More Like this

1. Application of Causal Inference to the Analysis of Occupant Thermal State and Energy Behavioral Intentions in Immersive Virtual Environments

   https://doi.org/10.3389/frsc.2021.730474  

   Rentala, Girish ; Zhu, Yimin ; Mukhopadhyay, Supratik ( November 2021 , Frontiers in Sustainable Cities)

   Identification and quantitative understanding of factors that influence occupant energy behavior and thermal state during the design phase are critical in supporting effective energy-efficient design. To achieve this, immersive virtual environments (IVEs) have recently shown potential as a tool to simulate occupant energy behaviors and collect context-dependent behavior data for buildings under design. On the other hand, prior models of occupant energy behaviors and thermal states used correlation-based approaches, which failed to capture the underlying causal interactions between the influencing factors and hence were unable to uncover the true causing factors. Therefore, in this study, the authors investigate the applicability of causal inference for identifying the causing factors of occupant/participant energy behavioral intentions and their thermal states in IVE condition and compare those results with the baseline in-situ condition. The energy behavioral intentions here are a proximal antecedent of actual energy behaviors. A set of experiments involving 72 human subjects were performed through the use of a head-mounted device (HMD) in a climate chamber. The subjects were exposed to three different step temperatures (cool, neutral, warm) under an IVE and a baseline in-situ condition. Participants' individual factors, behavioral factors, skin temperatures, virtual experience factors, thermal states (sensation, acceptability, comfort), and energy behavioral intentions were collected during the experiments. Structural causal models were learnt from data using the elicitation method in conjunction with the PC-Stable algorithm. The findings show that the causal inference framework is a potentially effective method for identifying causing factors of thermal states and energy behavioral intentions as well as quantifying their causal effects. In addition, the study shows that in IVE experiments, the participants' virtual experience factors such as their immersion, presence, and cybersickness were not the causing factors of thermal states and energy behavioral intentions. Furthermore, the study suggests that participants' behavioral factors such as their attitudes toward energy conservation and perceived behavioral control to conserve energy were the causing factors of their energy behavioral intentions. Also, the indoor temperature was a causing factor of general thermal sensation and overall skin temperature. The paper also discusses other findings, including discrepancies, limitations of the study, and recommendations for future studies. 

   more » « less
2. Reduction of cybersickness in head mounted displays use: A systematic review and taxonomy of current strategies

   https://doi.org/10.3389/frvir.2023.1027552  

   Ang, Samuel ; Quarles, John ( March 2023 , Frontiers in Virtual Reality)

   This literature review examines the existing research into cybersickness reduction with regards to head mounted display use. Cybersickness refers to a collection of negative symptoms sometimes experienced as the result of being immersed in a virtual environment, such as nausea, dizziness, or eye strain. These symptoms can prevent individuals from utilizing virtual reality (VR) technologies, so discovering new methods of reducing them is critical. Our objective in this literature review is to provide a better picture of what cybersickness reduction techniques exist, the quantity of research demonstrating their effectiveness, and the virtual scenes testing has taken place in. This will help to direct researches towards promising avenues, and illuminate gaps in the literature. Following the preferred reporting items for systematic reviews and meta-analyses statement, we obtained a batch of 1,055 papers through the use of software aids. We selected 88 papers that examine potential cybersickness reduction approaches. Our acceptance criteria required that papers examined malleable conditions that could be conceivably modified for everyday use, examined techniques in conjunction with head mounted displays, and compared cybersickness levels between two or more user conditions. These papers were sorted into categories based on their general approach to combating cybersickness, and labeled based on the presence of statistically significant results, the use of virtual vehicles, the level of visual realism, and the virtual scene contents used in evaluation of their effectiveness. In doing this we have created a snapshot of the literature to date so that researchers may better understand what approaches are being researched, and the types of virtual experiences used in their evaluation. Keywords: Virtual reality cybersickness Simulator Sickness Visually induced motion sickness reduction Systematic review Head mounted display. 

