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The purpose of this study is to present a large-scale real-world comparative study using pre-COVID lockdown data versus post-COVID lockdown data on predicting shipment times of therapeutic supplies in e-pharmacy supply chains and show that our proposed methodology is robust to lockdown effects.

The researchers used organic data of over 5.9 million records of therapeutic shipments, with 2.87 million records collected pre-COVID lockdown and 3.03 million records collected post-COVID lockdown. The researchers built various Machine Learning (ML) classifier models on the two datasets, namely, Random Forest (RF), Extra Trees (XRT), Decision Tree (DT), Multi-Layer Perceptron (MLP), XGBoost (XGB), CatBoost (CB), Linear Stochastic Gradient Descent (SGD) and the Linear Naïve Bayes (NB). Then, the researchers stacked these base models and built meta models on top of them. Further, the researchers performed a detailed comparison of the performances of ML models on pre-COVID lockdown and post-COVID lockdown datasets.

The proposed approach attains performance of 93.5% on real-world post-COVID lockdown data and 91.35% on real-world pre-COVID lockdown data. In contrast, the turn-around times (TAT) provided by therapeutic supply logistics providers are 62.91% accurate compared to reality in post-COVID lockdown times and 73.68% accurate compared to reality pre-COVID lockdown times. Hence, it is clear that while the TAT provided by logistics providers has deteriorated in the post-pandemic business climate, the proposed method is robust to handle pandemic lockdown effects on e-pharmacy supply chains.

The implication of the study provides a novel ML-based framework for predicting the shipment times of therapeutics, diagnostics and vaccines, and it is robust to COVID-19 lockdown effects.

E-pharmacy companies can readily adopt the proposed approach to enhance their supply chain management (SCM) capabilities and build resilience during COVID lockdown times.

The present study is one of the first to perform a large-scale real-world comparative analysis on predicting therapeutic supply shipment times in the e-pharmacy supply chain with novel ML ensemble stacking, obtaining robust results in these COVID lockdown times.

The purpose of this paper is to study the use of a heterogeneous and evolutionary team approach based on different sources of knowledge to address a real-world problem within the telecommunication domain: the frequency assignment problem (FAP). Evolutionary algorithms have been proved as very suitable strategies when they are used to solve NP-hard optimization problems. However, these algorithms can find difficulties when they fall into local minima and the generation of high-quality solutions when tacking real-world instances of the problem is computationally very expensive. In this scenario, the use of a heterogeneous parallel team represents a very interesting approach.

The results have been validated by using two real-world telecommunication instances which contain real information about two GSM networks. Contrary to most of related publications, this paper is focussed on aspects which are relevant for real communication networks. Moreover, due to the stochastic nature of metaheuristics, the results are validated through a formal statistical analysis. This analysis is divided in two stages: first, a complete statistical study, and after that, a full comparative study against results previously published.

Comparative study shows that a heterogeneous evolutionary proposal obtains better results than proposals which are based on a unique source of knowledge. In fact, final results provided in the work surpass the results of other relevant studies previously published in the literature.

The paper provides a complete study of the contribution provided by the different metaheuristics included in the team and the impact of using different sources of evolutionary knowledge when the system is applied to solve a real-world FAP problem. The conclusions obtained in this study represent an original contribution never reached before for FAP.

The purpose of this paper is to conduct a comprehensive review of the noteworthy contributions made in the area of the Feedforward neural network (FNN) to improve its generalization performance and convergence rate (learning speed); to identify new research directions that will help researchers to design new, simple and efficient algorithms and users to implement optimal designed FNNs for solving complex problems; and to explore the wide applications of the reviewed FNN algorithms in solving real-world management, engineering and health sciences problems and demonstrate the advantages of these algorithms in enhancing decision making for practical operations.

The FNN has gained much popularity during the last three decades. Therefore, the authors have focused on algorithms proposed during the last three decades. The selected databases were searched with popular keywords: “generalization performance,” “learning rate,” “overfitting” and “fixed and cascade architecture.” Combinations of the keywords were also used to get more relevant results. Duplicated articles in the databases, non-English language, and matched keywords but out of scope, were discarded.

