Knowledge Model Research Articles

Knowledge and innovation management model in the mezcal industry in Mexico

Knowledge and innovation management model in the mezcal industry in Mexico

Investigating the role of lifestyle, knowledge and health beliefs in performing preventive behaviours against human papilloma virus in married women.

The aim of this research was investigating the role of lifestyle, knowledge and health beliefs in performing preventive behaviours against HPV in married women. Human Papilloma Virus (HPV) is a significant public health concern in Iran, with a relatively high prevalence in the general female population. The study utilized a cross-sectional survey among 260 married women working at the university. The data collection instruments comprised knowledge, lifestyle, and health belief model (HBM) questionnaires. The data analysis was performed using SPSS. The study's notable strength lies in its examination of the influential factors affecting HPV preventive behaviours through the HBM, utilizing a culturally relevant questionnaire tailored to Iranian circumstances. Notably, self-efficacy and perceived benefits merged as the strongest predictors of engaging in preventive behaviours against HPV, respectively. Notably, self-efficacy and perceived benefits merged as the strongest predictors of engaging in preventive behaviours against HPV, respectively. Therefore, it is crucial for future interventions to focus on addressing these constructs to promote HPV vaccination and other preventive behaviours effectively. Besides, it is essential to recognize the close relationship between self-efficacy and perceived barriers. Understanding the key barriers to HPV vaccination and other preventive behaviours is crucial, as addressing these barriers can enhance individuals' self-efficacy and, in turn, promote the adoption of these preventive measures effectively.

An ontology-based knowledge modeling for the rite of Bai Sri Su Kwan: a ritual of the Greater Mekong Subregion

An ontology-based knowledge modeling for the rite of Bai Sri Su Kwan: a ritual of the Greater Mekong Subregion

Intelligent shipping: integrating autonomous maneuvering and maritime knowledge in the Singapore-Rotterdam Corridor

Designing safe and reliable routes is the core of intelligent shipping. However, existing methods for industrial use are inadequate, primarily due to the lack of considering company preferences and ship maneuvering characteristics. To address these challenges, here we introduce a methodological framework that integrates maritime knowledge and autonomous maneuvering model. Based on historical maritime big data, the framework offers customized routes for companies with specific routing preferences. The autonomous maneuvering model then evaluates the safety and reliability of the routes by considering ship motion characteristics and ocean hydrodynamics. We validate its effectiveness on the world’s longest Green and Digital Shipping Corridor between Singapore and Rotterdam. Results demonstrate that our model can provide customized route design for companies and enhance safety for shipping. The framework could serve as a fundamental structure to build a fully digitalized platform for route customization and evaluation for global shipping, optimizing operational decision-making and safety assurance.

Some Critical Thinking on Electric Vehicle Battery Reliability: From Enhancement to Optimization

Electric vehicle (EV) batteries play a crucial role in sustainable transportation, with reliability being pivotal to their performance, longevity, and environmental impact. This study explores battery reliability from micro (individual user), meso (industry), and macro (societal) perspectives, emphasizing interconnected factors and challenges across the lifecycle. A novel lifecycle framework is proposed, introducing the concept of “Zero-Life” reliability to expand traditional evaluation methods. By integrating the reliability ecosystem with a dynamic system approach, this research offers comprehensive insights into the optimization of EV battery systems. Furthermore, an expansive Social–Industrial Large Knowledge Model (S-ILKM) is presented, bridging micro- and macro-level insights to enhance reliability across lifecycle stages. The findings provide a systematic pathway to advance EV battery reliability, aligning with global sustainability objectives and fostering innovation in sustainable mobility.

Rethinking Indigenous Community-Led Water Sustainability: Decolonial and Relational Approaches in Western Canada

This study examines Indigenous community-led water sustainability in Western Canada through relational and decolonial lenses, addressing the interplay between traditional knowledge, environmental governance, and cultural identity. The relational and decolonial frameworks emphasize water as a living entity integral to environmental sustainability and community well-being, contrasting with extractive Western models that prioritize economic gains. Using a community-led collaborative methodology, the research engaged Elders, Knowledge-keepers, and youth in discussions and land-based activities, reinforcing intergenerational knowledge transfer. The findings showcase critical challenges to Indigenous water governance, including industrial encroachments, climate change, and colonial environmental management systems that marginalize Indigenous perspectives. These human-created challenges threaten Indigenous water quality and disrupt Indigenous sustainable governance, underlining the need for alternative, adaptive frameworks. Despite these challenges, Indigenous communities are reclaiming water sustainability through initiatives that implement traditional knowledge, cultural revitalization, and collaborative governance models. Such efforts emphasize respect, reciprocity, and stewardship, promoting long-term environmental sustainability.