   more » « less
3. Author-Driven Computable Data and Ontology Production for Taxonomists

   https://doi.org/10.3897/biss.5.75741  

   Cui, Hong ; Ford, Bruce ; Starr, Julian ; Macklin, James ; Reznicek, Anton ; Giebink, Noah ; Longert, Dylan ; Léveillé-Bourret, Étienne ; Zhang, Limin ( September 2021 , Biodiversity Information Science and Standards)

   It takes great effort to manually or semi-automatically convert free-text phenotype narratives (e.g., morphological descriptions in taxonomic works) to a computable format before they can be used in large-scale analyses. We argue that neither a manual curation approach nor an information extraction approach based on machine learning is a sustainable solution to produce computable phenotypic data that are FAIR (Findable, Accessible, Interoperable, Reusable) (Wilkinson et al. 2016). This is because these approaches do not scale to all biodiversity, and they do not stop the publication of free-text phenotypes that would need post-publication curation. In addition, both manual and machine learning approaches face great challenges: the problem of inter-curator variation (curators interpret/convert a phenotype differently from each other) in manual curation, and keywords to ontology concept translation in automated information extraction, make it difficult for either approach to produce data that are truly FAIR. Our empirical studies show that inter-curator variation in translating phenotype characters to Entity-Quality statements (Mabee et al. 2007) is as high as 40% even within a single project. With this level of variation, curated data integrated from multiple curation projects may still not be FAIR. The key causes of this variation have been identified as semantic vagueness in original phenotype descriptions and difficulties in using standardized vocabularies (ontologies). We argue that the authors describing characters are the key to the solution. Given the right tools and appropriate attribution, the authors should be in charge of developing a project's semantics and ontology. This will speed up ontology development and improve the semantic clarity of the descriptions from the moment of publication. In this presentation, we will introduce the Platform for Author-Driven Computable Data and Ontology Production for Taxonomists, which consists of three components: a web-based, ontology-aware software application called 'Character Recorder,' which features a spreadsheet as the data entry platform and provides authors with the flexibility of using their preferred terminology in recording characters for a set of specimens (this application also facilitates semantic clarity and consistency across species descriptions); a set of services that produce RDF graph data, collects terms added by authors, detects potential conflicts between terms, dispatches conflicts to the third component and updates the ontology with resolutions; and an Android mobile application, 'Conflict Resolver,' which displays ontological conflicts and accepts solutions proposed by multiple experts. a web-based, ontology-aware software application called 'Character Recorder,' which features a spreadsheet as the data entry platform and provides authors with the flexibility of using their preferred terminology in recording characters for a set of specimens (this application also facilitates semantic clarity and consistency across species descriptions); a set of services that produce RDF graph data, collects terms added by authors, detects potential conflicts between terms, dispatches conflicts to the third component and updates the ontology with resolutions; and an Android mobile application, 'Conflict Resolver,' which displays ontological conflicts and accepts solutions proposed by multiple experts. Fig. 1 shows the system diagram of the platform. The presentation will consist of: a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). The key difference of this system from other ontology-aware systems is that authors can directly add needed terms to the ontology as they wish and can update their data according to ontology updates. a report on the findings from a recent survey of 90+ participants on the need for a tool like Character Recorder; a methods section that describes how we provide semantics to an existing vocabulary of quantitative characters through a set of properties that explain where and how a measurement (e.g., length of perigynium beak) is taken. We also report on how a custom color palette of RGB values obtained from real specimens or high-quality specimen images, can be used to help authors choose standardized color descriptions for plant specimens; and a software demonstration, where we show how Character Recorder and Conflict Resolver can work together to construct both human-readable descriptions and RDF graphs using morphological data derived from species in the plant genus Carex (sedges). The key difference of this system from other ontology-aware systems is that authors can directly add needed terms to the ontology as they wish and can update their data according to ontology updates. The software modules currently incorporated in Character Recorder and Conflict Resolver have undergone formal usability studies. We are actively recruiting Carex experts to participate in a 3-day usability study of the entire system of the Platform for Author-Driven Computable Data and Ontology Production for Taxonomists. Participants will use the platform to record 100 characters about one Carex species. In addition to usability data, we will collect the terms that participants submit to the underlying ontology and the data related to conflict resolution. Such data allow us to examine the types and the quantities of logical conflicts that may result from the terms added by the users and to use Discrete Event Simulation models to understand if and how term additions and conflict resolutions converge. We look forward to a discussion on how the tools (Character Recorder is online at http://shark.sbs.arizona.edu/chrecorder/public) described in our presentation can contribute to producing and publishing FAIR data in taxonomic studies. 

