Development and implementation of utility relocation cost estimation system

This thesis explores how to leverage information management techniques in developing a database system which can store, access and query data to generate preliminary cost estimate reports for utility relocations in highway construction projects. Although cost estimation for utility relocation is an essential part of most transportation projects, there are very few ready-to-use cost database or software platform available to fulfill this purpose for state DOTs personnel. Therefore, the research aimed to develop a database system that can provide estimates with historical cost data. The unit cost data used in this database are derived either from the executed utility agreements between TxDOT office and utility owners or a publicly available open source database. The estimated costs are computed with these pre-stored data. As a result of the research, the Utility Relocation Cost Estimation Database system was completed and has been handed over to TxDOT Austin District for further tests and implementations.Civil, Architectural, and Environmental Engineerin

Mean value coordinates–based caricature and expression synthesis

We present a novel method for caricature synthesis based on mean value coordinates (MVC). Our method can be applied to any single frontal face image to learn a specified caricature face pair for frontal and 3D caricature synthesis. This technique only requires one or a small number of exemplar pairs and a natural frontal face image training set, while the system can transfer the style of the exemplar pair across individuals. Further exaggeration can be fulfilled in a controllable way. Our method is further applied to facial expression transfer, interpolation, and exaggeration, which are applications of expression editing. Additionally, we have extended our approach to 3D caricature synthesis based on the 3D version of MVC. With experiments we demonstrate that the transferred expressions are credible and the resulting caricatures can be characterized and recognized

Inferior vestibular neuritis in a fighter pilot: A case report

Pilot spatial disorientation is a leading factor contributing to many fatal flying accidents. Spatial orientation is the product of integrative inputs from the proprioceptive, vestibular, and visual systems. Vestibular neuritis (VN) can lead to sudden pilot incapacitation in flight. VN is commonly diagnosed by demonstration of unilateral vestibular failure, as unilateral loss of caloric response. As this test reflects the function of the superior part of the vestibular nerve only, cases of pure inferior nerve neuritis will be lost. This paper describes a fighter pilot with symptoms suggestive of VN but with normal caloric test results. Further test showed unilateral loss of vestibular evoked myogenic potential. We believe that the pilot suffered from pure inferior nerve vestibular neuritis. VEMP plays a major role in the diagnosis of inferior nerve vestibular neuritis in pilots. Aeromedical concerns are also discussed

Kinetics of coherent order-disorder transition in Al3ZrAl_3 Zr

Within a phase field approach which takes the strain-induced elasticity into account, the kinetics of the coherent order-disorder transition is investigated for the specific case of $Al_3 Zr$ alloy. It is shown that a microstructure with cubic $L1_2$ precipitates appears as a transient state during the decomposition of a homogeneous disordered solid solution into a microstructure with tetragonal $DO_{23}$ precipitates embedded into a disordered matrix. At low enough temperature, favored by a weak internal stress, only $L1_2$ precipitates grow in the transient microstructure preceding nucleation of the $DO_{23}$ precipitates that occurs exclusively at the interface of the solid solution with the $L1_2$ precipitates. Analysis of microstructures at nanoscopic scale shows a characteristic rod shape for the $DO_{23}$ precipitates due to the combination of their tetragonal symmetry and their large internal stress.Comment: 2 postscript figures and 1 JPG pag

Exploring the transferability of large supramolecular assemblies to the vacuum-solid interface

We present an interplay of high-resolution scanning tunneling microscopy imaging and the corresponding theoretical calculations based on elastic scattering quantum chemistry techniques of the adsorption of a gold-functionalized rosette assembly and its building blocks on a Au(111) surface with the goal of exploring how to fabricate functional 3-D molecular nanostructures on surfaces. The supramolecular rosette assembly stabilized by multiple hydrogen bonds has been sublimed onto the Au(111) surface under ultra-high vacuum conditions; the resulting surface nanostructures are distinctly different from those formed by the individual molecular building blocks of the rosette assembly, suggesting that the assembly itself can be transferred intact to the surface by in situ thermal sublimation. This unanticipated result will open up new perspectives for growth of complex 3-D supramolecular nanostructures at the vacuum-solid interface

