Knowledge Transfer Mechanisms Research Articles

Multitask Linear Genetic Programming with Shared Individuals and its Application to Dynamic Job Shop Scheduling

Multitask genetic programming methods have been applied to various domains, such as classification, regression, and combinatorial optimization problems. Most existing multitask genetic programming methods are designed based on tree-based structures, which are not good at reusing building blocks since each sub-tree passes its outputs to only one parent. It may limit the design and performance of knowledge sharing in multitask optimization. Different from tree-based genetic programming, building blocks in linear genetic programming can be easily reused by more than one parent. Besides, existing multitask genetic programming methods always allocate each individual to a specific task and have to duplicate genetic materials from task to task in knowledge transfer, which is inefficient and often produces redundancy. Contrarily, it is natural for a linear genetic programming individual to produce multiple distinct outputs, which enables each linear genetic programming individual to solve multiple tasks simultaneously. With this in mind, we propose a new multitask linear genetic programming method that transfers knowledge via multi-output individuals (i.e., shared individuals among tasks). By integrating different solutions into one multi-output individual, the proposed method efficiently reuses common knowledge among tasks and maintains distinct behaviors for each task. The empirical results show that the proposed method has a significantly better test performance than state-of-the-art multitask genetic programming methods. Further analyses verify that the new knowledge transfer mechanism can adjust the transfer rate automatically and thus improves its effectiveness.

Impact of the Choice of Knowledge Transfer Mechanism on the Performance of Brazilian

Impact of the Choice of Knowledge Transfer Mechanism on the Performance of Brazilian

Dünyada ve Kazakistan'da Sanatın Geliştirilmesi ve Yaratıcı Eğitim: Tasarım ile Karşılaştırmalı Analiz

Teachers of art and creative education are assigned more and more complex and effective tasks. However, the current system of art education does not always prepare students for these challenges. When we study what and how our system teaches young designers, we find that the most valuable elements of a designer's perspective and process are rarely taught. Instead, some designers go beyond their education through industry experience, essentially learning by accident. Many project programs still maintain a closed perspective and an inefficient mechanism for tacit knowledge transfer. At the same time, the skills to develop creative solutions to complex problems are becoming increasingly important. Organizations are beginning to realize that signers bring something special to this type of work, a rational belief based on numerous studies that link commercial success to a design-based approach. So, what should we do? Other learned professions, such as medicine, law, and business, provide excellent advice and guidance embedded in their own professionalization history. In this article, we will borrow their experience to recommend a course of action for designing. This will not be easy: it will require a research team to make recommendations on how to compile a list of design and educational practices that schools can use to create a curriculum that is appropriate for their goals and abilities. And then it will take a conscious effort for the design profession to become both a reliable community of practitioners and an effective professor, able together to fully understand the value of design in the 21st century. In this article, we will outline this path.

Remaining useful life prediction across machines using multi-source adversarial online knowledge distillation

Deep transfer learning has been extensively developed in the remaining useful life prediction of rolling bearings because it can decrease the dependence on massive labeled data and robustly extract adaptive features. Most existing studies perform remaining useful life predictions across different working conditions within the same machine. Therefore, these methods are unsuitable for data with severe distribution drifts. Only a few studies investigate the remaining useful life prediction across different devices, but their success depends on the prerequisite that a large amount of labeled data exists. In brief, the models' applicabilities and prediction performances are significantly constrained toward industrial scenarios. To address these drawbacks, a multi-source adversarial online knowledge distillation approach is proposed for rolling bearing remaining useful life prediction across machines. A dual knowledge transfer mechanism, including multi-level domain adaptation and online knowledge distillation with dynamic weighting, is designed in the ensemble learning architecture. The proposed approach allows the more comprehensive extraction and transition of prognostic knowledge of multiple working conditions across different machines, thereby improving the performance of remaining useful life prediction. Experiments on two open-access rolling bearing datasets demonstrate the effectiveness and superiority of the proposed approach in terms of prediction accuracy.

Medication use of Canadians with chronic obstructive pulmonary disease: a cohort study

ObjectivesThe objectives of this study were to describe medication use, treatment patterns and adherence, as well as acute exacerbations of chronic obstructive pulmonary disease (AECOPD) among adults living with COPD.MethodsA...

