword network analysis, co-term network analysis, and co-category network analysis, this analytical approach facilitates the comprehension of the interdisciplinary research by identifying various topics and disciplines in compasing the application of inferentialism in education, as well as its relation to logic, metaethics, mathematics, perception and cognition. Through the co-author and collaborating country/region of the study, the question who are the main researchers of a study and which countries/regions are they affiliated with can be answered. It can provide the reference for us to introduce academic resources, carry out cooperation and evaluate academic achievements. All in all, a systematic analysis in CiteSpace is helpful for researchers to capture the salient literature, understand the historical evolution and development trend of inferentialism, and lay a foundation for further research and cooperation according to their interest and specialities.

Potential of Mangifera Indica as a Green Corrosion Inhibitor Against Different Metals: A Review

Mangifera indica (MIn, commonly known as “mango”), is a most widely cultivated flowering plant species in the family Anacardiaceae capable of growing to a height of 30 meters (100 feet). Being rich in nutritional value and phytochemical constituents, it is known for its tremendous chemical and biological actions. As a contribution to the current interest in green corrosion inhibitors, this review aims at broadening the application of MIn extracts for metallic corrosion inhibition by investigating the inhibiting properties of MIn on various metals of day-to-day importance. The adsorption isotherm studies, chemical composition of MIn, effect of temperature on inhibition efficiency and computational analysis related to MIL adsorption on metals have also been discussed in detail. This work will further help in the understanding of the adsorption mechanism involved. The overarching objective is to provide researchers with a concise but comprehensive knowledge base of MIn as a green corrosion inhibitor.

Sustainable integration of artificial intelligence and machine learning approaches within the African infectious disease vaccine research and development ecosystem

Artificial Intelligence and Machine Learning (AI/ML) techniques, including reverse vaccinology and predictive models, have already been applied for developing vaccine candidates for COVID-19, HIV, and Hepatitis, streamlining the vaccine development lifecycle from discovery to deployment. The application of AI and ML technologies for improving heath interventions, including drug discovery and clinical development, are expanding across Africa, particularly in South Africa, Kenya, and Nigeria. Further initiatives are required however to expand AI/ML capabilities across the continent to ensure the development of a sustainable ecosystem including enhancing the requisite knowledge base, fostering collaboration between stakeholders, ensuring robust regulatory and ethical frameworks and investment in requisite infrastructure.

Students’ Reflections in an Online Service-Learning (OS-L): An Analysis on Learning Outcomes

One of the functions of education includes a growth in cognitive process that supports the social aspect. The service-learning as a pedagogy is reported to develop students’ personal aspect and social engagement. As a teaching tool, it provides students the opportunity to reflect, and to better understand their experiences by writing reflections in a concluded service-learning activities. However, the unprecedented COVID-19 pandemic, posed a challenge in the accomplishment of course deliverables that required educators to redesign learning delivery and on how service learning pedagogy be facilitated virtually. Following Kolb’s experiential learning theory, the researchers utilized the rubric that assesses the academic service-learning (AS-L) of gathered reflection papers of Grade 12. This study aims to explore how their online experiences can contribute to a positive learning outcome using the following dimensions: awareness of purpose of service, critical thinking, application of the AS-L experience to the academic knowledge base and objectives of the course, responsibility to the community and impact on student’s personal life. These were analyzed using reflexive thematic analysis (RTA) with computer-assisted data analysis software Max Weber Qualitative Data Analysis (MAXQDA). Given that service-learning plays a vital role in the academic milieu, the outcomes of this study may be used as a benchmark in the sphere of Philippine education and how it can contribute in enhancing students’ reflective process.

