Expert Evaluation Research Articles

Golden Breath: Feasibility and acceptability of a biofeedback-based virtual reality game on reducing children's needle-related pain and fear

BackgroundNeedle procedures can cause pain and fear in children. Current literature reports that biofeedback-based virtual reality applications may help alleviate children's pain and fear. This study aims to evaluate the feasibility, acceptability, and safety of the newly developed game Golden Breath, which uses biofeedback-based virtual reality to reduce children's needle-related pain and fear. MethodsThe development of Golden Breath includes; (1) development of the application's features based on needs assessment, (2) gamification of the application content based on literature, (3) development of the prototype, (4) expert evaluation and feedback on the application content, (5) usability testing by 11 children (4–12 years). ResultsRegarding acceptability, the expert evaluation showed a high usability of the system. All children provided positive feedback and reported high satisfaction with Golden Breath. The game was feasible and effective for reducing children's pain and fear levels during needle procedures. Golden Breath was deemed safe for children because it did not cause symptoms such as dizziness, vomiting, or nausea. ConclusionThe expert evaluation, children's feedback, and pilot study results showed that the Golden Breath game is feasible, acceptable, and safe for children during the needle-related procedure. The pilot study revealed that the Golden Breath game effectively reduced pain and fear during blood sampling in children. Practice to implicationsIt is recommended that healthcare professionals use Golden Breath to optimize the well-being of children receiving treatment for chronic and acute diseases.

Enhancing teaching materials development course with the ICARE learning model in e-learning

Using e-learning in higher education has many important benefits and advantages in todays world of education. Therefore, this research aims to develop e-learning based on the introduction, connection, application, reflection, extend (ICARE) learning model for developing teaching materials courses in the Faculty of Education, Universitas Pendidikan Ganesha. The Hannafin and Peck development model was adopted as a foundational method, and the digital content developed was subjected to formative evaluation methods, including i) expert validation, ii) individual assessment, and iii) small group evaluation. To ascertain the quality of the developed content, the research engaged 3 expert evaluators, each specializing in media, instructional design, and content. Concurrently, individual evaluation was carried out with 3 students, followed by a small group evaluation involving 9 additional students. Data collection techniques involved both observational measures and questionnaire-based inquiries. The expert assessment showed that the design, media, and content dimensions all fell within the "very good" category. Student reactions, as measured through both individual and small group evaluations, consistently aligned with the "very good" category. Therefore, the attractiveness and practicality of electronic learning (e-learning) fell under the "very good" classification, due to the implementation of the ICARE learning model as the foundational design principle.

Optimizing Supplier Selection using Fuzzy MCDM

This study explores the supplier selection problem within technology manufacturing by employing the Fuzzy Analytic Hierarchy Process (FAHP). Data was collected from a technology manufacturing company, and expert evaluations were sought from professionals with over 10 years of experience in the industry. The criteria considered for supplier selection included profitability of the supplier (C1), relationship closeness (C2), technological capability (C3), conformance quality (C4), and conflict (C5). The FAHP results indicated that relationship closeness (C2) ranked highest among the criteria, highlighting its significance in the supplier selection process, while conflict (C5) received the lowest ranking. These findings emphasize the importance of nurturing strong relationships with suppliers and vendors to enhance overall supply chain performance. The proposed FAHP model effectively addresses the complexities and uncertainties inherent in supplier evaluation, offering a robust decision-making tool tailored to the specific needs of technology manufacturing companies

Restoration and Segmentation of Old Jawi Manuscripts using Variational Image Inpainting and Active Contour Models

