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"MODELS OF KNOWLEDGE"
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Adult numeracy and the totally pedagogised society: PIAAC and other international surveys in the context of global educational policy on lifelong learning
This paper aims to discuss the emergence, form and likely effects of international surveys of adults' skills by locating them in the global context of policies on education and Life Long Learning (LLL). It focuses on adults' numeracy and discusses its conceptualisation and assessment in the Project for the International Assessment of Adult Competencies (PIAAC), which is the most recent survey. Drawing on critical theoretical resources about new forms of governance in education and transformations in the pedagogic discourse, the paper further substantiates existing critiques of global policy trends, namely that they are motivated by human capital approaches to education and LLL. In particular, we show that the apparently commonsensical appeal of evaluative instruments like PISA and PIAAC is based on a competency model of knowledge, which embodies a narrow notion of competence. Relatedly, the notional curricula promoted by such surveys potentially articulate a more radical idea of LLL, captured by Bernstein's conception of trainability as the mode of socialisation into a Totally Pedagogised Society. The paper presents a dual approach to understanding international adult performance surveys in general—in that, besides deploying the theoretical resources already indicated, it also raises a number of methodological issues relevant to the valid interpretation of these studies' results. Ultimately, it argues for the importance of mobilising resources from critical educational perspectives to support the development of potentially powerful knowledge like numeracy and to prevent its being reduced to a narrow competency.
Journal Article
Predictive maintenance approaches: A systematic literature review
Purpose: Predictive maintenance (PdM) aims to optimize maintenance operations by detecting operational anomalies and potential equipment failures before they lead to costly unplanned downtime. The goal is to minimize reactive maintenance and reduce the frequency of preventive maintenance interventions. This paper evaluates PdM strategies using knowledge-based, physics-based, and data-driven models to propose an integrated approach that enhances prediction accuracy, aligning with Industry 4.0 goals.Design/methodology/approach: A Systematic Literature Review (SLR) is conducted to examine the strengths and weaknesses of knowledge-based, physics-based, and data-driven models in predictive maintenance. The study assesses existing research, compares methodologies, and identifies opportunities for integrating these models to improve PdM outcomes.Findings: The review indicates that no single approach—whether knowledge-based, physics-based, or data-driven—is sufficient to meet the comprehensive demands of predictive maintenance. Instead, an integrated approach that combines these three models provides more accurate and cost-effective maintenance solutions, supporting the automation and efficiency goals of Industry 4.0.Research limitations/implications: The study's findings are limited by the availability of real-world data and case studies. Future research should focus on testing the proposed integrated model in diverse industrial contexts to validate its effectiveness across different sectors.Practical implications: The proposed approach offers industries a more reliable framework for optimizing maintenance strategies, improving operational efficiency, and reducing costs associated with equipment failures and excessive preventive measures.Social implications: By enhancing predictive maintenance, the integrated model supports sustainability efforts by reducing waste, improving resource utilization, and contributing to the longevity of machinery and equipment.Originality/value: This research offers a novel contribution by integrating knowledge-based, physics-based, and data-driven models into a unified PdM approach. It provides valuable insights for both academia and industry, especially in the context of Industry 4.0.
Journal Article
Formation of Future Teachers’ Skills to Create and Use Visual Models of Knowledge
The article proposes a solution of the problem of formation of future teachers' skills to create and use visual models of knowledge in professional activities. It is substantiated that modern students, as representatives of the generation Z, have the majority of visual-oriented perceptions. Modelling of special future teachers’ preparation for the creating of visual models of knowledge is made. The content of the special course, aimed at forming the future teachers’ ability to create and use visual models of knowledge, is described. The pedagogical experiment included two areas of research: the study of the dynamics of the readiness and the formation of skills to create and use visual models of knowledge. Statistical methods confirmed the effectiveness of the author's special course at the significant level of 0.05.
Journal Article
Models of knowledge management in micro and small enterprises
The paper analyzes models of knowledge management (KM), based on the profile of the micro and small enterprises (MSE) in the state of Sergipe, Brazil, specifically the models proposed by C. R. Silva Jr. (2006); E. E. Thiel (2002); M. C. Rumizen (2002) and G. Von Krogh and K. Ichijo and T. Nonaka (2000). The characteristics of the MSE in the Brazilian economy emphasize their place of prominence as responsible for 28% of gross revenues from the formal sector and 20% of Gross Domestic Product. However, the lack of researches which emphasize the reality of the MSE may be one reason which interferes in their more significant role in the Brazilian economy. The corpus consists of 60 (sixty) employees from 10 (ten) MSE installed in the Technological Park of Sergipe, incorporating managers, key professionals and members of the operating body. Through the techniques of interview, questionnaire and direct observation, it identifies the attributes of technology in the MSE, as well as the characteristics of the adopted processes and the ones considered ideal for employees. The most important results reveal the inadequacy of the analyzed models, because they are always elaborated by considering the reality of medium and big enterprises. It concludes, finally, that none of analyzed models are fully adequate to the reality of the MSE, and even the model of Von Kroch, Ichijo and Nonaka approaching closely to the profile of these companies, still requires modifications to its implementation. It is recommended, therefore, the creation of a model through further analysis of the activities from other adoption models to establish a new model suitable to the limitations of MSE.