The authors studied a total of 80 articles and classified them into six categories according to the nature of the algorithms proposed in these articles which aimed at improving the generalization performance and convergence rate of FNNs. To review and discuss all the six categories would result in the paper being too long. Therefore, the authors further divided the six categories into two parts (i.e. Part I and Part II). The current paper, Part I, investigates two categories that focus on learning algorithms (i.e. gradient learning algorithms for network training and gradient-free learning algorithms). Furthermore, the remaining four categories which mainly explore optimization techniques are reviewed in Part II (i.e. optimization algorithms for learning rate, bias and variance (underfitting and overfitting) minimization algorithms, constructive topology neural networks and metaheuristic search algorithms). For the sake of simplicity, the paper entitled “Machine learning facilitated business intelligence (Part II): Neural networks optimization techniques and applications” is referred to as Part II. This results in a division of 80 articles into 38 and 42 for Part I and Part II, respectively. After discussing the FNN algorithms with their technical merits and limitations, along with real-world management, engineering and health sciences applications for each individual category, the authors suggest seven (three in Part I and other four in Part II) new future directions which can contribute to strengthening the literature.

The FNN contributions are numerous and cannot be covered in a single study. The authors remain focused on learning algorithms and optimization techniques, along with their application to real-world problems, proposing to improve the generalization performance and convergence rate of FNNs with the characteristics of computing optimal hyperparameters, connection weights, hidden units, selecting an appropriate network architecture rather than trial and error approaches and avoiding overfitting.

This study will help researchers and practitioners to deeply understand the existing algorithms merits of FNNs with limitations, research gaps, application areas and changes in research studies in the last three decades. Moreover, the user, after having in-depth knowledge by understanding the applications of algorithms in the real world, may apply appropriate FNN algorithms to get optimal results in the shortest possible time, with less effort, for their specific application area problems.

The existing literature surveys are limited in scope due to comparative study of the algorithms, studying algorithms application areas and focusing on specific techniques. This implies that the existing surveys are focused on studying some specific algorithms or their applications (e.g. pruning algorithms, constructive algorithms, etc.). In this work, the authors propose a comprehensive review of different categories, along with their real-world applications, that may affect FNN generalization performance and convergence rate. This makes the classification scheme novel and significant.

To connect students with the real world of management practice, the purpose of this paper is to extend and operationalize the situated cultural learning approach (SiCuLA) through five learning processes occurring within communities of practice. These include integration of cultural contexts, authentic activities, reflections, facilitation, and the construction of a collaborative learning community.

To investigate the complex processes and principles of cultural learning, a multi-method approach is applied to an extensive comparative study of default and intervened cases within three management classes. Evidence is drawn from multiple sources of qualitative data including class observations, meeting minutes, focus groups, and group interviews with students and instructors.

Results indicated that in default cases, little explicit attention was given to a situated perspective of culture, or to the rich sources of cultural knowledge available among members of the classroom community. In contrast, following the intervention cases where SiCuLA was applied, there was strong evidence that much more attention was given to enhancing student contextual knowledge. Nonetheless, there were some challenges in applying these processes within the classroom context.

This is the first study to extend and operationalize SiCuLA in a classroom setting. More importantly, the evidence forms the empirical basis for deriving theoretical principles for cross-cultural management (CCM) education and training. It contributes to studying cultural contexts as sources of knowledge for learning through active co-participation. It also contributes to positive CCM learning with an emphasis on human agency that encourages students to take more responsibility and ownership of their cultural learning.

Automated dust monitoring in workplaces helps provide timely alerts to over-exposed workers and effective mitigation measures for proactive dust control. However, the cluttered nature of construction sites poses a practical challenge to obtain enough high-quality images in the real world. The study aims to establish a framework that overcomes the challenges of lacking sufficient imagery data (“data-hungry problem”) for training computer vision algorithms to monitor construction dust.

This study develops a synthetic image generation method that incorporates virtual environments of construction dust for producing training samples. Three state-of-the-art object detection algorithms, including Faster-RCNN, you only look once (YOLO) and single shot detection (SSD), are trained using solely synthetic images. Finally, this research provides a comparative analysis of object detection algorithms for real-world dust monitoring regarding the accuracy and computational efficiency.

This study creates a construction dust emission (CDE) dataset consisting of 3,860 synthetic dust images as the training dataset and 1,015 real-world images as the testing dataset. The YOLO-v3 model achieves the best performance with a 0.93 F1 score and 31.44 fps among all three object detection models. The experimental results indicate that training dust detection algorithms with only synthetic images can achieve acceptable performance on real-world images.

This study provides insights into two questions: (1) how synthetic images could help train dust detection models to overcome data-hungry problems and (2) how well state-of-the-art deep learning algorithms can detect nonrigid construction dust.

This paper aims to showcase and critically review the value of selected pedagogies in which real-world engagements are used to enhance sustainability learning in an open, distance and e-learning (ODeL) context in the Global South. The paper considers opportunities, issues, alternatives and implementation guidelines.

