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Assessing the failure probability of engineering structures is still a challenging task in the presence of various uncertainties due to the involvement of expensive-to-evaluate computational models. The traditional simulation-based approaches require tremendous computational effort, especially when the failure probability is small. Thus, the use of more efficient surrogate modeling techniques to emulate the true performance function has gained increasingly more attention and application in recent years. In this paper, an active learning method based on a Kriging model is proposed to estimate the failure probability with high efficiency and accuracy.

To effectively identify informative samples for the enrichment of the design of experiments, a set of new learning functions is proposed. These learning functions are successfully incorporated into a sampling scheme, where the candidate samples for the enrichment are uniformly distributed in the n-dimensional hypersphere with an iteratively updated radius. To further improve the computational efficiency, a parallelization strategy that enables the proposed algorithm to select multiple sample points in each iteration is presented by introducing the K-means clustering algorithm. Hence, the proposed method is referred to as the adaptive Kriging method based on K-means clustering and sampling in n-Ball (AK-KBⁿ).

The performance of AK-KBⁿ is evaluated through several numerical examples. According to the generated results, all the proposed learning functions are capable of guiding the search toward sample points close to the LSS in the critical region and result in a converged Kriging model that perfectly matches the true one in the regions of interest. The AK-KBⁿ method is demonstrated to be well suited for structural reliability analysis and a very good performance is observed in the investigated examples.

In this study, the statistical information of Kriging prediction, the relative contribution of the sample points to the failure probability and the distances between the candidate samples and the existing ones are all integrated into the proposed learning functions, which enables effective selection of informative samples for updating the Kriging model. Moreover, the number of required iterations is reduced by introducing the parallel computing strategy, which can dramatically alleviate the computation cost when time demanding numerical models are involved in the analysis.

The purpose of this paper is to develop e-health and patient monitoring systems remotely to overcome the difficulty of patients going to hospitals especially in times of epidemics such as virus disease (COVID-19). Artificial intelligence (AI) technology will be combined Internet of Things (IoT) in this research to overcome these challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the neural network (NN). Then, define the patient data sent through protocols of the IoT. NN checks the patient’s medical sensors data to make the appropriate decision. Then it sends this diagnosis to the doctor. Using the proposed solution, the patients can diagnose and expect the disease automatically and help physicians to discover and analyze the disease remotely without the need for patients to go to the hospital.

AI technology will be combined with the IoT in this research. The research aims to select the most appropriate’ best-hidden layers numbers’ and the activation function types for the NN.

Decision support health-care system based on IoT and deep learning techniques was proposed. The authors checked out the ability to integrate the deep learning technique in the automatic diagnosis and IoT abilities for speeding message communication over the internet has been investigated in the proposed system. The authors have chosen the appropriate structure of the NN (best-hidden layers numbers and the activation function types) to build the e-health system is performed in this work. Also, depended on the data from expert physicians to learn the NN in the e-health system. In the verification mode, the overall evaluation of the proposed diagnosis health-care system gives reliability under different patient’s conditions. From evaluation and simulation results, it is clear that the double hidden layer of feed-forward NN and its neurons contain Tanh function preferable than other NN.

AI technology will be combined IoT in this research to overcome challenges. The research aims to select the most appropriate, best-hidden layers numbers and the activation function types for the NN.

Although the field of neuroscience has evolved dramatically, little research has attempted to conceptualize the impact of neuroscience on the field of human resource development (HRD). The purpose of this study is an integrative review of the influential relationship between neuroscience and workplace learning including applicable implications for HRD research and practice.

By reviewing 93 studies on neuroscience and brain-based learning published between 1995 and 2017, the authors synthesized their findings.

This study discusses the basic concepts of neuroscience such as the structure and functions of the brain, neuroscientific findings about memory and cognition, the effect of neural transmitters on memory and cognition and the neuroscience of learning. This study also illustrates brain-based learning styles affecting learning and describes various neuroscientific learning principles and models that can be applied to practical planning and the delivery of workplace learning and HRD activities.

This study concludes with brain-based learning principles called neuroscientism compared with traditional learning theories. It also includes several brain-based learning cases from workplace settings and implications for future research and further HRD practices.

This purpose of this paper is to provide an overview of the theoretical background and applications of inverse reinforcement learning (IRL).

Reinforcement learning (RL) techniques provide a powerful solution for sequential decision making problems under uncertainty. RL uses an agent equipped with a reward function to find a policy through interactions with a dynamic environment. However, one major assumption of existing RL algorithms is that reward function, the most succinct representation of the designer's intention, needs to be provided beforehand. In practice, the reward function can be very hard to specify and exhaustive to tune for large and complex problems, and this inspires the development of IRL, an extension of RL, which directly tackles this problem by learning the reward function through expert demonstrations. In this paper, the original IRL algorithms and its close variants, as well as their recent advances are reviewed and compared.

This paper can serve as an introduction guide of fundamental theory and developments, as well as the applications of IRL.

This paper surveys the theories and applications of IRL, which is the latest development of RL and has not been done so far.

This paper aims to deal with the trade-off of the stability and the accuracy in learning human control strategy from demonstrations. With the stability conditions and the estimated stability region, this paper aims to conveniently get rid of the unstable controller or controller with relatively small stability region. With this evaluation, the learning human strategy controller becomes much more robust to perturbations.

In this paper, the criterion to verify the stability and a method to estimate the domain of attraction are provided for the learning controllers trained with support vector machines (SVMs). Conditions are formulated based on the discrete-time system Lyapunov theory to ensure that a closed-form of the learning control system is strongly stable under perturbations (SSUP). Then a Chebychev point based approach is proposed to estimate its domain of attraction.

