Bridging Tacit Knowledge and Explicit Knowledge: An Ontological Model for Effective Knowledge Conversion

Knowledge management (KM) involves a structured approach to creating, sharing, utilizing, and organizing knowledge and information within an organization, aiming to enhance its efficiency, productivity, and competitive advantage. A core element of KM is the distinction between tacit and explicit knowledge (EK). Tacit knowledge (TK) refers to personal insights and skills that are difficult to articulate or transfer, as they are deeply embedded in individual experiences. In contrast, EK consists of information that can be easily documented, communicated, and shared. The process of converting TK into EK is essential for fostering innovation and organizational growth, particularly in today’s fast-paced business environment. By translating TK into a more formal, shareable format, it becomes easier for others to access and apply valuable insights. However, capturing TK presents challenges because it is subjective and linked to personal experience. The practice of externalization involves developing creative methods to articulate and share this kind of knowledge, making it accessible and actionable for others. This paper introduces a model for representing TK and outlines strategies for transforming it into explicit forms. It also discusses techniques for effectively capturing this valuable expertise, which is crucial for making informed decisions. Converting TK into EK ensures that organizations can preserve and utilize critical insights across different roles and functions

Systematic Review and Framework for AI-Driven Tacit Knowledge Conversion Methods and Machine Learning Algorithms for Ontology-Based Chatbots in E-Learning Platforms

The conversion of tacit knowledge, which is deeply rooted in personal experience and often difficult to articulate, presents a significant challenge within knowledge management systems. Ontology-based chatbots offer a promising solution by leveraging structured knowledge representations and advanced natural language processing (NLP) techniques to facilitate this transformation. This paper explores the various methods and algorithms used in developing ontology-based chatbots, with a particular focus on their role in converting tacit knowledge into more accessible forms. Additionally, it provides a comparative analysis of the algorithms employed, highlighting their respective strengths and weaknesses. Ultimately, this study addresses the critical challenge of managing and converting tacit knowledge, with the aim of enhancing the overall effectiveness of knowledge management systems

A Novel Hybrid Classification Approach for Sentiment Analysis of Text Document

Sentiment analysis is a more popular area of highly active research in Automatic Language Processing. She assigns a negative or positive polarity to one or more entities using different natural language processing tools and also predicted high and low performance of various sentiment classifiers. Our approach focuses on the analysis of feelings resulting from reviews of products using original text search techniques. These reviews can be classified as having a positive or negative feeling based on certain aspects in relation to a query based on terms. In this paper, we chose to use two automatic learning methods for classification: Support Vector Machines (SVM) and Random Forest, and we introduce a novel hybrid approach to identify product reviews offered by Amazon. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. The results summarize that the proposed method outperforms these individual classifiers in this amazon dataset

Enhancing tabular data analysis for classification of airline passenger satisfaction using TabNet deep neural network

In an era of air travel, understanding and enhancing passenger satisfaction are pivotal to the success of airlines and the overall passenger experience. Analyzing airline passenger satisfaction using tabular data can pose various challenges, both when employing classical statistical methods and when leveraging machine learning and deep learning techniques. On the one hand, statistical approaches pose various challenges including limited feature engineering techniques, the assumption of linearity of the data sets and limited predictive power. Then again, the use of machine learning and deep learning techniques may face other challenges such as the problem of overfitting, difficulty in interpreting data, intensive resource requirements, and the generalization problem in deploying machine learning-based methods. This paper presents a novel deep learning approach utilizing TabNet to classify airline passenger satisfaction. Leveraging a comprehensive dataset comprising various passenger-related attributes, our TabNet-based model demonstrates exceptional performance in distinguishing between satisfied and dissatisfied passengers. Our model’s robustness in handling tabular data, underscores its power as a valuable tool for the aviation industry. Comparing out results to recent papers show that out model outperforms these studies in terms of accuracy, precision, recall and area under the curve. The results show that our TabNet Network model outperforms all implemented machine learning models by reaching respectively the following results: 96.47%, 96.41% and 96.24% for accuracy, F1-score and G-mean score

Increased Mast Cell Density and Airway Responses to Allergic and Non-Allergic Stimuli in a Sheep Model of Chronic Asthma

BACKGROUND: Increased mast cell (MC) density and changes in their distribution in airway tissues is thought to contribute significantly to the pathophysiology of asthma. However, the time sequence for these changes and how they impact small airway function in asthma is not fully understood. The aim of the current study was to characterise temporal changes in airway MC density and correlate these changes with functional airway responses in sheep chronically challenged with house dust mite (HDM) allergen. METHODOLOGY/PRINCIPAL FINDINGS: MC density was examined on lung tissue from four spatially separate lung segments of allergic sheep which received weekly challenges with HDM allergen for 0, 8, 16 or 24 weeks. Lung tissue was collected from each segment 7 days following the final challenge. The density of tryptase-positive and chymase-positive MCs (MC(T) and MC(TC) respectively) was assessed by morphometric analysis of airway sections immunohistochemically stained with antibodies against MC tryptase and chymase. MC(T) and MC(TC) density was increased in small bronchi following 24 weeks of HDM challenges compared with controls (P<0.05). The MC(TC)/MC(T) ratio was significantly increased in HDM challenged sheep compared to controls (P<0.05). MC(T) and MC(TC) density was inversely correlated with allergen-induced increases in peripheral airway resistance after 24 weeks of allergen exposure (P<0.05). MC(T) density was also negatively correlated with airway responsiveness after 24 challenges (P<0.01). CONCLUSIONS: MC(T) and MC(TC) density in the small airways correlates with better lung function in this sheep model of chronic asthma. Whether this finding indicates that under some conditions mast cells have protective activities in asthma, or that other explanations are to be considered requires further investigation

