An experimental study of satisfaction response: Evaluation of online collaborative learning

On the one hand, a growing amount of research discusses support for improving online collaborative learning quality, and many indicators are focused to assess its success. On the other hand, thinkLets for designing reputable and valuable collaborative processes have been developed for more than ten years. However, few studies try to apply thinkLets to online collaborative learning. This paper introduces thinkLets to online collaborative learning and experimentally tests its effectiveness with participants' responses on their satisfaction. Yield Shift Theory (YST), a causal theory explaining inner satisfaction, is adopted. In the experiment, 113 students from Universities in Beijing, China are chosen as a sample. They were divided into two groups, collaborating online in a simulated class. Then, YST in student groups under online collaborative learning is validated, a comparison study of online collaborative learning with and without thinkLets is implemented, and the satisfaction response of participants are analyzed. As a result of this comparison, YST is proved applicable in this context, and satisfaction is higher in online collaborative learning with thinkLets

A new model based on adiabatic flame temperature for evaluation of the upper flammable limit of alkane-air-CO2 mixtures

© 2017 Elsevier B.V. For security issue of alkane used in Organic Rankine Cycle, a new model to evaluate the upper flammability limits for mixtures of alkanes, carbon dioxide and air has been proposed in present study. The linear relationship was found at upper flammability limits between molar fraction of diluent in alkane-CO 2 mixture and calculated adiabatic flame temperature. The prediction ability of the variable calculated adiabatic flame temperature model that incorporated the linear relationship above is greatly better than the models that adopted the fixed calculated adiabatic flame temperature at upper flammability limit. The average relative differences between results predicted by the new model and observed values are less than 3.51% for upper flammability limit evaluation. In order to enhance persuasion of the new model, the observed values of n-butane-CO 2 and isopentane-CO 2 mixtures measured in this study were used to confirm the validity of the new model. The predicted results indicated that the new model possesses the capacity of practical application and can adequately provide safe non-flammable ranges for alkanes diluted with carbon dioxide

Numerical Study of Surfactant Dynamics during Emulsification in a T‑Junction Microchannel

Microchannel emulsification requires large amounts of surfactant to prevent coalescence and improve emulsions lifetime. However, most numerical studies have considered surfactant-free mixtures as models for droplet formation in microchannels, without taking into account the distribution of surfactant on the droplet surface. In this paper, we investigate the effects of nonuniform surfactant coverage on the microfluidic flow pattern using an extended lattice-Boltzmann model. This numerical study, supported by micro-particle image velocimetry experiments, reveals the likelihood of uneven distribution of surfactant during the droplet formation and the appearance of a stagnant cap. The Marangoni effect affects the droplet breakup by increasing the shear rate. According to our results, surfactant-free and surfactant-rich droplet formation processes are qualitatively different, such that both the capillary number and the Damköhler number should be considered when modeling the droplet generation in microfluidic devices. The limitations of traditional volume and pressure estimation methods for determining the dynamic interfacial tension are also discussed on the basis of the simulation results

Numerical Study of Surfactant Dynamics during Emulsification in a T‑Junction Microchannel

Microchannel emulsification requires large amounts of surfactant to prevent coalescence and improve emulsions lifetime. However, most numerical studies have considered surfactant-free mixtures as models for droplet formation in microchannels, without taking into account the distribution of surfactant on the droplet surface. In this paper, we investigate the effects of nonuniform surfactant coverage on the microfluidic flow pattern using an extended lattice-Boltzmann model. This numerical study, supported by micro-particle image velocimetry experiments, reveals the likelihood of uneven distribution of surfactant during the droplet formation and the appearance of a stagnant cap. The Marangoni effect affects the droplet breakup by increasing the shear rate. According to our results, surfactant-free and surfactant-rich droplet formation processes are qualitatively different, such that both the capillary number and the Damköhler number should be considered when modeling the droplet generation in microfluidic devices. The limitations of traditional volume and pressure estimation methods for determining the dynamic interfacial tension are also discussed on the basis of the simulation results

Elite Model for the Generation of Induced Pluripotent Cancer Cells (iPCs)

<div><p>The inefficiency of generating induced pluripotent somatic cells (iPS) engendered two contending models, namely the Stochastic model and Elite model. Although the former is more favorable to explain the inherent inefficiencies, it may be fallible to extrapolate the same working model to reprogramming of cancer cells. Indeed, tumor cells are known to be inherently heterogeneous with respect to distinctive characteristics thus providing a suitable platform to test whether the reprogramming process of cancer cells is biased. Here, we report our observations that all randomly picked induced pluripotent cancer cells (iPCs) established previously do not possess mutations known in the parental population. This unanticipated observation is most parsimoniously explained by the Elite model, whereby putative early tumor progenies were selected during induction to pluripotency.</p> </div

Evidence rejecting the Stochastic model for generating iPCs.

