# Iowa State University

• Ames, IA, United States
Recent publications
While the construction industry always strives for better project performance, it has failed to achieve improved project performance in capital projects. To reap maximum benefits, the capital projects sector needs to better understand facility standardization, and its’ optimum implementation. By capturing the best practices for successful planning and execution of facility standardization, the achievement of a higher degree of facility standardization, and consequently, improved project performance is possible in capital projects. The purpose of this paper is to identify best practices, i.e., the key deliverables for standardization work process tasks, and the optimization strategy for facility standardization. To that extent, the researchers present the phase deliverables for the lifecycle of a standardized project, and outline the phases recommended for the execution of key tasks in a standardized project. Additionally, the paper investigates pertinent critical success factors (CSFs) for each work process key task, and identifies the optimization process of facility standardization. The paper also proposes a facility standardization optimization chart that serves as a visual representation of the work process. The researchers enlisted the support of the Construction Industry Institute (CII) research team (RT-UMM-01) to aid in identifying the deliverables for the work process key tasks, which were previously identified by RT-UMM-01. The researchers adopted a mixed-method methodology: a comprehensive literature review; an in-house survey amongst subject matter experts in the research team; and detailed discussions in virtual and in-person meetings. This study helps practitioners by providing standardization work process deliverables, and identifies CSFs for standardization projects’ lifecycles.
Predicting phenotype from genotype is a central challenge in biology. By understanding genomic information to predict and improve traits, scientists can address the challenges and opportunities of achieving sustainable genetic improvement of complex, economically important traits in agriculturally relevant species. Converting the enormous, recent technical advances in all areas of genomics and phenomics into sustained and ecologically responsible improvements in food and fuel production is complex. It will require engaging agricultural genome to phenome (G2P) experts, drawing from a broad community, including crop and livestock scientists and essential integrative disciplines (e.g., engineers, economists, data and social scientists). To achieve this vision, the USDA NIFA-funded project inaugurating the Agricultural Genome to Phenome Initiative (AG2PI) is working to: Develop a cohesive vision for agricultural G2P research by identifying research gaps and opportunities; advancing community solutions to these challenges and gaps; and rapidly disseminating findings to the broader community. Towards these ends, this AG2PI project is organizing virtual field days, conferences, training workshops, and awarding seed grants to conceive new insights (details at www.ag2pi.org). Since October 2020, more than 10,000 unique participants from every inhabited continent have engaged in these activities. To illustrate AG2PI’s scope, we present survey results on agricultural G2P research needs and opportunities, highlighting opinions and suggestions for the future. We invite stakeholders interested in this complex but critical effort to help create an optimal, sustainable food supply for society and challenge the community to add to our vision for future accomplishments by a fully actualized AG2PI enterprise.
Background: Insulinomas are the most common tumour of the endocrine pancreas in dogs. These malignant tumours have a high metastatic rate and limited chemotherapeutic options. The multi-receptor tyrosine kinase inhibitor sunitinib malate has benefit in the treatment of metastatic insulinoma in people. Toceranib phosphate, an analogous veterinary agent, may provide benefit for dogs. Methods: A retrospective study describing the extent and duration of clinical outcomes and adverse events (AEs) in dogs diagnosed with insulinoma and receiving toceranib. Results: Records for 30 dogs diagnosed with insulinoma and having received toceranib were identified from a medical record search of five university and eight referral hospitals. The median progression-free interval and overall survival time were 561 days (95% confidence interval (CI): [246, 727 days]) and 656 days (95% CI: [310, 1045 days]), respectively. Of the dogs for which the canine Response evaluation criteria for solid tumours tool could be applied, the majority (66.7%) showed either a complete response, partial response or stable disease. Time to clinical progression was associated with prior intervention and type of veterinary practice. Larger dogs were at increased risk for disease progression and death. No novel AEs were reported. Conclusions: Most dogs diagnosed with insulinoma and receiving toceranib appeared to have a clinical benefit. Randomised, prospective studies are needed to better elucidate and objectively quantify the potential effect and survival benefit of toceranib therapy for management of insulinoma in dogs.
