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Forecasting population movement trends is crucial for implementing effective policies to regulate labor force growth and understand demographic changes. Combining migration theory expertise and neural network technology can bring a fresh perspective to international migration forecasting research.

This study proposes a conditional generative adversarial neural network model incorporating the migration knowledge – conditional generative adversarial network (MK-CGAN). By using the migration knowledge to design the parameters, MK-CGAN can effectively address the limited data problem, thereby enhancing the accuracy of migration forecasts.

The model was tested by forecasting migration flows between different countries and had good generalizability and validity. The results are robust as the proposed solutions can achieve lesser mean absolute error, mean squared error, root mean square error, mean absolute percentage error and R² values, reaching 0.9855 compared to long short-term memory (LSTM), gated recurrent unit, generative adversarial network (GAN) and the traditional gravity model.

This study is significant because it demonstrates a highly effective technique for predicting international migration using conditional GANs. By incorporating migration knowledge into our models, we can achieve prediction accuracy, gaining valuable insights into the differences between various model characteristics. We used SHapley Additive exPlanations to enhance our understanding of these differences and provide clear and concise explanations for our model predictions. The results demonstrated the theoretical significance and practical value of the MK-CGAN model in predicting international migration.

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.

We applied a true-experimental randomized control posttest design to collect and analyze quantitative and qualitative data to compare the effects of the AR and traditional board games on students’ SDG learning achievements.

We applied a true-experimental randomized control posttest design to collect and analyze quantitative and qualitative data to compare the effects of AR and traditional board games on students' SDG learning achievements.

Our analysis of the quantitative and qualitative data revealed that the effects of AR and traditional board games on the students' cognitive outcomes differed significantly, indicating the importance of providing a situated learning environment in SDG education. Moreover, the students perceived that the incorporation of the AR game into SDG learning improved their learning effectiveness – including both cognitive and affective dimensions – thus confirming its educational value and potential in SDG learning.

To the best of our knowledge, this is the first study to explore the effectiveness of different learning tools (AR and traditional board games) and to evaluate the importance of providing a situated learning environment through a true-experimental randomized control posttest design.

The purpose of our research is to better understand inquiry-based pedagogy in the context of leadership education. Specifically, we sought to learn about how leadership learning is characterized in an immersive inquiry course, and how inquiry-based pedagogy is experienced by students engaged in interdisciplinary leadership learning.

We used a case study approach as an overarching methodology. The research methods employed to collect data were World Cafe and episodic narrative interview. Further, we used collocation analysis and systematic text condensation as analytical strategies to interpret data.

Our findings led us to four primary conclusions: (1) inquiry-based learning helps to foster an inquiry mindset amongst leadership education students; (2) the challenges and tensions associated with inquiry-based learning are worth the learning gains for leadership students; (3) the opportunity to learn in relationship is beneficial for leadership development outcomes and (4) students’ experiences of inquiry-based learning in leadership education often included instances of transformation.

Limitations of the research were: (1) it is a case study situated within a unique, particular social and educational context; (2) demographic data were not collected from participants, so results cannot be disaggregated based on particular demographic markers and (3) the small sample size involved in the study makes it impossible to generalize across a broad population.

This research has enabled a deep understanding of structural and relational supports that can enable effective inquiry-based learning in leadership education. It also offers evidence to support institutional shifts to inquiry-based pedagogy in leadership education.

Our research demonstrates that use of inquiry-based pedagogy in leadership education has long-lasting positive effects on students' capacity for applied leadership practice. Consequently, participants in this type of leadership learning are better positioned to effectively lead social change that is pressing in our current global context.

There is scant (if any) published research that has focused on using inquiry-based pedagogies in leadership education. This research makes a significant contribution to the scholarship of leadership education.

This study aims to provide new insights into the relationship between individual characteristics, particularly personality traits and mature students' intention to use (ITU) mobile learning (m-learning).

The research model was constructed by integrating the Big Five personality traits into the unified theory of acceptance and use of technology (UTAUT) model. The data were collected from mature students at a university research center in Macau. Partial least squares structural equation modeling (PLS-SEM) was used to analyze the data and test the proposed hypotheses.

