Exchanging social support on online teacher groups: Relation to teacher self-efficacy

The present study explored the relation between exchanging social support on online teacher groups and teacher self-efficacy. An online survey was conducted inside a massive Facebook teacher group, and 584 elementary school teachers provided information regarding Facebook use, self-efficacy for creative teaching, and the extent to which they provided and received support on the online teacher group. Results showed that teacher self-efficacy varied as a function of years of teaching experience, length of group membership, and the extent to which teachers provided and received social support on the online teacher group. Furthermore, the providing of online support predicted teacher self-efficacy after taking into account the effects of teaching experience and group membership. The findings are discussed in terms of the features and importance of social support exchange on online teacher support groups.     

Detecting removed attributes in the cyber system for smart manufacturing

The Journal of Supercomputing - According to the concept of edge computing, some service and data should be moved from the centralized data server to the data source for enhancing the computational...     

Validating the moderating role of age in multi-perspective acceptance model of wearable healthcare technology

The recent trend of aging population, not to mention the unprecedented pandemic, draws great attention from the general public about health concerns. Since healthcare information technology is different from non-healthcare information technology, additional contexts should be properly incorporated into technology acceptance research to accurately identify influential factors affecting the acceptance of wearable healthcare technology. Thus, we selectively reconfigured factors from health, privacy, and socio-demographic contexts to formulate a health-aware acceptance model. Then, it was empirically analyzed using structural equation modeling. Based on the results, whereas privacy concerns were directly associated with intention to use wearable healthcare technology, health concerns were not. Moreover, age had a moderating effect on social influence and facilitating conditions. These findings suggest valuable insights that the adoption rate of healthcare technology is increased by 1) keeping personal information securely, 2) facilitating social interaction among users, and 3) offering intuitive user experience for elderly people.     

Defect Engineering in Ambipolar Layered Materials for Mode-Regulable Nociceptor

Defect engineering represents a significant approach for atomically thick 2D semiconductor material development to explore the unique material properties and functions. Doping-induced conversion of conductive polarity is particularly beneficial for optimizing the integration of layered electronics. Here, controllable doping behavior in palladium diselenide (PdSe₂) transistor is demonstrated by manipulating its adatom-vacancy groups. The underlying mechanisms, which originate from reversible adsorption/desorption of oxygen clusters near selenide vacancy defects, are investigated systematically via their dynamic charge transfer characteristics and scanning tunneling microscope analysis. The modulated doping effect allows the PdSe₂ transistor to emulate the essential characteristics of photo nociceptor on a device level, including firing signal threshold and sensitization. Interestingly, electrostatic gating, acting as a neuromodulator, can regulate the adaptive modes in nociceptor to improve its adaptability and perceptibility to handle different danger levels. An integrated artificial nociceptor array is also designed to execute unique image processing functions, which suggests a new perspective for extension of the promise of defect engineered 2D electronics in simplified sensory systems toward use in advanced humanoid robots and artificial visual sensors.     

Prediction of risk factors of cyberbullying-related words in Korea: Application of data mining using social big data

The study examined a decision tree analysis using social big data to conduct the prediction model on types of risk factors related to cyberbullying in Korea. The study conducted an analysis of 103,212 buzzes that had noted causes of cyberbullying and data were collected from 227 online channels, such as news websites, blogs, online groups, social network services, and online bulletin boards. Using opinion-mining method and decision tree analysis, the types of cyberbullying were sorted using SPSS 25.0. The results indicated that the total rate of types of cyberbullying in Korea was 44%, which consisted of 32.3% victims, 6.4% perpetrators, and 5.3% bystanders. According to the results, the impulse factor was also the greatest influence on the prediction of the risk factors and the propensity for dominance factor was the second greatest factor predicting the types of risk factors. In particular, the impulse factor had the most significant effect on bystanders, and the propensity for dominance factor was also significant in influencing online perpetrators. It is necessary to develop a program to diminish the impulses that were initiated by bystanders as well as victims and perpetrators because many of those bystanders have tended to aggravate impulsive cyberbullying behaviors.     

Provably secure lightweight client authentication scheme with anonymity for TMIS using chaotic hash function

The Journal of Supercomputing - Telecare medicine information system (TMIS) is recognized as an important tool for improving the quality and protection of healthcare services. In addition to...     

Study on related simulations between exit characteristics and evacuation performance

Microsystem Technologies - During a building disaster, building occupants typically engage in herd behavior, which results in friction, pushing, and even injury and death. Building occupants...     

High-performance NdSrCo2O5+δ–Ce0.8Gd0.2O2-δ composite cathodes for electrolyte-supported microtubular solid oxide fuel cells

NdSrCo₂O_5+δ (NSCO) is a perovskite with an electrical conductivity of 1551.3 S cm⁻¹ at 500 °C and 921.7 S cm⁻¹ at 800 °C and has a metal-like temperature dependence. This perovskite is used as the cathode material for Ce_0.8Gd_0.2O_2-δ (GDC)-supported microtubular solid oxide fuel cells (MT-SOFCs). The MT-SOFCs fabricated in this study consist of a bilayer anode, comprising a NiO–GDC composite layer and a NiO layer, and a NSCO–GDC composite cathode. Three cell designs with different outer tube diameters, GDC thicknesses, and NSCO/GDC ratios are designed. The MT-SOFC with an outer tube diameter of 1.86 mm, an electrolyte thickness of 180 μm, and a 5NSCO–5GDC composite cathode presents the best performance. The flexural strength of the aforementioned cell is 177 MPa, which is sufficient to confer mechanical integrity to the cell. Moreover, the ohmic and polarization resistance values of the cell are 0.22 and 0.09 Ω cm² at 700 °C, respectively, and 0.15 and 0.03 Ω cm² at 800 °C, respectively. These results indicate that the NSCO-GDC composite exhibits high electrochemical activity. The maximum power densities of the cell at 700 and 800 °C are 0.46 and 0.67 W cm⁻², respectively, exceeding those of existing electrolyte-supported MT-SOFCs with similar electrolyte thicknesses.