In situ visualization of Li/Ag 2 VP 2 O 8 batteries revealing rate-dependent discharge mechanism
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This paper introduces a simulation-based workflow to assess annual circadian lighting performance, glare risk and energy demand for an array of seated view positions under operated shading and electric lighting conditions. This workflow uses a combination of 9-band and 81-band simulations to represent daylight and electric lighting respectively. Annual daylight-driven calculations rely on a python-based code provided in the LARK simulation engine which was used to run 180-degree high dynamic range (HDR) renderings with 9-bands of spectral resolution. The results reveal the impact of shading systems on equivalent melanopic lux (EML) and daylight glare probability (DGP) over time and the energy demand (kWh) from electric lighting systems to supplement eye-level recommendations from the WELL v2 Building Standard.
We report the first confirmation of a hot Jupiter discovered by the Transiting Exoplanet Survey Satellite (TESS) mission: HD 202772A b. The transit signal was detected in the data from TESS Sector 1, and was confirmed to be of planetary origin through radial velocity (RV) measurements. HD 202772A b is orbiting a mildly evolved star with a period of 3.3 days. With an apparent magnitude of V = 8.3, the star is among the brightest and most massive known to host a hot Jupiter. Based on the 27 days of TESS photometry and RV data from the CHIRON, HARPS, and Tillinghast Reflector Echelle Spectrograph, the planet has a mass of 1.017{sub −0.068}{sup +0.070} M{sub J} and radius of 1.545{sub −0.060}{sup +0.052} R{sub J}, making it an inflated gas giant. HD 202772A b is a rare example of a transiting hot Jupiter around a quickly evolving star. It is also one of the most strongly irradiated hot Jupiters currently known.
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Heuristics have been used since the early 1990's in the Human Computer Interaction (HCI) domain, for evaluating user interfaces. Most notably, heuristics developed by Nielsen and Molich (1990) have been used by the HCI community. Forsell and Johansson (2010) used the same methodology as Nielsen and Molich and developed a set of 10 heuristics for information visualization. Currently, neither the information visualization or the visual analytics community has adopted these heuristics. However, several studies have been conducted using heuristics. Hearst et al. (2015) found that using the heuristics for visualizations along with asking questions about the data resulted in complementary results as the questions about the data could compensate for heuristics that were difficult to understand and apply. Heli Väätäjä et al. (2016) found that heuristics related to interaction, veracity, and aesthetics needed to be added to the Forsell and Johansson (2010) set. They also noted that a lack of domain knowledge caused the evaluators issues in conducting a heuristic evaluation. We conducted a controlled experiment to determine what factors might impact the expert evaluators' capability to conduct a heuristic evaluation of a static information visualization. In particular, we wanted to understand how their familiarity with the visualizations and their experience in conducting heuristic evaluations impacted their evaluations of visualizations. We recruited 10 participants who were domain experts in the visualization field each having experience with one of more of the following: user experience design or research, user interface development, and human computer interaction research. We used a heuristic set proposed by Forsell and Johansson. Participants were also asked to complete a demographic survey in which they rated their experience with each visualization type generally, their experience conducting heuristic evaluations, their experience using each visualization type to extract information, and their experience developing and creating each visualization type. The participants were shown a series of 5 common visualizations (Scatterplots, Sunbursts, Tree Maps, Parallel Sets, and Area Graphs) all displaying the VAST 2008 Mini Challenge Two- Migrant Boats data . Participants were given a suggested analytical question related to each visualization to keep in mind as they applied the 10 heuristics to each visualization. They were asked if they could find any usability problems in the visualizations and which heuristic(s) helped them to identify these problems. The participants were asked to rate how relevant each heuristic was for evaluating each visualization along with rating how confident they felt in their ability to apply the heuristic to evaluate each visualization. At the conclusion of the study, participants provided qualitative feedback on the usability issues of each visualization. Additionally, we collected eye tracking data on 6 of the 10 participants. We will be using this data to determine how long participants looked at the various visualizations and what parts of the visualizations they focused on. We had two hypotheses for this empirical evaluation: 1.The Forsell and Johansson heuristics of Orientation and help, Data set reduction, and Prompting are less useful for static visualizations because they require some interaction in order to test completely. 2.Participants that do not have experience with heuristic evaluation will provide lower evaluation scores for the heuristics compared to those that have experience. Related to hypothesis 1, as our study used only static visualizations, interaction-related usability challenges should be harder to identify. This should result in lower usefulness ratings for those heuristics. Preliminary results indicate that participants found it more difficult to apply heuristics to unfamiliar visualizations.