The results reveal that personality traits play a significant role in determining mature students' ITU m-learning technology. In particular, social influence (SI) mediates the relationship between agreeableness (AGB) and ITU.

This study examines how personality traits collectively influence mature students' receptiveness and intentions toward m-learning. As mature learners' motivations and preferences remain underexplored, insights into trait-technology links could address current gaps and optimize mobile educational support tailored to their distinct characteristics and needs.

The purpose of this study is to document the process of transformative learning during students’ internships.

A qualitative study was conducted with 13 interviewed students to gain deeper insights into their learning experiences during their internships. Their weekly reflections from their 6 month’s internship experience were also coded for common themes.

The study found numerous trigger events ranging from task-related challenges to interpersonal challenges to environmental challenges led to mindset shifts in students during their internships. The mindset shifts are enabled by students engaging in the trigger events through asking questions, seeking information and reflecting. Other enablers of these mindset shifts are workplace psychological safety, social support and individual learning orientation. The conclusion drawn is that trigger events and enabling resources such as external support are central to healthy mindset shifts and learning.

This paper provides important guidance for supporting transformative learning during student internships.

This paper provides important guidance for supporting transformative learning during student internships.

In this study, the authors propose a novel digital twinning approach specifically designed for controlling transient thermal systems. The purpose of this study is to harness the combined power of deep learning (DL) and physics-based methods (PBM) to create an active virtual replica of the physical system.

To achieve this goal, we introduce a deep neural network (DNN) as the digital twin and a Finite Element (FE) model as the physical system. This integrated approach is used to address the challenges of controlling an unsteady heat transfer problem with an integrated feedback loop.

The results of our study demonstrate the effectiveness of the proposed digital twinning approach in regulating the maximum temperature within the system under varying and unsteady heat flux conditions. The DNN, trained on stationary data, plays a crucial role in determining the heat transfer coefficients necessary to maintain temperatures below a defined threshold value, such as the material’s melting point. The system is successfully controlled in 1D, 2D and 3D case studies. However, careful evaluations should be conducted if such a training approach, based on steady-state data, is applied to completely different transient heat transfer problems.

The present work represents one of the first examples of a comprehensive digital twinning approach to transient thermal systems, driven by data. One of the noteworthy features of this approach is its robustness. Adopting a training based on dimensionless data, the approach can seamlessly accommodate changes in thermal capacity and thermal conductivity without the need for retraining.

This paper provides practical advice for archaeologists and heritage specialists wishing to use ML approaches to identify archaeological features in high-resolution satellite imagery (or other remotely sensed data sources). We seek to balance the disproportionately optimistic literature related to the application of ML to archaeological prospection through a discussion of limitations, challenges and other difficulties. We further seek to raise awareness among researchers of the time, effort, expertise and resources necessary to implement ML successfully, so that they can make an informed choice between ML and manual inspection approaches.

Automated object detection has been the holy grail of archaeological remote sensing for the last two decades. Machine learning (ML) models have proven able to detect uniform features across a consistent background, but more variegated imagery remains a challenge. We set out to detect burial mounds in satellite imagery from a diverse landscape in Central Bulgaria using a pre-trained Convolutional Neural Network (CNN) plus additional but low-touch training to improve performance. Training was accomplished using MOUND/NOT MOUND cutouts, and the model assessed arbitrary tiles of the same size from the image. Results were assessed using field data.

Validation of results against field data showed that self-reported success rates were misleadingly high, and that the model was misidentifying most features. Setting an identification threshold at 60% probability, and noting that we used an approach where the CNN assessed tiles of a fixed size, tile-based false negative rates were 95–96%, false positive rates were 87–95% of tagged tiles, while true positives were only 5–13%. Counterintuitively, the model provided with training data selected for highly visible mounds (rather than all mounds) performed worse. Development of the model, meanwhile, required approximately 135 person-hours of work.