   more » « less
4. Florida mangrove saltmarsh reference surface soils

   https://doi.org/10.6073/pasta/0e08cbe07c84488cb7b9dd16669946d0  

   Lewis, David Bruce ( January 2021 , Environmental Data Initiative)

   Site description. This data package consists of data obtained from sampling surface soil (the 0-7.6 cm depth profile) in black mangrove (Avicennia germinans) dominated forest and black needlerush (Juncus roemerianus) saltmarsh along the Gulf of Mexico coastline in peninsular west-central Florida, USA. This location has a subtropical climate with mean daily temperatures ranging from 15.4 °C in January to 27.8 °C in August, and annual precipitation of 1336 mm. Precipitation falls as rain primarily between June and September. Tides are semi-diurnal, with 0.57 m median amplitudes during the year preceding sampling (U.S. NOAA National Ocean Service, Clearwater Beach, Florida, station 8726724). Sea-level rise is 4.0 ± 0.6 mm per year (1973-2020 trend, mean ± 95 % confidence interval, NOAA NOS Clearwater Beach station). The A. germinans mangrove zone is either adjacent to water or fringed on the seaward side by a narrow band of red mangrove (Rhizophora mangle). A near-monoculture of J. roemerianus is often adjacent to and immediately landward of the A. germinans zone. The transition from the mangrove to the J. roemerianus zone is variable in our study area. An abrupt edge between closed-canopy mangrove and J. roemerianus monoculture may extend for up to several hundred meters in some locations, while other stretches of ecotone present a gradual transition where smaller, widely spaced trees are interspersed into the herbaceous marsh. Juncus roemerianus then extends landward to a high marsh patchwork of succulent halophytes (including Salicornia bigellovi, Sesuvium sp., and Batis maritima), scattered dwarf mangrove, and salt pans, followed in turn by upland vegetation that includes Pinus sp. and Serenoa repens. Field design and sample collection. We established three study sites spaced at approximately 5 km intervals along the western coastline of the central Florida peninsula. The sites consisted of the Salt Springs (28.3298°, -82.7274°), Energy Marine Center (28.2903°, -82.7278°), and Green Key (28.2530°, -82.7496°) sites on the Gulf of Mexico coastline in Pasco County, Florida, USA. At each site, we established three plot pairs, each consisting of one saltmarsh plot and one mangrove plot. Plots were 50 m^2 in size. Plots pairs within a site were separated by 230-1070 m, and the mangrove and saltmarsh plots composing a pair were 70-170 m apart. All plot pairs consisted of directly adjacent patches of mangrove forest and J. roemerianus saltmarsh, with the mangrove forests exhibiting a closed canopy and a tree architecture (height 4-6 m, crown width 1.5-3 m). Mangrove plots were located at approximately the midpoint between the seaward edge (water-mangrove interface) and landward edge (mangrove-marsh interface) of the mangrove zone. Saltmarsh plots were located 20-25 m away from any mangrove trees and into the J. roemerianus zone (i.e., landward from the mangrove-marsh interface). Plot pairs were coarsely similar in geomorphic setting, as all were located on the Gulf of Mexico coastline, rather than within major sheltering formations like Tampa Bay, and all plot pairs fit the tide-dominated domain of the Woodroffe classification (Woodroffe, 2002, "Coasts: Form, Process and Evolution", Cambridge University Press), given their conspicuous semi-diurnal tides. There was nevertheless some geomorphic variation, as some plot pairs were directly open to the Gulf of Mexico while others sat behind keys and spits or along small tidal creeks. Our use of a plot-pair approach is intended to control for this geomorphic variation. Plot center elevations (cm above mean sea level, NAVD 88) were estimated by overlaying the plot locations determined with a global positioning system (Garmin GPS 60, Olathe, KS, USA) on a LiDAR-derived bare-earth digital elevation model (Dewberry, Inc., 2019). The digital elevation model had a vertical accuracy of ± 10 cm (95 % CI) and a horizontal accuracy of ± 116 cm (95 % CI). Soil samples were collected via coring at low