Background The association of cough with Mycoplasma hyopneumoniae (MHP) DNA detection in specimens was evaluated under conditions in which the MHP status of inoculated and contact-infected pen mates was closely monitored for 59 days post-inoculation (DPI). Methods Seven-week-old pigs (n = 39) were allocated to five rooms (with one pen). Rooms contained 9 pigs each, with 1, 3, 6, or 9 MHP -inoculated pigs, respectively, except Room 5 (three sham-inoculated pigs). Cough data (2 × week) and specimens, tracheal swabs (2 × week), oral fluids (daily), drinker wipes (~ 1 × week), and air samples (3 × week) were collected. At 59 DPI, pigs were euthanized, and lung and trachea were evaluated for gross and microscopic lesions. Predictive cough value to MHP DNA detection in drinker and oral fluid samples were estimated using mixed logistic regression. Results Following inoculation, MHP DNA was first detected in tracheal swabs from inoculated pigs (DPI 3), then oral fluids (DPI 8), air samples (DPI 10), and drinker wipes (21 DPI). MHP DNA was detected in oral fluids in 17 of 59 (Room 1) to 43 of 59 (Room 3) samples, drinker wipes in 4 of 8 (Rooms 2 and 3) to 5 of 8 (Rooms 1 and 4) samples, and air samples in 5 of 26 (Room 2) or 3 of 26 (Room 4) samples. Logistic regression showed that the frequency of coughing pigs in a pen was associated with the probability of MHP DNA detection in oral fluids ( P < 0.01 ) and nearly associated with drinker wipes ( P = 0.08 ). Pathology data revealed an association between the period when infection was first detected and the severity of gross lung lesions. Conclusions Dry, non-productive coughs suggest the presence of MHP , but laboratory testing and MHP DNA detection is required for confirmation. Based on the data from this study, oral fluids and drinker wipes may provide a convenient alternative for MHP DNA detection at the pen level when cough is present. This information may help practitioners in specimen selection for MHP surveillance.
The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.
More than \$270 billion is spent on combatting corrosion annually in the USA alone. As such, we present a machine-learning (ML) approach to down select corrosion-resistant alloys. Our focus is on a non-traditional class of alloys called multi-principal element alloys (MPEAs). Given the vast search space due to the variety of compositions and descriptors to be considered, and based upon existing corrosion data for MPEAs, we demonstrate descriptor optimization to predict corrosion resistance of any given MPEA. Our ML model with descriptor optimization predicts the corrosion resistance of a given MPEA in the presence of an aqueous environment by down selecting two environmental descriptors (pH of the medium and halide concentration), one chemical composition descriptor (atomic % of element with minimum reduction potential), and two atomic descriptors (difference in lattice constant (Δa) and average reduction potential). Our findings show that, while it is possible to down select corrosion-resistant MPEAs by using ML from a large search space, a larger dataset and higher quality data are needed to accurately predict the corrosion rate of MPEAs. This study shows both the promise and the perils of ML when applied to a complex chemical phenomenon like corrosion of alloys.