Specification of a smart-analysis system of sound events for smart environments

PurposeIn this work, we proposed a smart-analysis system of sound events for smart/intelligent environments based on an autonomic cycle of data analysis tasks.Design/methodology/approachWe propose an autonomic cycle of data analysis tasks. An autonomic cycle of data analysis tasks is a set of data analysis tasks that supervise and control a process anonymously, which are based on knowledge models (of prediction, recognition, etc.), interacting with each other to reach a common goal. Each task has a different function in the cycle: observation of the process, or its analysis, or decision-making.FindingsThis work presents the autonomic cycle. With its components, this autonomic cycle detects sound information using a taxonomic model of the sound events to analyze them and give a recommendation about the context. The taxonomic model is a hierarchical pattern that considers different aspects to recognize the sound events. This work defines the architecture of this autonomic cycle, specifies its machine-learning-based analysis tasks and evaluates its capabilities of reasoning, adaptation and communication in case studies.Research limitations/implicationsIt is important to work in the future on the improvement of the accuracy of the system by implementing neural networks or more sophisticated techniques. To take the implemented autonomic cycle to a higher level, it could use parallel function management. The automation also needs improvement. In addition to that, future works are going to be directed not only to sound events but also to include emotion recognition and its relation with sound events happening simultaneously.Originality/valueThe main contributions of this paper are as follows: the detailed description of the intelligent sound analysis (ISA) autonomic cycle for the smart sound analysis of sound events (SAS-SE) in an intelligent environment (IE); the specification of the machine-learning-based analysis tasks of ISA for the smart sound analysis and the development of a case study that settles the use of the system in different IEs.

Conceptual design of smart product-service system based on function-oriented search

Unlike traditional products, Smart Product-Service System design requires the broad design knowledge support. However, traditional knowledge representation does not consider the characteristic elements of Smart Product-Service System, which can lead to the loss of essential information when organising and reusing design knowledge. This limitation affects the application of cross-domain functional knowledge in the conceptual design of Smart Product-Service System. To address this issue, a process model for the conceptual design of Smart Product-Service System based on function-oriented search is proposed. An initial functional modelling is first constructed based on the characteristics of the Smart Product-Service System. The extended Function Behaviour Structure model is then used to construct a design knowledge model of the system. Based on Function-Oriented Search to search for patent knowledge with highly similar functions in the patent database, the knowledge fusion is used to solve the derived problems after pruning, so as to obtain high-quality system conceptual solutions. Finally, the effectiveness of the method is verified by taking the scheme construction of intelligent forklift product service system as an example.

P0956 Predicting Clinical Remission in Crohn’s Disease: A Comparative Study of Expert-Generated and Computer-Generated Bayesian Networks