Our attempt to deploy a pre-trained CNN demonstrates the limitations of this approach when it is used to detect varied features of different sizes within a heterogeneous landscape that contains confounding natural and modern features, such as roads, forests and field boundaries. The model has detected incidental features rather than the mounds themselves, making external validation with field data an essential part of CNN workflows. Correcting the model would require refining the training data as well as adopting different approaches to model choice and execution, raising the computational requirements beyond the level of most cultural heritage practitioners.

Improving the pre-trained model’s performance would require considerable time and resources, on top of the time already invested. The degree of manual intervention required – particularly around the subsetting and annotation of training data – is so significant that it raises the question of whether it would be more efficient to identify all of the mounds manually, either through brute-force inspection by experts or by crowdsourcing the analysis to trained – or even untrained – volunteers. Researchers and heritage specialists seeking efficient methods for extracting features from remotely sensed data should weigh the costs and benefits of ML versus manual approaches carefully.

Our literature review indicates that use of artificial intelligence (AI) and ML approaches to archaeological prospection have grown exponentially in the past decade, approaching adoption levels associated with “crossing the chasm” from innovators and early adopters to the majority of researchers. The literature itself, however, is overwhelmingly positive, reflecting some combination of publication bias and a rhetoric of unconditional success. This paper presents the failure of a good-faith attempt to utilise these approaches as a counterbalance and cautionary tale to potential adopters of the technology. Early-majority adopters may find ML difficult to implement effectively in real-life scenarios.

Unlike many high-profile reports from well-funded projects, our paper represents a serious but modestly resourced attempt to apply an ML approach to archaeological remote sensing, using techniques like transfer learning that are promoted as solutions to time and cost problems associated with, e.g. annotating and manipulating training data. While the majority of articles uncritically promote ML, or only discuss how challenges were overcome, our paper investigates how – despite reasonable self-reported scores – the model failed to locate the target features when compared to field data. We also present time, expertise and resourcing requirements, a rarity in ML-for-archaeology publications.

Talent management research today is increasing as organizational requirements attempt to meet the challenges of effectively managing talent to achieve organizations’ strategic agendas. However, in learning organizations specifically, investigations of talent management practices are limited, with this study exploring the role of talent management practices in employee satisfaction and organizational performance in Pakistan.

The study was conducted in various universities (public and private) in Pakistan using a quantitative approach. Cross-sectional data are collected through a questionnaire, with analysis and conclusions based on completed questionnaires from 403 respondents.

The study’s findings from the analysis by structural equation modeling (SEM) emphasize the positive and significant effects of most talent management practices (i.e. talent identification, talent development, talent culture and talent retention) on employee satisfaction and organizational performance (talent attraction is the exception). Employee satisfaction positively and significantly affects organizational performance and is found to have a mediating effect, bridging the relationships of most talent management practices (talent identification, talent development, talent culture and talent retention) with organizational performance.

The study’s findings support human resource professionals, academics and policymakers in managing talent practices to enhance organizational performance. The findings assist in developing core skills and talent-related competencies to achieve organizational goals and success.

The study fills the research gaps by developing a framework of talent management practices for employee satisfaction and organizational performance in learning organizations, which warrants further consideration.

This paper shares findings from a multi-year literacy professional development partnership between a school district and university (2014–2019). We share this case of a Literacy Cohort initiative as an example of cross-institutional professional development situated within several of NAPDS’ nine essentials, including professional learning and leading, boundary-spanning roles and reflection and innovation (NAPDS, 2021).

We asked, “In what ways did the Cohort initiative create conditions for community and collaboration in the service of meaningful literacy reforms?” Drawing on social design methodology (Gutiérrez & Vossoughi, 2010), we sought to generate and examine the educational change associated with this multi-year initiative. Our data set included programmatic data, interviews (N = 30) and artifacts of literacy teaching, learning and leading.

Our findings reflect the emphasis areas that are important to educators in the partnership: diversity by design, building relationships through collaboration and rooting literacy reforms in teacher leadership. Our discussion explores threads of reciprocity, simultaneous renewal and boundary-spanning leadership and their role in sustaining partnerships over time.

This paper contributes to our understanding of building and sustaining a cohort model of multi-year professional development through the voices, perspectives and experiences of teachers, faculty and district administrators.