High Dynamic Range (HDR) imaging has traditionally been used to create photorealistic images by combining multiple Low Dynamic Range (LDR) images. The application of HDR imaging has been since expanded to study the lighting environment by extraction of metrics such as luminance, illuminance, and glare. While luminance mapping of HDR images has been extensively studied in the recent past, research on illuminance mapping has been limited because of its strong dependency on surface materiality. This document reviews recent research on HDR techniques as it pertains to illuminance measurement and describes results of a pilot study comparing illuminance values captured and calculated using HDR against illuminance measurements collected with a calibrated illuminance meter, from a turf-based surface.

Transitioning to electric vehicles (EV) can lower emission to mitigate the effects of climate change. Labeling an EV as completely `zero emission' is incorrect, as it ignores the emissions produced by the electricity generation used to charge the vehicle. The local electricity provider's energy mix will determine the emissions of an EV, and the fuel efficiency will determine economic savings. This paper will describe the development of two indices to gauge the regional environmental and economic impact of a new EV as compared to a new conventional internal combustion engine vehicle (ICEV) and hybrid electric vehicles (HEV), which will be used to create an interface for vehicle comparison in the future.

A key factor for the successful deployment of electrified transportation is determining locations for the electric vehicle charging infrastructures and develop a proper charging infrastructure network. In this paper, an algorithm has been developed to identify locations of charging infrastructures when driving a particular Electric Vehicle (EV) on a particular Interstate or US-Highway in a given state. The algorithm utilizes the concept of a ruler whose length is determined using constraints that include: range anxiety, driver's driving habits and EV battery's State of Health (SoH), State of Charge (SoC), and Depth of Discharge (DoD). A case study is presented to demonstrate the application of this algorithm.

ANSI/IES TM-30-24 uses 99 Color Evaluation Samples (CES) selected to provide broad, approximately uniform sampling of object-color space for evaluating color rendition in built environments. Although TM-30 is not intended as a dedicated skin-tone fidelity assessment method, the standard designates two CES, #15 and #18, as skin-tone samples. This study examines how this sampling structure intersects the color space region occupied by measured human skin tones. The analysis used 100 skin reflectance spectra from two independent datasets grouped into five lightness-based categories from Very Light to Very Dark. CAM02-UCS coordinates were computed under D65 using TM-30-specified viewing conditions, and Euclidean nearest-neighbor distances (ΔE′) to the 99 CES were calculated. In both datasets, the Very Dark category showed substantially larger nearest-CES distances than the Very Light category. In the ISSA dataset, Very Dark samples had a mean distance of 8.13 ± 2.17, compared with 3.99 ± 0.59 for Very Light samples. In the CIE dataset, the corresponding values were 7.96 ± 0.94 and 4.24 ± 0.85. Across the combined dataset, Very Dark samples were approximately 95% farther from their nearest CES than Very Light samples. The designated skin CES, #15 and #18, served as nearest neighbors for 51 of 100 samples, primarily in the Very Light, Light, and Medium categories. These findings provide a structural map of how measured skin tones relate to the TM-30 CES set and clarify the role of CES #15 and #18 within the broader skin-tone region.

ANSI/IES TM-30-24 designates CES #15 and CES #18 as skin-representative color-evaluation samples, summarized by their mean individual fidelity (R_f,CES15,18). This analysis tested how well that two-sample mean represents average fidelity and category-level variation across a 100-spectrum skin-reflectance library stratified into five lightness-defined categories, evaluated across 1,432 architectural LED sources from the DTU PhotoLED database. R_f,CES15,18 closely tracked mean fidelity across the full reference library (r = 0.9976; RMSE = 0.84 Rf points). Benchmarked against all 4,950 possible two-spectrum means from the same library, it exceeded 85.7% of pairs in correlation and 82.7% in RMSE, favorable performance that was not unique. Raw category means within individual sources differed by 4.74 R_f points on average and by as much as 10.66 points. This ordering tracked chroma and was largely captured by the lightness-chroma geometry of the sampled spectra; little category-specific structure remained once that geometry was modeled, and the ordering was not fixed across the two reflectance libraries. Compact subsets were tested under source-only holdout and under simultaneous source-and-skin-spectrum holdout. Greedy selection held a modest source-held-out error advantage over an optimized category-matched random search at 9 to 10 samples. Under simultaneous holdout, subsets of that size preserved source-rank recovery, while reconstruction of unseen category means carried median errors near 1 R_f point. CES #15/#18 therefore serves as a favorable aggregate indicator within this library. Characterizing variation across the sampled skin-reflectance locus requires broader reflectance sets.