Data-driven methodologies for supporting decision-making in roadway safety and pavement management

There has been a significant rise in the utilization of data-driven methods within the contemporary realm of transportation engineering. This trend is primarily attributed to the limitations associated with experience-based methods, such as subjectivity and non-reproducibility. In contrast, data-driven methods have proven to offer a more objective and effective approach to problem analysis, thereby providing decision-makers with a reliable basis for informed decision-making. This present research focuses on two types of data-driven methodologies: geostatistical analyses utilizing geographic information systems (GIS) and cutting-edge algorithms associated with artificial intelligence (AI). In numerical analysis, data provides a means to gain valuable insights into a problem of interest. While AI-oriented methods have been shown in many studies to be more effective than traditional approaches, the accuracy of the analysis still heavily depends on the quality of the data. This dissertation endeavors to shed light on the pivotal role that data plays in both roadway safety analysis and pavement management. To accomplish this, four distinct studies are proposed that examine different aspects of data-driven methods. The studies encompass an evaluation of data consistency in motor vehicle crash databases, the identification of crash hot spots within a road network, a synthesis of advancements in the application of AI algorithms to various activities of pavement management, and an exploration of the relationship between pavement conditions and roadway safety using AI-oriented methods. The knowledge acquired from these studies serves as a foundation for future research, advancements, and the adoption of innovative approaches to enhance the efficiency of safety analysis and pavement management. This research ultimately facilitates informed decision-making, effective resource allocation, and the implementation of cost-effective interventions to enhance roadway safety and optimize pavement management practices.Civil, Architectural, and Environmental Engineerin

Parity Problem With A Cellular Automaton Solution

The parity of a bit string of length $N$ is a global quantity that can be efficiently compute using a global counter in ${O} (N)$ time. But is it possible to find the parity using cellular automata with a set of local rule tables without using any global counter? Here, we report a way to solve this problem using a number of $r=1$ binary, uniform, parallel and deterministic cellular automata applied in succession for a total of ${O} (N^2)$ time.Comment: Revtex, 4 pages, final version accepted by Phys.Rev.

Low Mach number effect in simulation of high Mach number flow

In this note, we relate the two well-known difficulties of Godunov schemes: the carbuncle phenomena in simulating high Mach number flow, and the inaccurate pressure profile in simulating low Mach number flow. We introduced two simple low-Mach-number modifications for the classical Roe flux to decrease the difference between the acoustic and advection contributions of the numerical dissipation. While the first modification increases the local numerical dissipation, the second decreases it. The numerical tests on the double-Mach reflection problem show that both modifications eliminate the kinked Mach stem suffered by the original flux. These results suggest that, other than insufficient numerical dissipation near the shock front, the carbuncle phenomena is strongly relevant to the non-comparable acoustic and advection contributions of the numerical dissipation produced by Godunov schemes due to the low Mach number effect.Comment: 9 pages, 1 figur

Evidence for internal field in graphite: A conduction electron spin resonance study

We report conduction electron spin resonance measurements performed on highly oriented pyrolitic graphite samples between 10 K and 300 K using S (f = 4 GHz), X (f = 9.4 GHz), and Q (f = 34.4 GHz) microwave bands for the external dc-magnetic field applied parallel (H || c) and perpendicular (H perp c) to the sample hexagonal c-axis. The results obtained in the H || c geometry are interpreted in terms of the presence of an effective internal ferromagnetic-like field Heff-int(T,H) that increases as the temperature decreases and the applied dc-magnetic field increases. We associate the occurrence of the Heff-int(T,H) with the field-induced metal-insulator transition in graphite and discuss its origin in the light of relevant theoretical models.Comment: 10 pages (tex), 5 figures (ps

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support