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By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

The purpose of this research is to achieve multi-task autonomous driving by adjusting the network architecture of the model. Meanwhile, after achieving multi-task autonomous driving, the authors found that the trained neural network model performs poorly in untrained scenarios. Therefore, the authors proposed to improve the transfer efficiency of the model for new scenarios through transfer learning.

First, the authors achieved multi-task autonomous driving by training a model combining convolutional neural network and different structured long short-term memory (LSTM) layers. Second, the authors achieved fast transfer of neural network models in new scenarios by cross-model transfer learning. Finally, the authors combined data collection and data labeling to improve the efficiency of deep learning. Furthermore, the authors verified that the model has good robustness through light and shadow test.

This research achieved road tracking, real-time acceleration–deceleration, obstacle avoidance and left/right sign recognition. The model proposed by the authors (UniBiCLSTM) outperforms the existing models tested with model cars in terms of autonomous driving performance. Furthermore, the CMTL-UniBiCL-RL model trained by the authors through cross-model transfer learning improves the efficiency of model adaptation to new scenarios. Meanwhile, this research proposed an automatic data annotation method, which can save 1/4 of the time for deep learning.

This research provided novel solutions in the achievement of multi-task autonomous driving and neural network model scenario for transfer learning. The experiment was achieved on a single camera with an embedded chip and a scale model car, which is expected to simplify the hardware for autonomous driving.

This study aims to examine the relationships among structured-on-the job training (ST), mentoring, job rotation and the work environment factors on tacit knowledge transfer from training.

This study used quantitative research techniques to examine the causal relationships among the key study variables. A questionnaire-based survey has developed to evaluate the research model by drawing a convenience sample includes 239 employees working in the Arab Potash Company located in Jordan. Surveyed data were examined following the structural equation modeling procedures.

The results revealed that adapting of the ST, mentoring and job rotation in industrial firms had direct effect on the employees’ abilities to learn and transfer tacit knowledge from training to the actual work, and how these learning strategies strengthen employees’ abilities in solving work problems, improving customers’ satisfaction and quality of products and services. As well as, it affirmed the strong direct effect of work environment factors such as supervisor and peer support on the employees’ abilities to learning and transferring tacit knowledge to their jobs. However, this study showed that work environment factors have no significant mediating role on the relationship among ST, mentoring, job rotation and the employees’ abilities to learn and transfer tacit knowledge to their jobs.

The study results are opening the doors for future studies to examine the relationships among the methods of training and learning in the workplace, the work environment factors and tacit knowledge transfer from training to the jobs as prerequisites for improving the employees and organization performance. These results would be validated by conducting future research, examining larger samples of industrial companies to give more accurate data and clear explanations to the relationships among the study variables. It also suggests to replace the characteristics of work environment (supervisor support and peer support) by trainees’ characteristics (self-efficacy and career commitment) to give a better understanding to the relationships among the key study variables.

With regard to improving the employees’ competency while doing their jobs, this study developed a conceptual framework that guides managers to recognize the importance of ST, mentoring and job rotation in increasing the employees’ learning together; and giving them the chance to use the new learned experiences and knowledge to improve the organization performance and its competitive advantage. This study helps managers build a positive work environment that encourages social interaction, respect and mutual interest among employees, and increases their sense of responsibility for learning and transferring skills and knowledge to the jobs.

The training methods in the workplace go beyond immediate work performance to act as a promising tool make employees’ learning more easily and faster, and help them to transfer and retain new skills and knowledge, adapt with changing environments, build stronger relationships with stakeholders and at the same time, make the organizations ensure that employees comply with their societal goals.

The authors have noticed that large portions of the studies on training and human resources development neglected the role effect of (ST, mentoring and job rotation) on the tacit knowledge transfer from training to the jobs. Hence, these gaps in researches have motivated to develop a theoretical model that helps to examine the relationship between the two constructs. This study also suggests to examine the mediating role effects of work environment factors on the relationships among (ST, mentoring and job rotation) and tacit knowledge transfer, as well as it extends to examine the mediating role of work environment factors on transferring knowledge to jobs, attributed to the demographic variables such as gender, age, work experience and education level.

This study aims to explore barriers and facilitators for knowledge transfer and learning processes by examining a cross-departmental collaborative project in the municipal organization. It is based on a R&D collaboration between University West and a Swedish municipality.

