Conditional Reliability, Sub-Monthly Time Step, Flood Control, and Salinity Features of WRAP

WRAP is a generalized river/reservoir system simulation model providing flexible capabilities for analyzing water resources development, management, control, allocation, and use. This supplemental reference and users manual documents expanded WRAP modeling capabilities that are not covered in the following basic reference and users manuals. Water Rights Analysis Package (WRAP) Modeling System Reference Manual, TWRI TR-255, 1st Edition August 2003, 2nd Edition April 2005. Water Rights Analysis Package (WRAP) Modeling System Users Manual, TWRI TR-256, 1st Edition August 2003, 2nd Edition April 2005. A Water Availability Modeling (WAM) System was developed by the Texas Commission on Environmental Quality (TCEQ) and its partner agencies and contractors during 1997-2003 pursuant to Senate Bill 1 enacted by the Texas Legislature in 1997 and subsequent legislation. The WAM System includes the generalized WRAP simulation model and input datasets for the river basins of the state. The Reference and Users Manuals cited above cover the WRAP capabilities that are reflected in the original Texas WAM System datasets plus several recently added enhancements. This Supplemental Manual covers the following other major modeling capabilities added to WRAP since completion of the original TCEQ WAM System datasets.Texas Commission on Environmental Quality and Fort Worth District U.S. Army Corps of Engineer

Inhibition of bolting and flowering of a beta vulgaris plant

The present invention provides means for inhibiting the bolting and flowering of a Beta vulgaris plant, including an isolated nucleic acid, which can be used to produce a transgenic Beta vulgaris plant, where bolting and flowering is inhibited after vernalization. Furthermore, the invention discloses vectors, transgenic and non-transgenic, non-bolting plants and parts thereof, and methods for producing such plants

Long-Term Effects of Cancer Survivorship on the Employment of Older Workers

Reservoir is one of the emergency environments that required fast an accurate decision to reduce flood risk during heavy rainfall and contain water during less rainfall. Typically, during heavy rainfall, the water level increase very fast, thus decision of the water release is timely and crucial task. In this paper, intelligent decision support model based on neural network (NN) is proposed. The proposed model consists of situation assessment, forecasting and decision models. Situation assessment utilized temporal data mining technique to extract relevant data and attribute from the reservoir operation record. The forecasting model utilize NN to perform forecasting of the reservoir water level, while in the decision model, NN is applied to perform classification of the current and changes of reservoir water level. The simulations have shown that the performances of NN for both forecasting and decision models are acceptably good