Detection and use of QTL for complex traits in multiple environments.

QTL mapping methods for complex traits are challenged by new developments in marker technology, phenotyping platforms, and breeding methods. In meeting these challenges, QTL mapping approaches will need to also acknowledge the central roles of QTL by environment interactions (QEI) and QTL by trait interactions in the expression of complex traits like yield. This paper presents an overview of mixed model QTL methodology that is suitable for many types of populations and that allows predictive modeling of QEI, both for environmental and developmental gradients. Attention is also given to multi-trait QTL models which are essential to interpret the genetic basis of trait correlations. Biophysical (crop growth) model simulations are proposed as a complement to statistical QTL mapping for the interpretation of the nature of QEI and to investigate better methods for the dissection of complex traits into component traits and their genetic controls.

Boosting the productivity and profitability of northern Australian beef enterprises: Exploring innovation options using simulation modelling and systems analysis

The financial health of beef cattle enterprises in northern Australia has declined markedly over the last decade due to an escalation in production and marketing costs and a real decline in beef prices. Historically, gains in animal productivity have offset the effect of declining terms of trade on farm incomes. This raises the question of whether future productivity improvements can remain a key path for lifting enterprise profitability sufficient to ensure that the industry remains economically viable over the longer term. The key objective of this study was to assess the production and financial implications for north Australian beef enterprises of a range of technology interventions (development scenarios), including genetic gain in cattle, nutrient supplementation, and alteration of the feed base through introduced pastures and forage crops, across a variety of natural environments. To achieve this objective a beef systems model was developed that is capable of simulating livestock production at the enterprise level, including reproduction, growth and mortality, based on energy and protein supply from natural C4 pastures that are subject to high inter-annual climate variability. Comparisons between simulation outputs and enterprise performance data in three case study regions suggested that the simulation model (the Northern Australia Beef Systems Analyser) can adequately represent the performance beef cattle enterprises in northern Australia. Testing of a range of development scenarios suggested that the application of individual technologies can substantially lift productivity and profitability, especially where the entire feedbase was altered through legume augmentation. The simultaneous implementation of multiple technologies that provide benefits to different aspects of animal productivity resulted in the greatest increases in cattle productivity and enterprise profitability, with projected weaning rates increasing by 25%, liveweight gain by 40% and net profit by 150% above current baseline levels, although gains of this magnitude might not necessarily be realised in practice. While there were slight increases in total methane output from these development scenarios, the methane emissions per kg of beef produced were reduced by 20% in scenarios with higher productivity gain. Combinations of technologies or innovative practices applied in a systematic and integrated fashion thus offer scope for providing the productivity and profitability gains necessary to maintain viable beef enterprises in northern Australia into the future.

Physiological basis of yield variation in response to row spacing and plant density of mungbean grown in subtropical environments

In this study, we investigated the extent and physiological bases of yield variation due to row spacing and plant density configuration in the mungbean Vigna radiata (L.) Wilczek variety “Crystal” grown in different subtropical environments. Field trials were conducted in six production environments; one rain-fed and one irrigated trial each at Biloela and Emerald, and one rain-fed trial each at Hermitage and Kingaroy sites in Queensland, Australia. In each trial, six combinations of spatial arrangement of plants, achieved through two inter-row spacings of 1 m or 0.9 m (wide row), 0.5 m or 0.3 m (narrow row), with three plant densities, 20, 30 and 40 plants/m2, were compared. The narrow row spacing resulted in 22% higher shoot dry matter and 14% more yield compared to the wide rows. The yield advantage of narrow rows ranged from 10% to 36% in the two irrigated and three rain-fed trials. However, yield loss of up to 10% was also recorded from narrow rows at Emerald where the crop suffered severe drought. Neither the effects of plant density, nor the interaction between plant density and row spacing, however, were significant in any trial. The yield advantage of narrow rows was related to 22% more intercepted radiation. In addition, simulations by the Agricultural Production Systems Simulator model, using site-specific agronomy, soil and weather information, suggested that narrow rows had proportionately greater use of soil water through transpiration, compared to evaporation resulting in higher yield per mm of soil water. The long-term simulation of yield probabilities over 123 years for the two row configurations showed that the mungbean crop planted in narrow rows could produce up to 30% higher grain yield compared to wide rows in 95% of the seasons.