Old Jawi Manuscripts (OJM) are crucial to historical studies, offering insights into past societies. However, degradation from mishandling and environmental factors can impair their legibility. To preserve OJM, image inpainting and segmentation are essential for restoring corrupted areas and identifying text. Recently, the Gaussian Regularization Segmentation (GRS) model has shown effectiveness in intensity inhomogeneity grayscale image segmentation, though it was not designed for corrupted OJM images. Therefore, this study aimed to reformulate the GRS model to restore and segment text from real corrupted OJM images. The methodology begins with the incorporation of the Mumford-Shah and Bertalmio inpainting models into the GRS model as new fitting terms, resulting in the Modified Gaussian Regularization Segmentation Mumford-Shah (MGRSM) model and the Modified Gaussian Regularization Segmentation Bertalmio (MGRSB) model, respectively. MATLAB was used to implement these models, and their performance was assessed on 30 corrupted OJM samples from Malay Ethnomathematics Research Group, with expert evaluations and efficiency measured by average elapsed time. The MGRSM model achieved 38.4 percent and 12.4 percent higher overall total scores from experts in terms of segmentation accuracy compared to the GRS and MGRSB models, respectively. While the GRS model is the fastest, the MGRSM model provides superior accuracy, with an average processing time of 9.35 seconds, making it the most optimal for restoring and segmenting OJM images. This approach not only enhances the preservation of historical manuscripts but also provides a practical tool for researchers and historians in safeguarding our cultural heritage.

An operational risk assessment method for petrochemical plants based on deep learning

The petrochemical industry is an important guarantee for the development of people’s lives, and the operational risk assessment method of personnel in the operation of petrochemical enterprises is the most important. Based on a deep learning algorithm, a new method based on the micro-Doppler effect and fuzzy analytic hierarchy process is proposed to evaluate the operational risk of personnel in petrochemical enterprises. The original monitoring image of petrochemical equipment is invoked, and micro-Doppler analysis is performed based on the original image to identify the activity target of the personnel on the job site and generate the activity map of the petrochemical plant operators. Based on the analysis data of the micro-Doppler effect, the fuzzy function and hierarchical analysis method are combined. The fuzzy theory is introduced into the analytic hierarchy process, and the inherent expert evaluation scores are transformed into fuzzy numbers. It makes the expert evaluation of petrochemical plants more accurate in the subsequent coupling and improves the objectivity of the traditional analytic hierarchy process. The scheme proposed in this paper can monitor real-time operation safety and provide a guarantee for the personal safety of field operators. This method plays an important role in improving the safety of petrochemical plants.

Network Integration as a Tool for Sustainable Business Development

Business enterprises are required to adopt new economic models and business strategies in line with global trends such as the green agenda, the shortening of innovation cycles, digitalization (networking platforms) and socio-economic security. The models of network integrations (clusters, industrial symbioses, innovation ecosystems, etc.) are developing faster than other business models. Networking allows businesses to synergetically, based on the principles of collaboration, trust and self-organization, increase intellectual, innovative, social, and environmental values, which leads to economic growth and sustainable development. Through networking integration, the business performance of all participants in multi-company projects can be improved, regardless of their size and activities. The purpose of this study is to develop a methodological framework for assessing the synergistic effect and the level of collaborative maturity of participants in network industrial integration. This paper provides a comprehensive analysis and evaluation of network business integration efficiency. The authors’ methodology is based on the AHP hierarchy method, expert evaluation and Harrington’s level of desirability. The methodology is validated on Russian industrial integrations: industrial cluster of Nizhny Novgorod region; pipeline valve manufacturing cluster; South Ural industrial cluster producing spare parts and assembly units for roadbuilding, construction and agricultural vehicles. The results show that the level of collaborative maturity of business partners and the synergies from network integration effect the sustainability of business development. Recommendations for businesses are provided to improve their network integration and collaborative maturity. Furthermore, the findings allow the authors to define directions for further research, considering the interests of the business community, regional authorities, and financial institutions, and provide a framework for researchers to investigate the link between industrial network integration and economic growth.

Establishing disease models for the Belgian national burden of disease study: challenges and perspectives

Abstract The Belgian national burden of disease study (BeBOD) was launched in 2022, and currently provides disease burden estimates for 38 key diseases. The calculation of the non-fatal component of the disease burden, the Years Lived with Disability (YLD), follows a stepwise approach that aims to establish priority outcomes; quantify prevalence “best estimates”; establish disease models; and perform expert evaluation of methods and results. The disease models visualize the relationship between the different health states associated with a given outcome. Health states include the different acute and chronic stages of the outcome (including complications), which may be stratified in different severity levels (e.g., mild, moderate, severe). Disease models used in burden of disease studies primarily aim to document the considered health states, and do not aim at a representation of the complete clinical picture of the condition. The disease models instead help in understanding how the number of cases for each health state is calculated. Models typically start with one “parent node”, which contains all cases. This parent node then gives rise to multiple “child nodes”, with the terminal child nodes representing the individual health states. In BeBOD, disease models and severity distributions are adapted from the Global Burden of Disease (GBD) study, and complemented with local data where possible. This process ensures consistency with available disability weights, while still allowing for local data to feed into the calculations. In this presentation, we will demonstrate the BeBOD process for establishing disease models based on a number of archetypical examples, which represent varying challenges linked to disease model complexity, clarity and completeness of the GBD disease model documentation, and availability of local data. Future perspectives will be discussed, including the improved leveraging of local clinical data, and the implementation of tailored active data collection.

