Presented is a validation study for high fidelity numerical modeling of earthquake soil structure interaction (ESSI) for a building, hotel structure in Ventura, California. A detailed finite element (FE) model of the ESSI system, featuring the 12-story concrete structure, pile group foundation, and underlying soil, is developed using the Real-ESSI Simulator (Jeremić et al., The Real-ESSI Simulator System 1988–2022, 2022a). The domain reduction method (DRM) (Bielak et al., Bulletin of the Seismological Society of America, 2003, 93(2), 817–824; Yoshimura et al., Bulletin of the Seismological Society of America, 2003, 93(2), 825–841) is used to apply seismic loads, in this case the 1994 Northridge earthquake motions. Direct comparison between simulation results and California Strong Motion Instrumentation Program (CSMIP) recordings shows a high level of agreement in acceleration and displacement responses at all instrumented locations. Sensitivity study on a number of modeling choices and analysis parameters is conducted to investigate controlling factors for the ESSI response. For example, the soil-structure interaction effect and structural damping ratios are shown to have significant influence on system dynamic response. In addition, the soil inelasticity is shown to be highly influenced by the magnitude of seismic motion. Both effects are important for validation as they contribute to sensitivity of response to parametric variability.

Abstract Successful placement and curing of cement behind casing is critical for the environment, operational safety, economics and lifespan of any oil and gas well. Cement bond logging technologies have evolved, from basic CBL, to include different measurement physics, sensor technologies and deployment options. Add to that, a better understanding of downhole conditions now enables us to interpret the measurements in a more insightful manner. We will present a modern review of the cement logging technologies including current and best practices in log interpretation. Introduced in the oil and gas industry in the mid-1950’s, Cement Bond Log (CBL) tools have stood the test of time despite the continuous development of increasingly sophisticated technologies. Where used by itself, CBL technology presents several inherent limitations, however, when used together with modern measurements, it frequently provides valuable complementary information. Our methodology consists of using a collection of recent case examples, using multiple technologies and measurement physics, and review both the weaknesses of a standalone CBL and the value it brings when integrated with new methods. Initially designed and tested in laboratory conditions, cement slurries are expected to achieve certain downhole performance with respect to curing time and compressive strength. The casing is also expected as centralized, in good borehole, surrounded by a uniform cement sheath for zonal isolation. A deviation from the ideal conditions, can impact the interpretation of cement logs and lead to erroneous decision making. Low density and or contaminated cements, unexpected downhole pressure and temperature, gasified borehole fluid, enlarged and or rugose open holes, casing eccentricity, multiple concentric casings, fast formations, micro-annulus, etc. are all real-life conditions which influence the condition of cement, as related to both coverage and strength, and challenge the interpretation of the logs. However, a majority of these challenges are effectively overcome, through integration of technologies, as will be demonstrated through presented field examples. Advancement in cement evaluation technologies, over the last 70 years, and our increased understanding of downhole conditions makes the case of an all-encompassing, modern review of cement bond logging. Log responses are easy to understand in extreme conditions of "free" or fully bonded pipe. However, as downhole conditions start to differ from ideal conditions, the log responses can often be misunderstood. This paper proposes to provide a comprehensive and additive information on interpretating the time-tested CBL all the way to modern services in use today. This will lead to unambiguous evaluation of downhole cement status and confident decision making by practicing engineers.