Uncertainty based online planning for UAV target finding in cluttered and GPS-denied environments

There are some scenarios in which Unmmaned Aerial Vehicle (UAV) navigation becomes a challenge due to the occlusion of GPS systems signal, the presence of obstacles and constraints in the space in which a UAV operates. An additional challenge is presented when a target whose location is unknown must be found within a confined space. In this paper we present a UAV navigation and target finding mission, modelled as a Partially Observable Markov Decision Process (POMDP) using a state-of-the-art online solver in a real scenario using a low cost commercial multi rotor UAV and a modular system architecture running under the Robotic Operative System (ROS). Using POMDP has several advantages to conventional approaches as they take into account uncertainties in sensor information. We present a framework for testing the mission with simulation tests and real flight tests in which we model the system dynamics and motion and perception uncertainties. The system uses a quad-copter aircraft with an board downwards looking camera without the need of GPS systems while avoiding obstacles within a confined area. Results indicate that the system has 100% success rate in simulation and 80% rate during flight test for finding targets located at different locations.

Automatic wildfire detection and simulation using optical information from unmanned aerial systems

In many parts of the world, uncontrolled fires in sparsely populated areas are a major concern as they can quickly grow into large and destructive conflagrations in short time spans. Detecting these fires has traditionally been a job for trained humans on the ground, or in the air. In many cases, these manned solutions are simply not able to survey the amount of area necessary to maintain sufficient vigilance and coverage. This paper investigates the use of unmanned aerial systems (UAS) for automated wildfire detection. The proposed system uses low-cost, consumer-grade electronics and sensors combined with various airframes to create a system suitable for automatic detection of wildfires. The system employs automatic image processing techniques to analyze captured images and autonomously detect fire-related features such as fire lines, burnt regions, and flammable material. This image recognition algorithm is designed to cope with environmental occlusions such as shadows, smoke and obstructions. Once the fire is identified and classified, it is used to initialize a spatial/temporal fire simulation. This simulation is based on occupancy maps whose fidelity can be varied to include stochastic elements, various types of vegetation, weather conditions, and unique terrain. The simulations can be used to predict the effects of optimized firefighting methods to prevent the future propagation of the fires and greatly reduce time to detection of wildfires, thereby greatly minimizing the ensuing damage. This paper also documents experimental flight tests using a SenseFly Swinglet UAS conducted in Brisbane, Australia as well as modifications for custom UAS.

Ship collision avoidance and COLREGS compliance using simulation-based control behavior selection with predictive hazard assessment

This paper describes a concept for a collision avoidance system for ships, which is based on model predictive control. A finite set of alternative control behaviors are generated by varying two parameters: offsets to the guidance course angle commanded to the autopilot and changes to the propulsion command ranging from nominal speed to full reverse. Using simulated predictions of the trajectories of the obstacles and ship, compliance with the Convention on the International Regulations for Preventing Collisions at Sea and collision hazards associated with each of the alternative control behaviors are evaluated on a finite prediction horizon, and the optimal control behavior is selected. Robustness to sensing error, predicted obstacle behavior, and environmental conditions can be ensured by evaluating multiple scenarios for each control behavior. The method is conceptually and computationally simple and yet quite versatile as it can account for the dynamics of the ship, the dynamics of the steering and propulsion system, forces due to wind and ocean current, and any number of obstacles. Simulations show that the method is effective and can manage complex scenarios with multiple dynamic obstacles and uncertainty associated with sensors and predictions.

Frontier Based Multiple Goal Search in Unknown Environments

We present a frontier based algorithm for searching multiple goals in a fully unknown environment, with only information about the regions where the goals are most likely to be located. Our algorithm chooses an ``active goal'' from the ``active goal list'' generated by running a Traveling Salesman Problem (Tsp) routine with the given centroid locations of the goal regions. We use the concept of ``goal switching'' which helps not only in reaching more number of goals in given time, but also prevents unnecessary search around the goals that are not accessible (surrounded by walls). The simulation study shows that our algorithm outperforms Multi-Heuristic LRTA* (MELRTA*) which is a significant representative of multiple goal search approaches in an unknown environment, especially in environments with wall like obstacles.