Human role in the concept of «Computational Leviathan»

Computational sociology is a subfield of social philosophy that views social relations in the context of comparing them to a computer. «Computational Leviathan» is a concept of computational sociology that draws an analogy between society and social relations and a set of organs of a giant animal in constant interaction. The interaction consists of information exchange, production and consumption of resources, and other mutually beneficial processes. Like organs in an organism, the components of the “computational Leviathan” cannot do without each other, being in mutual dependence. The cessation or disruption of one of the components immediately affects the activity of the others, which, in turn, leads to fundamental changes in the functioning of the “Leviathan” or to death, i.e. to the end of human civilization. The purpose of this article is to reveal the role of the individual in the concept of “computational Leviathan.” Research methods: theoretical: information analysis, synthesis of judgments and observations, analogy; modeling; empirical: comparison, cross-sectional study, text analysis, method of expert evaluations. Sources of research: literature, observation, experience, logical reasoning. Scientific novelty: the role of an individual in the concept of “computational Leviathan” has been clarified.

Current State of Serious Games in Human Trafficking: Evaluation, Gaps, and Future Research Directions

Addressing human trafficking is crucial due to its severe impact on human rights, dignity, and well-being. Serious games refer to digital games that are designed to entertain while also accomplishing at least one additional objective, such as learning or health promotion. Serious games play a significant role in raising awareness, training professionals, fostering empathy, and advocating for policy improvements related to human trafficking. In this study, we systematically examine and assess the current landscape of serious games addressing human trafficking to unveil the existing state, pinpoint gaps, and propose future research avenues. Our investigation encompassed academic publications, gray literature, and commercial games related to human trafficking. Furthermore, we conducted a thorough review of evaluation criteria and heuristics for the comprehensive assessment of serious games. Subsequently, incorporating these evaluation metrics and heuristics, the games were subjected to evaluation by both players and experts. Following a combined qualitative and quantitative analysis, the results were deliberated upon, and their implications were expounded. Five serious games related to human trafficking were identified and evaluated using the SGES and EGameFlow scales, along with both game-specific and serious game heuristics. Player and expert evaluations ranked “(Un)TRAFFICKED” and “Missing” as the best-performing games, while “SAFE Travel” received the lowest ratings. Players generally rated the games higher than experts, particularly in usability, feedback, and goal clarity, although the games scored poorly in audiovisual quality and relevance. Experts highlighted deficiencies in motivation, challenge, and learning outcomes. The lack of personalization and the absence of social gaming elements point to the need for more targeted human trafficking games adapted to different demographics, cultures, and player types.

A novel multi-criteria decision making method to evaluate green innovation ecosystem resilience

Green innovation ecosystem resilience evaluation is crucial for ensuring sustainable development in dynamic environments. By comprehensively assessing resilience, it helps identify risks and facilitate effective responses, thus promoting sustainable development. To effectively evaluate the resilience of green innovation ecosystems, this paper proposes a model based on the interval-valued intuitionistic fuzzy multi-criteria decision-making (MCDM) method, designed to support decision-makers (DMs) in resilience evaluation. In this model, we improve the Criteria Importance Through Intercriteria Correlation (CRITIC) method by introducing Spearman's correlation coefficient to more accurately determine the objective weights of various indicators based on their interrelationships. Furthermore, we propose an Interval-Valued Intuitionistic Fuzzy Hybrid Average Aggregation (IVIFHAA) operator, which considers both the subjective and objective weight order for aggregating expert evaluation results. Additionally, a scoring function is introduced to generate ranked evaluation results. We also establish a resilience evaluation criteria system covering interconnectivity, sustainability, and diversity within green innovation ecosystems to assist DMs in resilience evaluation. Finally, to validate this method, we apply it to assess the resilience of regional green innovation ecosystems and compare it with other methods. The results demonstrate that the improved operator effectively reduces information loss and improves evaluation efficiency, while the improved CRITIC method handles outliers during the evaluation process.

