Search results

The synthetic application and interaction of/between the internet, Internet of Things, cloud computing, big data, Industry 4.0 and other new patterns and new technologies shall breed future Web-based industrial operation system and social operation management patterns, manifesting as a crowd cyber eco-system composed of multiple interconnected intelligent agents (enterprises, individuals and governmental agencies) and its dynamic behaviors. This paper aims to explore the basic principles and laws of such a system and its behavior.

The authors propose the concepts of crowd science and engineering (CSE) and expound its main content, thus forming a research framework of theories and methodologies of crowd science.

CSE is expected to substantially promote the formation and development of crowd science and thus lay a foundation for the advancement of Web-based industrial operation system and social operation management patterns.

This paper is the first one to propose the concepts of CSE, which lights the beacon for the future research in this area.

With the rapid growth of the Internet of Things (IoT) market and requirement, low power wide area (LPWA) technologies have become popular. In various LPWA technologies, Narrow Band IoT (NB-IoT) and long range (LoRa) are two main leading competitive technologies. Compared with NB-IoT networks, which are mainly built and managed by mobile network operators, LoRa wide area networks (LoRaWAN) are mainly operated by private companies or organizations, which suggests two issues: trust of the private network operators and lack of network coverage. This study aims to propose a conceptual architecture design of a blockchain built-in solution for LoRaWAN network servers to solve these two issues for LoRaWAN IoT solution.

The study proposed modeling, model analysis and architecture design.

The proposed solution uses the blockchain technology to build an open, trusted, decentralized and tamper-proof system, which provides the indisputable mechanism to verify that the data of a transaction has existed at a specific time in the network.

To the best of our knowledge, this is the first work that integrates blockchain technology and LoRaWAN IoT technology.

This paper aims to adapt and computerize the Trail Making Test (TMT) to support long-term self-assessment of cognitive abilities.

The authors propose a divide-and-combine (DAC) approach for generating different instances of TMT that can be used in repeated assessments with nearly no discernible practice effects. In the DAC approach, partial trails are generated separately in different layers and then combined to form a complete TMT trail.

The proposed approach was implemented in a computerized test application called iTMT. A pilot study was conducted to evaluate iTMT. The results show that the instances of TMT generated by the DAC approach had an adequate level of difficulty. iTMT also achieved a stronger construct validity, higher test–retest reliability and significantly reduced practice effects than existing computerized tests.

The preliminary results suggest that iTMT is suitable for long-term monitoring of cognitive abilities. By supporting self-assessment, iTMT also can help to crowdsource the assessment processes, which need to be administered by healthcare professionals conventionally, to the patients themselves.

The parent-child relationship is important to the solidarity of families and the emotional well-being of family members. Since people are more dependent on their close social relationships as they age, understanding the quality of relationships between aged parents and their adult children is a critical topic. Previous research shows that this relationship is complicated with both kinship and ambivalence. However, there is little research on the causes of this complexity. This paper proposes a role model to explain this complexity by studying the leadership transition within a family as the child grows.

In this paper, we proposed a novel perception to understand this transition process and explain related problems based on the analysis of the leader-follower relationship between the parents and their children.

When a child is born, his/her parents become the leader of this family because of their abilities, responsibilities and the requirements of the infant. This leader-follower role structure will last a long time in this family. Decades later, when the parents become old and the child grows up, the inter-generational contracts within the family and the requirement of each members change. This transition weakens the foundation of the traditional leader-follower role structure within the family. If either the parent or the child does not want to accept their new roles, both of them will suffer and struggle in this relationship. This role conflict will cause ambivalence in the relationship between aged parents and their adult children.

Based on the quantitative study model provided in this paper, we can moderate the relationships between aged parents and their adult children. This effort is meaningful in enhancing the quality of life and emotional wellbeing for senior citizens.

Recent years have witnessed the rapid development of massive open online courses (MOOCs). With more and more courses being produced by instructors and being participated by learners all over the world, unprecedented massive educational resources are aggregated. The educational resources include videos, subtitles, lecture notes, quizzes, etc., on the teaching side, and forum contents, Wiki, log of learning behavior, log of homework, etc., on the learning side. However, the data are both unstructured and diverse. To facilitate knowledge management and mining on MOOCs, extracting keywords from the resources is important. This paper aims to adapt the state-of-the-art techniques to MOOC settings and evaluate the effectiveness on real data. In terms of practice, this paper also tries to answer the questions for the first time that to what extend can the MOOC resources support keyword extraction models, and how many human efforts are required to make the models work well.