To explore the barriers and facilitators, the data collection was made through observation of the project implementation process, as well as 20 interviews with public servants and external actors. To conduct a systematic qualitative-oriented content analysis, the article constructs and applies a theoretical analytical framework consisting of different factors influencing knowledge transfer and learning processes within a municipal organizational setting.

This study explores the facilitators and barriers to knowledge transfer and learning processes, specifically focusing on strategic communication, individual roles, common goals, time pressure, group learning, trust and relationships and absorptive capability. Lack of communication affected the group learning process, while the close relation between time pressure, group learning and trust in colleagues is also pointed out as crucial areas. Trust developed through dialogue efforts helped overcome project fatigue. Coaching with a human rights-based approach improved organizational absorptive capabilities.

The study gives important insights into organizational learning within a municipality in Sweden for the successful implementation of collaborative projects. Knowledge must be transferred for the organization to learn to develop and tackle future challenges and its complex responsibilities. The theoretical analytical framework provided in this article has proven to be effective and is therefore transferable to other organizations in both the public and private sectors.

The present study used the job-demands and resources (JD-R) framework to understand how the training is transferred to an extreme working context through the analysis of job and personal resources (social support from the leader and colleagues and adaptability). Specifically, the authors tested the mediating role of motivation to transfer in the relationship (1) between the perceived support from the supervisor and colleagues and performance after training and (2) between adaptability and performance in an extreme context of the pandemic crisis – the first peak of COVID-19 in Portugal. Further, an inspection of the factors that predicted knowledge transfer and adaptability under an extreme context was carried out.

To do so, necessary training about the new safety rules regarding the pandemic crisis of COVID-19 was implemented in a healthcare institution as a strategy to help healthcare workers deal with the increasing uncertainty and complexity that was threatening their work. It consisted of three sessions (each with one hour of training) regarding procedures, rules and safety norms. The training occurred in May 2020. Overall, 291 healthcare workers participated in the study and answered one online questionnaire one week after training completion.

The results showed that the motivation to transfer had a significant indirect effect on the relationship between colleagues' and supervisors' support and performance and between adaptability and performance. Additionally, complementary analyses showed that the mediations depended on the levels of self-efficacy in such a way that the indirect relationships were stronger when self-efficacy was higher. Thus, adaptability and support, both from colleagues and the supervisor, are determining factors for knowledge transfer and resultant performance in extreme contexts, such as the COVID-19 pandemic crisis. Lastly, the results showed that the most significant predictors of transference were self-efficacy and the motivation to transfer the learned knowledge. On the other hand, self-efficacy, peer support and the opportunity to use the knowledge were the most significant predictors of adaptability.

These findings provide support for the role of employee motivation to transfer as a mechanism connecting both perceived support and adaptability to performance outcomes under extreme working contexts.

This study, conducted in the middle of the COVID-19 pandemic context – an extreme and uncertain working context – shows the relevance of both job and individual factors to predict employees' adaptability to such contexts.

Compared with traditional methods relying on manual teaching or system modeling, data-driven learning methods, such as deep reinforcement learning and imitation learning, show more promising potential to cope with the challenges brought by increasingly complex tasks and environments, which have become the hot research topic in the field of robot skill learning. However, the contradiction between the difficulty of collecting robot–environment interaction data and the low data efficiency causes all these methods to face a serious data dilemma, which has become one of the key issues restricting their development. Therefore, this paper aims to comprehensively sort out and analyze the cause and solutions for the data dilemma in robot skill learning.

First, this review analyzes the causes of the data dilemma based on the classification and comparison of data-driven methods for robot skill learning; Then, the existing methods used to solve the data dilemma are introduced in detail. Finally, this review discusses the remaining open challenges and promising research topics for solving the data dilemma in the future.

This review shows that simulation–reality combination, state representation learning and knowledge sharing are crucial for overcoming the data dilemma of robot skill learning.

To the best of the authors’ knowledge, there are no surveys that systematically and comprehensively sort out and analyze the data dilemma in robot skill learning in the existing literature. It is hoped that this review can be helpful to better address the data dilemma in robot skill learning in the future.

The study proposes to empirically test a model that illustrates how identical elements (IEs), transfer design and trainer performance as training predictors affect trainees' motivation to improve work through learning (MTIWL) and training transfer (TT) in the Indian context.