Abstract Background In Crohn’s disease (CD), artificial intelligence (AI) may improve treatment optimization and aid in clinical decision-making 1. The aim of the study is to compare the results of Bayesian Networks (BNs) of both Expert Knowledge Model (EKM) and Computer Algorithm Generated Model (CAGM) in predicting corticosteroid-free clinical remission at 52 weeks after introducing ustekinumab and vedolizumab treatment in patients with CD. Methods Data were extracted from the Dutch Initiative on Crohn and Colitis (ICC) registry. Observations were conducted on patients with CD (n = 440) based on remission criteria including Harvey Bradshaw Index (HBI<5) assessment and no corticosteroid use. Data were divided into training (70%, N = 309) and validation (30%, N = 131) subsets. Based on these, two Bayesian network models were developed. Expert knowledge gathered through three consultation rounds with 12 specialists was used to generate an EKM using a Directed Acyclic Graph and the K2-scoring method to refine and accurately represent node relationships in the model. Furthermore, the Hill Climbing search algorithm was applied to build CAGM 2. Finally, node elimination was applied to reduce complexity and further optimize the performance of both EKM and CAGM. Results The EKM contained 21 expert-defined variables and 38 edges between them. There were ten direct parent nodes in the EKM final round of node elimination, including age at diagnosis, disease duration, gender, smoking status, calprotectin, C-reactive protein (CRP), thrombocytes, prior surgery, previous medication, and perianal disease. The EKM showed AUC, accuracy, sensitivity, and specificity values of 0.70, 0.64, 0.15, and 0.92, respectively. On the other hand, the CAGM contained 14 variables and 17 edges. Six direct parents were left in the final round, including previous medication, prior surgery, CRP, perianal disease, thrombocytes, and age at diagnosis. The CAGM showed an AUC, accuracy, sensitivity, and specificity values of 0.59, 0.62, 0.17, and 0.88, respectively. Consequently, age at diagnosis, previous medication, thrombocytes, and perianal diseases were common nodes left in both models. Conclusion In this comparative study, after rounds of node elimination, the expert-generated model has a higher AUC than that of the computer-generated model. The findings indicate that expert knowledge models provide a deeper, more comprehensive influence on Crohn’s remission outcomes compared to computer-generated models. With BN’s capacity to incorporate expert information and surpass algorithm-only methods in remission prediction, they may provide physicians with a potent, interpretable tool for individualized decision-making and enhancing diagnostic precision. References Sandborn WJ, Rutgeerts P, Gasink C, et al. Long-term efficacy and safety of ustekinumab for Crohn’s disease through the second year of therapy. Aliment Pharmacol Ther. 2018;48(1):65-77. doi:10.1111/apt.14794 Park E, Chang H jae, Nam HS. A Bayesian Network Model for Predicting Post-stroke Outcomes With Available Risk Factors. Front Neurol. 2018;9. doi:10.3389/fneur.2018.00699

Interweaving academic insights: advancing university knowledge management through a strategic data fabric framework

PurposeEffective knowledge management in large academic institutions is crucial for fostering innovation and improving educational practices. However, these institutions often face challenges, such as data fragmentation, siloed information systems and the complexity of integrating different data sources from various departments with complex hierarchical structures. To address these problems, the authors proposed a data fabric strategic framework that improves and enhances knowledge management by leveraging ontologies and knowledge graphs. This study aims to investigate the potential of knowledge graphs, ontological knowledge modelling and knowledge representation to improve knowledge management in large academic institutions. It also describes how technology can enhance knowledge accessibility and exchanges and improve decision-making processes based on insights from complex educational systems.Design/methodology/approachThis study uses coordination theory as a foundational framework to analyse intricate data systems in preparation for constructing, the Wizard of Oz method to facilitate the systematic organisation and management of information and the execution of an ontology-based data fabric framework and knowledge graphs. The authors propose a data fabric strategic framework aimed at improving knowledge management by leveraging ontologies and knowledge graphs.FindingsThe final evaluation demonstrates that this approach effectively breaks down data silos, promotes research collaboration and improves decision-making processes in large academic settings, offering solution-oriented data fabric technologies applicable to universities and university federations globally.Practical implicationsThe proposed system provides a more efficient way of managing and connecting fragmented academic resources, improving accessibility for both learners and educators. By interconnecting and streaming knowledge management process, the system can reduce not only operational costs but also expenses on doing scientific research.Originality/valueAcademic institutions prioritise time efficiency when acquiring vital data for improved scientific results. This emphasis extends beyond data governance to focus on how collective intelligence might improve organisational performance. The academic community has enhanced data utilisation through the implementation of data fabric technologies to improve data accessibility and data line tracking.

Las políticas educativas de la ingeniería civil en México. Una estratigrafía histórica

As a result of the social approach adopted in recent years by the Mexican Federal Government, a new strategy has emerged to promote higher education, focusing on the most marginalized individuals and communities, with the goal of fostering their inclusion in professional training at this level. This research presents the philosophical foundations of this initiative, based on the concept of “episteme”; it includes a review of the evolution of higher education in Mexico from the perspective of civil engineering, utilizing a stratigraphic model of knowledge. In particular, the creation of the Benito Juárez García System of Universities for Welfare and the Civil Engineering School in Texcoco, State of Mexico, is described. The latter serves as an alternative to address the consequences of educational policies implemented under the so-called neoliberal framework, representing a potential option for improved social participation in the development of specialized technical capacities. The results and conclusions demonstrate that the educational reforms implemented do not follow a continuous discursive line but rather emerge in response to the episteme or discursive stratum of the moment. As a case study, the current higher education model in civil engineering is presented. In the same way, the results of the new model of the University of Civil Engineering are presented.

