Effective Knowledge Transfer Research Articles

Exploring the antecedents of knowledge transfer among stakeholders in green building projects: From the configuration perspective

With the rapid development of green buildings around the world, knowledge transfer among stakeholders is of great significance in promoting the innovation and application of green building technologies. However, due to the complexity and specificity of green building projects, current studies have not fully established a comprehensive understanding of how elements from various dimensions (i.e., subject, relationship, context, and activity) collectively impact knowledge transfer among stakeholders in green building projects. Therefore, this study aims to explore the key factors that affect knowledge transfer among stakeholders in green building projects and how these factors work together to enhance the effectiveness of knowledge transfer (EKT). Firstly, the key factors influencing knowledge transfer were pinpointed: cognitive differences, connection strength, organizational climate, information technology capability, transmit willingness, receive willingness, perceived policy support, and richness of transfer channels. Then, an examination of the collected valid samples was conducted through configuration analysis utilizing the fuzzy set qualitative comparative analysis (fsQCA) method. The findings reveal five configurations, categorized as strong connection-driven, policy support-driven, and both-driven. Lastly, some combination strategies were outlined for different categories to assist green building companies in enhancing EKT. This study not only enhances the existing green building literature by developing a framework of factors that influence knowledge transfer among stakeholders, but also provides a reference for corporate executives to choose the best strategies to enhance EKT in green building projects.

MFWOA: Multifactorial Whale Optimization Algorithm

Multi-task optimization is an emerging research topic in the field of evolutionary computation, which can exploit the synergy between tasks to solve multiple optimization problems simultaneously and efficiently. However, the correlation and negative transfer problems between tasks are the main challenges faced by multi-task optimization. To this end, this paper proposes a new multi-task optimization algorithm, named Multifactorial Whale Optimization Algorithm (MFWOA). MFWOA uses the Whale Optimization Algorithm (WOA) as a search mechanism and designs an adaptive knowledge transfer strategy to effectively exploit the correlation between tasks. This strategy includes two ways: one is to exchange search experience by adding distance terms from other tasks; the other is to generate new random individuals or optimal individuals through crossover and mutation operations and use them to guide position updates. By combining these two methods, MFWOA can explore a wider area. In addition, in order to better balance the useful information transfer between and within tasks, MFWOA also designs a random mating probability parameter adaptive strategy. Experimental results show that MFWOA can achieve effective and efficient knowledge transfer, and outperforms other multi-task optimization algorithms in terms of convergence speed and accuracy. It is a promising multi-task optimization algorithm.

Multimodal Cross-Lingual Summarization for Videos: A Revisit in Knowledge Distillation Induced Triple-Stage Training Method.

Multimodal summarization (MS) for videos aims to generate summaries from multi-source information (e.g., video and text transcript), showing promising progress recently. However, existing works are limited to monolingual scenarios, neglecting non-native viewers' needs to understand videos in other languages. It stimulates us to introduce multimodal cross-lingual summarization for videos (MCLS), which aims to generate cross-lingual summaries from multimodal input of videos. Considering the challenge of high annotation cost and resource constraints in MCLS, we propose a knowledge distillation (KD) induced triple-stage training method to assist MCLS by transferring knowledge from abundant monolingual MS data to those data with insufficient volumes. In the triple-stage training method, a video-guided dual fusion network (VDF) is designed as the backbone network to integrate multimodal and cross-lingual information through diverse fusion strategies in the encoder and decoder; What's more, we propose two cross-lingual knowledge distillation strategies: adaptive pooling distillation and language-adaptive warping distillation (LAWD), designed for encoder-level and vocab-level distillation objects to facilitate effective knowledge transfer across cross-lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle lingual sequences of varying lengths between MS and MCLS models. Specifically, to tackle the challenge of unequal length of parallel cross-language sequences in KD, LAWD can directly conduct cross-language distillation while keeping the language feature shape unchanged to reduce potential information loss. We meticulously annotated the How2-MCLS dataset based on the How2 dataset to simulate MCLS scenarios. Experimental results show that the proposed method achieves competitive performance compared to strong baselines, and can bring substantial performance improvements to MCLS models by transferring knowledge from the MS model.