Some of such learning controllers have been implemented in the vertical balance control of a dynamically stable, statically unstable wheel mobile robot.

Intelligent agents are becoming an essential part of collaborative virtual environments. The purpose of this paper is to present an architecture of a learning agent that is able to utilize machine learning techniques to monitor the user's actions.

A learning agent is developed and integrated into federated collaborative virtual workspace.

The experimental results showed that the combination of genetic algorithms and reinforcement learning algorithms provides the agent with better learning capability resulting in better predictions for the user.

This paper provides experimental results and a performance analysis in terms of accuracy of predictions, processing time, and memory utilization of the agent.

The purpose of this study is to explore the potential of trainable activation functions to enhance the performance of deep neural networks, specifically ResNet architectures, in the task of image classification. By introducing activation functions that adapt during training, the authors aim to determine whether such flexibility can lead to improved learning outcomes and generalization capabilities compared to static activation functions like ReLU. This research seeks to provide insights into how dynamic nonlinearities might influence deep learning models' efficiency and accuracy in handling complex image data sets.

This research integrates three novel trainable activation functions – CosLU, DELU and ReLUN – into various ResNet-n architectures, where “n” denotes the number of convolutional layers. Using CIFAR-10 and CIFAR-100 data sets, the authors conducted a comparative study to assess the impact of these functions on image classification accuracy. The approach included modifying the traditional ResNet models by replacing their static activation functions with the trainable variants, allowing for dynamic adaptation during training. The performance was evaluated based on accuracy metrics and loss profiles across different network depths.

The findings indicate that trainable activation functions, particularly CosLU, can significantly enhance the performance of deep learning models, outperforming the traditional ReLU in deeper network configurations on the CIFAR-10 data set. CosLU showed the highest improvement in accuracy, whereas DELU and ReLUN offered varying levels of performance enhancements. These functions also demonstrated potential in reducing overfitting and improving model generalization across more complex data sets like CIFAR-100, suggesting that the adaptability of activation functions plays a crucial role in the training dynamics of deep neural networks.

This study contributes to the field of deep learning by introducing and evaluating the impact of three novel trainable activation functions within widely used ResNet architectures. Unlike previous works that primarily focused on static activation functions, this research demonstrates that incorporating trainable nonlinearities can lead to significant improvements in model performance and adaptability. The introduction of CosLU, DELU and ReLUN provides a new pathway for enhancing the flexibility and efficiency of neural networks, potentially setting a new standard for future deep learning applications in image classification and beyond.

The purpose of this paper is to identify the coaching structures that aspiring principals associate with developmentally consequential coaching interactions; identify structural features/functions/attributes that shape a structure’s developmental utility and use; and consider how a multifarious coaching structure might advantage the learning experiences of aspiring principals.

This qualitative study included multiple interviews with two cohorts of aspiring principals (n=20) from one preparation program and with their leadership coaches (n=5) and was framed using the theories of social capital and networks, situated learning, and distributed cognition.

The authors identified eight coaching structures that aspirants identified as consequential to their learning and development. The authors identified four structural features/functions/attributes that shape a structure’s developmental utility. The authors identified three factors that contribute to the developmental utility of this multifarious coaching model.

This study includes a relatively small participant sample –70 percent of the aspiring principals from two cohorts within one preparation program. Data do not include direct observations of coaching interactions within the context of individual coaching structures.

The findings suggest that the structuring of leadership coaching is a critical consideration for those designing leadership coaching programs. This multifarious structuring of leadership coaching created three developmental affordances.

This paper generates new knowledge for the field of principal preparation related to the structuring of leadership coaching and ways in which structuring can shape aspirant learning experiences. These findings are likely to also be instructive to those interested in coaching more generally.

The purpose of this paper is to present a prototype ontology‐based application that has been developed for competency management and learning paths.

The paper provides an overview of competency management and related work in this area, a description of the competency ontology, and a functional and architectural analysis.

The paper provides information on work related to ontology‐based competency management systems, indicating an enhanced approach with a detailed analysis of system architecture and functional analysis.

The proposed application will be implemented through a .NET deployment, in Microsoft Hellas, the Greek subsidiary of the multinational IT company.

Ontologies have already been created in different scientific areas, including knowledge and competency management. However, only a few ontology‐based applications are available today within the domain of competency management. In this paper an ontology‐based application is presented has been developed for competency management and learning paths. Specifically, the paper provides an overview of competency management and related work in this area, a description of the competency ontology, and a functional and architectural analysis.

Technological advancements and global societal changes reshapes manufacturing industry emphasizing needs for competence development of industrial professionals. The purpose of this paper is to study how organizational learning supports the development of academic structures, creating agile and sustainable formal educational models meeting novel competence needs.

The qualitative case study, part of a longitudinal research study, focuses on internal academic processes supporting a new formal educational model. Qualitative data was collected through five focus groups, incorporating 32 informants from different HEI function categories.

Changing traditional academic structures requires joint engagement between all HEI functions, emphasizing organizational learning with subprocesses of searching, creating, sustaining and exchanging knowledge in a learning loop. Results show a consensus among the different HEI functions regarding the value of the HEI’s coproduction with society; however, bureaucracy and academic structure hinder flexibility. Cross-functional teams building a “chain-of-trust” throughout the HEI coupled with full management support show opportunities to progress into a learning organization.

Organizational learning within HEIs requires trustful and open communication, multifunction knowledge exchange, holistic views of processes and system thinking, achieved through cross-functional teams and continuous improvement through learning loops.

Industry-academic collaboration on formal education for lifelong learning needs to become both agile and resilience to meet technological advancement and sustainability.

Novel technology, digitalization and sustainability gain ground and require that society and organizations, including academia, change and learn. This means that academia is meeting new challenges and needs to develop internal processes.