Light exposure has a profound impact on human physiology and behaviour. For example, light exposure at the wrong time can disrupt our circadian rhythms and acutely suppress the production of melatonin. In turn, appropriately timed light exposure can support circadian photoentrainment. Beginning with the discovery that melatonin production is acutely suppressed by bright light more than 40 years ago, understanding which aspects of light drive the 'non-visual' responses to light remains a highly active research area, with an important translational dimension and implications for "human-centric" or physiologically inspired architectural lighting design. In 2018, the International Commission on Illumination (CIE) standardised the spectral sensitivities for predicting the non-visual effects of a given spectrum of light with respect to the activation of the five photoreceptor classes in the human retina: the L, M and S cones, the rods, and the melanopsin-containing intrinsically photosensitive retinal ganglion cells (ipRGCs). Here, we described a novel, lean, user-friendly, open-access and open-source platform for calculating quantities related to light. The platform, called luox, enables researchers and research users in vision science, lighting research, chronobiology, sleep research and adjacent fields to turn spectral measurements into reportable quantities. The luox code base, released under the GPL-3.0 License, is modular and therefore extendable to other spectrum-derived quantities. luox calculations of CIE quantities and indices have been endorsed by the CIE following black-box validation.

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As a primary user group, Deaf or Hard of Hearing (D/HOH) audiences use Closed Captioning (CC) service to enjoy the TV programs with audio by reading text. However, the D/HOH communities are not completely satisfied with the quality of CC even though the government regulators entail certain rules in the CC quality factors. The measure of the CC quality is often interpreted as an accuracy on translation and regulators use the empirical models to assess. The need of a subjective quality scale comes from the gap in between current empirical assessment models and the audience perceived quality. It is possible to fill the gap by including the subjective assessment by D/HOH audiences. This research proposes a design of an automatic quality assessment system for CC which can predict the D/HOH audience subjective ratings. A simulated rater is implemented based on literature and the CC quality factor representative value extraction algorithm is developed. Three prediction models are trained with a set of CC quality values and corresponding rating scores, then they are compared to find the feasible prediction model.