Can AI-powered avatars replace human trainers? An empirical test of synthetic humanlike spokesperson applications

Purpose This study aims to investigate the effectiveness of widely adopted but under-studied synthetic humanlike spokespersons (SHS) compared to organic human spokespersons in workplace training videos. The primary aim is to evaluate whether employees will rate training videos more negatively when they perceive their trainer to be synthetic such as those videos made with the AI-powered tools Synthesia or HeyGen. Results suggest that while ratings are more negative when the viewer perceives syntheticness, the change is only mild across most measures deployed here. When measures were calculated by using actual modality and not perceived modality, no significant change in rating was found. This study deployed three measures: actual knowledge transfer, perceived effectiveness and brand impression. It is guided by a convergence of AI Literacy Theory, the Technology Acceptance Model and the Theory of Reasoned Action. Design/methodology/approach Over 250 professionals assessed the effectiveness of training videos in a 2 × 2 trial design. Participants were randomly assigned to view one of four training videos featuring either a synthetic or organic spokesperson for a fictional business. After watching the video, participants answered Likert-scale survey questions and multiple-choice quiz-style questions to provide comparative measurements of actual knowledge transfer effectiveness, perceived effectiveness, brand impression and the effectiveness of the synthetic deception. The study used both ANOVA and multiple regression analyses to control for potential confounding variables and ensure the robustness of the findings. Findings The results indicate no significant differences between SHS and organic human spokespersons in terms of actual effectiveness (quiz scores), perceived effectiveness or brand impression, based on actual modality comparisons. However, when respondents perceived syntheticness, the avatar triggered the negative uncanny valley phenomenon and slightly decreased overall scores in perceived effectiveness and brand impression. Notably, more than half of respondents did not detect that the SHS was synthetic. Demographic variables such as gender, age or household income had no significant impact on the results. Practical implications Organizations can justifiably consider incorporating SHS into their training programs, leveraging these synthetic agents to deliver cost-effective and scalable learning solutions. The findings suggest that SHS can be used effectively for goals such as actual knowledge transfer without compromising training quality, and that other perceptual goals may be within reach as well. This may offer a viable alternative to traditional, organic human spokespersons, opening up new opportunities for enhancing training efficiency and accessibility across various industries. Originality/value Synthetic avatars, as outlined here, are a demonstrably effective new option in the array of available learning technologies. They are being adopted en masse but without significant study of their effectiveness. This research provides foundational quantitative assessments designed to address that gap in the literature and practice. The data presented here is highly valuable for practitioners and scholars interested in cutting-edge learning tools. Given the rapid advancement of technology, this study provides an important benchmark for evaluating future improvements in these tools. Furthermore, it offers actionable recommendations for integrating AI-powered avatars into professional development, enhancing understanding of the roles advanced technologies play in educational and training programs.

Exploring the key drivers of inter-organizational knowledge transfer in projects: evidence from international construction projects

PurposeDespite the knowledge transfer in project contexts which has been extensively studied by scholars, the study on inter-organizational knowledge transfer in international construction projects is still scattered and unsystematic. This research aims to explore the key factors influencing inter-organizational knowledge transfer of international construction projects and analyze how these factors interact to create a synthetic framework that enhances the effectiveness of knowledge transfer (EKT).Design/methodology/approachAt first, eight factors influencing inter-organizational knowledge transfer within international construction projects were identified, which were from the four dimensions of subject, relationship, channel and context, namely cultural distance, connection strength, organizational climate, intercultural competence, information technology capability, transmit willingness, receive willingness and richness of transfer channels. Then, a conceptual model was developed and 13 hypotheses were formulated, which were derived from a literature review and in-depth survey. After that, data from 353 respondents were collected and analyzed, and the hypotheses were tested by structural equation modeling analysis and bootstrapping test.FindingsThe results suggest that cultural distance hinders transfer willingness, which further affects EKT. Connection strength affects EKT by positively influencing transfer willingness and channel. Besides, organizational climate and intercultural competence positively influence transfer willingness and further affect EKT, while information technology capability affects the richness of transfer channels.Originality/valueThis research gives a thorough examination of the determinants influencing inter-organizational knowledge transfer of international construction projects, thus formulating available approaches that project managers and personnel can employ to effectively facilitate EKT.

