Search results

Student engagement is a key factor that connects with student achievement and retention. This paper aims to identify individuals' engagement automatically in the classroom with multimodal data for supporting educational research.

The video and electroencephalogram data of 36 undergraduates were collected to represent observable and internal information. Since different modal data have different granularity, this study proposed the Fast–Slow Neural Network (FSNN) to detect engagement through both observable and internal information, with an asynchrony structure to preserve the sequence information of data with different granularity.

Experimental results show that the proposed algorithm can recognize engagement better than the traditional data fusion methods. The results are also analyzed to figure out the reasons for the better performance of the proposed FSNN.

This study combined multimodal data from observable and internal aspects to improve the accuracy of engagement detection in the classroom. The proposed FSNN used the asynchronous process to deal with the problem of remaining sequential information when facing multimodal data with different granularity.

The purpose of this study is to conduct a systematic review to understand state-of-art research related to learning preferences from the aspects of impacts, influential factors and evaluation methods.

This paper uses the systematic synthesis method to provide state-of-the-art knowledge on learning preference research by summarizing published studies in major databases and attempting to aggregate and reconcile the scientific results from the individual studies. The findings summarize aggregated research efforts and improve the quality of future research.

After analyzing existing literature, this study proposed three possible research directions in the future. First, researchers might focus on how to use the real-time tracking mechanism to further understand other impacts of learning preferences within the learning environments. Second, existing studies mainly focused on the influence of singular factors on learning preferences. The joint effects of multiple factors should be an important topic for future research. Finally, integrated algorithms might become the most popular evaluation method of learning preference in the era of smart learning environments.

This review used the search results generated by Google Scholar and Web of Science databases. There might be published papers available in other databases that have not been taken into account.

The research summarizes the state-of-art research related to learning preferences. This paper is one of the first to discuss the development of learning preference research in smart learning environments.

The purpose of this paper is to study an electrolytic etching method to prepare fine lines on printed circuit board (PCB). And the influence of organics on the side corrosion protection of PCB fine lines during electrolytic etching is studied in detail.

In this paper, the etching factor of PCB fine lines produced by new method and the traditional method was analyzed by the metallographic microscope. In addition, field emission scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS) were used to study the inhibition of undercut of the four organometallic corrosion inhibitors with 2,5-dimercapto-1,3,4-thiadiazole, benzotriazole, l-phenylalanine and l-tryptophan in the electrolytic etching process.

The SEM results show that corrosion inhibitors can greatly inhibit undercut of PCB fine lines during electrolytic etching process. XPS results indicate that N and S atoms on corrosion inhibitors can form covalent bonds with copper during electrolytic etching process, which can be adsorbed on sidewall of PCB fine lines to form a dense protective film, thereby inhibiting undercut of PCB fine lines. Quantum chemical calculations show that four corrosion inhibitor molecules tend to be parallel to copper surface and adsorb on copper surface in an optimal form. COMSOL Multiphysics simulation revealed that there is a significant difference in the amount of corrosion inhibitor adsorbed on sidewall of the fine line and the etching area.

As a clean production technology, electrolytic etching method has a good development indicator for the production of high-quality fine lines in PCB industry in the future. And it is of great significance in saving resources and reducing environmental pollution.

The purpose of this paper is to characterize distracted driving by quantifying the response time and response intensity to an emergency stop using the driver’s physiological states.

Field tests with 17 participants were conducted in the connected and automated vehicle test field. All participants were required to prioritize their primary driving tasks while a secondary nondriving task was asked to be executed. Demographic data, vehicle trajectory data and various physiological data were recorded through a biosignalsplux signal data acquisition toolkit, such as electrocardiograph for heart rate, electromyography for muscle strength, electrodermal activity for skin conductance and force-sensing resistor for braking pressure.

This study quantified the psychophysiological responses of the driver who returns to the primary driving task from the secondary nondriving task when an emergency occurs. The results provided a prototype analysis of the time required for making a decision in the context of advanced driver assistance systems or for rebuilding the situational awareness in future automated vehicles when a driver’s take-over maneuver is needed.

The hypothesis is that the secondary task will result in a higher mental workload and a prolonged reaction time. Therefore, the driver states in distracted driving are significantly different than in regular driving, the physiological signal improves measuring the brake response time and distraction levels and brake intensity can be expressed as functions of driver demographics. To the best of the authors’ knowledge, this is the first study using psychophysiological measures to quantify a driver’s response to an emergency stop during distracted driving.

This paper seeks to examine the way quality management practices (QMPs) impact quality outcome, R&D process, and business performance, using investigation data from Chinese firms. The possible moderating effects of industrial types and competition on the above influencing relationships were investigated as well.

A two‐round questionnaire survey was conducted to 196 manufacturing and service firms in West China, and hypotheses were verified using a structural equation model with LISREL software.

The results suggest that quality management practices do not have a positive impact on firms' business performance directly, but have an indirect impact on business performance mediated by quality performance and R&D performance. Furthermore, the authors find that industrial type can moderate the relationships between quality management practices and business performance, while competition does not.

The findings make a significant contribution to understanding how QMPs impact firms' performance. In addition, the authors' research provides empirical evidence for the fact that QMPs' contribution to firms' financial and marketing performance is greater in service firms, which partly reflects the actual situation in China and other similar developing countries.