Emerald ash borer (Agrilus planipennis; EAB) has devastated populations of ash (Fraxinus spp.) trees in dozens of U.S. states and Canada over the past few decades. The continued survival of scattered ash trees known as “lingering ash” in heavily infested natural stands, however, offers evidence of genetic resistance or tolerance to EAB. These surviving or “lingering” ash individuals may form the basis for reforestation programs in EAB-impacted areas, and clonal mass-propagation of these genotypes can help accelerate these efforts. Between 2013 and 2018, we initiated embryogenic cultures by culturing immature zygotic embryos from open-pollinated (OP) seeds collected from several surviving white ash and green ash trees in Michigan and Pennsylvania. In addition, in 2018, we initiated cultures from crosses made between lingering green ash parents from the USDA Forest Service ash breeding program in Ohio. Somatic embryos were produced by growing cultures in liquid suspension, followed by fractionation and plating on semisolid medium to produce developmentally synchronous populations of somatic embryos. Somatic embryo germination and conversion were enhanced by a combination of pre-germination cold treatment and inclusion of activated charcoal and gibberellic acid in the germination medium. Ash somatic seedlings derived from OP explants grew rapidly following transfer to potting mix and somatic seedlings representing nine ash clones were acclimatized, grown in the greenhouse and planted in a preliminary field test, along with EAB-resistant Manchurian ash (F. mandshurica) and EAB-susceptible control seedlings. Somatic seedlings have now been produced from cultures that originated from seeds derived from the progeny of lingering green ash parents and an ex vitro germination protocol has shown some promise for accelerating early somatic seedling growth. Results of this research could provide the basis for scaled-up production of EAB-resistant ash varieties for seed orchard production for forest restoration and cultivar development for urban tree restoration.

Rainfall is arguably the most important yet most variable input for rainfall-runoff hydrologic models. In this study, the authors search for the characteristics of radar-rainfall estimates that are most important for skillful streamflow predictions. They perform comprehensive hydrologic investigations of radar-rainfall characteristics, including spatiotemporal resolution, radar range visibility, statistical characterization of rainfall variability, all vis-a-vis basin characteristics such as size and river network topology. Since the true rainfall fields are unknown, the authors exploit a paradigm of using two independently constructed radar-rainfall products i.e., Multi-Radar Multi-Sensor and IFC-ZR used operationally by the Iowa Flood Center (IFC). Using the distributed hydrologic model called the Hillslope-Link Model for the domain of the state of Iowa, they evaluate streamflow prediction at 140 USGS gauge stations that monitor rivers in Iowa. Through spatial and temporal rainfall aggregation experiments, the authors show that the impact of spatial and temporal resolution of rainfall is significant typically for smaller basins while starts reducing significantly for basins larger than 1,000 km2. Other rainfall characteristics they explored do not reveal a strong signature in the relationship of rainfall differences between the two products and hydrograph errors. However, exploring the product similarities rather than differences reveals that the basin-wide rainfall volume has the most significant effect on streamflow prediction. The results from this study are generalizable for all rainfall observing systems.

Estimating bird and bat fatalities caused by wind-turbine facilities is challenging when carcasses are rare and produce counts that are either exactly or very near zero. The rarity of found carcasses is exacerbated when live members of a particular species are rare and when carcasses degrade quickly, are removed by scavengers, or are not detected by observers. With few observed carcass counts, common statistical methods like logistic, Poisson, or negative binomial regression are unreliable (statistically biased) and often fail to provide answers (i.e., fail to converge). Here, we propose a binomial N-mixture model that estimates fatality rates as well as the total number of carcasses when rates are expanded. Our model extends the "evidence of absence" model by relating carcass deposition rates to study covariates and by incorporating terms that naturally scale counts from facilities of different sizes. Our model, which we call Evidence of Absence Regression (EoAR), can estimate the total number of birds or bats killed at a single wind energy facility or a fleet of wind energy facilities based on covariate values. Furthermore, with accurate prior distributions the model's results are extremely robust to sparse data and unobserved combinations of covariate values. In this paper, we describe the model, show its low bias and high precision via computer simulation, and apply it to bat carcass counts observed at 21 wind energy facilities in Iowa.

A solution manifold is the collection of points in a $d$-dimensional space satisfying a system of $s$ equations with $s<d$. Solution manifolds occur in several statistical problems including hypothesis testing, curved-exponential families, constrained mixture models, partial identifications, and nonparametric set estimation. We analyze solution manifolds both theoretically and algorithmically. In terms of theory, we derive five useful results: the smoothness theorem, the stability theorem (which implies the consistency of a plug-in estimator), the convergence of a gradient flow, the local center manifold theorem and the convergence of the gradient descent algorithm. To numerically approximate a solution manifold, we propose a Monte Carlo gradient descent algorithm. In the case of likelihood inference, we design a manifold constraint maximization procedure to find the maximum likelihood estimator on the manifold. We also develop a method to approximate a posterior distribution defined on a solution manifold.