Search results

The purpose of this study is to examine the effect of students’ self-regulation, co-regulation and behavioral engagement on their performance in flipped learning environments in higher education.

The subjects were college students taking an education course offered at a 4-year university in South Korea. Structural equation modeling was adopted to analyze 221 student responses.

The findings indicated that the more students self-regulated, the more likely they were to engage in co-regulation with other students in the class. Students’ self-regulation and co-regulation also significantly affected their behavioral engagement. Finally, students’ self-regulation positively affected their academic performance, while co-regulation and behavioral engagement did not affect their performance.

Based on these findings, this study provides meaningful implications for scholars and practitioners on how to select and use more appropriate instructional and evaluation strategies to improve students’ positive behavior, engagement and performance in a flipped learning environment.

The purpose of this paper is to apply what can be learned from the emergence of nature tourism to understand some current and future trends of tourism.

This study adopted the evolutionary paradigm for investigation.

The emergence of nature tourism in early medieval China can be attributed to four major factors, including transformation of value orientations, seeking longevity, interest in suburbs and population migration.

Historical studies help understand the current and future trends. When the contributing factors for nature tourism are linked to the contemporary world, it can be found that these factors are still playing a part in shaping tourism trends or patterns in their original or alternative forms. These trends or patterns are worthy of scholarly investigations.

This paper offers a comprehensive understanding of the origins of nature tourism.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

The electromechanical brake system is leading the latest development trend in railway braking technology. The tolerance stack-up generated during the assembly and production process catalyzes the slight geometric dimensioning and tolerancing between the motor stator and rotor inside the electromechanical cylinder. The tolerance leads to imprecise brake control, so it is necessary to diagnose the fault of the motor in the fully assembled electromechanical brake system. This paper aims to present improved variational mode decomposition (VMD) algorithm, which endeavors to elucidate and push the boundaries of mechanical synchronicity problems within the realm of the electromechanical brake system.

The VMD algorithm plays a pivotal role in the preliminary phase, employing mode decomposition techniques to decompose the motor speed signals. Afterward, the error energy algorithm precision is utilized to extract abnormal features, leveraging the practical intrinsic mode functions, eliminating extraneous noise and enhancing the signal’s fidelity. This refined signal then becomes the basis for fault analysis. In the analytical step, the cepstrum is employed to calculate the formant and envelope of the reconstructed signal. By scrutinizing the formant and envelope, the fault point within the electromechanical brake system is precisely identified, contributing to a sophisticated and accurate fault diagnosis.

This paper innovatively uses the VMD algorithm for the modal decomposition of electromechanical brake (EMB) motor speed signals and combines it with the error energy algorithm to achieve abnormal feature extraction. The signal is reconstructed according to the effective intrinsic mode functions (IMFS) component of removing noise, and the formant and envelope are calculated by cepstrum to locate the fault point. Experiments show that the empirical mode decomposition (EMD) algorithm can effectively decompose the original speed signal. After feature extraction, signal enhancement and fault identification, the motor mechanical fault point can be accurately located. This fault diagnosis method is an effective fault diagnosis algorithm suitable for EMB systems.

By using this improved VMD algorithm, the electromechanical brake system can precisely identify the rotational anomaly of the motor. This method can offer an online diagnosis analysis function during operation and contribute to an automated factory inspection strategy while parts are assembled. Compared with the conventional motor diagnosis method, this improved VMD algorithm can eliminate the need for additional acceleration sensors and save hardware costs. Moreover, the accumulation of online detection functions helps improve the reliability of train electromechanical braking systems.

Even though the crowdfunding (CF) literature is rapidly reaching its maturity phase, the topic of serial CF (i.e. the participation in more than one CF campaign) is as much promising as still largely under explored. This study thus aims to offer a thorough view of the dynamic and complex processes characterizing the participation of the start-ups to more than one campaign adopting a business network perspective.

In line with an explorative research aim, a multiple case study analysis is performed by taking into consideration four start-ups engaged in more than one CF campaigns with different combinations of equity and non-equity CF, adopting the actor–resource–activity (ARA) model as theoretical framework.

Multiple CF campaigns are embedded in the overall changing startup’s network and are affected by the concurrent and overlapping startup’s development processes. From this standpoint, the adoption of the ARA model suggests to reconsider the “serial” dimension of multiple CF campaigns. These processes can be more or less “linear” as they could be affected by the combination of CF schemes and by the degree of alignment of actors, activities and resources, whose “assembly” can be facilitated by learning processes and impaired by unexpected circumstances.

This paper explores in depth the startup’s serial CF journey, building on recent studies calling for stronger analyses of the directions and outcomes of innovative funding trajectories pursued and implemented by new business ventures. From this standpoint, to the best of the authors’ knowledge, this is the first study to consider a complete spectrum of combinations between CF schemes within serial CF, thus allowing for a better understanding of the role of such a factor within a dynamic and contextual view, that is, that offered by the business network perspective. This paper also contributes to the Industrial Marketing and Purchasing research on start-ups.

Library intelligence institutions, which are a kind of traditional knowledge management organization, are at the frontline of the big data revolution, in which the use of unstructured data has become a modern knowledge management resource. The paper aims to discuss this issue.

This research combined theme logic structure (TLS), artificial neural network (ANN), and ensemble empirical mode decomposition (EEMD) to transform unstructured data into a signal-wave to examine the research characteristics.

Research characteristics have a vital effect on knowledge management activities and management behavior through concentration and relaxation, and ultimately form a quasi-periodic evolution. Knowledge management should actively control the evolution of the research characteristics because the natural development of six to nine years was found to be difficult to plot.

Periodic evaluation using TLS-ANN-EEMD gives insights into journal evolution and allows journal managers and contributors to follow the intrinsic mode functions and predict the journal research characteristics tendencies.