The School of Ecological and Human Sustainability (University of South Africa) serves as case study, with blended and fully online learning used as examples of pedagogies. The assessment of these pedagogies uses examples of learning activities and exercises, critical reflections on feedback by lecturers and students and consideration against criteria for real-world learning.

The experiences showcased illustrate that despite challenges in ODeL, real-world engagements can be used successful as pedagogy for sustainability learning in the Global South context. Limited access to ICTs can be mitigated through mobile technologies and free and open software applications, as illustrated by the examples in this paper.

The case study approach and qualitative methodology present limitations, with focus on only two examples. However, significant depth is achieved with the assessment of these examples, while the recommendations and lessons learnt can be applied in other contexts, thus expanding on the knowledge and experience in this field.

This paper showcases innovative approaches to incorporate real-world engagements for sustainability learning in ODeL. Application of real-world engagements in ODeL in the Global South context is original and addresses the need for teaching and learning strategies responding to the digital divide and contributing to expand access to higher education and an Afrocentric discourse to best practice.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

This study concerned aspiring educational leaders’ problem-solving skill development, specifically through classroom instruction with real-world scenarios. Professional educators obtaining an advanced degree in educational administration at a public university were surveyed in the fall and spring about their problem-solving abilities. Participants were also asked to respond to real-world principal scenarios. Focus group interviews were conducted in the spring. Results indicated that participants’ confidence in their problem-solving abilities did improve, though their ability to address the real-world problems did not improve significantly. Participants identified the value of learning from real-world scenarios and professors who had experience as administrators, and they also recognized the importance of learning from one another during discussions of the scenarios. Participants indicated that they still needed experience working in actual administrative contexts.

The purpose of this paper is to examine young learners’ attitudes towards enterprise education within the context of a university led initiative to construct a sustainable framework which benefits identified stakeholders.

The research used self-completed questionnaires with 117 business studies students in Stages S4-S6 from secondary schools across Dundee and business students from Years 1-4 at one university in Dundee, Scotland.

The research reveals that respondents positively engage with enterprise education and felt that their project management, creative thinking, communication skills and confidence were enhanced by the activity of real-world business challenges. The findings support the notion that an enterprising spine embedded in the academic curriculum better equip the learner with the necessary hard and soft skills required for the employment market but not necessarily to be entrepreneurial.

A limitation of this research was the sample size, which although representative of the pupil and student cohorts associated with the various stages of education being studied at the particular time of data collection, and is suitable for an exploratory study, the research would have benefited from being both larger and complimented by more of a qualitative component beyond the inclusion of open-ended questions.

As an exploratory study which informs a wider comparative study into enterprise education, the research examines learner’s perspectives and the measures they feel are required for effective engagement with enterprise education activities in schools and universities. The findings should assist education providers to deliver a better learning experience and the learners with improved enterprising and social skills, particularly the building of confidence.

As an exploratory study which informs a wider comparative study into enterprise education, the research examines learner’s perspectives and the measures they feel are required for effective engagement with enterprise education activities in schools and universities. The findings should assist education providers to deliver a better learning experience and the learners with improved enterprising and social skills, particularly the building of confidence.

The research should prove useful to educational establishments who are considering the implementation of, or further engagement with, enterprise education and involvement with the business community and how such activities impact on their learners.

This study aims to explore DroneSim, a virtual reality (VR)-based flight training simulator, as an alternative for real-world drone-mediated building inspection training.

Construction, engineering and management students were asked to pilot drones in the VR-based DroneSim space and perform common flight operations and inspection tasks within the spatiotemporal context of a building construction project. Another student group was also recruited and asked to perform a similar building inspection task in real world. The National Aeronautics and Space Administration (NASA)–Task Load Index (TLX) survey was used to assess students’ inflight workload demand under both Real and DroneSim conditions. Post-assessment questionnaires were also used to analyze students’ feedback regarding the usability and presence of DroneSim for drone building inspection training.

None of the NASA–TLX task load levels under Real and DroneSim conditions were highly rated by students, and both groups experienced comparable drone-building inspection training. Students perceived DroneSim positively and found the VR experience stimulating.

This study’s contribution is twofold: to better understand the development stages involved in the design of a VR-based drone flight training simulator, specifically for building inspection tasks; and to improve construction students’ drone operational and flight training skills by offering them the opportunity to enhance their drone navigation skills in a risk-free, repeatable yet realistic environment. Such contributions ultimately pave the way for better integration of drone-mediated building inspection training in construction education while meeting industry needs.