3.I. Scientific session: Increasing EU preparedness and improving European Vaccination Beyond COVID-19: the EUVABECO approach

Abstract This workshop provides a platform for delegates to enhance their understanding of the interdisciplinary approach developed in the European Vaccination Beyond COVID-19 (EUVABECO) project, funded under the European Commission’s EU4Health programme. The project is to build and validate implementation plans for EU Member States (MS) to equip them with operational and technical tools to enhance vaccination practices both in routine vaccination and in case of future outbreaks. Validation will be based on innovative practices that emerged in the context of the COVID-19 pandemic. Ultimately, EUVABECO will provide MS with robust implementation plans for common tools to support said innovative vaccination practices, thus, enhancing preparedness and increasing resilience against future health threats in Europe and globally. Guided by two chairs, the workshop is structured around five brief interactive presentations to deliver the conceptualisation, methodologies, and early results and vision for vaccination beyond COVID-19. It is designed to highlight how project activities strengthen resilience and increase preparedness against communicable diseases across the EU and beyond its borders, whilst brokering dialogue on related public health priorities. The presentations will focus on: 1. Preparing for future public health crises by fostering a conducive environment. 2. Enhancing preparedness by developing robust implementation plans. 3. An interdisciplinary approach: Exploring the five dimensions (medical/clinical, social, industrial, modelling and forecasting, and digital) of the tools deployed by EUVABECO. 4. The vital role of stakeholders and of the National Immunization Technical Advisory Groups (NITAGs). 5. Future actions and a call for ongoing support and collaboration in a structured manner. The session will also give the opportunity to formally present EUVABECO’s multistakeholder approach and to formally launch the EU Vaccination Stakeholder Forum, where early access will be granted to EU MS for the various resources generate. This will allow the audience to give input about the needs of the broader public health community involved in vaccination across the EU, for the Stakeholder Forum to effectively promote and support: • The scaling up of existing implementation plans within MS; • The development of new implementation plans for additional tools; • Integration between public health and primary care; • Inform the targeted interaction with EU and MS bodies involved in related policies and decision-making. The presentations will be augmented by real-time polling to maximize participant engagement. This approach will maximize interaction whilst eliciting critical insights into stakeholders’ knowledge, attitudes, and practices concerning EUVABECO’s tools and strategies. This type of engagement will ensure effective knowledge transfer and a comprehensive understanding of the project’s objectives and implementation strategies. Key messages • Enhancing vaccination preparedness requires an evidence-based and harmonized set of tools to be deployed across EU MS. EUVABECO will deliver validated implementation plans to achieve this. • An interdisciplinary approach and multistakeholder engagement are critical for evidence-informed decision-making and harmonization to successfully deploy vaccination initiatives across the EU.

Pengembangan buku ajar pembuatan kemeja pria dan efektivitasnya bagi siswa kelas XI Tata Busana SMK di Kabupaten Gunungkidul

Background: Textbooks are essential in facilitating independent and structured learning for students, supporting teachers in delivering curriculum-aligned material, and ensuring vocational high school students gain relevant industry skills for effective knowledge transfer and achievement of learning objectives. Purpose: This study aims to produce textbooks for making men's shirts that are worthy of being used as instructional materials and to know the effectiveness of using textbooks for students of class XI at SMK Negeri Gunungkidul District. Method: This study is research and development (R&D) with reference to the Borg and Gall development model, namely:1) Conducting product analysis, 2) Developing initial products, 3) Expert validation and revision, 4) Small-scale field trials and product revisions, 5) large scale field trials and final products. The product trial design consists of media experts and material experts, as well as field trials. Data collection techniques are, observation, questionnaires and tests. The feasibility test of teaching books was analyzed using descriptive analysis. Whereas to test the effectiveness of textbooks, data were analyzed using the t-test with a significance level of 0.05. Findings: The results showed that textbooks for making men's shirts were categorized as feasible, with the assessment of media experts getting an average score of 4.6 and based on the assessment of material experts, they got an average score of 4.28. The resulting textbook is effective to be used for students of Class XI Fashion Design at SMK N 1 Saptosari, as evidenced by the average pretest value of 64.12 increasing in the posttest of 83.21, the result of tcount ≥ t table (22.028≥ 1.696).