Goal Seeking for Robots in Unknown Environments

We consider the problem of goal seeking by robots in unknown environments. We present a frontier based algorithm for finding a route to a goal in a fully unknown environment, where information about the goal region (GR), the region where the goal is most likely to be located, is available. Our algorithm efficiently chooses the best candidate frontier cell, which is on the boundary between explored space and unexplored space, having the maximum ``goal seeking index'', to reach the goal in minimal number of moves. Modification of the algorithm is also proposed to further reduce the number of moves toward the goal. The algorithm has been tested extensively in simulation runs and results demonstrate that the algorithm effectively directs the robot to the goal and completes the search task in minimal number of moves in bounded as well as unbounded environments. The algorithm is shown to perform as well as a state of the art agent centered search algorithm RTAA*, in cluttered environments if exact location of the goal is known at the beginning of the mission and is shown to perform better in uncluttered environments.

``Conformational Simulation'' of Sulfamethizole by Molecular Complexation and Insights from Charge Density Analysis: Role of Intramolecular S center dot center dot center dot O Chalcogen Bonding

Intramolecular S center dot center dot center dot O chalcogen bonding and its potential to lock molecular conformation have been examined in the crystal forms of sulfamethizole, a sulfonamide antibiotic. Molecular complexes of sulfamethizole, including salts and cocrystal, have been synthesized, and their crystal structures were analyzed in order to examine the possible conformational preferences of the molecule in various ionic states and supramolecular environments (neutral/cocrystal, anionic salt, and cationic salt forms). The electrostatic potential mapped on Hirshfeld surfaces generated for these crystal forms provides insights into the possible binding modes of the drug in different environments. Further, the observed conformation locking feature has been rationalized in terms of the experimental charge density features of the intramolecular S center dot center dot O chalcogen bonding in sulfamethizole. The study quantitatively illustrates and rationalizes an intriguing case of a local minimum of molecular conformation being exclusively preferred over the global minimum, as it facilitates more efficient intermolecular interactions in a supramolecular environment.

QM/MM and Classical Molecular Dynamics Simulation of Histidine-Tagged Peptide Immobilization on Nickel Surface

The hybrid quantum mechanics (QM) and molecular mechanics (MM) method is employed to simulate the His-tagged peptide adsorption to ionized region of nickel surface. Based on the previous experiments, the peptide interaction with one Ni ion is considered. In the QM/MM calculation, the imidazoles on the side chain of the peptide and the metal ion with several neighboring water molecules are treated as QM part calculated by “GAMESS”, and the rest atoms are treated as MM part calculated by “TINKER”. The integrated molecular orbital/molecular mechanics (IMOMM) method is used to deal with theQMpart with the transitional metal. By using the QM/MM method, we optimize the structure of the synthetic peptide chelating with a Ni ion. Different chelate structures are considered. The geometry parameters of the QM subsystem we obtained by QM/MM calculation are consistent with the available experimental results. We also perform a classical molecular dynamics (MD) simulation with the experimental parameters for the synthetic peptide adsorption on a neutral Ni(1 0 0) surface. We find that half of the His-tags are almost parallel with the substrate, which enhance the binding strength. Peeling of the peptide from the Ni substrate is simulated in the aqueous solvent and in vacuum, respectively. The critical peeling forces in the two environments are obtained. The results show that the imidazole rings are attached to the substrate more tightly than other bases in this peptide.

Molecular Dynamics Simulation of Barnacle Cement

Barnacle cement is an underwater adhesive that is used for permanent settlement. Its main components are insoluble protein complexes that have not been fully studied. In present article, we chose two proteins of barnacle cement for study, 36-KD protein and Mrcp-100K protein. In order to investigate the characteristic of above two proteins, we introduced the method of molecular modeling. And the simulation package GROMACS was used to simulate the behavior of these proteins. In this article, before the simulations, we introduce some theories to predict the time scale for polymer relaxation. During the simulation, we mainly focus on two properties of these two proteins: structural stability and adhesive force to substrate. First, we simulate the structural stability of two proteins in water, and then the stability of 36-KD protein in seawater environment is investigated.We find that the stability varies in the different environments. Next, to study adhesive ability of two proteins, we simulate the process of peeling the two proteins from the substrate (graphite). Then, we analyze the main reasons of these results. We find that hydrogen bonds in proteins play an important role in the protein stability. In the process of the peeling, we use Lennard–Jones 12-6 potential to calculate the van der Waals interactions between proteins and substrate.