Background Linkage disequilibrium (LD) is commonly measured based on the squared coefficient of correlation $$\left({r}^{2}\right)$$ r 2 between the alleles at two loci that are carried by haplotypes. LD can also be estimated as the $${r}^{2}$$ r 2 between unphased genotype dosage at two loci when the allele frequencies and inbreeding coefficients at both loci are identical for the parental lines. Here, we investigated whether $${r}^{2}$$ r 2 for a crossbred population (F1) can be estimated using genotype data. The parental lines of the crossbred (F1) can be purebred or crossbred. Methods We approached this by first showing that inbreeding coefficients for an F1 crossbred population are negative, and typically differ in size between loci. Then, we proved that the expected $${r}^{2}$$ r 2 computed from unphased genotype data is expected to be identical to the $${r}^{2}$$ r 2 computed from haplotype data for an F1 crossbred population, regardless of the inbreeding coefficients at the two loci. Finally, we investigated the bias and precision of the $${r}^{2}$$ r 2 estimated using unphased genotype versus haplotype data in stochastic simulation. Results Our findings show that estimates of $${r}^{2}$$ r 2 based on haplotype and unphased genotype data are both unbiased for different combinations of allele frequencies, sample sizes (900, 1800, and 2700), and levels of LD. In general, for any allele frequency combination and $${r}^{2}$$ r 2 value scenarios considered, and for both methods to estimate $${r}^{2}$$ r 2 , the precision of the estimates increased, and the bias of the estimates decreased as sample size increased, indicating that both estimators are consistent. For a given scenario, the $${r}^{2}$$ r 2 estimates using haplotype data were more precise and less biased using haplotype data than using unphased genotype data. As sample size increased, the difference in precision and biasedness between the $${r}^{2}$$ r 2 estimates using haplotype data and unphased genotype data decreased. Conclusions Our theoretical derivations showed that estimates of LD between loci based on unphased genotypes and haplotypes in F1 crossbreds have identical expectations. Based on our simulation results, we conclude that the LD for an F1 crossbred population can be accurately estimated from unphased genotype data. The results also apply for other crosses (F2, F3, Fn, BC1, BC2, and BCn), as long as (selected) individuals from the two parental lines mate randomly.
Background Disease resilience is the ability to maintain performance across environments with different disease challenge loads (CL). A reaction norm describes the phenotypes that a genotype can produce across a range of environments and can be implemented using random regression models. The objectives of this study were to: (1) develop measures of CL using growth rate and clinical disease data recorded under a natural polymicrobial disease challenge model; and (2) quantify genetic variation in disease resilience using reaction norm models. Methods Different CL were derived from contemporary group effect estimates for average daily gain (ADG) and clinical disease phenotypes, including medical treatment rate (TRT), mortality rate, and subjective health scores. Resulting CL were then used as environmental covariates in reaction norm analyses of ADG and TRT in the challenge nursery and finisher, and compared using model loglikelihoods and estimates of genetic variance associated with CL. Linear and cubic spline reaction norm models were compared based on goodness-of-fit and with multi-variate analyses, for which phenotypes were separated into three traits based on low, medium, or high CL. Results Based on model likelihoods and estimates of genetic variance explained by the reaction norm, the best CL for ADG in the nursery was based on early ADG in the finisher, while the CL derived from clinical disease traits across the nursery and finisher was best for ADG in the finisher and for TRT in the nursery and across the nursery and finisher. With increasing CL, estimates of heritability for nursery and finisher ADG initially decreased, then increased, while estimates for TRT generally increased with CL. Genetic correlations for ADG and TRT were low between high versus low CL, but high for close CL. Linear reaction norm models fitted the data significantly better than the standard genetic model without genetic slopes, while the cubic spline model fitted the data significantly better than the linear reaction norm model for most traits. Reaction norm models also fitted the data better than multi-variate models. Conclusions Reaction norm models identified genotype-by-environment interactions related to disease CL. Results can be used to select more resilient animals across different levels of CL, high-performance animals at a given CL, or a combination of these.
Background Deterministic predictions of the accuracy of genomic estimated breeding values (GEBV) when combining information sources have been developed based on selection index theory (SIT) and on Fisher information (FI). These two approaches have resulted in slightly different results when considering the combination of pedigree and genomic information. Here, we clarify this apparent contradiction, both for the combination of pedigree and genomic information and for the combination of subpopulations into a joint reference population. Results First, we show that existing expressions for the squared accuracy of GEBV can be understood as a proportion of the variance explained. Next, we show that the apparent discrepancy that has been observed between accuracies based on SIT vs. FI originated from two sources. First, the FI referred to the genetic component that is captured by the marker genotypes, rather than the full genetic component. Second, the common SIT-based derivations did not account for the increase in the accuracy of GEBV due to a reduction of the residual variance when combining information sources. The SIT and FI approaches are equivalent when these sources are accounted for. Conclusions The squared accuracy of GEBV can be understood as a proportion of the variance explained. The SIT and FI approaches for combining information for GEBV are equivalent and provide identical accuracies when the underlying assumptions are equivalent.
