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Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

The effectiveness of infrastructure resilience relies on the seamless extraction of information, timely acquisition of critical knowledge, and heightened situational awareness. The ongoing utilization of digital citizen communication through social media with response organizations during disasters remains a valuable avenue for disseminating information, ensuring the effective utilization of public resources in emergency response to crisis events. Public agencies can use this information to examine community sentiments and discussions to assess, determine, and prioritize critical areas in need of assistance. However, there are limitations on harnessing precise geolocation information from social media, as well as a lack of mitigating bias of machine learning models used during such events. These limitations can restrict emergency management personnel's ability to locate and promptly delineate actionable insights. Here, we propose a semisupervised machine learning model that utilizes approaches such as transfer learning, topic modeling (i.e., Latent Dirichlet Allocation), and natural language processing to augment data from historical and current social media posts (i.e., Twitter) with community-driven application alerts (i.e., Waze) to achieve further evidence on the location and context of emergency events. The model is designed to also mitigate machine learning bias using the Wells–Du Bois protocol. A framework was developed for this process and is illustrated through a case study on Hurricane Ian and three previous hurricanes that occurred in Florida. This fusion provides increased situational awareness and may enhance the speed of emergency response. This study establishes a foundation for equitable, real-time crisis event detection, expanding organizations' response capacity in allocating resources and reducing harmful effects of disaster, particularly within public infrastructure systems. [ABSTRACT FROM AUTHOR]

A floristic quality assessment was conducted at Reber Woods, owned by the Red-Tail Land Conservancy, and located in Muncie (Delaware County), Indiana. For the entire study area, 346 taxa representing 211 genera in 82 families were documented; 251 taxa were native and 95 were non-native. The native floristic quality index (FQI) and average coefficient of conservatism (mean C) for the study site were 50.2 and 3.2, respectively. A comparison of the floristic quality of the woodlands vs. the fields was made and it was proposed that the combined woodlands were nature preserve quality but not the fields due to the high number of ruderal species present. At the same time, the small size of Reber Woods and anthropogenic pressures have reduced FQI and mean C below that of Ginn Woods, a regional old growth forest preserve. Especially in light of recent loss of ash species due to emerald ash borer, the structure and composition of the large woodland were determined. Using ESRI ArchMap software, a grid of 30 m * 30 m plots was established. In the field each plot center was marked with a flag. All trees with a dbh [greater than or equal to] 20 cm were measured. For each tree in a plot, data collected included species name, distance from the plot center in m, azimuth, diameter at breast height (dbh), and notes on tree health, if necessary. Results were calculated for all trees including dead but still standing Fraxinus spp. (ASH), then recalculated for all trees minus the dead ASH stems. With ASH included, the large woodland is classified as an eastern deciduous forest, or more broadly as a temperate broadleaf deciduous forest. This woodland was dominated by Fraxinus-Acer-Quercus-Carya (RIV = 78.2). Without ASH, the woodland is dominated by Acer-Quercus-Carya (RIV = 73.1). Lastly, according to the Division of Nature Preserves' list of the Natural Communities of Indiana, Reber Woods is classified as a typical Central Till Plain flatwoods. Keywords: floristic quality index (FQI), Central Till Plain flatwoods, old-fields, Indiana flora, vascular flora, Delaware County, Acer-Quercus-Carya, INTRODUCTION Delaware County, in the Central Till Plain of Indiana, is largely a level to gently rolling, rather homogeneous landscape that historically was clothed with hardwood forest (Hedge 1997). Through [...]

The results of a floristic quality assessment conducted on the two detention ponds, the 100year flood canal system, and associated mesic woodland at Hayes Arboretum, Richmond (Wayne County), Indiana are presented. The ponds were constructed in 2007 and the assessment was conducted in 2015. For the entire study area, 335 taxa representing 211 genera in 79 families were reported; 229 taxa were native and 106 were non-native. The native floristic quality index (FQI) and average coefficient of conservatism (mean C) for the study site were 45.2 and 3.0, respectively. The assessment of Pond 1: 80 taxa, 56 native, native FQI = 17.8, and native mean C = 2.4. The assessment of Pond 2: 71 species, 56 native, native FQI = 22.3, and native mean C = 3.0. The assessment for all drainage canals combined: 113 species, 89 native, native FQI = 28.7 and native mean C = 3.0. As expected, the assessed quality of the constructed wetland at Hayes Arboretum falls far below those of natural sites in Wayne County and in Indiana in general. However, when compared to other constructed wetland sites, the floristic quality of the arboretum's constructed wetland is similar for 10 years post construction. Point-centered quarter (PCQ) analysis of the associated mesic woodland determined the three most important species (based on relative importance value, RIV) were Acer saccharum (34.4%), Tilia americana (17.8%) and Liquidambar styraciflua (12.8%), for a combined RIV = 65.0%. The other 35% is divided between 13 species, with none exceeding 4.2% RIV. The unusual composition of this woodland is the result of past management. In summary, based on the floristic quality assessment and PCQ analysis, it was determined that the study area lacks nature preserve quality from a floristic point of view. Keywords: Hayes Arboretum, constructed wetlands, detention ponds, floristic quality, floristic quality index (FQI), point-centered quarter method (PCQ), nature preserve, INTRODUCTION Hayes Arboretum (HA), an 188.5 ha (466 ac) nature preserve in Wayne County, Indiana (Fig. 1), lies in the Tipton Till Plain Section or the New Castle Till Plains [...]

ABSTRACT. Located on the cast side of the city of Muncie in Delaware County, Indiana, Craddock Wetland Nature Preserve (CWNP) is a 10.9 ha (27 acres) property. A floristic survey [...]

ABSTRACT. Owned by Ball State University, both Cooper Woods (CW) and Skinner Woods (SK) are located in northwest Muncie, Indiana. Together the two woodlands are ~19 ha. A floristic survey [...]