Budgeting under martial law

In the conditions of martial law, the issues of ensuring the preservation of the national economy and further economic development are becoming relevant. An important condition for this is an efficient mechanism for the formation and implementation of the state budget. The budget formation mechanism is an important tool for ensuring sustainable economic develop­ment of the country while maintaining the stability of the budget system. The aim of the article is to determine the peculiarities of budgeting in the conditions of martial law. The research presented in the article is based on the hypothesis of the need to strengthen the financial potential of budgets of all levels at the expense of their revenues. Strengthening the income part will contribute to achieving economic stability and balance and determine the pace of social and economic development. The research methodology is based on general scientific and special methods. The main research methods are dialectical and institutional. Structural, compa­rative, method of scientific abstraction, analysis and expert evaluations were also used. Adap­tation of the budget formation system to social and economic changes and determination of priority directions for the further development of financial and budgetary relations require attention. For the thoroughness of the study, the structure of the revenue part of the state budget was analyzed during 2019–2023. The dynamics of attracting international financial aid and its share in the state budget were evaluated. In the course of the research, modern adaptation and transformation mechanisms implemented in budget policy under martial law conditions were determined. New approaches to the formation of the revenue part of the budget have been studied. On the basis of the conducted research, promising directions for improving the forma­tion of state budget revenues have been deter­mined, which will allow the budget system to function more effectively and efficiently. It is expedient to further improve the system of bud­get formation in terms of creating and imple­menting effective financial mechanisms of bud­get policy, with the aim of further social and eco­nomic development of the country. To increase the effectiveness of budget poli­cy in the conditions of martial law, it is necessary to strengthen the use of the principles of stability, financial and economic soundness, social jus­tice, and budget balance in the budget process

Development and cognitive testing of a food frequency questionnaire to assess intake of plant-based protein foods among older adults.

To develop a web-based food frequency questionnaire (FFQ) measuring intake of plant-based protein foods (PBPs) among older adults from the province of Quebec, Canada. The questionnaire was adapted from an existing self-administered FFQ and first underwent expert panel evaluation for face and content validity. Then, three phases of cognitive testing were conducted in French, using the probe and think aloud approaches. Between each phase, the questionnaire was modified based on participants' feedback to improve the clarity and comprehension of the questions. Quebec City, Quebec, Canada. Twenty adults aged 65 years and older. Purposive sampling was used to maximize variation in sociodemographic characteristics, including gender, age, education level, and PBP consumption. The expert panel found the 28-item questionnaire to be a comprehensive measure of PBP intake and suggested minor changes to improve its clarity. The cognitive interviews showed that our PBP-FFQ was generally well understood and identified issues requiring modifications to improve comprehension and accuracy. Our FFQ provides a comprehensive measure of PBP intake, is well understood by older adults in Québec, and will support rigorous assessment of PBP intake in this population, but requires further validation to confirm validity and reproducibility.

Specific features of production and use of transportation biofuels in selected developing countries (part II)

Aim. To identify specific features of production and use of transportation biofuels in selected developing countries, as well as to assess the potential of bioethanol industry development in the Russian Federation (RF).Objectives. To assess the economic efficiency of bioethanol production in Russia; to reveal the potential of applying foreign experience in the development of bioethanol industry in our country.Methods. In the process of the research the methods of system analysis, comparative analysis, method of expert evaluations, mathematical, statistical methods were applied.Results. The actual estimation of economic efficiency of bioethanol production in Russia was obtained. The cost of a unit of bioethanol, which is equivalent to a liter of gasoline in terms of energy content, is 5-36% higher than the cost of a liter of gasoline (depending on the production technology). At the same time, state support for the production and use of bioethanol in our country is reasonable. This will allow to create additional demand for grain crops and reduce carbon dioxide emissions from motor transport. The production and use of bioethanol will form an additional supply in the gasoline market, will give an opportunity to support projects on deep processing of grain and wood chemistry.Conclusions. The experience of Brazil, China and Indonesia in the development of bioethanol industry is relevant for Russia. At the same time, in order to minimize possible risks in the field of food security at the initial stage of bioethanol industry development, low-quality grain stocks should be used as feedstock and the production of second-generation bioethanol should be stimulated.