Safedrive dreamer: Navigating safety–critical scenarios in autonomous driving with world models

Achieving stable and reliable autonomous driving in complex traffic environments while ensuring safety under unpredictable conditions is a critical challenge in autonomous driving technology. To address this issue, this study proposes the Safedrive Dreamer navigation framework, which aims to reduce the reliance on trial-and-error learning in real-world scenarios, thereby mitigating the risks associated with dynamic driving conditions and enhancing vehicle foresight. This framework integrates the predictive capabilities of world models with the constrained Markov decision process (CMDP) and safety reinforcement learning to accurately anticipate future environmental changes. This ensures the reliability of autonomous driving routes, thereby improving both safety and efficiency. Furthermore, to reduce trial-and-error costs in real-world applications, this study employs PAC-Bayesian methods to derive generalization error bounds between simulations and reality, enabling a more effective transfer of knowledge and experience from simulations to real-world scenarios. Validation experiments in simulated and real environments showed that Safedrive Dreamer significantly outperformed existing autonomous driving solutions by 3.8% in key safety metrics, excelling in collision avoidance and risk reduction. This study provides new insights into the integration of world models into decision-making processes to enhance decision-making capabilities in safety–critical applications, thereby contributing significantly to the improvement of autonomous driving safety and reliability.

ADA-UDA: A transferable transformer framework for rumor detection using Adversarial Domain Alignment within Unsupervised Domain Adaptation

The rapid spread of misinformation on social media has posed significant challenges, particularly in the early detection of rumors, which is critical to mitigating their negative impact. However, the limited data available during the initial stages of trending topics poses significant challenges, including analysis constraints and model overfitting. Therefore, the adoption of specific methods are needed to identify patterns from historical sample data and enable effective knowledge transfer, facilitating inductive reasoning with limited data on new topics. To address these challenges, we propose an Adversarial Domain Alignment Unsupervised Domain Adaptation (ADA-UDA) method based on the Transformer architecture. Our approach leverages labeled historical data from the source domain alongside a limited quantity of unlabeled data from new trending topics in the target domain. At the heart of our method is the Parameter Transferable Module (PTM), which guides the Transformer to focus on transferable and distinguishable features, thereby enhancing the model’s ability to perform effective inductive reasoning with limited data. We conducted extensive experiments to evaluate our method, benchmarking it against current mainstream rumor detection techniques. The findings indicate that our ADA-UDA method outperforms existing approaches, underscoring its potential for early and accurate rumor detection in emerging topics.

Job roles for digital twinning building construction processes. Introducing the digital twin manager position

Digital Twins (DT) holds the potential to enhance management and monitoring in the construction sector by means of an interconnected information environment. Although DT theoretical frameworks have been proposed and numerous literature reviews conducted, real-world demo cases showing requirements and challenges are yet limited. These existing frameworks lack of grounded verification on real scenarios to identify essential roles and skills for effective knowledge transfer. This paper contributes to bridge these gaps by harvesting the knowledge of twinning a real-world office building. Key takeaways include a general mind map and two complementary workflows for DT generation and operation for building construction processes. The information pipeline from data collection to decision-making is described and required skills and roles are identified along this data journey. An identification of the emerging role of DT Managers is presented, a vital yet underexplored position in current construction literature. The presented practical roadmap paves the way for increasing maturity on DTs and for promoting their industry adoption.