A Knowledge Guided Transfer Strategy for Evolutionary Dynamic Multiobjective Optimization

The key task in dynamic multiobjective optimization problems (DMOPs) is to find Pareto-optima closer to the true one as soon as possible once a new environment occurs. Previous dynamic multiobjective evolutionary algorithms (DMOEAs) normally focus on DMOPs with regular environmental changes, but neglect widespread random one, limiting their applications in real-world fields. To address this issue, a knowledge guided transfer strategy based DMOEA is proposed in the paper. First, knowledge described as a two-tuple is extracted under each historical environment and preserved to a knowledge pool. Redundant knowledge is recognized and adaptively removed so as to guarantee the diversity of the pool. Second, a knowledge matching strategy is developed to re-evaluate the representative of each stored knowledge under a new environment, with the purpose of finding the most valuable one to promote positive knowledge transfer. Third, an improved knowledge transfer mechanism based on subspace alignment is introduced. By integrating it with knowledge reuse mechanism, a hybrid transfer strategy is constructed to adaptively select the most suitable one in terms of the similarity degree of selected knowledge on the current environment, and then generate a new initial population. Experiments on 20 benchmark problems demonstrate that the knowledge guided transfer strategy outperforms five state-of-the-art algorithms, achieving good versatility in solving DMOPs with both regular and random changes.

A multipopulation multitasking evolutionary scheme with adaptive knowledge transfer to solve the clustered minimum routing cost tree problem

The advancement in communication technology nowadays has increased the number of networked devices exponentially. Thus, network applications begin to cluster terminals to address scalability, privacy, and device connectivity with low routing costs. A recently introduced NP-hard problem called the Clustered Minimum Routing Cost Tree (CluMRCT) problem is assessed to be a fundamental key in designing an efficient network with low routing costs. Lately, some evolutionary multitasking meta-heuristics have been proposed to tackle multiple CluMRCT problems concurrently. However, these previous studies still have some limitations, such as only being applicable to complete graphs or there is no mechanism for effective knowledge transfer between problems when solving them simultaneously. To overcome these limitations, this paper proposes a novel multi-population multitasking evolutionary framework to address the problem. The individual representation is developed based on Network random key scheme and can be applied to both sparse and complete graphs. Moreover, our proposal can adaptively exploit positive knowledge transfer by adjusting the number of individuals migrating from other populations to a particular task. Then, the proposed algorithm is experimented on various data types and compared with other state-of-the-art algorithms. The experimental results show the proposal's effectiveness in most cases of the dataset, followed by detailed comparison, evaluation, and analysis.

Performance of knowledge transfer mechanisms: The case of franchising networks

Franchisor’s system knowledge is considered one of the most important resources for achieving competitive advantage in franchise networks. Although franchisors use a variety of knowledge transfer mechanisms, such as training, intranet, video conferences, manuals, and emails, to transfer the system knowledge to their local franchise partners, previous research has not investigated the performance outcomes of the different knowledge transfer mechanisms in franchising networks. In this study, we address this research question by examining the impact of various knowledge transfer mechanisms on franchise network performance using primary data collected from 168 franchise systems in four Central European countries. Based on the application of PLS-SEM methodology, our findings highlight that high information-rich knowledge transfer mechanisms (training, meetings, visits, video conferences) increase performance by facilitating the transfer of tacit system knowledge, while IT-based knowledge transfer mechanisms (emails, instant messages, intranet, and social media) increase performance by facilitating the transfer of explicit system knowledge. We contribute to the franchise literature and the knowledge management of franchising networks by showing that superior network performance requires co-location between the tacitness of system knowledge and information richness of knowledge transfer mechanisms.

The Relationship of Parental Control with Children’s Digital Literacy Skills in Pesisir Selatan

The digital divide in Indonesia demands that digital literacy education be delivered through several knowledge transfer mechanisms. The purpose of this research is to look at the relationship between parental control and digital literacy skills in children in Pesisir Selatan. This type of research is quantitative correlational. Sources of data in this study are parents and children. The population in this study amounted to 30 parents and the sampling technique was Simple Random Sampling and 70% were taken, namely 21 children. The data collection method uses a questionnaire with a statement list format as a data collection tool, data analysis techniques use a percentage formula and to find correlations use the Rank Order formula. The results of the study found: 1) Parental control of this child was classified in the unfavorable category. 2) digital literacy skills in children are classified in the low category. 3) There is a significant relationship between parental control and digital literacy skills in children in Pesisir Selatan. Furthermore, some suggestions are: 1) According to the respondents' assessment, parental control over children is still much that has not been implemented, when viewed from the duties and responsibilities of parents, it is suggested to parents to better understand their duties and responsibilities so that they are able to protect children from the dangers of digital media. 2) It is necessary for children to be aware of how important it is to understand increasingly sophisticated digital media that can develop various potentials in children so that they have knowledge and experience. 3) It is hoped that other studies will be able to see several other factors that have not been studied in this study

