Yes / Modern manufacturing companies face increased pressures to adapt to shorter product life cycles and the need to reconfigure more frequently their production systems to offer new product variants. This paper proposes a new multi-agent architecture utilising “plug and produce” principles for configuration and reconfiguration of production systems with minimum human intervention. A new decision-making approach for system reconfiguration based on tasks re-allocation is presented using goal driven methods. The application of the proposed architecture is described with a number of architectural views and its deployment is illustrated using a validation scenario implemented on an industrial assembly platform. The proposed methodology provides an innovative application of a multi-agent control environment and architecture with the objective of significantly reducing the time for deployment and ramp-up of small footprint assembly systems. / The reported research has been part of the EU FP7 research project “PRIME”

Indiana University-Purdue University Indianapolis (IUPUI) / Complex biological systems are composed of many regulatory components, which can be measured with the advent of genomics technology. Each molecular assay is normally designed to interrogate one aspect of the cell state. However, a comprehensive understanding of the regulatory mechanism requires characterization from multiple levels such as genome, epigenome, and transcriptome. Integration of multi-omics data is urgently needed for understanding the global regulatory mechanism of gene expression. In recent years, single-cell technology offers unprecedented resolution for a deeper characterization of cellular diversity and states. High-quality single-cell suspensions from tissue biopsies are required for single-cell sequencing experiments. Tissue biopsies need to be processed as soon as being collected to avoid gene expression changes and RNA degradation. Although cryopreservation is a feasible solution to preserve freshly isolated samples, its effect on transcriptome profiles still needs to be investigated. Investigation of multi-omics data at the single-cell level can provide new insights into the biological process. In addition to the common method of integrating multi-omics data, it is also capable of simultaneously profiling the transcriptome and epigenome at single-cell resolution, enhancing the power of discovering new gene regulatory interactions. In this dissertation, we integrated bulk RNA-seq with ATAC-seq and several additional assays and revealed the complex mechanisms of ER–E2 interaction with nucleosomes. A comparison analysis was conducted for comparing fresh and frozen multiple myeloma single-cell RNA sequencing data and concluded that cryopreservation is a feasible protocol for preserving cells. We also analyzed the single-cell multiome data for mesenchymal stem cells. With the unified landscape from simultaneously profiling gene expression and chromatin accessibility, we discovered distinct osteogenic differentiation potential of mesenchymal stem cells and different associations with bone disease-related traits. We gained a deeper insight into the underlying gene regulatory mechanisms with this frontier single-cell mutliome sequencing technique.

The ovarian hormone 17β-estradiol (E2) is one of the central regulators of the female reproductive system. E2 is also a pleiotropic regulator since it can exert its non-reproductive role on other organ systems. E2 is neuroprotective, it maintains body's energy homeostasis, participates in various repair mechanism and is required for neural development. However, there is a substantial evidence suggesting that there might be a molecular reprogramming of E2's action when it is supplied exogenously after E2 deprivation. Though the length of E2 deprivation and age has been linked to this phenomenon, the molecular components and how they activate this reprogramming is still elusive. Our main goal was to perform global proteomics and metabolomics study to identify the molecular components and their interaction networks that are being altered in the brain and serum after a short-term E2 treatment following ovariectomy (OVX) in Sprague Dawley rats. One of the strength of our global study is that it gave us extensive information on the brain proteome itself by identification of a wide number of proteins in different brain sections. By analyzing the differentially expressed proteins, our proteomics study revealed 49 different networks to be altered in 7 sections of the brain. Most of the perturbed networks were involved in cell metabolism, neural development, protein synthesis, cellular trafficking and degradation, and several stress response signaling pathways. We assessed the neuroenergetic status of the brain based on E2's response to various energy generating pathways, including glycolysis, TCA cycle, and oxidative phosphorylation, and several signaling pathways. All energetics pathways were shown to be downregulated in E2 treatment, which suggests that E2 exerts its neuroprotective role by restoring energy homeostasis in OVX rat model by regulating complex signaling and metabolic networks. Our second focus was to determine the metabolite response (amino acids and lipids) after E2 treatment in the brain and serum by employing targeted metabolomics study. We have found that in rat brain cortex there was significant upregulation of a wide number of amino acids suggesting alternate route of metabolism. Another alternate explanation is that E2 replacement replenished the amino acid pool in the tissue. Pathway enrichment analysis revealed upregulation of several pathways, including amino sugar metabolism, purine metabolism, and glutathione metabolism. By combining proteomics and metabolomics in two different biological matrices we were able to gather a vast array of information on how E2 replacement after E2 deprivation can confer neuroprotection. Our findings will help to create a foundation of basic science to be used for developing potentially effective hormone therapies.