Semantic Analysis of Natural Language Texts: Ontological Approach

The development of information (intelligent) learning systems, electronic document management systems, web-oriented systems working with text information in natural language has led to an increase in the volume of educational content and/or arrays of processed full-text documents. All this requires new means of organizing access to information, many of which should be classified as intelligent systems for knowledge processing. One of the effective approaches to identifying and processing the meaning of educational content (and/or text documents) is the use of ontologies. The purpose of the article is research, analysis of various approaches to determining the semantic content of texts in natural language, consideration of existing concepts of text analysis and prospects for using the proposed ontological approach to semantic analysis of texts in natural language. Research methods are methods of semantic analysis of the main concepts of the analyzed subject area (semantic analysis of texts in natural language). The article considers an approach to the linguistic analysis of texts based on ontological modeling. The novelty of the research is the application of the proposed ontological approach to the semantic analysis of texts in natural language to determine the meaning (semantics) of text information, which is used in intelligent systems of various classes. The conclusion of the research carried out in the article is as follows: an ontological approach to the semantic analysis of natural language text, its tasks and methods is proposed. The use of the proposed approach to text analysis leads to the understanding of semantic analysis as a single triad: &lt;Ontology – Text – Meaning&gt;. For effective and correct extraction of knowledge, it is suggested to use a multi-level ontology. The result of the interaction of a specific natural language text with an ontology is an ontological meaning – a set of interconnected subgraphs of the ontograph. The ontological content is extracted from the ontograph using a semantic analyzer. The dialogue processor examines the syntactic tree of sentence parsing (a connected element of the natural language text) and, based on the given question, finds a fragment in the text that is the answer to the question. Computer understanding (in information or intellectual systems) of natural language text is achieved, in particular, by: immersion of the text in a single environment of knowledge – ontology; formal presentation of meaning (semantics) in the knowledge base of the corresponding system; the possibility of operations on the ontological content. The proposed approach can be used to create intelligent information repositories that work in a single knowledge environment. The proposed approach to the semantic analysis of texts in natural language is focused on the automatic extraction of metadata from texts of various nature (for example, a text document, Internet content, educational content of relevant online courses, description of computer and board games). With further development of the proposed approach, it can be used in systems of automatic referencing of scientific publications, meaningful interpretation of multimedia content, training and testing (including elements of visual display of information and elements of gamification).

Schema Retrieval for Korean Geographic Knowledge Base Question Answering Using Few-Shot Prompting

Geographic Knowledge Base Question Answering (GeoKBQA) has garnered increasing attention for its ability to process complex geographic queries. This study focuses on schema retrieval, a critical step in GeoKBQA that involves extracting relevant schema items (classes, relations, and properties) to generate accurate operational queries. Current GeoKBQA studies primarily rely on rule-based approaches for schema retrieval. These predefine words or descriptions for each schema item. This rule-based method has three critical limitations: (1) poor generalization to undefined schema items, (2) failure to consider the semantic meaning of user queries, and (3) an inability to adapt to languages not used in the predefined step. In this study, we present a schema retrieval model by using few-shot prompting on GPT-4 Turbo to address these issues. Using the SKRE dataset, we searched for the best prompt in terms of enabling the model to handle Korean geographic questions across various generalization levels. Notably, this method outperformed fine-tuning in zero-shot scenarios, underscoring its adaptability to unseen data. To our knowledge, this is the first attempt to develop a schema retrieval model for GeoKBQA that purely utilizes a language model and is capable of processing Korean geographic questions.

Trophic ecology of the clingfish Gobiesox marmoratus (Gobiesocidae): experimental evaluation of constraints to foraging behaviour

ABSTRACT The Chilean clingfish Gobiesox marmoratus Jenyns (Gobiesocidae) is a common carnivorous fish inhabiting the rocky intertidal pools along the Chilean coast. In spite of its wide-ranging distribution, basic knowledge about this species is scarce and as a consequence important aspects of its biology such as dietary composition, dietary selectivity and trophic behaviour have received little attention. The goals of this work were to assess quantitatively the dietary composition of this species, its trophic selectivity, assimilation rate of its prey and to experimentally analysed the role of the prey energy value, pre-digestive and post-digestive limitations in the dietary selection of this species. The diet of this species is mainly based on invertebrates, particularly amphipods, crabs and limpets as the most important food items. In laboratory, crabs and amphipods were the most preferred and consumed items over limpets and snails. Our results show that the amphipod is the prey item that provides to G. marmoratus with greater energy per unit of time of manipulation, which would explain its preference in the laboratory experiments and suggests that G. marmoratus feeds according to the net energy gain of their prey, supporting energy maximization, an underlying assumption of foraging theory.