Based on which side generates the data, i.e instructors or learners, the data are classified to teaching resources and learning resources, respectively. The approach used on teaching resources is based on machine learning models with labels, while the approach used on learning resources is based on graph model without labels.

From the teaching resources, the methods used by the authors can accurately extract keywords with only 10 per cent labeled data. The authors find a characteristic of the data that the resources of various forms, e.g. subtitles and PPTs, should be separately considered because they have the different model ability. From the learning resources, the keywords extracted from MOOC forums are not as domain-specific as those extracted from teaching resources, but they can reflect the topics which are lively discussed in forums. Then instructors can get feedback from the indication. The authors implement two applications with the extracted keywords: generating concept map and generating learning path. The visual demos show they have the potential to improve learning efficiency when they are integrated into a real MOOC platform.

Conducting keyword extraction on MOOC resources is quite difficult because teaching resources are hard to be obtained due to copyrights. Also, getting labeled data is tough because usually expertise of the corresponding domain is required.

The experiment results support that MOOC resources are good enough for building models of keyword extraction, and an acceptable balance between human efforts and model accuracy can be achieved.

This paper presents a pioneer study on keyword extraction on MOOC resources and obtains some new findings.

The purpose of this paper is to put forward a nvew reconfigurable multi-mode walking-rolling robot based on the single-loop closed-chain four-bar mechanism, and the robot can be changed to different modes according to the terrain.

Based on the topological analysis, singularity analysis, feasibility analysis, gait analysis and the motion strategy based on motor time-sharing control, the paper theoretically verified that the robot can switch between the four motion modes.

The robot integrates four-bar walking, self-deforming and four-bar and six-bar rolling modes. A series of simulation and prototype experiment results are presented to verify the feasibility of multiple motion modes of the robot.

The work presented in this paper provides a good theoretical basis for further exploration of multiple mode mobile robots. It is an attempt to design the multi-mode mobile robot based on single loop kinematotropic mechanisms. It is also a kind of exploration of the new unknown movement law.

To reveal the effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations, the leadership rapport orientation is subdivided into two types of values-based/instrumental rapport orientation. The mechanism of supervisor developmental feedback in mediating between leadership rapport orientation and employees' innovative behaviour and the moderating effect of ambidextrous environments is explored. This paper aims to discuss the aforementioned objective.

Leadership rapport orientation is divided into value-based and instrumental rapport orientation to reveal effective ways for leaders to motivate employees' innovative behaviour in complex environmental situations.

The results show that the values-based (instrumental) rapport orientation of leaders impacts employees' innovative behaviour positively (negatively).

Leaders' values-based/instrumental rapport orientation indirectly influences employees' innovative behaviour through supervisor developmental feedback, which positively moderates the relationship between the values-based or instrumental rapport orientation of leaders and employees' innovative behaviour and further moderates the partially mediating role of supervisor developmental feedback between leaders' values-based/instrumental rapport orientation and employees' innovative behaviour.

The purpose of this paper is to create a better understanding of how entrepreneurial education research has evolved with regard to pedagogy over the past decades.

The authors employed systematic review methodology to enable an in-depth analysis of the literature in a process that was both replicable and transparent. Guided by the research purpose, the systematic review of 395 articles published between January 1980 and December 2018 was influenced by a configurative approach aimed at interpreting and understanding the phenomenon under study.

The analysis suggests that the scholarly discourse on pedagogy in entrepreneurial education research has developed over time from teacher-guided instructional models to more constructivist perspectives. A shift in the literature was also observed, where scholarly discussions moved from addressing the issue of teachability to a greater emphasis on learnability. Contemporary discussions centre on the theoretical and philosophical foundations of experience-based teaching and learning.

The study illustrates how entrepreneurial education has evolved into a distinct research theme, characterized by a practice-oriented research agenda that emphasizes the need to connect teaching to “real-world” environments. The practice-oriented agenda has led to continued societal interest in promoting entrepreneurial education, while at the same time creating low academic legitimacy.