The ability to predict and control laminar-to-turbulent transition in high-speed flow has a substantial effect on heat transfer and skin friction, thus improving the design and operation of hypersonic vehicles. The control of transition on blunt bodies is essential to improve the performance of lifting and control surfaces. The objective of this Ph.D. research is to develop efficient and accurate algorithms for the detection of high-frequency heat flux fluctuations supported by hypersonic flow in transitional boundary layers. The focus of this research is on understanding the mathematical properties of the reconstruction such as regularity, sensitivity to noise, multi-resolution, and accuracy. This research is part of an effort to develop small-footprint heat flux sensors able to measure high-frequency fluctuations on test articles in a hypersonic wind tunnel with a small curvature radius. In the present theoretical/numerical study a multi-resolution formulation for the direct and inverse reconstruction of the heat flux from temperature sensors distributed over a multidimensional solid in a hypersonic flow was developed and validated. The solution method determines the thermal response by approximating the system Green's function with the Galerkin method and optimizes the heat flux distribution by fitting the distributed surface temperature data. Coating and glue layers are treated as separate domains for which the Green's function is obtained independently. Connection conditions for the system Green's function are derived by imposing continuity of heat flux and temperature concurrently at all interfaces. The solution heat flux is decomposed on a space-time basis with the temporal basis a multi-resolution wavelet with arbitrary scaling function. Quadrature formulas for the convolution of wavelets and the Green's function, a reconstruction approach based on isoparametric mapping of three-dimensional geometries, and a boundary wavelet approach for inverse problems were developed and verified. This approach was validated against turbulent conjugate heat transfer simulations at Mach 6 on a blunted wedge at 0 angle of attack and wind tunnel experiments of round impinging jet at Mach 0.7 It was found that multidimensional effects were important near the wedge shoulder in the short time scale, that the L-curve regularization needed to be locally corrected to analyze transitional flows and that proper regularization led to sub-cell resolution of the inverse problem. While the L2 regularization techniques are accurate they are also computationally inefficient and lack mathematical rigor. Optimal non-linear estimators were researched both as means to promote sparsity in the regularization and to pre-threshold the inverse heat conduction problem. A novel class of nonlinear estimators is presented and validated against wind tunnel experiments for a flat-faced cylinder also at Mach 6. The new approach to hypersonic heat flux reconstruction from discrete temperature data developed in this thesis is more efficient and accurate than existing techniques. / Doctor of Philosophy / The harsh environment supported by hypersonic flows is characterized by high-frequency turbulent bursts, acoustic noise, and vibrations that pollute the signals of the sensors that probe at high frequencies the state of the boundary layers developing on the walls. This research describes the search for optimal estimators of the noisy signal, i.e., those that lead to the maximum attenuation of the risk of error pollution by non-coherent scales. This research shows that linear estimators perform poorly at high-frequency and non-linear estimators can be optimized over a sparse projection of the signal in a discrete wavelet basis. Optimal non-linear estimators are developed and validated for wind tunnel experiments conducted at Mach 6 in the Advanced Propulsion and Power Laboratory at Virginia Tech.

Multi-robot systems of autonomous mobile robots offer many benefits but also many challenges. This work addresses collision avoidance of robots solving continuous problems in known environments. The approach to handling collision avoidance is here to enhance a motion planning method for single-robot systems to account for auxiliary robots. A few assumptions are made to put the focus of the work on path planning, rather than on localization. A method, based on exact cell decomposition and extended with a few rules, was developed and its consistency was proven. The method is divided into two steps: path planning, which is off-line, and path monitoring, which is on-line. This work also introduces the notion ofpath obstacle, an essential tool for this kind of path planning with many robots. Furthermore, an implementation was performed on a system of omni-directional robots and tested in simulations and experiments. The implementation practices centralized control, by letting an additional computer handle the motion planning, to relieve the robots of strenuous computations. A few drawbacks with the method are stressed, and the characteristics of problems that the method is suitable for are presented. / QC 20100705

Nowadays, researchers have shown convolutional neural network (CNN) can achieve an improved performance in multi-object tracking (MOT) by performing detection and re-identification (ReID) simultaneously. Many models have been created to overcome challenges and bring the state-of-the-art performance to a new level. However, due to the fact the CNN models only utilize feature from a local region, the potential of the model has not been fully utilized. The long range dependencies in spatial domain are usually difficult for a network to capture. Hence, how to obtain such dependencies has become the new focus in MOT field. One approach is to adopt the self-attention mechanism named transformer. Since it was successfully transferred from natural language processing to computer vision, many recent works have implemented it to their trackers. With the introduce of global information, the trackers become more robust and stable. There are also traditional methods which are re-designed in the manner of CNN and achieve satisfying performance such as optical flow. It can generate a correlated relation between feature maps and also obtain non-local information. However, the introduces of these mechanism usually causes a significant surge in computational power and memory. They also requires huge amount of epochs to train thus the training time is largely increased. To solve this issue, we propose a new method to gather non-local information based on the existing self-attention methods, we named it dual attention with regional-representation, which significantly reduces the training time as well as the inference time, but only causes a small increase in computational memory and are able to run with a reasonable speed. Our experiments shows this module can help the ReID be more stable to improve the performance in different tasks. / Thesis / Master of Applied Science (MASc)

No description available.

No description available.

No description available.

No description available.