Research on the knowledge transfer mechanism of digital platform in the digital innovation ecosystem: an improved model of SIR embedded in symbiosis theory

PurposeIn the digital economy era, digital platforms are vital infrastructure for innovation subjects to perform digital innovation activities. Achieving efficient and smooth knowledge transfer between innovation subjects through digital platforms has become a novel research subject. This study aims to examine the knowledge transfer mechanism of digital platforms in the digital innovation ecosystem through modeling and simulation to offer a theoretical basis for digital innovation subjects to acquire digital value through knowledge-sharing and thus augment their competitive advantage.Design/methodology/approachThis study explores the optimal symbiotic interaction rate between different users based on the classic susceptible-infected-removed (SIR) model. Additionally, it constructs a knowledge transfer mechanism model for digital platforms in the digital innovation ecosystem by combining the theories of communication dynamics and symbiosis. Finally, Matrix Laboratory (MATLAB) software is used for the model and numerical simulation.FindingsThe results demonstrate that (1) the evolutionary path of the symbiotic model is key to digital platforms' knowledge transfer in the digital innovation ecosystem. In the symbiotic model, the knowledge transfer path of digital platforms is “independent symbiosis—biased symbiosis (user benefit)—reciprocal symbiosis,” aligning with the overall interests of the digital innovation ecosystem. (2) Digital platforms' knowledge transfer effects within the digital innovation ecosystem show significant differences. The most effective knowledge transfer model for digital platforms is reciprocal symbiosis, whereas the least effective is parochial symbiosis (platform benefit). (3) The symbiotic rate has a significant positive impact on the evolutionary dynamics of knowledge transfer on digital platforms, especially in the reciprocal symbiosis model.Originality/valueThis study's results aid digital innovators in achieving efficient knowledge transfer through digital platforms and identify how symbiotic relationships affect the knowledge transfer process across the ecosystem. Accordingly, the authors propose targeted recommendations to promote the efficiency of knowledge transfer on digital platforms.

As the crow flies: tracking policy diffusion through stakeholder networks

AbstractPolicy diffusion is an important element of the policy formation process. However, understanding of the micro-level interactions governing policy spread remains limited. Much of the literature focuses on macro-level proxies for intergovernmental connectivity. These proxies outline broad diffusion patterns without specifying the micro-level mechanisms that govern how individuals facilitate that diffusion. The role of stakeholders in diffusion in the policy subsystem is also poorly understood. We construct a panel dataset covering the spread of the US ecotourism programs from 1993 to 2016 to investigate how micro-level movement within stakeholder networks explains state-level policy diffusion over time. Using fixed-effects regression, we find that stakeholder movement significantly drives diffusion, acting as a mechanism of knowledge transfer. Our findings provide a more precise theoretical understanding of how policy knowledge diffuses at the micro level, empirically explain the role of policy stakeholders in diffusion, and highlight the value of citizen-science data for policy research.

Knowledge Transfer Within Organizations: Mechanisms, Motivation, and Consideration.

Knowledge transfer-the extent to which one unit learns from or is affected by the experience of another-has the potential to improve the performance of organizations. Through knowledge transfer, developments made in one unit of an organization can benefit others. Studies have found, however, considerable variation in the extent to which knowledge transfers across organizational units. In some cases, knowledge transfers seamlessly, whereas in others, knowledge transfer is far from complete. This article reviews research with the aim of explaining the variation observed in knowledge transfer. Key factors identified as explaining the variation include knowledge transfer opportunities, knowledge characteristics, mechanisms for knowledge transfer, motivation for transfer, and the depth of consideration of knowledge. These factors are integrated into a theoretical framework that predicts when knowledge transfer will be successful. The article concludes with a discussion of directions for future research to increase our understanding of knowledge transfer in organizations.