An online survey was conducted to validate the study model. The quantitative data collected from 360 executives and managers were analyzed using the covariance-based structural equation modeling (CB-SEM) technique.

The study finds that trainees' MTIWL has a full mediation impact between transfer design, trainer performance and TT. However, a partial mediating impact of MTIWL was found between IEs and TT.

This is the first study that empirically explores the mediating mechanism of MTIWL between IEs, transfer design, trainer performance and TT. This study extends the current understanding of trainees' MTIWL that links the cumulative influence of training predictors to TT.

This study aims to investigate the relationship between intrinsic motivators and the transfer of knowledge/skills gained during training to work. The intrinsic motivators considered for the study were self-efficacy and motivation to transfer the training knowledge. The study also examined how work conditions mediate the association of intrinsic motivators and training transfer. The working conditions considered in the study were autonomy and the opportunity to perform in the job.

A cross-sectional study was conducted among 426 participants from microfinance institutions in Karnataka, India, who had received a three-week job training six months earlier. Data were collected using a questionnaire and structural equation modelling was performed for the analysis of the data.

The study found positive significant relationships between motivation motivators and training transfer of learning. Positive relationships were also seen between work conditions and training transfer of learning acquired via training. The study also established the role of intrinsic motivators in predicting training transfer through work conditions.

This study stands among the pioneering works to investigate the influence of intrinsic motivators on training transfer, while also examining the mediating role of work conditions. It focuses on an emerging economy, specifically India, thereby contributing valuable insights to the field.

Diabetic retinopathy (DR) is one of the dangerous complications of diabetes. Its grade level must be tracked to manage its progress and to start the appropriate decision for treatment in time. Effective automated methods for the detection of DR and the classification of its severity stage are necessary to reduce the burden on ophthalmologists and diagnostic contradictions among manual readers.

In this research, convolutional neural network (CNN) was used based on colored retinal fundus images for the detection of DR and classification of its stages. CNN can recognize sophisticated features on the retina and provides an automatic diagnosis. The pre-trained VGG-16 CNN model was applied using a transfer learning (TL) approach to utilize the already learned parameters in the detection.

By conducting different experiments set up with different severity groupings, the achieved results are promising. The best-achieved accuracies for 2-class, 3-class, 4-class and 5-class classifications are 86.5, 80.5, 63.5 and 73.7, respectively.

In this research, VGG-16 was used to detect and classify DR stages using the TL approach. Different combinations of classes were used in the classification of DR severity stages to illustrate the ability of the model to differentiate between the classes and verify the effect of these changes on the performance of the model.

This study aims to analyze the effects of microentrepreneurs’ knowledge transfer (KT) on personal improvement (PI) and business improvement (BI).

The study was developed in two stages: a literature review based on KT and the learning process in microenterprises to have managerial competence and PI and BI to acquire the managerial competence that entrepreneurs need. The second stage was constructing a structural model based on 107 questionnaires and bootstrapping of 5,000 replications of microentrepreneurs who went through a training program (quantitative) and a focus group (qualitative). This study had a mixed approach, exploratory scope and experimental design.

The research showed real evidence about the performance level of microentrepreneurs when they passed through the process of KT and its impact on PI and BI. This research considers their managerial competencies, and the findings show a relationship between the theory of individual and organizational learning.

This study considered Peruvian microentrepreneurs who participated in a virtual training program that included several courses related to their current environments and topics of interest. The analyzed period covered the years affected by COVID-19.

The model reveals that KT is relevant to PI and BI. Performance was measured regarding growth, income, innovation, productivity and responsibility before and after the program.

This research analyzed the need for training microentrepreneurs for personal and private reasons under a COVID-19 scenario to foster their businesses and assume financial responsibilities. This study considered Peru’s reality, a country in which 94.9% of companies are microenterprises. The study revealed that microentrepreneurs improved their personal and professional lives and addressed relevant social problems that affect their environments because of the KT effects.

This study bridges the gap in the literature on how the theory of KT can be applied to entrepreneurs. This study revealed significant findings in terms of PI and BIs. The impact of KT indicates the relevance of managerial competencies related to the performance level obtained in terms of growth, income, innovation, productivity and responsibility.