QM/MM and Classical Molecular Dynamics Simulation of Histidine-Tagged Peptide Immobilization on Nickel Surface

The hybrid quantum mechanics (QM) and molecular mechanics (MM) method is employed to simulate the His-tagged peptide adsorption to ionized region of nickel surface. Based on the previous experiments, the peptide interaction with one Ni ion is considered. In the QM/MM calculation, the imidazoles on the side chain of the peptide and the metal ion with several neighboring water molecules are treated as QM part calculated by "GAMESS", and the rest atoms are treated as MM part calculated by "TINKER". The integrated molecular orbital/molecular mechanics (IMOMM) method is used to deal with the QM part with the transitional metal. By using the QM/MM method, we optimize the structure of the synthetic peptide chelating with a Ni ion. Different chelate structures are considered. The geometry parameters of the QM subsystem we obtained by QM/MM calculation are consistent with the available experimental results. We also perform a classical molecular dynamics (MD) simulation with the experimental parameters for the synthetic peptide adsorption on a neutral Ni(100) surface. We find that half of the His-tags are almost parallel with the substrate, which enhance the binding strength. Peeling of the peptide from the Ni substrate is simulated in the aqueous solvent and in vacuum, respectively. The critical peeling forces in the two environments are obtained. The results show that the in-tidazole rings are attached to the substrate more tightly than other bases in this peptide.

Molecular Dynamics Simulation of Barnacle Cement

Barnacle cement is an underwater adhesive that is used for permanent settlement. Its main components are insoluble protein complexes that have not been fully studied. In present article, we chose two proteins of barnacle cement for study, 36-KD protein and Mrcp-100K protein. In order to investigate the characteristic of above two proteins, we introduced the method of molecular modeling. And the simulation package GROMACS was used to simulate the behavior of these proteins. In this article, before the simulations, we introduce some theories to predict the time scale for polymer relaxation. During the simulation, we mainly focus on two properties of these two proteins: structural stability and adhesive force to substrate. First, we simulate the structural stability of two proteins in water, and then the stability of 36-KD protein in seawater environment is investigated. We find that the stability varies in the different environments. Next, to study adhesive ability of two proteins, we simulate the process of peeling the two proteins from the substrate (graphite). Then, we analyze the main reasons of these results. We find that hydrogen bonds in proteins play an important role in the protein stability. In the process of the peeling, we use Lennard-Jones 12-6 potential to calculate the van der Waals interactions between proteins and substrate.

Interactions between building integrated photovoltaics and microclimate in urban environments

BIPV (building integrated photovoltaics) has progressed in the past years and become an element to be considered in city planning. BIPV has significant influence on microclimate in urban environments and the performance of BIPV is also affected by urban climate. The thermal model and electrical performance model of ventilated BIPV are combined to predict PV temperature and PV power output in Tianjin, China. Then, by using dynamic building energy model, the building cooling load for installing BIPV is calculated. A multi-layer model AUSSSM of urban canopy layer is used to assess the effect of BIPV on the Urban Heat Island (UHI). The simulation results show that in comparison with the conventional roof, the total building cooling load with ventilation PV roof may be decreased by 10%. The UHI effect after using BIPV relies on the surface absorptivity of original building. In this case, the daily total PV electricity output in urban areas may be reduced by 13% compared with the suburban areas due to UHI and solar radiation attenuation because of urban air pollution. The calculation results reveal that it is necessary to pay attention to and further analyze interactions between BIPV and microdimate in urban environments to decrease urban pollution, improve BIPV performance and reduce cooling load. Copyright © 2006 by ASME.

simulation and interaction of fluid dynamics

In the fluid simulation, the fluids and their surroundings may greatly change properties such as shape and temperature simultaneously, and different surroundings would characterize different interactions, which would change the shape and motion of the fluids in different ways. On the other hand, interactions among fluid mixtures of different kinds would generate more comprehensive behavior. To investigate the interaction behavior in physically based simulation of fluids, it is of importance to build physically correct models to represent the varying interactions between fluids and the environments, as well as interactions among the mixtures. In this paper, we will make a simple review of the interactions, and focus on those most interesting to us, and model them with various physical solutions. In particular, more detail will be given on the simulation of miscible and immiscible binary mixtures. In some of the methods, it is advantageous to be taken with the graphics processing unit (GPU) to achieve real-time computation for middle-scale simulation.