Exploration of Establishing Evaluation Indicators for Hand Trauma Rehabilitation Nursing Based on Delphi Method.

This study aimed to develop an evaluation index system for hand surgery rehabilitation nursing using the Delphi method. A survey questionnaire on evaluation indicators for hand surgery rehabilitation nursing was developed, and 18 professionals including orthopedic surgeons, orthopedic nursing, and rehabilitation departments were selected. The survey was conducted using the letter method, and a database was established to screen the items, forming a hand surgery rehabilitation nursing evaluation scale. The expert's positive coefficient, authority coefficient, and coordination coefficient were analyzed, and the mean, standard deviation, coefficient of variation, and weight coefficient were calculated. Based on the correlation assignment results, the Item-level Content Validity Index (I-CVI), Scale-level Content Validity Index (S-CVI), average S-CVI, Probability of random consistency (Pc), and corrected I-CVI Kendall's coefficient of concordance (K*) were determined. In the first round of expert inquiry, a total of 18 questionnaires were distributed, and 15 valid questionnaires were collected, with an effective response rate of 83.33%. In the second round, 15 questionnaires were distributed and 15 valid questionnaires were collected, with an effective response rate of 100%. The two rounds of expert evaluations yielded judgment scores of 0.91 and 0.95, with proficiency levels of 0.83 and 0.87 and authority coefficients of 0.87 and 0.91, respectively. The Kendall's W values for the two rounds were 0.313 and 0.224, respectively, with a statistically significant difference (p < 0.01). After screening, 23 indicators were retained, with a coefficient of variation ranging from 0.072 to 0.166. Among the third-level indicators, 12 had an I-CVI of 1.00, 4 had an I-CVI of 0.93, and 7 had an I-CVI of 0.87. The overall S-CVI was 0.80, with an average S-CVI of 0.95. The K* values for the 23 indicators ranged from 0.8662 to 1.0000. The evaluation index system for hand injury rehabilitation nursing based on the Delphi method is highly scientific, and is expected to be used to guide the clinical evaluation of hand injury rehabilitation nursing.

The Impact of Sociological Situations, Psychological and Psychological-Situational Characteristics on Curious in Foreign Languages Learning Behaviors of Secondary Students of the Northern British International School

Background and Aim: This research explores the impact of sociological situations, psychological characteristics, and psychological-situational characteristics on the curiosity and behaviors of secondary students learning foreign languages at the Northern British International School. In today's globalized world, proficiency in foreign languages is crucial for academic and professional success. Understanding the factors that influence students' motivation and behaviors in learning languages like German and Chinese can help educators develop effective strategies to enhance their engagement and performance. Materials and Methods: The study involves 42 students from grades 7 to 12 enrolled in foreign language courses at the Northern British International School. A purposive sampling method was used to select the participants. Data were collected using a questionnaire designed based on a comprehensive review of relevant literature. The validity of the instrument was ensured by expert evaluation, and its reliability was tested using Cronbach’s alpha. Descriptive statistics and multiple regression analysis were employed to analyze the relationships between sociological situations, psychological characteristics, and psychological-situational characteristics with students’ foreign language learning behaviors. Results: The analysis revealed that psychological-situational characteristics had the strongest influence on foreign language learning behaviors, followed by sociological situations and psychological characteristics. The regression model indicated that these factors collectively explained 60.3% of the variance in learning behaviors. Specifically, positive learning environments and supportive relationships were found to significantly enhance students' curiosity and engagement in language learning. Conclusion: The findings highlight the importance of creating a supportive and stimulating learning environment that considers both individual and contextual factors. Educators and policymakers should focus on fostering positive sociological and psychological-situational conditions to enhance students' curiosity and motivation to learn foreign languages, ultimately improving their proficiency and academic performance.