Knowledge Transfer in the Precommercialization Phase of a New Industry: Star Scientists’ (im)Mobility in the Presence of a Potential Lead User

We extend prior research on industry evolution by focusing attention to knowledge transfer in the precommercialization phase of a new industry. Specifically, we build on research that highlights the role of star scientists’ mobility as a mechanism of knowledge transfer to mitigate technological uncertainty and the role of potential lead users as the mechanism for knowledge transfer to reduce demand uncertainty. Using the image sensor industry's precommercialization phase as our context, we find that the star scientists of Texas Instruments, Tektronix, Jet Propulsion Laboratory, Bell Laboratories, and other organizations were largely immobile, that is, rooted to the same organization. Our article not only uncovers the role of star scientists’ mobility and the potential lead user as complementary mechanisms of knowledge transfer, but also highlights the boundary condition of the benefits of star scientists’ mobility in the precommercialization phase of a new industry.

PKAMNet: A Transmission Line Insulator Parallel- Gap Fault Detection Network Based on Prior Knowledge Transfer and Attention Mechanism

Insulator parallel gaps in a transmission line protect the insulator from arc burn and provide protection from lightning. Detection of insulator parallel-gap faults based on aerial inspection images of transmission lines may be hindered by various factors, such as complex background, scale distortion, and occlusion. In this study, we propose a prior knowledge transfer and attention mechanism network (PKAMNet) to detect faulty insulator parallel gaps. First, the capability of PKAMNet to learn the features of different types of parallel-gap faults is improved by constructing a prior knowledge transfer model based on visual saliency. Owing to the difficulty of effectively expressing the features of the fault target due to the deviation of the shooting angle and complex background of the inspection image, we enhance the feature expression ability of the fault area by embedding the coordinate attention block. Additionally, the parameters of the detection network are simplified, and the modules are optimized to improve detection efficiency. Comparison results on 3-year aerial inspection videos of 500 kV high-voltage transmission lines show that PKAMNet has a high detection accuracy and can ameliorate the phenomenon of missed and erroneous detection caused by insufficient expression of parallel-gap faults.

Синтез методов дистанционного цифрового обучения портретной живописи (на примере темы «Женский портрет на светлом фоне»)

Conventional art education at the present stage is actively developing. Nowadays there is a purposeful search for means and mechanisms of knowledge transfer from teacher to students using digital technologies. The article reviews the existing teaching methods, the use of which can be applied in the system of remote art edu-cation to improve the level of practical skills in painting, drawing and composition. As an example, the theme “Portrait of a Woman on a Light Background in Painting” was chosen as a training course for a teacher of art disciplines at the Russian State Pedagogical University. A.I. Herzen. The main five methods of online learning are considered, which can be applied using platforms, social networks and instant messengers that can elimi-nate communication difficulties as well as helping to achieve effective solution of educational tasks.

An Investigation on Knowledge-Based Entrepreneurship in Higher Education

Purpose: This section provides an overview of the article's main objective and scope. It introduces the concept of knowledge-based entrepreneurship in higher education institutions and sets the stage for the research's exploration of its various aspects, including drivers, challenges, and transformative impacts.
 
 Theoretical Framework: This section discusses the theoretical underpinnings and concepts that guide the research. It mentions how universities have evolved from traditional knowledge disseminators to hubs for entrepreneurial initiatives and outlines the theoretical foundation upon which the study is built.
 
 Methodology: This part briefly outlines the research methodology employed in the study. It mentions the systematic analysis of existing literature as the primary research approach and highlights the study's focus on knowledge transfer, collaborative relationships, and the role of Technology Transfer Offices (TTOs).
 
 Findings: This section summarizes the key findings of the research. It touches upon the pivotal role of universities in the knowledge economy, the mechanisms of knowledge transfer, and the significance of TTOs in facilitating knowledge-based entrepreneurship. It provides an overview of the transformative potential highlighted in the study.
 
 Research, Practical & Social Implications: This part discusses the broader implications of the research. It highlights how the findings impact academia, industry, and society at large. It mentions the need for collaborative efforts and how knowledge-based entrepreneurship can drive economic growth, technological advancement, and societal progress.
 
 Originality/Value: This section underscores the uniqueness and value of the research. It emphasizes the contribution of the study to the understanding of knowledge-based entrepreneurship and its role in fostering collaboration between academia and industry. It also mentions the potential for innovation and resilience in the future.

An Investigation on Knowledge-Based Entrepreneurship in Higher Education

Purpose: This section provides an overview of the article's main objective and scope. It introduces the concept of knowledge-based entrepreneurship in higher education institutions and sets the stage for the research's exploration of its various aspects, including drivers, challenges, and transformative impacts.
 