The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.
Background Bayesian genomic prediction methods were developed to simultaneously fit all genotyped markers to a set of available phenotypes for prediction of breeding values for quantitative traits, allowing for differences in the genetic architecture (distribution of marker effects) of traits. These methods also provide a flexible and reliable framework for genome-wide association (GWA) studies. The objective here was to review developments in Bayesian hierarchical and variable selection models for GWA analyses. Results By fitting all genotyped markers simultaneously, Bayesian GWA methods implicitly account for population structure and the multiple-testing problem of classical single-marker GWA. Implemented using Markov chain Monte Carlo methods, Bayesian GWA methods allow for control of error rates using probabilities obtained from posterior distributions. Power of GWA studies using Bayesian methods can be enhanced by using informative priors based on previous association studies, gene expression analyses, or functional annotation information. Applied to multiple traits, Bayesian GWA analyses can give insight into pleiotropic effects by multi-trait, structural equation, or graphical models. Bayesian methods can also be used to combine genomic, transcriptomic, proteomic, and other -omics data to infer causal genotype to phenotype relationships and to suggest external interventions that can improve performance. Conclusions Bayesian hierarchical and variable selection methods provide a unified and powerful framework for genomic prediction, GWA, integration of prior information, and integration of information from other -omics platforms to identify causal mutations for complex quantitative traits.
Background An important goal in animal breeding is to improve longitudinal traits. The objective of this study was to explore for longitudinal residual feed intake (RFI) data, which estimated breeding value (EBV), or combination of EBV, to use in a breeding program. Linear combinations of EBV (summarized breeding values, SBV) or phenotypes (summarized phenotypes) derived from the eigenvectors of the genetic covariance matrix over time were considered, and the linear regression method (LR method) was used to facilitate the evaluation of their prediction accuracy. Results Weekly feed intake, average daily gain, metabolic body weight, and backfat thickness measured on 2435 growing French Large White pigs over a 10-week period were analysed using a random regression model. In this population, the 544 dams of the phenotyped animals were genotyped. These dams did not have own phenotypes. The quality of the predictions of SBV and breeding values from summarized phenotypes of these females was evaluated. On average, predictions of SBV at the time of selection were unbiased, slightly over-dispersed and less accurate than those obtained with additional phenotypic information. The use of genomic information did not improve the quality of predictions. The use of summarized instead of longitudinal phenotypes resulted in predictions of breeding values of similar quality. Conclusions For practical selection on longitudinal data, the results obtained with this specific design suggest that the use of summarized phenotypes could facilitate routine genetic evaluation of longitudinal traits.
A naturalistic data-driven study was conducted in Chennai, Tamil Nadu, to study drivers’ physiological stress behavior under Indian traffic conditions. Six male participants with professional driving experience were recruited and asked to complete daily trips in urban roadways of Chennai. The participants completed their trips in a sedan-type four-wheeler vehicle mounted with two cameras (near the rearview mirror) to record the traffic data and driver’s in-vehicle behavior. An android phone mounted on the dashboard collected GPS and accelerometer data. The drivers wore an Empatica E4 device to record their physiological data, such as electrodermal activity, a key indicator of physiological stress. A holistic database assembled by linking different kinds of information was used to identify traffic events and road characteristics that can potentially impact the driver’s physiological stress levels. This data was analyzed using a mixed-effects model. It was observed that U-turns and Protected Right turns in an intersection significantly increased the driver’s stress. Additionally, slow-moving traffic in such scenarios further increased the effect. Merge Points serve as undesignated spaces for vehicle and pedestrian crossing and were identified as a contributor to the driver’s stress. Alternatively, the presence of a median and a greater number of lanes significantly decreased the driver’s stress. This paper reports a first-of-its-kind study conducted in India to assess the factors impacting driver’s stress. Adopting measures and initiatives to modify the identified factors can help create a stress-free traffic environment for Indian drivers.
