Investigating factors that influence social presence and learning outcomes in distance higher education

There are many factors that influence distance learning especially in higher education where collaborative and communicative discourse is necessary for pursuing knowledge. Social presence, among other factors, is an important concept to be facilitated, developed and sustained in distance higher education as it promotes and supports discourse based learning. This study examines the relationship among demographic and other variables, social presence and learning satisfaction. Results showed demographic variables, such as gender, online learning experience and work status were not significant factors in terms of influencing on either social presence or learning satisfaction. While media integration and instructor’s quality teaching were significant predictors of both social presence and learning satisfaction, interactivity among participants was a predictor of social presence but not of learning satisfaction. Along with the study findings, some implications were discussed for online learning practitioners in higher education setting.     

Recent developments in scale-up of microfluidic emulsion generation via parallelization

Microfluidics affords precise control over the flow of multiphasic fluids in micron-scale channels. By manipulating the viscous and surface tension forces present in multiphasic flows in microfluidic channels, it is possible to produce highly uniform emulsion droplets one at a time. Monodisperse droplets generated based on microfluidics are useful templates for producing uniform microcapsules and microparticles for encapsulation and delivery of active ingredients as well as living cells. Also, droplet microfluidics have been extensively exploited as a means to enable highthroughput biological screening and assays. Despite the promise droplet-based microfluidics hold for a wide range of applications, low production rate (<<10mL/hour) of emulsion droplets has been a major hindrance to widespread utilization at the industrial and commercial scale. Several reports have recently shown that one way to overcome this challenge and enable mass production of microfluidic droplets is to parallelize droplet generation, by incorporating a large number of droplet generation units (N>>100) and networks of fluid channels that distribute fluid to each of these generators onto a single chip. To parallelize droplet generation and, at the same time, maintain high uniformity of emulsion droplets, several considerations have to be made including the design of channel geometries to ensure even distribution of fluids to each droplet generator, methods for large-scale and uniform fabrication of microchannels, device materials for mechanically robust operation to withstand high-pressure injection, and development of commercially feasible fabrication techniques for three-dimensional microfluidic devices. We highlight some of the recent advances in the mass production of highly uniform microfluidics droplets via parallelization and discuss outstanding issues.     

The studies of physical properties of dimeric fatty acid-modified thiodiphenyl epoxy resins

A thermally stable thiodiphenyl epoxy resin was modified with a dimeric fatty acid at an epoxy resin:fatty acid molar ratio of 4:1. The thermal and mechanical properties of the modified epoxy resin were studied by preparing an epoxy composition with an amine curing agent and a catalyst, followed by curing at 170 °C to produce a neat plastic epoxy resin. The tensile and impact strengths of the resin indicated improved flexibility and toughness compared to other epoxy resins. Enhanced toughness was confirmed by the increased lap shear strength in single lap joints prepared with steel substrates attached by the resin.     

A heuristic method of variable selection based on principal component analysis and factor analysis for monitoring in a 300 kW MCFC power plant

In a commercialized 300 kW molten carbonate fuel cell (MCFC) power plant, a univariate alarm system that has only upper and lower limits is usually employed to identify abnormal conditions in the system. Even though univariate alarms have already been adopted for system monitoring, this simple monitoring system is limited for using in an extended monitoring system for fault diagnosis. Therefore, based on principal component analysis (PCA), a recursive variable grouping method for a multivariate monitoring system in a commercialized MCFC power plant is presented in this paper. In terms of development, since a principal component analysis model that contains all system variables cannot isolate a system fault, heuristic recursive variable selection method using factor analysis is presented here. To verify the performance of the fault detection, real plant operations data are used. Furthermore, comparison between type 1 and type 2 errors for four different variable groups demonstrates that the developed heuristic method works well when system faults occur. These monitoring techniques can reduce the number of false alarms occurring on site at MCFC power plant.     

Rapid Screening of Glucocorticoid Receptor (GR) Effectors Using Cortisol-Detecting Sensor Cells

Cortisol, a stress hormone, plays key roles in mediating stress and anti-inflammatory responses. As abnormal cortisol levels can induce various adverse effects, screening cortisol and cortisol analogues is important for monitoring stress levels and for identifying drug candidates. A novel cell-based sensing system was adopted for rapid screening of cortisol and its functional analogues under complex cellular regulation. We used glucocorticoid receptor (GR) fused to a split intein which reconstituted with the counterpart to trigger conditional protein splicing (CPS) in the presence of targets. CPS generates functional signal peptides which promptly translocate the fluorescent cargo. The sensor cells exhibited exceptional performance in discriminating between the functional and structural analogues of cortisol with improved sensitivity. Essential oil extracts with stress relief activity were screened using the sensor cells to identify GR effectors. The sensor cells responded to peppermint oil, and L-limonene and L-menthol were identified as potential GR effectors from the major components of peppermint oil. Further analysis indicated L-limonene as a selective GR agonist (SEGRA) which is a potential anti-inflammatory agent as it attenuates proinflammatory responses without causing notable adverse effects of GR agonists.     

All-nanoparticle thin-film coatings

All-nanoparticle thin-film coatings that exhibit antireflection, antifogging (superhydrophilicity), and self-cleaning properties have been prepared via layer-by-layer deposition of TiO(2) and SiO(2) nanoparticles. The porosity and chemical composition of the coatings were determined using a simple method that is based on ellipsometry and does not require any assumptions about the refractive indices of the constituent nanoparticles. The presence of nanopores in the TiO(2)/SiO(2) nanoparticle coatings results in superhydrophilicity as well as antireflection properties. The superhydrophilicity of contaminated coatings could also be readily recovered and retained after ultraviolet irradiation.