Diagnosing and developing the IT skills of new entrants to higher education

This paper presents an approach to the diagnosis and development off IT skills using Computer Aided Assessment (CAA). It looks at the rationale for the assessment of IT skills and the relevance for higher education in general. It reflects on some of the outcomes of the project and staff and student thoughts on the use of CAA in this context

Energy spread of ultracold electron bunches extracted from a laser cooled gas

Ultrashort and ultracold electron bunches created by near-threshold femtosecond photoionization of a laser-cooled gas hold great promise for single-shot ultrafast diffraction experiments. In previous publications the transverse beam quality and the bunch length have been determined. Here the longitudinal energy spread of the generated bunches is measured for the first time, using a specially developed Wien filter. The Wien filter has been calibrated by determining the average deflection of the electron bunch as a function of magnetic field. The measured relative energy spread $\frac{\sigma_{U}}{U} = 0.64 \pm 0.09\%$ agrees well with the theoretical model which states that it is governed by the width of the ionization laser and the acceleration length

Descriptive Statistics for all variables.

<p>Hypomanic Personality Scale (HPS); Altman Self Rating Mania Index (ASRM); Responses to Positive Affect Scale (RPA); Positive Urgency Measure (PUM); Inspiration Scale (IS); External and Internal Scale of Inspiration (EISI).</p

Component loading table for External and Internal Inspiration Scale.

<p><i>Note: Factor</i> 1 = Self-focus; Factor 2 = Others-focus; Factor 3 = Achievement-focus; Factor 4 = Emotion = focus; Factor 5 = External-focus h² = Communality; r_i(t-i) =Corrected item-total correlation; Corrected α = Cronbach's α if item delete.</p

Correlations between the EISI, EISI subscales with IS, RPA, PUM, HPS and ASRM.

<p><i>Note:</i> EISI = External and Internal Scale of Inspiration; IS = Inspiration Scale; HPS = Hypomanic Personality Scale; ASRM = Altman Self Rating Mania Index; RPA = Responses to Positive Affect Scale; PUM = Positive Urgency Measure; Hypothesis 1b: ** <i>p<</i>0.001 (Adjusted α-level after Bonferroni correction = 0.004); Hypothesis 1c: *<i>p</i><0.002 **<i>p</i><0.001 (Adjusted α-level after Bonferroni correction = 0.002); Hypothesis 2a: <i>*p</i><0.003 **<i>p<</i>0.001 (Adjusted α-level after Bonferroni correction = 0.003).</p

Hypothesis 2b: Associations between HPS, ASRM, and EISI subscales.

<p>Note: EISI = External and Internal Scale of Inspiration; HPS = Hypomanic Personality Scale; ASRM = Altman Self Rating Mania Index. EISI, R² = .11** at step 1, ΔR² = .004 at step 2.</p><p>Self, R² = .10** at step 1, ΔR² = .004 at step 2.</p><p>Others, <i>R</i>² = .02** at step 1, <i>ΔR</i>² = .004 at step 2.</p><p>Achievement, <i>R</i>² = .06** at step 1, <i>ΔR</i>² = .001 at step 2.</p><p>Prosocial, <i>R</i>² = .06** at step 1, <i>ΔR</i>² = .000 at step 2.</p><p>External, <i>R</i>² = .02** at step 1, <i>ΔR</i>² = .002 at step 2.</p><p>*<i>p</i><0.003.</p><p>**<i>p<</i>0.001 (Adjusted α for Bonferroni correction = .003).</p

Hypothesis 2b: Associations between EISI subscales and bipolar risk, when controlling for current mania.

<p>Note: <i>R</i>² = .11** at step 1, <i>ΔR</i>² = .06** at step 2. HPS = Hypomanic Personality Scale; ASRM = Altman Self-Rating Mania Scale.</p><p>**<i>p<</i>0.001 (Adjusted α for Bonferroni correction = .003).</p

External and Internal Scale of Inspiration (EISI).

<p>External and Internal Scale of Inspiration (EISI).</p

Description of Questionnaire Measures.

<p><i>Note:</i> Hypomanic Personality Scale (HPS); Altman Self Rating Mania Index (ASRM); Inspiration Scale (IS); External and Internal Scale of Inspiration (EISI); Responses to Positive Affect Scale (RPA); Positive Urgency Measure (PUM).</p

Photothermal Heating of Plasmonic Nanoantennas: Influence on Trapped Particle Dynamics and Colloid Distribution

Plasmonic antennas are well-known and extremely powerful platforms for optical spectroscopy, sensing, and manipulation of molecules and nanoparticles. However, resistive antenna losses, resulting in highly localized photothermal heat generation, may significantly compromise their applicability. Here we investigate how the interplay between plasmon-enhanced optical and thermal forces affects the dynamics of nanocolloids diffusing in close proximity to gold bowtie nanoantennas. The study is based on an anti-Stokes thermometry technique that can measure the internal antenna temperature with an accuracy of ∼5 K over an extended temperature range. We argue that Kapitza resistances have a significant impact on the local thermal landscape, causing an interface temperature discontinuity of up to ∼20% of the total photothermal temperature increase of the antenna studied. We then use the bowties as plasmonic optical tweezers and quantify how the antenna temperature influences the motion and distribution of nearby fluorescent colloids. We find that colloidal particle motion within the plasmonic trap is primarily dictated by a competition between enhanced optical forces and enhanced heating, resulting in a surprising insensitivity to the specific resonance properties of the antenna. Furthermore, we find that thermophoretic forces inhibit diffusion of particles toward the antenna and drive the formation of a thermal depletion shell that extends several microns. The study highlights the importance of thermal management at the nanoscale and points to both neglected problems and new opportunities associated with plasmonic photothermal effects in the context of nanoscale manipulation and analysis