Auditor Skill Demands and Audit Quality: Evidence from Job Postings

This study empirically examines the relation between audit quality and auditors’ cognitive and social skills. Using a novel data set of online job postings by accounting firms, we document substantial variation in the stated demand for auditors’ cognitive and social skills, suggesting that audit offices are not homogeneous in their preferences for such skills. We find a positive relation between audit quality and the prevalence of cognitive and social skills within an audit office’s job postings. This association is stronger for audit engagements that are more complex or require greater coordination, suggesting that cognitive and social skills are particularly important in engagements where effective communication and knowledge transfer, as well as sound professional judgment and skepticism, are needed. The association is also stronger for audit offices with greater investments in new technology, consistent with the complementary relation between cognitive and social skills and the use of technology. Overall, our study offers empirical evidence linking specific auditor skills to audit quality. This paper was accepted by Suraj Srinivasan, accounting. Funding: C. Ham acknowledges financial support from the Blanche “Peg” Philpott Faculty Fellowship. R. N. Hann acknowledges financial support from KPMG. W. Wang acknowledges the financial support from the Hong Kong Research Grant Council [Grant 11503720]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.01640 .

Efficient urinary stone type prediction: a novel approach based on self-distillation

Urolithiasis is a leading urological disorder where accurate preoperative identification of stone types is critical for effective treatment. Deep learning has shown promise in classifying urolithiasis from CT images, yet faces challenges with model size and computational efficiency in real clinical settings. To address these challenges, we developed a non-invasive prediction approach for determining urinary stone types based on CT images. Through the refinement and improvement of the self-distillation architecture, coupled with the incorporation of feature fusion and the Coordinate Attention Module (CAM), we facilitated a more effective and thorough knowledge transfer. This method circumvents the extra computational expenses and performance reduction linked with model compression and removes the reliance on external teacher models, markedly enhancing the efficacy of lightweight models. achieved a classification accuracy of 74.96% on a proprietary dataset, outperforming current techniques. Furthermore, our method demonstrated superior performance and generalizability on two public datasets. This not only validates the effectiveness of our approach in classifying urinary stones but also showcases its potential in other medical image processing tasks. These results further reinforce the feasibility of our model for actual clinical deployment, potentially assisting healthcare professionals in devising more precise treatment plans and reducing patient discomfort.

For the sake of my job security: examining the effect of age-based stereotype threat on older workers’ knowledge hiding

Purpose Organizations can greatly benefit from the transfer of knowledge from older employees to younger generations. However, older workers often hesitate to share their expertise with their younger colleagues. Grounded in conservation of resources theory and the stereotype threat framework, this study aims at examining the negative impact of age-based stereotype threat on organizational knowledge management. Design/methodology/approach The authors developed a moderated mediation model and collected data from 219 older workers with a time-lagged design to explore the effect of age-based stereotype threat on older workers’ knowledge hiding. Findings The results showed that age-based stereotype threat could lead to knowledge hiding behaviors among older workers due to increased job insecurity, and leader-member exchange can potentially mitigate these effects. Originality/value The findings underscore the detrimental effect of age-based stereotype threat on organizational knowledge management and suggest that organizations should foster an age-inclusive environment to facilitate effective intergenerational knowledge transfer.

Analyzing Junior High School Students’ Mathematical Communication Ability and Self Esteem using Discovery Learning Model

Mathematical reasoning, mathematical communication, mathematical problem solving, conceptual understanding, mathematical understanding, creative thinking and critical thinking are part of the mathematical abilities that students must have. One of the goals of learning mathematics according to NCTM is learning to communicate (mathematical communication). Mathematical communication skills in mathematics learning really need to be developed. This type of research design uses a quasi-experimental research design, namely pretest and post-test with non-equivalent control group design . The research used research subjects into two groups, namely the experimental group and the control group. The subjects of this research were students of class VII I MTs As-Sa'adah Padahanten Majalengka, numbering 64 people. Subjects were selected based on experimental class VIII A, 32 people and control class VIIIB, 32 people. Walkthroughs, questionnaires, and tests are techniques used in data collection. Data processing analysis was carried out qualitatively and quantitatively. The results of this research are that there is a difference in the effectiveness of the Discovery Learning model learning approach with the ordinary (conventional) learning model in terms of the aspects of mathematical communication skills and self-esteem of MTs in statistics material. The implications of the Discovery Learning learning model in this research are that it makes it easy for students to remember the lessons learned, integrate self concept according to their initial abilities, focus, be creative in exploratory projects, and the effect of knowledge transfer on learning outcomes can last a long time.

