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The purpose of this study was to design a multitask learning model so that biomedical entities can be extracted without having any ambiguity from biomedical texts.

In the proposed automated bio entity extraction (ABEE) model, a multitask learning model has been introduced with the combination of single-task learning models. Our model used Bidirectional Encoder Representations from Transformers to train the single-task learning model. Then combined model's outputs so that we can find the verity of entities from biomedical text.

The proposed ABEE model targeted unique gene/protein, chemical and disease entities from the biomedical text. The finding is more important in terms of biomedical research like drug finding and clinical trials. This research aids not only to reduce the effort of the researcher but also to reduce the cost of new drug discoveries and new treatments.

As such, there are no limitations with the model, but the research team plans to test the model with gigabyte of data and establish a knowledge graph so that researchers can easily estimate the entities of similar groups.

As far as the practical implication concerned, the ABEE model will be helpful in various natural language processing task as in information extraction (IE), it plays an important role in the biomedical named entity recognition and biomedical relation extraction and also in the information retrieval task like literature-based knowledge discovery.

During the COVID-19 pandemic, the demands for this type of our work increased because of the increase in the clinical trials at that time. If this type of research has been introduced previously, then it would have reduced the time and effort for new drug discoveries in this area.

In this work we proposed a novel multitask learning model that is capable to extract biomedical entities from the biomedical text without any ambiguity. The proposed model achieved state-of-the-art performance in terms of precision, recall and F1 score.

In gearbox fault diagnosis, identifying the fault type and severity simultaneously, as well as the compound fault containing multiple faults, is necessary.

To diagnose multiple faults simultaneously, this paper proposes a multichannel and multi-task convolutional neural network (MCMT-CNN) model.

Experiments were conducted on a bearing dataset containing different fault types and severities and a gearbox compound fault dataset. The experimental results show that MCMT-CNN can effectively extract features of different tasks from vibration signals, with a diagnosis accuracy of more than 97%.

Vibration signals at different positions and in different directions are taken as the MC inputs to ensure the integrity of the fault features. Fault labels are established to retain and distinguish the unique features of different tasks. In MCMT-CNN, multiple task branches can connect and share all neurons in the hidden layer, thus enabling multiple tasks to share information.

The higher education world around us is changing fast. Mobile learning (M-learning) and apps in education are new and exciting. However, this does not mean that the faculty is less relevant. On the contrary, the faculty are an ever-important factor that is needed to increase student engagement. Disengaged students are in fact more problematic than low achieving students. This is because of the disruption disengaged student bring in the classroom. Therefore, increasing student engagement is a key challenge in higher education today. Research suggests that active learning, value and expectations are important factors that determine student engagement. Some of these factors can be indirectly influenced by the faculty and course designers. Against this background, the objective of this chapter is to examine if dwindling student engagement in higher education can be addressed with CURPA, i.e. a CURriculum and course planning (CURP) App. CURPA is the result of our efforts at Utrecht University to help design courses and curricula by using cards to account for different activities, with the strategic aim being to stimulate student engagement in higher education. Our expert interviews resulted in a MoSCoW-prioritised list of requirements that can be implemented over time, structured in line with Becker's student engagement model.

The purpose of this paper is to study the incentive mechanism design problem of manager under the assumption of bounded rationality in the theoretical framework of principal‐agent relationship and provide some insights into the relationship between the economic agent's rationality and incentive mechanism.

The framework of principal‐agent relationship is based on the full rationality of the agents. However, the agents cannot make optimal decisions in the uncertain and imprecise environment. To capture the essence of incentive scheme, bounded rationality is introduced in the model and the gradient dynamics is applied in modelling bounded rational learning behavior. The corresponding single‐task principal‐agent problem is solved to obtain the second‐best contracts and the effort levels. Furthermore, the results are generalized for the multi‐task principal‐agent problem.

The second‐best contract under the assumption of bounded rationality converges to the optimal contract under the assumption of full rationality over time.

Accessibility of data for empirical research is the main limitation which model will be applied.

A useful reference for managerial decisions.

The new approach of bounded rationality modeling and simulation. This paper is aimed at the owners, enterprisers, managers, and workers.

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.

The purpose of this paper is to explore experiences of e-learners in relation to their time preferences, and to examine how technology changes temporal culture in e-learning.

A pilot study using multiple methods of data collection to provide a rich picture of the experience of e-learners in relation to temporal flexibility. Taking a narrative approach it analysed data from a survey, time tracking data as well as data from online discussion boards, learners’ assignments and short telephone interviews.

The study found e-learners may experience temporal culture shock when starting to study online for the first time. It highlights the need to explore an individual's time preference and to consider the temporal culture of the e-learning programme.

It highlights that the issue of time is still missing from educational studies and suggests that temporal aspects are included in e-learning research studies. This is a small exploratory study but it indicates the need for further research to explore the potential for temporal culture shock and influence of e-learner time preferences.