Gully erosion, one of the most damaging forms of land degradation, destroys farmlands and threatens grain and ecological stability. Approximately 295,000 gullies have formed in the Mollisols region of northeast China (NEC), while the actual severity of gully erosion remains unclear. This study aims to clarify gully morphology, factors influencing gully morphology, and contribution of gully erosion to soil loss through a 393 km² field investigation in a typical and representative Mollisols region of NEC. There were 1048 gullies observed, with an average length, top width, depth and width-depth ratio of 522.32 m, 15.05 m, 2.77 m and 7.16, respectively. Over 70% of gullies had a length, top width and depth of 100–500 m, 3–20 m and 0.5–3 m, respectively. The three types of gullies, classified based on where the gully head developed (GF, farmland; GR, unpaved road; GW, woodland), had significantly distinct morphologies, with GW being broader and deeper and GF being longer. Notably, 56.2% of gullies are GF, accounted for 74.84% of the total gully area and 67.04% of the total gully volume (V). V of GF, GR and GW could be well explained by a power function of gully area (A). Gully density (gully length per unit area, GD, km km⁻²) and ground lacerative degree (gully area per unit area, GLD, km km⁻²) increased with slope gradient, and first increased and then decreased with slope length. Gully development was more favorable of sunny slope and a larger angle between gully and ridge orientation. The annual eroded soil from the gully was 1.86 times of that from the hillslope across the region during 2013–2018, contributing 65% of region eroded soil. This clearly identifies the severity of gully erosion and substantiates gully erosion risks to valuable mollisols and therefore food security. An urgent need exists to implement gully erosion control practices in this area.
The tight upper bound pt+(G)≤⌈|V(G)|−Z+(G)2⌉ is established for the positive semidefinite propagation time of a graph in terms of its positive semidefinite zero forcing number. To prove this bound, two methods of transforming one positive semidefinite zero forcing set into another and algorithms implementing these methods are presented. Consequences of the bound, including a tight Nordhaus-Gaddum sum upper bound on positive semidefinite propagation time, are established.
Very-high-resolution (VHR) land cover and land use (LCLU) is an essential baseline data for understanding fine-scale interactions between humans and the heterogeneous landscapes of urban environments. In this study, we developed a Fine-resolution, Large-area Urban Thematic information Extraction (FLUTE) framework to address multiple challenges facing large-area, high-resolution urban mapping, including the view angle effect, high intraclass and low interclass variation, and multiscale land cover types. FLUTE builds upon a teacher-student deep learning architecture, and includes two new feature extraction modules-Scale-aware Parsing Module (SPM) and View-aware Embedding Module (VEM). Our model was trained with a new benchmark database containing 52.43 million labeled pixels (from 2014 to 2017 NAIP airborne Imagery) to capture diverse LCLU types and spatial patterns. We assessed the credibility of FLUTE by producing a 1-meter resolution database named UrbanWatch for 22 major cities across the conterminous United States. UrbanWatch contains nine LCLU classes-building, road, parking lot, tree canopy, grass/shrub, water, agriculture, barren, and others, with an overall accuracy of 91.52%. We have further made UrbanWatch freely accessible to support urban-related research, urban planning and management, and community outreach efforts: https://urbanwatch.charlotte.edu.
Jeju Island in South Korea is a popular tourism destination designated as a biosphere reserve by the Natural Sciences Sector of UNESCO. However, a rapid influx of tourists into the island has caused major environmental problems, which may be reduced through volunteer tourism. This study predicts volunteer tourists' support for sustainable tourism development by applying the Value-Belief-Norm (VBN) theory and the concept of altruism. The study collected 308 responses from volunteer tourists who participated in an environmental program called Clean Olle. The findings confirmed the relationships between tourists' values, beliefs, and personal norms that predicted environmentally friendly behavior and support for sustainable tourism. Altruism, an important motivation for volunteering, reinforced tourists' environmentally friendly behavior. The study contributes to better understanding the volunteer tourists' role in sustainable tourism development and provides suggestions for destination management organizations (DMOs) in developing volunteer programs that motivate tourists’ altruism to engage in environmentally friendly behavior.