Journal Article
Critique and complexity: presenting a more effective way to conceptualise the knowledge adoption process
The process of 'knowledge adoption' is defined as the means through which policy-makers digest, accept then 'take on board' research findings. It is argued in Brown, however, that current models designed to explain knowledge adoption activity fail to fully account for the complexities that affect its operation. Within this paper, existing frameworks are explored and critiqued, and an alternative approach is presented. It is argued that this alternative conceptualisation provides a more effective explanation of the knowledge adoption process and significantly improves on extant work in this area.
Journal Article
Agriculture Knowledge Graph Construction and Application
For the purpose of establishing vertical knowledge graph and auxiliary applications in the agricultural field, a set of agricultural knowledge graph construction methods, calculation frameworks and practical application systems are proposed. Firstly, the existing storage form and knowledge representation of knowledge in the agricultural field are integrated and regularized. On the basis of this data processing, the intelligent construction method of automatic and manual dual mode of knowledge graph in the agricultural field is proposed, and the key technology of entity relationship joint model to extract entity relationship and intelligent retrieval of irregular data. Then, similarity calculation will be used to perform entity knowledge fusion on knowledge graph in the agricultural field, making the graph more standardized, accurate and complete. A good graph is visualized and applied to the mainstream functions of intelligent question answering, which makes the whole system sort out the messy agricultural knowledge and apply it better to better assist learning and research.
Journal Article
Operational Interval Extraction Based on Long‐Short Term Memory Networks for Building More Feasible Reservoir Operation Models
Advances in data analytics create an opportunity to enhance reservoir operation. A challenge arising is how to utilize operational data to form realistic constraints of the reservoir operation practice. To address this issue, a novel approach is proposed to extract operational intervals of reservoir outflow by a deep learning method, namely the physics‐guided long‐short term memory network. The knowledge‐informed reservoir operation (KIRO) model was built by adding derived operational intervals of outflow as constraints for the traditional reservoir operation (TRO) model. KIRO couples (a) an optimization model to search for optimal operation schemes, and (b) operational intervals of reservoir operators' decisions based on realistic factors. China's Qingjiang cascade reservoir including Shuibuya, Geheyan, and Gaobazhou reservoirs is used as a case study. Results show that KIRO can yield more physically feasible operation schemes than TRO due to its additional constraints. Specifically, KIRO avoids excessive reservoir water level fluctuations and outflow variations compared with TRO. Moreover, the extracted operational interval can help uncover implicit demands of real‐world operation, for example, the KIRO model accurately identified the cascade reservoir unit maintenance events from 31 January 2019, to 31 March 2019, and the operation schemes were aligned more closely with the power demands. This study provides a new method for building more feasible reservoir operation models based on deep learning. Key Points Deep learning is used to build more feasible reservoir operation optimization models Physics‐guided long‐short term memory identifies operational intervals of reservoir outflow The extracted interval is helpful to identify implicit demands of real‐world operation
Journal Article
New Knowledge Discovery for Creating Terminological Profiles of Diseases
The paper focuses on discovering new knowledge for creating and updating terminological profiles of diseases. A profile is understood as the complex of related annotations of terms describing a disease and its stages. An annotation of each term contains a structured definition of the term meaning (=concept) in which its sub-meanings can be defined, term synonyms, inter-concept relationships, links to external information resources, term contexts extracted from scientific texts on medicine and a linkage between each context and a relevant text, and term associations with disease stages. The growth of scientific knowledge in medicine results in new terms that need to be regularly added to disease profiles. Creating and updating disease profiles requires an advanced model as a theoretical basis for developing a knowledge base and information technology that support the discovery of new knowledge in large collections. The proposed model combines the automatic and expert stages of finding new meanings of existing terms and new terms representing new knowledge concepts that are not described in medical dictionaries and handbooks used by experts. The paper aims to compare our informationtechnology-oriented model with the spiral model of knowledge creation.
Conference Proceeding
SOFTWARE FOR FAULT DIAGNOSIS USING KNOWLEDGE MODELS IN PETRI NETS
Los sistemas de diagnóstico de fallas en empresas asociadas al sector eléctrico requieren propiedades de precisión y flexibilidad cuando surgen eventos de falla. Actualmente existen sistemas que pretenden mejorar el proceso de diagnóstico mediante varios métodos y técnicas computacionales, reduciendo el tiempo de respuesta a perturbaciones. Sin embargo, son pocas las propuestas que unifican modelos gráficos de conocimiento con las señales de un proceso que pueden ofrecer dispositivos como controladores lógicos programables (PLCs). Este artículo propone un software novedoso guiado por modelos basados en redes de Petri e integrado con señales del proceso, para el diagnóstico de falla en centrales de generación eléctrica. Un caso de estudio demuestra la flexibilidad y adaptabilidad del software cuando nuevas nociones en los modelos de conocimiento cambian, sin realizar procedimientos de reingeniería al software.
Journal Article