Designing an e-learning environment for learners needs to consider learner time preferences to ensure that temporal flexibility is a realised benefit, not a barrier to learning. The temporal culture of the online learning environment also needs to be made explicit so that learner expectations can be managed.

It contributes to the literature on e-learning in the higher education context by providing a temporal lens to explore learner experiences. It is of value to educationalists and learning technologists designing, delivering or evaluating e-learning programmes. Online learners may also benefit from its findings.

This chapter aims to provide an overview and conceptualization of multiscreening in the field of advertising effectiveness.

By means of the multi-dimensions of media multitasking, it is possible to differentiate different forms of media multitasking. This framework is used to describe and explain the phenomenon of multiscreening. The framework consists of four categories each with its own dimensions: (1) task relations (e.g., task hierarchy, task switch, shared modality), (2) task inputs (e.g., information flow), (3) task outputs (e.g., behavioral responses), and (4) user differences. The description of multiscreening per dimensions is completed with a review of recent literature in the field of multiscreening, media multitasking and persuasion.

Literature in the field of media multitasking often assumes detrimental effects. Practical implications for advertisers are discussed by presenting an overview of the existing literature on multiscreening and advertising effectiveness. At the end of the chapter, a summary of the different dimensions is presented and an answer is formulated to the question: Is multiscreening a challenge or opportunity for advertisers?

In addition to practical implications, this chapter also offers an overview of the current research in the field of multiscreening and advertising effects. By presenting recent literature in this field, it becomes clear where knowledge is lacking. Directions for future research are discussed.

This chapter is the first to present a structured overview of the phenomenon of multiscreening. It will provide practitioners and researchers with the current status in the field of multiscreening and advertising effectiveness. In addition, the chapter can also be seen as a guide for future directions in the field of multiscreening.

This article focuses on the assessment of entrepreneurship competence by selected vocational teachers in Italy. The exploratory research question addresses the extent to which entrepreneurship assessments are competence based, and the research seeks to identify fully fledged assessment programmes with both a formative and summative component, and the use of assessment rubrics. It also explores the extent to which entrepreneurship competence is referred to in school documentation and later assessed, and the tools and strategies used for such assessment.

This case study is part of a larger European research project promoted by Cedefop; in Italy it focused on six selected vocational IVET and CVET programmes and apprenticeship schemes. It used a wide range of instruments to ensure triangulation and multiple perspectives: analysed policy documents and undertook online interviews with experts and policy makers. At VET providers' premises it deployed: analysis of school documents; observations of learning environments; interviews and focus groups with (in schools) teachers, directors and vice directors, learners and alumni (in companies) instructors, company tutors and employers, apprentices and alumni.

Assessment tasks were rarely embedded within fully fledged assessment programmes involving both formative and summative tasks, and assessment rubric for grading. Most of the time, entrepreneurship programmes lacked self-assessment, peer assessment and structured feedback and did not involve learners in the assessment process. Some instructors coached the students, but undertook no clear formative assessment. These findings suggest institutions have a testing culture with regard to assessment, at the level of both policy and practice. In most cases, entrepreneurship competence was not directly assessed, and learning outcomes were only loosely related to entrepreneurship.

One limitation concerned the selection of the VET providers: these were chosen not on a casual basis, but because they ran programmes that were relevant to the development of entrepreneurship competence.

At the policy level, there is a need for new guidelines on competence development and assessment in VET, guidelines that are more aligned with educational research on competence development. To ensure the development of entrepreneurship competence, educators need in-service training and a community of practice.

So far, the literature has concentrated on entrepreneurship education at the tertiary level. Little is known about how VET instructors assess entrepreneurship competence. This study updates the picture of policy and practice in Italy, illustrating how entrepreneurship competence is developed in selected IVET and CVET programmes and apprenticeships.

A striking feature of Jaques' work is his “no nonsense” attitude to the “manager‐subordinate” relationship. His blunt account of the origins of this relationship seems at first sight to place him in the legalistic “principles of management” camp rather than in the ranks of the subtler “people centred” schools. We shall see before long how misleading such first impressions can be, for Jaques is not making simplistic assumptions about the human psyche. But he certainly sees no point in agonising over the mechanism of association which brings organisations and work‐groups into being when the facts of life are perfectly straightforward and there is no need to be squeamish about them.

This chapter outlines an augmented reality project developed as part of a master’s course on eTourism within a curriculum. It discusses opportunities to foster community engagement with local tourism actors and experiential learning for international students. It also contributes to the literature on experiential education in this field. Moreover, the chapter discusses cross-cultural learning implications as international students were asked to study a local destination. Results show how the introduction of a practical project into the tourism curriculum proved to provide better learning of the application of eTourism, and a powerful pedagogical approach to raise global citizenship awareness.