Dynamic optimization has several key advantages. This includes the ability to work on binary code in the absence of sources and to perform optimization across module boundaries. However, it has a significant disadvantage viz-a-viz traditional static optimization: it has a significant runtime overhead. There can be performance gain only if the overhead can be amortized. In this paper, we will quantitatively analyze the runtime overhead introduced by a dynamic optimizer, DynamoRIO. We found that the major overhead does not come from the optimizer's operation. Instead, it comes from the extra code in the code cache added by DynamoRIO. After a detailed analysis, we will propose a method of trace construction that ameliorate the overhead introduced by the dynamic optimizer, thereby reducing the runtime overhead of DynamoRIO. We believe that the result of the study as well as the proposed solution is applicable to other scenarios such as dynamic code translation and managed execution that utilizes a framework similar to that of dynamic optimization. / Singapore-MIT Alliance (SMA)

Fiber reinforced polymer (FRP) deck has some significant advantages compared to concrete deck in use of bridges, such as light self-weight, high stiffness and strength, good durability and easy to install. FRP deck has already been used in some bridge rehabilitation and short span bridges. But for widely used in bridges, FRP deck bridges still need further research. Currently many research efforts focus on the filed tests of FRP deck bridges. Compared to field tests, Finite element analysis also has great advantages, such as low cost and convenient to conduct. Therefore, in this thesis finite element analysis is conducted by ABAQUS on the Boyer Bridge in Pennsylvania. The finite element model is verified by the static field test result. Then a simplified moving truck load is applied on the bridge model in order to analyze the dynamic responses of the FRP deck bridge, including the displacements and stress of each girder at the middle span. The dynamic effect is shown by comparing the dynamic responses and the static responses of the bridge. The connection between the FRP deck and girder is very important to the behavior of the bridge. In this thesis shear studs serve to connect the FRP deck and girder. This thesis also analyzes the effect of shear studs to the dynamic responses of the bridge by changing the number of the shear studs.

The goals of gene prediction using computational approaches are to determine gene location and the corresponding functionality of the coding region. A subset of gene prediction is the gene structure prediction problem, which is to define the exon-intron boundaries of a gene. Gene prediction follows two general approaches: statistical patterns identification and sequence similarity comparison. Similarity based approaches have gained increasing popularity with the recent vast increase in genomic data in GenBank. The proposed gene prediction algorithm is a similarity based algorithm which capitalizes on the fact that similar sequences bear similar functions. The proposed algorithm, like most other similarity based algorithms, is based on dynamic programming. Given a genomic DNA, X = x1 xn and a closely related cDNA, Y = y1 yn, these sequences are aligned with matching pairs stored in a data set. These indexes of matching sets contain a large jumble of all matching pairs, with a lot of cross over indexes. Dynamic programming alignment is again used to retrieve the longest common non-crossing subsequence from the collection of matching fragments in the data set. This algorithm was implemented in Java on the Unix platform. Statistical comparisons were made against other software programs in the field. Statistical evaluation at both the DNA and exonic level were made against Est2genome, Sim4, Spidey, and Fgenesh-C. The proposed gene structure prediction algorithm, by far, has the best performance in the specificity category. The resulting specificity was greater than 98%. The proposed algorithm also has on par results in terms of sensitivity and correlation coeffcient. The goal of developing an algorithm to predict exonic regions with a very high level of correctness was achieved.

In this paper a partial least squares (PLS) approach to dynamic modelling with latent variables is proposed. Let Y be a matrix of manifest variables and H the matrix of the corresponding latent variables. And let H = BH+ε be a structural PLS model with a coefficient matrix B. Then this model can be made a dynamic one by substituting for B a matrix F = B + CL containing the lag operator L. Then the structural dynamic model H = FH+ε is formally estimated like an ordinary PLS model. In an exploratory way the model can be used for forecasting purposes. The procedure is being programmed in ISP.

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Traffic assignment is the last step in the conventional four-step transportation planning model, following trip generation, trip distribution, and mode choice. It concerns selection of routes between origins and destinations on the traffic network. Traditional traffic assignment methods do not consider trip chaining behavior. Since commuters always make daily trips in the form of trip chains, meaning a traveler’s trips are sequentially made with spatial correlation, it makes sense to develop models to feature this trip chaining behavior. Network performance in congested areas depends not only on the total daily traffic volume but also on the trip distribution over the course of a day. Therefore, this research makes an effort to propose a network traffic assignment framework featuring commuters’ trip chaining behavior. Travelers make decisions on their departure time and route choices under a capacity-constrained network. The modeling framework sequentially consists of an activity origin-destination (OD) choice model and a dynamic user equilibrium (DUE) traffic assignment model. A heuristic algorithm in an iterative process is proposed. A solution tells commuters’ daily travel patterns and departure distributions. Finally, a numerical test on a simple transportation network with simulation data is provided. In the numerical test, sensitivity analysis is additionally conducted on modeling parameters.

