The next generation of gene-based crop models offers the potential of predicting crop vegetative and reproductive development based on genotype and weather data as inputs. Here, we illustrate an approach for developing a dynamic modular gene-based model to simulate changes in main stem node numbers, time to first anthesis, and final node number on the main stem of common bean (Phaseolus vulgaris L.). In the modules, these crop characteristics are functions of relevant genes (quantitative trait loci (QTL)), the environment (E), and QTL × E interactions. The model was based on data from 187 recombinant inbred (RI) genotypes and the two parents grown at five sites (Citra, FL; Palmira, Colombia; Popayan, Colombia; Isabela Puerto Rico; and Prosper, North Dakota). The model consists of three dynamic QTL effect models for node addition rate (NAR, No. d− 1), daily rate of progress from emergence toward flowering (RF), and daily maximum main stem node number (MSNODmax), that were integrated to simulate main stem node number vs. time, and date of first flower using daily time steps. Model evaluation with genotypes not used in model development showed reliable predictions across all sites for time to first anthesis (R2 = 0.75) and main stem node numbers during the linear phase of node addition (R2 = 0.93), while prediction of the final main stem node number was less reliable (R2 = 0.27). The use of mixed-effects models to analyze multi-environment data from a wide range of genotypes holds considerable promise for assisting development of dynamic QTL effect models capable of simulating vegetative and reproductive development.

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An adaptive optical system for precise control of a laser beam's mode structure has been developed. The system uses a dynamic lens based on controlled optical path deformation in a dichroic optical element that is heated with an auxiliary laser. Our method is essentially aberration free, has high dynamic range, and can be implemented with high average power laser beams where other adaptive optics methods fail. A quantitative model agrees well with our experimental data and demonstrates the potential of our method as a mode-matching and beam-shaping element for future large-scale gravitational wave detectors.

Cloud computing is a hot research topic and is quickly gaining recognition and application in combination with Building Information Modeling (BIM). However, since there is still lack of an authoritative definition of cloud computing, different parties in Architectural / Engineering / Construction (AEC) industry may have very different understanding on what is qualified as BIM cloud computing.This paper proposes a system under which some of the currently available cloud computing frameworks are classified and the adoption of cloud computing in a specific context can be assessed, and reviews and compares several currently available BIM cloud computing frameworks under this system. The BIM cloud computing frameworks compared include dedicated cloud services for AEC industry such as Revit Server, Revit Cloud and STRATUS, and general purpose cloud services such as Advance2000 and Amazon. The proposed system can be used by IT implementers in the AEC industry when making decisions on adopting cloud computing for BIM applications.