tide in June 2011. From each plot, we collected a composite soil sample consisting of three discrete 5.1 cm diameter soil cores taken at equidistant points to 7.6 cm depth. Cores were taken by tapping a sleeve into the soil until its top was flush with the soil surface, sliding a hand under the core, and lifting it up. Cores were then capped and transferred on ice to our laboratory at the University of South Florida (Tampa, Florida, USA), where they were combined in plastic zipper bags, and homogenized by hand into plot-level composite samples on the day they were collected. A damp soil subsample was immediately taken from each composite sample to initiate 1 y incubations for determination of active C and N (see below). The remainder of each composite sample was then placed in a drying oven (60 °C) for 1 week with frequent mixing of the soil to prevent aggregation and liberate water. Organic wetland soils are sometimes dried at 70 °C, however high drying temperatures can volatilize non-water liquids and oxidize and decompose organic matter, so 50 °C is also a common drying temperature for organic soils (Gardner 1986, "Methods of Soil Analysis: Part 1", Soil Science Society of America); we accordingly chose 60 °C as a compromise between sufficient water removal and avoidance of non-water mass loss. Bulk density was determined as soil dry mass per core volume (adding back the dry mass equivalent of the damp subsample removed prior to drying). Dried subsamples were obtained for determination of soil organic matter (SOM), mineral texture composition, and extractable and total carbon (C) and nitrogen (N) within the following week. Sample analyses. A dried subsample was apportioned from each composite sample to determine SOM as mass loss on ignition at 550 °C for 4 h. After organic matter was removed from soil via ignition, mineral particle size composition was determined using a combination of wet sieving and density separation in 49 mM (3 %) sodium hexametaphosphate ((NaPO_3)_6) following procedures in Kettler et al. (2001, Soil Science Society of America Journal 65, 849-852). The percentage of dry soil mass composed of silt and clay particles (hereafter, fines) was calculated as the mass lost from dispersed mineral soil after sieving (0.053 mm mesh sieve). Fines could have been slightly underestimated if any clay particles were burned off during the preceding ignition of soil. An additional subsample was taken from each composite sample to determine extractable N and organic C concentrations via 0.5 M potassium sulfate (K_2SO_4) extractions. We combined soil and extractant (ratio of 1 g dry soil:5 mL extractant) in plastic bottles, reciprocally shook the slurry for 1 h at 120 rpm, and then gravity filtered it through Fisher G6 (1.6 μm pore size) glass fiber filters, followed by colorimetric detection of nitrite (NO_2^-) + nitrate (NO_3^-) and ammonium (NH_4^+) in the filtrate (Hood Nowotny et al., 2010,Soil Science Society of America Journal 74, 1018-1027) using a microplate spectrophotometer (Biotek Epoch, Winooski, VT, USA). Filtrate was also analyzed for dissolved organic C (referred to hereafter as extractable organic C) and total dissolved N via combustion and oxidation followed by detection of the evolved CO_2 and N oxide gases on a Formacs HT TOC/TN analyzer (Skalar, Breda, The Netherlands). Extractable organic N was then computed as total dissolved N in filtrate minus extractable mineral N (itself the sum of extractable NH_4-N and NO_2-N + NO_3-N). We determined soil total C and N from dried, milled subsamples subjected to elemental analysis (ECS 4010, Costech, Inc., Valencia, CA, USA) at the University of South Florida Stable Isotope Laboratory. Median concentration of inorganic C in unvegetated surface soil at our sites is 0.5 % of soil mass (Anderson, 2019, Univ. of South Florida M.S. thesis via methods in Wang et al., 2011, Environmental Monitoring and Assessment 174, 241-257). Inorganic C concentrations are likely even lower in our samples from under vegetation, where organic matter would dilute the contribution of inorganic C to soil mass. Nevertheless, the presence of a small inorganic C pool in our soils may be counted in