Knowledge Sharing vs. Knowledge Appropriation: A Case Study of Contingent Relationships in the Role of Boundary Spanners in Asian Subsidiaries of Japanese Multi-National Enterprises

In recent decades, Asian subsidiaries of Japanese manufacturing multi-national enterprises (MNEs) have faced heightened competitions from local firms to underscore the strategic importance of developing competent “host country nationals” (HCNs) to overcome their liability of foreignness. In addition, in recent literature on global MNEs, the importance of “boundary spanners” (BSs) has been pointed out in facilitating knowledge sharing across intra-organizational boundaries. Given these discussions, this study focuses on an interesting contingent relationship in the role of BSs observed in author’s interviews. i.e., In cases of “bad BSs,” “knowledge appropriation” can take place where newly promoted HCNs would monopolize their knowledge and information, and the mitigating processes of notion gap problem in grey areas management are heavily stagnated. Whereas in cases of “good BSs,” “knowledge sharing” would take place where newly promoted HCNs would be willing to share them with other members, and the mitigating processes of the notion gap problem are steadily progressed. The study proposes a framework illustrating conditions under which HCNs choose knowledge sharing over appropriation. The analysis identifies two key factors influencing this choice: dependence on personal skills and prospects for growth opportunities. The findings suggest a dynamic relationship where knowledge sharing leads to effective control and efficient knowledge transfer, fostering virtuous cycles, while knowledge appropriation results in inefficiencies, creating vicious cycles. The study calls for further research on dynamic aspects of this contingent relationship and suggests practical strategies for shifting from knowledge appropriation to sharing, thereby enhancing organizational performance in foreign subsidiaries.

Enhancing microgrid forecasting accuracy with SAQ-MTCLSTM: A self-adjusting quantized multi-task ConvLSTM for optimized solar power and load demand predictions

Accurate forecasting of solar power output and load demand is critical for the efficient operation and management of isolated microgrids, where reliability and sustainability are paramount. Traditional methods often struggle with data scarcity, limitations in capturing intricate temporal dynamics, and lack of scalability. This research introduces a novel multi-task learning (MTL) model, the Self-Aware Quantized Multi-Task ConvLSTM (SAQ-MTCLSTM), which addresses these challenges by jointly forecasting solar power and load demand while leveraging shared representations across these interdependent time series. The SAQ-MTCLSTM incorporates a sophisticated architecture that combines convolutional and LSTM layers with self-aware quantization to enhance computational efficiency and model adaptability. This allows for effective knowledge transfer, improved data utilization, and the capture of intricate temporal patterns. Evaluated on a real-world isolated microgrid dataset from the Micro Reseau Mafate research project, the model demonstrates significant improvements in forecasting accuracy, with MSE values of 0.0021 for solar power output and 0.0037 for load demand forecasting, compared to single-task and other advanced models. Extensive experiments highlight the impact of data scarcity, seasonality patterns, and microgrid topology on forecasting performance. Such forecasting is essential to optimize the integration and utilization of renewable resources, enhancing operational stability and reducing dependency on external energy supplies.

