Chronology of Dune Development in the White River Badlands, Northern Great Plains, USA

Aeolian dune field chronologies provide important information on drought history on the Great Plains. The White River Badlands (WRB) dunes are located approximately 60 km north of the Nebraska Sand Hills (NSH), in the western section of the northern Great Plains. Clifftop dunes, sand sheets, and stabilized northwest-southeast trending parabolic dunes are found on upland mesas and buttes, locally called tables. The result of this study is a dune stabilization history determined from samples collected from stratigraphic exposures and dune crests. Thirty-seven OSL ages, from this and previous investigations, show three periods of dune activity: 1) ∼21,000 years ago to 12,000 years ago (a), 2) ∼9 to 6 ka, and 3) post-700 a. Stratigraphic exposures and low-relief dune forms preserve evidence of late Pleistocene and middle Holocene dune development, while high-relief dune crests preserve evidence of late Holocene dune development. Results of 12 OSL ages from the most recent dune activation event indicate that Medieval Climate Anomaly (MCA) droughts and Little Ice Age (LIA) droughts caused dune reactivation on the tables. Dune reactivation was accompanied by other drought-driven geomorphological responses in the WRB, including fluvial incision of the prairie and formation of sod tables. Regional significance of the MCA and LIA droughts is supported by similarities in the aeolian chronologies of the NSH at 700–600 a and some western Great Plains dune fields at 420–210 a. Aerial photographs of the WRB show little activity during the Dust Bowl droughts of the 1930s

Isogeometric analysis: an overview and computer implementation aspects

Isogeometric analysis (IGA) represents a recently developed technology in computational mechanics that offers the possibility of integrating methods for analysis and Computer Aided Design (CAD) into a single, unified process. The implications to practical engineering design scenarios are profound, since the time taken from design to analysis is greatly reduced, leading to dramatic gains in efficiency. The tight coupling of CAD and analysis within IGA requires knowledge from both fields and it is one of the goals of the present paper to outline much of the commonly used notation. In this manuscript, through a clear and simple Matlab implementation, we present an introduction to IGA applied to the Finite Element (FE) method and related computer implementation aspects. Furthermore, implemen- tation of the extended IGA which incorporates enrichment functions through the partition of unity method (PUM) is also presented, where several examples for both two-dimensional and three-dimensional fracture are illustrated. The open source Matlab code which accompanies the present paper can be applied to one, two and three-dimensional problems for linear elasticity, linear elastic fracture mechanics, structural mechanics (beams/plates/shells including large displacements and rotations) and Poisson problems with or without enrichment. The Bezier extraction concept that allows FE analysis to be performed efficiently on T-spline geometries is also incorporated. The article includes a summary of recent trends and developments within the field of IGA

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

A Mathematical Model for Intra-cellular Effects of Toxins on DNA Adduction and Repair

Bulletin of Mathematical Biology, 59, 1997, pp. 89-106.The processes by which certain classes of toxic compounds or their metabolites may react with DNA to alter the genetic information contained in subsequent generations of cells or organisms are a major component of hazard associated with exposure to chemicals in the environment. Many classes of chemicals may form DNA adducts and there may or may not be a defined mechanism to remove a particular adduct from DNA independent of replication. Many compounds and metabolites that bind DNA also readily bind existing proteins; some classes of toxins and DNA adducts have the capacity to inactivate a repair enzyme and divert the repair process competitively. This paper formulates an intracellular dynamic model for one aspect of the action of toxins that form DNA adducts, recognizing a capacity for removal of those adducts by a repair enzyme combined with reaction of the toxin and/or the DNA adduct to inactivate the repair enzyme. This particular model illustrates the possible saturation of repair enzyme capacity by the toxin dosage and shows that bistable behavior can occur, with the potential to induce abrupt shifts away from steady-state equilibria. The model suggests that bistable behavior, dose and variation between individuals or tissues may combine under certain conditions to amplify the biological effect of dose observed as DNA adduction and its consequences as mutation. A model recognizing stochastic phenomena also indicates that variation in within-cell toxin concentration may promote jumps between stable equilibria