Stigmatized HIV Over Knowledge and Support: A Quantitative Study to Develop and Measure Bangladeshi Women's Social Support When Living With HIV-Related Stigma

Abstract Recognizing the unique context of Bangladeshi women living with HIV-related stigma in a patriarchal society is central to social scientists developing a culturally appropriate instrument for examining the impact of meaningful interventions. Working with 200 Bangladeshi women with HIV who responded to 77 items from three instruments, we used exploratory factor analysis and identified five latent constructs: emotional support, felt normative stigma, incorrect HIV transmission knowledge, correct HIV transmission knowledge, and tangible support. Confirmatory factor analysis model specifications for the Comparative Fit Index, Tucker Lewis Index (TLI), and Root Mean Square Error of Approximation (RMSEA) were analyzed and all moderately fit. We thus present the Stigmatized HIV Over Knowledge and Support (SHOKS) model as pioneering work facilitating social scientists in developing a culturally appropriate instrument to generate interventions and better support Bangladeshi women living with HIV-related stigma.

Benchmarking large language models for genomic knowledge with GeneTuring.

Large language models have demonstrated great potential in biomedical research. However, their ability to serve as a knowledge base for genomic research remains unknown. We developed GeneTuring, a comprehensive Q&A database containing 1,200 questions in genomics, and manually scored 25,200 answers provided by six GPT models, including GPT-4o, Claude 3.5, and Gemini Advanced. GPT-4o with web access showed the best overall performance and excelled in most tasks. However, it still failed to correctly answer all questions and may not be fully reliable for providing answers to genomic inquiries.

Knowledge-aware evolutionary graph neural architecture search

Graph neural architecture search (GNAS) can customize high-performance graph neural network architectures for specific graph tasks or datasets. However, existing GNAS methods begin searching for architectures from a zero-knowledge state, ignoring the prior knowledge that may improve the search efficiency. The available knowledge base (e.g. NAS-Bench-Graph) contains many rich architectures and their multiple performance metrics, such as the accuracy (#Acc) and number of parameters (#Params). This study proposes exploiting such prior knowledge to accelerate the multi-objective evolutionary search on a new graph dataset, named knowledge-aware evolutionary GNAS (KEGNAS). KEGNAS employs the knowledge base to train a knowledge model and a deep multi-output Gaussian process (DMOGP) in one go, which generates and evaluates transfer architectures in only a few GPU seconds. The knowledge model first establishes a dataset-to-architecture mapping, which can quickly generate candidate transfer architectures for a new dataset. Subsequently, the DMOGP with architecture and dataset encodings is designed to predict multiple performance metrics for candidate transfer architectures on the new dataset. According to the predicted metrics, non-dominated candidate transfer architectures are selected to warm-start the multi-objective evolutionary algorithm for optimizing the #Acc and #Params on a new dataset. Empirical studies on NAS-Bench-Graph and five real-world datasets show that KEGNAS swiftly generates top-performance architectures, achieving 4.27% higher accuracy than advanced evolutionary baselines and 11.54% higher accuracy than advanced differentiable baselines. In addition, ablation studies demonstrate that the use of prior knowledge significantly improves the search performance.

FireExpert: Fire Event Identification and Assessment Leveraging Cross-Domain Knowledge and Large Language Model

FireExpert: Fire Event Identification and Assessment Leveraging Cross-Domain Knowledge and Large Language Model

A novel staged training strategy leveraging knowledge distillation and model fusion for heterogeneous federated learning

A novel staged training strategy leveraging knowledge distillation and model fusion for heterogeneous federated learning

A Novel Classification System of Smart Mechanical Plaque Control: A Cross-Sectional Survey with Cluster Analysis

