Facing Anxiety, Growing Up. Trait Emotional Intelligence as a Mediator of the Relationship Between Self-Esteem and University Anxiety

The current study analyzed how trait emotional intelligence (trait EI) mediates the relationship between self-esteem and state anxiety and trait anxiety. The sample was composed of 153 undergraduate students from the University of Cádiz, Spain (71.9% women and 28.1% men). Students completed measures of self-esteem, state anxiety, trait anxiety, and trait EI. Mediation analyses were completed with three trait EI dimensions (EA, emotional attention; EC, emotional clarity; and MR, mood repair) as mediating variables, self-esteem as the independent variable, and state anxiety and trait anxiety as the dependent ones. Our results confirmed that self-esteem scores explained and predicted both, state and trait anxiety values (13% for state and 21% for trait anxiety). This explanatory capacity is increased by 8% when accounting for all trait EI dimensions. Considering state anxiety, the results of the direct effects showed that a decrease in their levels is predicted through the increases in the levels of both, self-esteem and MR. Regarding trait anxiety, the results of the direct effects showed that a decrease in their levels is predicted, in addition to an increment of self-esteem and MR values, by an increase of EC and a decrease of EA. Conversely, indirect effects revealed that higher levels of self-esteem were associated with worse scores in EA and worse MR, which in turn would enhance both state and trait anxiety levels. Moreover, regarding trait anxiety higher levels of self-esteem were associated with worse scores in EA and worse EC, therefore increasing trait anxiety levels. As shown, the negative association found between self-esteem and EA becomes a key element. The effect of self-esteem on EA and the influence that the latter had on EC and MR exerts an indirect mediated effect with the power to invert the influence that self-esteem wields on both types of anxiety. In this sense, the apparent protective role of self-esteem changed, turning into a risk factor that promotes higher anxiety values

Penrose pixels: Super-resolution in the detector layout domain

We present a novel approach to reconstruction based superresolution that explicitly models the detector’s pixel layout. Pixels in our model can vary in shape and size, and there may be gaps between adjacent pixels. Furthermore, their layout can be periodic as well as aperiodic, such as Penrose tiling or a biological retina. We also present a new variant of the well known error back-projection super-resolution algorithm that makes use of the exact detector model in its back projection operator for better accuracy. Our method can be applied equally well to either periodic or aperiodic pixel tiling. Through analysis and extensive testing using synthetic and real images, we show that our approach outperforms existing reconstruction based algorithms for regular pixel arrays. We obtain significantly better results using aperiodic pixel layouts. As an interesting example, we apply our method to a retina-like pixel structure modeled by a centroidal Voronoi tessellation. We demonstrate that, in principle, this structure is better for super-resolution than the regular pixel array used in today’s sensors.

An LED-only BRDF Measurement Device

Light Emitting Diodes (LEDs) can be used as light detectors and as light emitters. In this paper, we present a novel BRDF measurement device consisting exclusively of LEDs. Our design can acquire BRDFs over a full hemisphere, or even a full sphere (for the bidirectional transmittance distribution function BTDF) , and can also measure a (partial) multi-spectral BRDF. Because we use no cameras, projectors, or even mirrors, our design does not suffer from occlusion problems. It is fast, significantly simpler, and more compact than existing BRDF measurement designs. 1

Online work force analyzes social media to identify consequences of an unplanned school closure – using technology to prepare for the next pandemic

We used the social media-monitoring platform Radian6 (San Francisco, CA) to retrospectively capture social media posts related to the Chicago City School District closure in September 2012. Social media in dataset include posts from Twitter, Facebook, blogs, forums, and comments between September 8 and September 12, (two days before the strike started to two days after the strike ended). We used the following combination of search terms: “strike Chicago” AND “breakfast” OR “childcare” OR “daycare” OR “lunch” OR “parent”.  A proximity score of “5” was applied to the terms “strike” and “Chicago” (on a scale of 1–20, with 1 being exact [i.e., strike and Chicago together]).<div><br></div><div>Column headings include:  Unique post identifying number (NUMBER), post content (CONTENT), social media provider (MEDIA_PROVIDER), and publishing date/time (PUBLISH_DATE).  </div><div><br></div><div>These posts were reviewed and categorized as relevant (related to impact of closure on students and their families) or irrelevant (describing political aspects of strike, welfare system, or other unrelated topics).  Relevant posts were further analyzed for underlying sentiment (positive, neutral, or negative).</div

Sentiment score of relevant posts identified from social media by date.

<p>Relevant posts mentioned impact of unplanned school closure due to Chicago teachers’ strike from September 8–21 (two days before to three days after strike) on students and their families (N = 930). Sentiment score was calculated as: (positive posts—negative posts)/(positive posts + negative posts + neutral posts)^a-b. ^aSentiment score < 0 suggests negative sentiment; score > 0 suggests positive sentiment. ^bScore on September 21 reflects only four relevant posts, all expressing negative sentiment.</p