The Effectiveness of ECE (Early Clinical Exposure) on the Integration of Basic Knowledge with Type 2 Diabetes Mellitus Clinical Knowledge and Interprofessional Collaboration in Medical Students of Prima Indonesia University

Early Clinical Exposure (ECE) is a teaching-learning methodology that involves exposing medical students to patients or clinical scenarios early in the first year of medical education. In this study, the focus is on fifth-semester students, particularly on the integration of basic knowledge with clinical knowledge of Type 2 Diabetes Mellitus (DMT2), the role of multidisciplinary teams, and interprofessional collaboration. The purpose of this study is to determine the effectiveness of early clinical exposure in integrating basic knowledge with clinical knowledge of DMT2 and knowledge of interprofessional collaboration among medical students at Universitas Prima Indonesia. The method used in this research is a quantitative approach with an experimental design, specifically quasi-experimental research. In this design, 124 fifth-semester medical students from Universitas Prima Indonesia will be given a pretest before participating in Early Clinical Exposure (ECE) and a posttest after completing Early Clinical Exposure (ECE). The results of this study indicate a significant difference in the integration of basic knowledge with clinical knowledge of DMT2 and knowledge of interprofessional collaboration among medical students at Universitas Prima Indonesia, with a p-value of 0.000 (p &lt; 0.05). Therefore, it can be concluded that Early Clinical Exposure (ECE) is effective in improving the integration of basic knowledge with clinical knowledge of DMT2 and knowledge of interprofessional collaboration among medical students at Universitas Prima Indonesia.

Information system for assessing reliability a decoder remote piloting station

Under conditions of armed aggression by Russia against Ukraine, which escalated into a full-scale military invasion on February 24, 2022, national security is facing the greatest threat, as it is about destroying Ukrainian statehood. As experience in preparing and conducting intelligence operations by the Armed Forces of Ukraine during the large-scale invasion by the armed forces of the Russian Federation into your country has shown, using class I unmanned aerial vehicles has proven their effectiveness, which has raised issues of improving intelligence gathering with their help. A key role in the operations of a first-class unmanned aerial vehicle is played by a remote pilot station decoder. Quality operation of airborne reconnaissance assets depends on its efficiency. The article considers main reliability indicators of a human-machine system operator. The analysis results of the existing methods for controlling the humanmachine system operator, which shows that indicators of his performance are not measured, but are determined by monitoring his functional state with further evaluation of performance indicators. Based on results of the analysis, it was found that using fuzzy set theory and convolution according to a nonlinear trade-off scheme in tasks of evaluating efficiency of human-machine systems makes it possible to identify reliability status of a remote piloting station decoder in real time, taking into account human performance and equipment efficiency. The article develops an information system for assessing the reliability of a remote piloting station decoder based on a generalized mathematical model in the form of a multilevel hierarchical tree of logical inference that reflects classification of indicators and intermediate estimates. The roots of the trees correspond to evaluation results, and tops correspond to reliability indicators of a remote piloting point. This process is based on mathematical apparatus of fuzzy logic and is carried out using available expert information in form of logical rules "IF - THEN" that link fuzzy terms of remote piloting point reliability indicators and the assessment result. Reliability of the obtained data is achieved by forming a fuzzy knowledge base using fuzzy terms that take into account specifics of process obtaining intelligence information at remote piloting points.