The nutrition-based comprehensive intervention study on childhood obesity in China (NISCOC): a randomised cluster controlled trial

<p>Abstract</p> <p>Background</p> <p>Childhood obesity and its related metabolic and psychological abnormalities are becoming serious health problems in China. Effective, feasible and practical interventions should be developed in order to prevent the childhood obesity and its related early onset of clinical cardiovascular diseases. The objective of this paper is to describe the design of a multi-centred random controlled school-based clinical intervention for childhood obesity in China. The secondary objective is to compare the cost-effectiveness of the comprehensive intervention strategy with two other interventions, one only focuses on nutrition education, the other only focuses on physical activity.</p> <p>Methods/Design</p> <p>The study is designed as a multi-centred randomised controlled trial, which included 6 centres located in Beijing, Shanghai, Chongqing, Shandong province, Heilongjiang province and Guangdong province. Both nutrition education (special developed carton style nutrition education handbook) and physical activity intervention (Happy 10 program) will be applied in all intervention schools of 5 cities except Beijing. In Beijing, nutrition education intervention will be applied in 3 schools and physical activity intervention among another 3 schools. A total of 9750 primary students (grade 1 to grade 5, aged 7-13 years) will participate in baseline and intervention measurements, including weight, height, waist circumference, body composition (bioelectrical impendence device), physical fitness, 3 days dietary record, physical activity questionnaire, blood pressure, plasma glucose and plasma lipid profiles. Data concerning investments will be collected in our study, including costs in staff training, intervention materials, teachers and school input and supervising related expenditure.</p> <p>Discussion</p> <p>Present study is the first and biggest multi-center comprehensive childhood obesity intervention study in China. Should the study produce comprehensive results, the intervention strategies would justify a national school-based program to prevent childhood obesity in China.</p> <p>Trial Registration</p> <p>Chinese clinical trial registry (Primary registry in the WHO registry network) Identifier: ChiCTR-TRC-00000402</p

Acinetobacter baumannii Infection Inhibits Airway Eosinophilia and Lung Pathology in a Mouse Model of Allergic Asthma

Allergic asthma is a dysregulation of the immune system which leads to the development of Th2 responses to innocuous antigens (allergens). Some infections and microbial components can re-direct the immune response toward the Th1 response, or induce regulatory T cells to suppress the Th2 response, thereby inhibiting the development of allergic asthma. Since Acinetobacter baumannii infection can modulate lung cellular and cytokine responses, we studied the effect of A. baumannii in modulating airway eosinophilia in a mouse model of allergic asthma. Ovalbumin (OVA)-sensitized mice were treated with live A. baumannii or phosphate buffered saline (PBS), then intranasally challenged with OVA. Compared to PBS, A. baumannii treatment significantly reduced pulmonary Th2 cytokine and chemokine responses to OVA challenge. More importantly, the airway inflammation in A. baumannii-treated mice was strongly suppressed, as seen by the significant reduction of the proportion and the total number of eosinophils in the bronchoalveolar lavage fluid. In addition, A. baumannii-treated mice diminished lung mucus overproduction and pathology. However, A. baumannii treatment did not significantly alter systemic immune responses to OVA. Serum OVA-specific IgE, IgG1 and IgG2a levels were comparable between A. baumannii- and PBS-treated mice, and tracheobronchial lymph node cells from both treatment groups produced similar levels of Th1 and Th2 cytokines in response to in vitro OVA stimulation. Moreover, it appears that TLR-4 and IFN-γ were not directly involved in the A. baumannii-induced suppression of airway eosinophilia. Our results suggest that A. baumannii inhibits allergic airway inflammation by direct suppression of local pulmonary Th2 cytokine responses to the allergen

Association of mast cells with lung function in chronic obstructive pulmonary disease

BACKGROUND: In asthma, higher chymase positive mast cell (MC-C) numbers are associated with less airway obstruction. In COPD, the distribution of MC-C and tryptase positive mast cells (MC-T) in central and peripheral airways, and their relation with lung function, is unknown. We compared MC-T and MC-C distributions in COPD and controls without airflow limitation, and determined their relation with lung function. METHODS: Lung tissue sections from 19 COPD patients (median [interquartile range] FEV(1)% predicted 56 [23–75]) and 10 controls were stained for tryptase and chymase. Numbers of MC-T and MC-C were determined in different regions of central and peripheral airways and percentage of degranulation was determined. RESULTS: COPD patients had lower MC-T numbers in the subepithelial area of central airways than controls. In COPD, MC-T numbers in the airway wall and more specifically in the epithelium and subepithelial area of peripheral airways correlated positively with FEV(1)/VC (Spearman's rho (r(s)) 0.47, p = 0.05 and r(s )0.48, p = 0.05, respectively); MC-C numbers in airway smooth muscle of peripheral airways correlated positively with FEV(1)% predicted (r(s )0.57, p = 0.02). Both in COPD patients and controls the percentage of degranulated MC-T and MC-C mast cells was higher in peripheral than in central airways (all p < 0.05), but this was not different between the groups. CONCLUSION: More MC-T and MC-C in peripheral airways correlate with better lung function in COPD patients. It is yet to determine whether this reflects a protective association of mast cells with COPD pathogenesis, or that other explanations are to be considered