Advancing health coaching: A comparative study of large language model and health coaches

ObjectiveRecent advances in large language models (LLM) offer opportunities to automate health coaching. With zero-shot learning ability, LLMs could revolutionize health coaching by providing better accessibility, scalability, and customization. The aim of this study is to compare the quality of responses to clients' sleep-related questions provided by health coaches and an LLM. Design, setting, and participantsFrom a de-identified dataset of coaching conversations from a pilot randomized controlled trial, we extracted 100 question-answer pairs comprising client questions and corresponding health coach responses. These questions were entered into a retrieval-augmented generation (RAG)-enabled open-source LLM (LLaMa-2-7b-chat) to generate LLM responses. Out of 100 question-answer pairs, 90 were taken out and assigned to three groups of evaluators: experts, lay-users, and GPT-4. Each group conducted two evaluation tasks: (Task 1) a single-response quality assessment spanning five criteria—accuracy, readability, helpfulness, empathy, and likelihood of harm—rated on a five-point Likert scale, and (Task 2) a pairwise comparison to choose the superior response between pairs. A suite of inferential statistical methods, including the paired and independent sample t-tests, Pearson correlation, and chi-square tests, were utilized to answer the study objective. Recognizing potential biases in human judgment, the remaining 10 question-answer pairs were used to assess inter-evaluator reliability among the human evaluators, quantified using the interclass correlation coefficient and percentage agreement metrics. ResultsUpon exclusion of incomplete data, the analysis included 178 single-response evaluations (Task 1) and 83 pairwise comparisons (Task 2). Expert and GPT-4 assessments revealed no discernible disparities in health coach and LLM responses across the five metrics. Contrarily, lay-users deemed LLM responses significantly more helpful than that of human coaches (p < 0.05). LLM responses were preferred in the majority (62.25 %, n = 155) of the aggregate 249 assessments, with all three evaluator groups favoring LLM over health coach inputs. While GPT-4 rated both health coach and LLM responses significantly higher than experts in terms of readability, helpfulness, and empathy, its ratings on accuracy and likelihood of harm aligned with those of experts. Response length positively correlated with accuracy and empathy scores, but negatively affected readability across all evaluator groups. Expert and lay-user evaluators demonstrated moderate to high inter-evaluator reliability. ConclusionOur study showed encouraging findings by demonstrating that RAG-enabled LLM has comparable performance to health coaches in the domain tested. Serving as an initial step towards the creation of more sophisticated, adaptive, round-the-clock automated health coaching systems, our findings call for more extensive evaluation which could assist in the development of the model that could in the future lead to potential clinical implementation.

SUBSTANTIATION OF THE PROFESSIONAL STANDARD OF "PERSONAL FITNESS TRAINER" IN UKRAINE

Professional staff is needed for the effective functioning of health fitness. There are no professional standards for fitness staff in Ukraine, so there is no single standardized approach to determining the list of job functions, general and special competencies, the required knowledge, skills and practical abilities. The purpose of the study is to substantiate and develop a professional standard of "Personal Fitness Trainer" taking into account international experience. Methods: analysis and generalization of scientific and methodical literature, pedagogical observation, functional analysis, documentary method, method of expert evaluations, methods of mathematical statistics. Results. The development of a professional standard for "Personal Fitness Trainer" was based on regulatory acts and the algorithm formed for the health fitness system. There have been identified 60 necessary labor actions which ensure the performance of 9 labor functions of a fitness trainer. The professional standard takes into account the needs of employers, consumer requirements and will be the basis for the industry standard of higher education in the relevant specialty. Conclusions. The developed professional standard for "Personal fitness trainer" is relevant, complies with regulatory acts and established algorithm, adapted to the system of fitness. The expediency of developing a professional standard "Personal fitness trainer" was based on the study of the actual state and prospects of application of fitness staff. The professional standard provides requirements for the content and quality of professional activity; list of general competencies; requirements for the level of qualification, medical contraindications to work, work experience, level of education of a fitness trainer; a list of labor functions and labor actions and the necessary knowledge, skills and abilities; list of subjects and means of work and definition of professional competencies, etc. The professional standard "Personal fitness trainer" should be the basis for the industry standard of higher education in the relevant specialty.