the total C values we report. Extractable organic C is necessarily of organic C origin given the method (sparging with HCl) used in detection. Active C and N represent the fractions of organic C and N that are mineralizable by soil microorganisms under aerobic conditions in long-term soil incubations. To quantify active C and N, 60 g of field-moist soil were apportioned from each composite sample, placed in a filtration apparatus, and incubated in the dark at 25 °C and field capacity moisture for 365 d (as in Lewis et al., 2014, Ecosphere 5, art59). Moisture levels were maintained by frequently weighing incubated soil and wetting them up to target mass. Daily CO_2 flux was quantified on 29 occasions at 0.5-3 week intervals during the incubation period (with shorter intervals earlier in the incubation), and these per day flux rates were integrated over the 365 d period to compute an estimate of active C. Observations of per day flux were made by sealing samples overnight in airtight chambers fitted with septa and quantifying headspace CO_2 accumulation by injecting headspace samples (obtained through the septa via needle and syringe) into an infrared gas analyzer (PP Systems EGM 4, Amesbury, MA, USA). To estimate active N, each incubated sample was leached with a C and N free, 35 psu solution containing micronutrients (Nadelhoffer, 1990, Soil Science Society of America Journal 54, 411-415) on 19 occasions at increasing 1-6 week intervals during the 365 d incubation, and then extracted in 0.5 M K_2SO_4 at the end of the incubation in order to remove any residual mineral N. Active N was then quantified as the total mass of mineral N leached and extracted. Mineral N in leached and extracted solutions was detected as NH_4-N and NO_2-N + NO_3-N via colorimetry as above. This incubation technique precludes new C and N inputs and persistently leaches mineral N, forcing microorganisms to meet demand by mineralizing existing pools, and thereby directly assays the potential activity of soil organic C and N pools present at the time of soil sampling. Because this analysis commences with disrupting soil physical structure, it is biased toward higher estimates of active fractions. Calculations. Non-mobile C and N fractions were computed as total C and N concentrations minus the extractable and active fractions of each element. This data package reports surface-soil constituents (moisture, fines, SOM, and C and N pools and fractions) in both gravimetric units (mass constituent / mass soil) and areal units (mass constituent / soil surface area integrated through 7.6 cm soil depth, the depth of sampling). Areal concentrations were computed as X × D × 7.6, where X is the gravimetric concentration of a soil constituent, D is soil bulk density (g dry soil / cm^3), and 7.6 is the sampling depth in cm. 

   more » « less
5. Reinforcement Learning for Flooding Mitigation in Complex Stormwater Systems during Large Storms

   https://doi.org/10.1109/EUROCON52738.2021.9535587  

   Wang, Cheng ; Bowes, Benjamin D. ; Beling, Peter A. ; Goodall, Jonathan L. ( July 2021 , IEEE EUROCON 2021 - 19th International Conference on Smart Technologies)

   Compared with capital improvement projects, real-time control of stormwater systems may be a more effective and efficient approach to address the increasing risk of flooding in urban areas. One way to automate the design process of control policies is through reinforcement learning (RL). Recently, RL methods have been applied to small stormwater systems and have demonstrated better performance over passive systems and simple rule-based strategies. However, it remains unclear how effective RL methods are for larger and more complex systems. Current RL-based control policies also suffer from poor convergence and stability, which may be due to large updates made by the underlying RL algorithm. In this study, we use the Proximal Policy Optimization (PPO) algorithm and develop control policies for a medium-sized stormwater system that can significantly mitigate flooding during large storm events. Our approach demonstrates good convergence behavior and stability, and achieves robust out-of-sample performance. 

   more » « less