 Theoretical Framework: This section discusses the theoretical underpinnings and concepts that guide the research. It mentions how universities have evolved from traditional knowledge disseminators to hubs for entrepreneurial initiatives and outlines the theoretical foundation upon which the study is built.
 
 Methodology: This part briefly outlines the research methodology employed in the study. It mentions the systematic analysis of existing literature as the primary research approach and highlights the study's focus on knowledge transfer, collaborative relationships, and the role of Technology Transfer Offices (TTOs).
 
 Findings: This section summarizes the key findings of the research. It touches upon the pivotal role of universities in the knowledge economy, the mechanisms of knowledge transfer, and the significance of TTOs in facilitating knowledge-based entrepreneurship. It provides an overview of the transformative potential highlighted in the study.
 
 Research, Practical & Social Implications: This part discusses the broader implications of the research. It highlights how the findings impact academia, industry, and society at large. It mentions the need for collaborative efforts and how knowledge-based entrepreneurship can drive economic growth, technological advancement, and societal progress.
 
 Originality/Value: This section underscores the uniqueness and value of the research. It emphasizes the contribution of the study to the understanding of knowledge-based entrepreneurship and its role in fostering collaboration between academia and industry. It also mentions the potential for innovation and resilience in the future.

University as an opportunity space enabler in a regional entrepreneurial ecosystem

ABSTRACT Universities are widely recognized as key players in entrepreneurial ecosystems that support entrepreneurial activities to create economic and social value in the region. Previous research has increased our understanding about the role of universities contributing to regional economic development and entrepreneurship through knowledge transfer mechanisms, increasing human capital and innovation activity. However, little is known about the contribution of universities to the actual entrepreneurial processes in spatial context. Utilizing the case study approach, this study examines university as an ecosystem actor, focusing particularly on the enabling role of universities in entrepreneurial opportunity formation. As a result, this study provides a conceptualization of four different entrepreneurial opportunity spaces, bounded by the varying degrees of knowledge applicability and opportunity proximity. This article contributes to research on regional entrepreneurial ecosystems and on the role of universities as ecosystem actors contributing to research knowledge-based entrepreneurial opportunity formation for innovative entrepreneurship.

Drivers of strengthening university–industry collaboration: implications for favorable outcomes

PurposeThe purpose of the study was to examine the factors that are important for strengthening university–industry collaboration (UIC). This study also investigates the outcome of UIC in the light of creativity, skill, knowledge, and research work.Design/methodology/approachA survey method has been used to collect data for the study. This study applied a purposive judgmental sampling technique where particular types of respondents like university faculty members and the top officials of the organizations were selected who are knowledgeable and can provide the desired information. The current study used the structural equation modeling method to analyze the data. In the first stage, this research assessed the demographic factors of the respondent. Then this study conducts confirmatory factors analysis and convergent and discriminant validity and reliability test. Finally, the hypotheses are tested by using nonparametric.FindingsThis study finds that knowledge transfer mechanism, governmental factors, organizational design factors, technology transfer and the collaborative network has a significant impact on strengthening UIC, which ultimately facilitates creativity, knowledge creation, skills development and supply of graduate according to the requirement of the industry, good research work.Originality/valueThe current study identified some important determinant that has a substantial influence on strengthening UIC. According to the study organizational design, government, technology, collaborative network and mechanism for knowledge transfer play very crucial roles in strengthening collaboration that ultimately increases the creativity, skills, knowledge and research capability of graduates.

ENHANCING PERFORMANCE THROUGH TOTAL QUALITY MANAGEMENT: UNVEILING THE MEDIATING INFLUENCE OF KNOWLEDGE TRANSFER

In today's dynamic and competitive business environment, the organizations strive to attain superior performance by executing effective management strategies. One such strategy that has gained significant attention is Total Quality Management (TQM). This study aims to delve into the relationship between TQM and organizational performance, with a specific focus on the mediating effects of knowledge transfer. Drawing on comprehensive review of literature and empirical analysis, this research investigates the impact of TQM practices upon organizational performance, considering the crucial role played by knowledge transfer mechanisms. The study employs quantitative research design, collecting data from a manufacturing SME’s operating in Lahore, Pakistan. SEM is used to analyze data and test proposed hypotheses. The findings shed light on the significant positive relationship amid TQM & organizational performance, highlighting vital role of knowledge transfer as a mediating mechanism. The organizations that aim to improve performance should focus not only on implementing TQM practices but on establishing effective knowledge transfer mechanisms.