<p>(A) Serial dilution assay showing that at 10^3.7 times dilution, <i>CDKN2B</i> in IMR90 is detectable at basal levels. Therefore, at most 1 in 5000 H460 cells are ‘mutation-free’. (B) The probability model to test the null hypothesis: “Generation of iPCs follows the Stochastic model”. Subsequent input of the parameters determined in (A) suggests the rejection of the Stochastic model in the reprogramming of H358 and H460.</p

Unexpected expression of wild-type <i>TP53</i> in iPCH358 but not in H358.

<p>(A) log-transformed intensity readouts from Illumina HumanHT-12 indicating a significant (FDR-adjusted P<0.05) upregulation of <i>TP53</i> transcript in iPCH358 compared to H358. This is unexpected since H358 is known to be <i>TP53</i>−/−. (B) PCR (upper panel) and Western Blot (lower panel) assays confirming expression of <i>TP53</i> in several randomly picked colonies of iPCH358. The coding region of <i>TP53</i> in Col#1, Col#3 and Col#11 are wild-type (GenBank accession: JQ694049–JQ694051). (C) PCR (left panel) and Western Blot (right panel) assays on iPCH358 colonies of >20 passages revealed that passage number is uninformative to the outcome of these assays. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056702#s3" target="_blank">Results</a> from experiments conducted on separate occasions or cropped from the same image are marked by a broken line; original images can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056702#pone.0056702.s005" target="_blank">Presentation S1</a> which includes detailed documentation of passage number.</p

<i>CDKN2A</i> and <i>CDKN2B</i> are not mutated in iPCH460 colonies.

<p>(A) Heat map indicating methylation array probes (rows) failing to hybridize to <i>CDKN2A</i> and <i>CDKN2B</i> promoters in H460 (empty bars), but not so in iPCH460. (B) PCR (upper panel) assay showing <i>CDKN2A</i> and <i>CDKN2B</i> are detectable in iPCH460 while Western Blot (lower panel) assay showing <i>CDKN2A</i>, which is homozygous deleted in H460, is detectable in iPCH460. (C) PCR (upper panel) and Western Blot (lower panel) assays on iPCH460 colonies of >20 passages revealed that passage number is uninformative to the outcome of these assays. <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056702#s3" target="_blank">Results</a> from experiments conducted on separate occasions or cropped from the same image are marked by a broken line; original images can be found in Presentation S1 which includes detailed documentation of passage number.</p

Single explant tumor of H358 enriches ‘mutation-free’ subpopulation.

<p>(A) One out of four H358 explant tumor generated show presence of <i>TP53</i> in the genome, indicating enrichment of the elusive ‘mutation-free’ subpopulation. (B) None of the H460 explant tumors were observed to be enriched for ‘mutation-free’ subpopulation. Genomic DNA of SCID mice tail was used to control for mice DNA contamination in explant tumors. Information of explant tumors can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0056702#pone.0056702.s007" target="_blank">Table S2</a>.</p

Fixation of variant alleles in the bacterial population progresses by a deterministic process.

<p>The squares indicate the proportion of variants in the inoculum and at specific recovery timepoints following serial infection of mice. The curves represent the modelled progression as influenced by a decision marker. Rv0272c (blue line) represents a variant that undergoes rapid purification leading to fixation after 3 passages; Rv2809 (black line) represents two-stage purification leading to fixation after 6 passages. Rapid purification resulting in either full or zero fixation within 3 passages occurred for all variant alleles present at ≥ 50% in the original inoculum; two-stage fixation occurred for all variant alleles present at <50% in the inoculum (<a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1006111#ppat.1006111.s001" target="_blank">S1 Table</a>).</p