Because of the highly complex structure of the load-sensing pump, its compensators and controlling elements, simulation of load-sensing pump system pose many challenges to researchers. One way to overcome some of the difficulties with creating complex computer model is the use of black box approach to create an approximation of the system behaviour by analyzing input/output relationships. That means the details of the physical phenomena are not so much of concern in the black box approach. Neural network can be used to implement the black box concept for system identification and it is proven that the neural network have the ability to model very complex behaviour and there is a well defined set of neural and neural network structures. Previous studies have shown the problems and limitations in dynamic system modeling using static neuron based neural networks. Some new neuron structures, Dynamic Neural Units (DNUs), have been developed which open a new area to the research associated with the system modelling.<p>The overall objective of this research was to investigate the feasibility of using a dynamic neural unit (DNU) based dynamic neural network (DNN) in modeling a hydraulic component (specifically a load-sensing pump), and the model could be used in a simulation with any other required component model to aid in hydraulic system design. To be truly representative of the component, the neural network model must be valid for both the steady state and the transient response. Due to three components (compensator, pump and control valve) in a load sensing pump system, there were three different pump model structures (the pump, compensator and valve model, the compensator and pump model, and the pump only model) from the practical point of view, and they were analysed thoroughly in this study. In this study, the DNU based DNN was used to model a pump only model which was a portion of a complete load sensing pump. After the trained DNN was tested with a wide variety of system inputs and due to the steady state error illustrated by the trained DNN, compensation equation approach and DNN and SNN combination approach were then adopted to overcome the steady state deviation. <p>It was verified, through this work, that the DNU based DNN can capture the dynamics of a nonlinear system, and the DNN and SNN combination can eliminate the steady state error which was generated by the trained DNN. <p>The first major contribution of this research was in investigating the feasibility of using the DNN to model a nonlinear system and eliminating the error accumulation problem encountered in the previous work. The second major contribution is exploring the combination of DNN and SNN to make the neural network model valid for both steady state and the transient response.

Previous research has shown that the productivity of sows housed in an Electronic Sow Feeding (ESF) system is affected by the housing management (static vs. dynamic), stage of gestation at mixing and parity. Familiarity has also been shown to affect the behaviour of group-housed sows. Thus, the objective of this experiment was to determine how the previously mentioned social factors affect the behaviour, physiology and productivity of sows housed in an ESF system. Sows were regrouped into either the static and dynamic pens. Within an introduction group, a subgroup of up to 24 focals sows was observed. The focal sows were chosen based on whether they were mixed pre vs. post-implantation (<12 vs. >46 days post-breeding), familiar vs. unfamiliar with group mates and parity (1st vs. 2nd and 3rd vs. 4th +). Aggression at mixing and at the feeder, injury scores, feeder entry order, space usage, salivary cortisol and farrowing productivity was recorded. The data was analyzed using Proc-Mixed and the General Model for SAS. Housing did not have a significant effect on the any of the parameters examined. Young sows had significantly more piglets born alive when housed in a dynamic system, while old sows had more piglets born alive when housed in a static system (p=0.03). Pre-implant sows initiated more aggressive encounters than post-implant sows (p=0.01). Post-implant sows ate later in the feeding cycle (p=0.03), rested on the slats more (p<0.001) and had higher salivary cortisol concentrations (p=0.0008). However, the cortisol concentrations increased throughout gestation for all sows (p<0.001). Familiarity did not have an effect on any of the variables examined except, familiar sows spent more time lying against the wall (p=0.03) and unfamiliar sows spent more time lying in the centre of the solid area of the pen (p=0.02). Old sows were involved in more aggressive encounters (p=0.04), spent more time fighting at mixing (p=0.02) and laid against the wall more (p<0.001). Young sows tended to received more scratches (p=0.07), ate later in the feeding cycle (p<0.001) and spent more time lying on the slats (p<0.001). Intermediate sows had significantly lower salivary cortisol concentrations (p=0.003). There was not a difference between the static and dynamic management systems. Sows should not be mixed until after embryonic implantation because they are more docile. The intermediate sows underwent the least amount of social stress due to their intermediate position within the dominance hierarchy.

The central thesis of this dissertation is that insight from queueing analysis can effectively guide standard (combinatorial) scheduling algorithms in dynamic environments. Scheduling is generally concerned with complex combinatorial decisions for static problems, whereas queueing theory simplifies the combinatorics and focuses on dynamic systems. We examine a queueing network with flexible servers under queueing and scheduling techniques. Based on the strengths of queueing analysis and scheduling, we develop a hybrid model that guides scheduling with results from the queueing model. In order to include setup times, we create a logic-based Benders decomposition model for a static representation of the queueing network. Our model is able to find optimal schedules up to 5 orders of magnitude faster than the only other model in the literature. A hybrid model is then developed for the dynamic problem and shown to achieve the best mean flow time while also guaranteeing maximal capacity.

The central thesis of this dissertation is that insight from queueing analysis can effectively guide standard (combinatorial) scheduling algorithms in dynamic environments. Scheduling is generally concerned with complex combinatorial decisions for static problems, whereas queueing theory simplifies the combinatorics and focuses on dynamic systems. We examine a queueing network with flexible servers under queueing and scheduling techniques. Based on the strengths of queueing analysis and scheduling, we develop a hybrid model that guides scheduling with results from the queueing model. In order to include setup times, we create a logic-based Benders decomposition model for a static representation of the queueing network. Our model is able to find optimal schedules up to 5 orders of magnitude faster than the only other model in the literature. A hybrid model is then developed for the dynamic problem and shown to achieve the best mean flow time while also guaranteeing maximal capacity.