Background: Recurring advances in the field of mechanical plaque control (MPC) devices have shown promise in improving the oral hygiene status of the community. However, little is known about the association between the knowledge of healthcare professionals regarding these advances, the manufacturers in understanding the demand of the community, and the community’s willingness to switch to these “smarter” advances in MPC. This article puts forth a novel classification system of smart MPC to bridge this research gap. Materials and Methods: After a Delphi consensus and a sample pretesting, a customized questionnaire survey based on the knowledge, attitude, and practice model was filled by 618 participants. The participants were divided into two groups: Group I from the healthcare profession (n = 236) and Group II from other professions (n = 382). A Chi-square test was used to determine the significant variables. These variables went through K-means and cluster silhouette scoring for cluster analysis. A correlation coefficient using regression line was used to analyze the relation between related variables. Results: The Chi-square test revealed nine components with statistically significant associations (P < 0.05). K-means clustering of the nine parameters revealed six clusters (silhouette score >0.5) that guided in drawing a classification of smart MPC. Conclusion: This study reveals the dearth of knowledge among the participants regarding advanced MPC and their negligence in following basic oral hygiene routines. The classification system derived through cluster analysis provides a basis for understanding the upgraded modes of MPC.

Practical Applications of Neutrosophic Logic in Enhancing the Accuracy of Economic Forecasting Models and Supporting Decision-Making in Banks

Using three machine knowledge models that utilise Neutrosophic Logic (NL)—Linear Regression, Random Forest, and Gradient Increasing—this study studies the possibilities of refining financial result forecast. The cognitive behind this is that NL recovers the prediction power of these models across dissimilar organisations by accounting for the inherent uncertainty, unpredictability, and lack of sureness in financial numbers. In this study, the models' presentation is evaluated using a variety of financial factors, including interest rates and stock prices. F1 score, recall, correctness, and exactness are some of the metrics used by this drive. When likened to other models, NL with Gradient Cumulative consistently outperforms them in terms of correctness and robustness. You might think of Abu Dhabi Islamic Bank and the National Bank of Bahrain as two such examples. Companies like Emirates Islamic Bank reap some benefits from Chance Forest's combination of cheap computation with precision, but only to a lower degree. Complex datasets used by businesses like Al Rajhi Bank are beyond the capabilities of Linear Reversion, even when combined with NL. By proving that cooperative techniques combined with NL positively reduce financial data volatility, our results lay the groundwork for improved financial forecasting and decision-making. The exercise has demonstrated that NL has great potential to enhance financial prediction models, which could have future applications in investment planning and risk organization.

From Measuring to Action: The Next Steps in Physics Teachers’ Technological Pedagogical Content Knowledge

The Technological, Pedagogical Content Knowledge (TPACK) model serves as a comprehensive conceptual framework that delineates the essential knowledge domains teachers must possess to effectively integrate technology within educational settings. By synthesizing technological knowledge, pedagogical knowledge, and content knowledge, TPACK empowers educators to create innovative, engaging, and meaningful learning experiences that cater to diverse student needs. Extensive research has indicated that teaching practices informed by the TPACK framework can significantly enhance student motivation, foster critical thinking skills, and improve overall academic performance. Moreover, the successful implementation of TPACK is contingent upon several critical factors, including ongoing professional development opportunities for educators, the availability of adequate technological resources, and the cultivation of a supportive school culture that encourages collaboration and experimentation. A recent paper presents, the results of a quantitative study conducted using the TPACK questionnaire. Data are collected utilizing the database of the Association of Physics Teachers of Georgia. Confirmatory factor analysis for the constructs’ reliability is conducted. The results of the factor analysis are grouped into four factors, which differ from those in other studies and are discussed below. Based on these findings, recommendations are provided for the improvement and integrating technology into physics teacher education.

A Deployment Method for Motor Fault Diagnosis Application Based on Edge Intelligence

The rapid advancement of Industry 4.0 and intelligent manufacturing has elevated the demands for fault diagnosis in servo motors. Traditional diagnostic methods, which rely heavily on handcrafted features and expert knowledge, struggle to achieve efficient fault identification in complex industrial environments, particularly when faced with real-time performance and accuracy limitations. This paper proposes a novel fault diagnosis approach integrating multi-scale convolutional neural networks (MSCNNs), long short-term memory networks (LSTM), and attention mechanisms to address these challenges. Furthermore, the proposed method is optimized for deployment on resource-constrained edge devices through knowledge distillation and model quantization. This approach significantly reduces the computational complexity of the model while maintaining high diagnostic accuracy, making it well suited for edge nodes in industrial IoT scenarios. Experimental results demonstrate that the method achieves efficient and accurate servo motor fault diagnosis on edge devices with excellent accuracy and inference speed.