DCST: Dual Cross-Supervision for Transformer-based Unsupervised Domain Adaptation

Unsupervised Domain Adaptation aims to leverage a source domain with ample labeled data to tackle tasks on an unlabeled target domain. However, this poses a significant challenge, particularly in scenarios exhibiting significant disparities between the two domains. Prior methods often fall short in challenging domains due to the impact of incorrect pseudo-labeling noise and the limits of handcrafted domain alignment rules. In this paper, we propose a novel method called DCST (Dual Cross-Supervision Transformer), which improves upon existing methods in two key aspects. Firstly, vision transformer is combined with dual cross-supervision learning strategy to enforce consistency learning from different domains. The network accomplishes domain-specific self-training and cross-domain feature alignment in an adaptive manner. Secondly, due to the presence of noise in challenging domain, and the need to reduce the risks of model collapse and overfitting, we propose a Domain Shift Filter. Specifically, this module allows the model to leverage the memory of source domain features to facilitate a smooth transition. It can also improve the effectiveness of knowledge transfer between domains with significant gaps. We conduct extensive experiments on four benchmark datasets and achieved the best classification results, including 94.3% on Office-31, 86.0% on Office-Home, 89.3% on VisDA-2017, and 48.8% on DomainNet. Code is available in https://github.com/Yislight/DCST.

Robust knowledge transfer for bearing diagnosis in neural network models using multilayer maximum mean discrepancy loss function

Abstract This paper introduces a robust transfer learning method to enhance bearing diagnosis, particularly in cross-machine scenarios. The method trains a shallow neural network using labeled data from a different machine and unlabeled data from the target monitoring machine. To facilitate effective knowledge transfer, a multilayer maximum mean discrepancy loss function is employed, enabling the model to adapt learned features from the source machine to the target machine’s unlabeled data. This approach addresses the challenges of low accuracy and robustness often seen in transfer learning, especially when dealing with different machines. Experiments conducted on the Hanoi University of Science and Technology bearing dataset validate the effectiveness of the proposed method. The results show significant improvements in prediction accuracy and robustness, making this method superior to existing transfer learning models for cross-machine bearing diagnosis.

Coevolutionary multitasking for constrained multiobjective optimization

Addressing the challenges of constrained multiobjective optimization problems (CMOPs) with evolutionary algorithms requires balancing constraint satisfaction and optimization objectives. Coevolutionary multitasking (CEMT) offers a promising strategy by leveraging synergies from distinct, complementary tasks. The primary challenge in CEMT frameworks is constructing suitable auxiliary tasks that effectively complement the main CMOP task. In this paper, we propose an adaptive CEMT framework (ACEMT), which customizes two adaptive auxiliary tasks to enhance CMOP-solving efficiency. The first auxiliary task dynamically narrows constraint boundaries, facilitating exploration in regions with smaller feasible spaces. The second task focuses specifically on individual constraints, continuously adapting to expedite convergence and uncover optimal regions. In solving the main CMOP task, this dual-auxiliary-task strategy not only improves search thoroughness but also clarifies the balance between constraints and objectives. Concretely, ACEMT incorporates an adaptive constraint relaxation technique for the first auxiliary task and a specialized constraint selection strategy for the second. These innovations foster effective knowledge transfer and task synergy, addressing the key challenge of auxiliary task construction in CEMT frameworks. Extensive experiments on three benchmark suites and real-world applications demonstrate ACEMT’s superior performance compared to state-of-the-art constrained evolutionary algorithms. ACEMT sets a new standard in CMOP-solving by strategically constructing and adapting auxiliary tasks, representing a significant advancement in this research direction.

Unlocking the Secrets of Successful Knowledge Transfer Among Software Project Managers: A Mixed-Methods Study

Objectives: This study combined qualitative and quantitative sequential exploratory mixed-method approaches to identify the success factors of knowledge transfer effectiveness with software project managers and investigate how these factors impacted knowledge transfer effectiveness. Methods/Analysis: The qualitative research component included twelve senior and junior project managers, while the quantitative component involved a cross-sectional survey of 212 completed responses from software project managers. Findings: The results suggested that two influential factors as knowledge providers and knowledge transfer environment had a significant effect on knowledge transfer effectiveness. Novelty/Improvement: The research results enhance the operational capability of software project supervision and contribute to the improvement of knowledge transfer for software project managers operating in teams. Doi: 10.28991/HEF-2024-05-03-06 Full Text: PDF