Dynamic Retrieval Augmented Generation of Ontologies using Artificial Intelligence (DRAGON-AI)

BackgroundOntologies are fundamental components of informatics infrastructure in domains such as biomedical, environmental, and food sciences, representing consensus knowledge in an accurate and computable form. However, their construction and maintenance demand substantial resources and necessitate substantial collaboration between domain experts, curators, and ontology experts.We present Dynamic Retrieval Augmented Generation of Ontologies using AI (DRAGON-AI), an ontology generation method employing Large Language Models (LLMs) and Retrieval Augmented Generation (RAG). DRAGON-AI can generate textual and logical ontology components, drawing from existing knowledge in multiple ontologies and unstructured text sources.ResultsWe assessed performance of DRAGON-AI on de novo term construction across ten diverse ontologies, making use of extensive manual evaluation of results. Our method has high precision for relationship generation, but has slightly lower precision than from logic-based reasoning. Our method is also able to generate definitions deemed acceptable by expert evaluators, but these scored worse than human-authored definitions. Notably, evaluators with the highest level of confidence in a domain were better able to discern flaws in AI-generated definitions. We also demonstrated the ability of DRAGON-AI to incorporate natural language instructions in the form of GitHub issues.ConclusionsThese findings suggest DRAGON-AI's potential to substantially aid the manual ontology construction process. However, our results also underscore the importance of having expert curators and ontology editors drive the ontology generation process.

Neurophysiological Correlates of Expert Knowledge: An Event-Related Potential (ERP) Study about Law-Relevant Versus Law-Irrelevant Terms.

The evaluation of evidence, which frequently takes the form of scientific evidence, necessitates the input of experts in relevant fields. The results are presented as expert opinions or expert evaluations, which are generally accepted as a reliable representation of the facts. A further issue that remains unresolved though is the process of evaluating the expertise and knowledge of an expert in the first instance. In general, earned certificates, grades and other objective criteria are typically regarded as representative documentation to substantiate an expert status. However, there is a possibility that these may not always be sufficiently representative. The goal of the present study was to provide evidence that the neural processing of law-relevant and law-irrelevant terms varies significantly between participants who have received training in the field of law (experts) and those who have not (novices). To this end, changes in brain activity were recorded via electroencephalography (EEG) during visual presentations of terms belonging to five different categories (fake right, democracy, filler word, basic right and rule of law). Event-related potentials (ERPs) were subsequently averaged for each category and subjected to statistical analysis. The results clearly demonstrate that participants trained in law processed fake rights and filler words in a similar manner. Furthermore, both of these conditions elicited different levels of brain activity compared to all law-relevant terms. This was not the case in participants who had not received legal training. The brains of untrained participants processed all five term categories in a strikingly similar manner. In light of prior knowledge regarding language processing, the primary focus was on two distinct electrode locations: one in the left posterior region, and the other in the left frontal region. In both locations, the most prominent differences in brain activity elicited by the aforementioned term categories in law-trained participants occurred approximately 450 milliseconds after stimulus onset. The results were further corroborated by a repeated-measures ANOVA and subsequent t-tests, which also demonstrated the absence of this effect in law-untrained participants. The findings of this study provide empirical evidence that brain activity measurements, in particular ERPs, can be used to distinguish between experts trained in a specific field of expertise and novices in that field. Such findings have the potential to facilitate objective assessments of expertise, enabling comparisons between experts and novices that extend beyond traditional criteria such as qualifications and experience. Instead, individuals can be evaluated based on their cognitive processes, as observed through brain activity.

Ontology-Guided Generation of Mechanized Construction Plan for Power Grid Construction Project

Mechanized construction is being fully implemented in the electric power infrastructure domain to ensure construction safety, enhance project quality, and improve efficiency. Traditional methods of designing mechanized construction plans are often inefficient due to their labor-intensive processes and heavy reliance on human expertise. This study introduces and evaluates an ontology-guided system designed to automate mechanized construction planning for power grid projects. The developed ontology effectively models domain-specific knowledge, enabling the semantic integration of data from various sources. By leveraging SPARQL queries, the ontology-guided system incorporates knowledge reasoning capabilities that facilitate the automated selection of construction equipment and the generation of comprehensive construction plans. A prototype system incorporating an ontology-guided mechanism has been developed, showcasing marked enhancements in efficiency and accuracy over traditional manual methods, as evidenced by case studies and expert evaluations. The research results emphasize the potential of ontology-guided systems in innovating architectural planning, providing an extensible and standardized approach. Expert evaluation indicates that the system achieves 71.38% effectiveness in generating mechanized construction plans.