A NATURALISTIC COMPUTATIONAL MODEL OF HUMAN BEHAVIOR IN NAVIGATION AND SEARCH TASKS

Planning, navigation, and search are fundamental human cognitive abilities central to spatial problem solving in search and rescue, law enforcement, and military operations. Despite a wealth of literature concerning naturalistic spatial problem solving in animals, literature on naturalistic spatial problem solving in humans is comparatively lacking and generally conducted by separate camps among which there is little crosstalk. Addressing this deficiency will allow us to predict spatial decision making in operational environments, and understand the factors leading to those decisions. The present dissertation is comprised of two related efforts, (1) a set of empirical research studies intended to identify characteristics of planning, execution, and memory in naturalistic spatial problem solving tasks, and (2) a computational modeling effort to develop a model of naturalistic spatial problem solving. The results of the behavioral studies indicate that problem space hierarchical representations are linear in shape, and that human solutions are produced according to multiple optimization criteria. The Mixed Criteria Model presented in this dissertation accounts for global and local human performance in a traditional and naturalistic Traveling Salesman Problem. The results of the empirical and modeling efforts hold implications for basic and applied science in domains such as problem solving, operations research, human-computer interaction, and artificial intelligence

Early analysis of VLSI systems with packaging considerations

There is an explosive growth in the size of the VLSI (Very Large Scale Integration) systems today. Microelectronic system designers are packing millions of transistors in a single IC chip. Packaging techniques like Multi-chip module (MCM) and flip-chip bonding offer faster interconnects and IC\u27s capable of accommodating a larger number of inputs and outputs. The complexity of today\u27s designs and the availability of advanced packaging techniques call for an early analysis of the system based on estimation of system parameters to select from a wide choice of circuit partitioning, architecture alternatives and packaging options which give the best cost/performance. A procedure for the early analysis of VLSI systems under packaging considerations has been developed and implemented in this dissertation work. The early analysis tool was used to evaluate the inter-relationship between partitioning and packaging and to determine the best system design considering cost, size and delays. The functional unit level description of a 750,000-transistor MicroSparc processor was studied using an exhaustive search technique. The early analysis performed on the MicroSparc design suggested that the three chip multi-chip design using flip-chip IC\u27s interconnected on a MCM-D substrate is the most cost effective. An early bond pitch analysis performed using the tool concluded that a 250-micron bond pitch is the best choice for the multi-chip MicroSparc designs. The tool was also used to perform an early cache analysis which showed that the use of separate memory and logic processes made it feasible to design the MicroSparc design with larger cache sizes than the use of a combined logic and memory process. The designs based on the separate processes gave equivalent or better performance than the design candidates with smaller cache sizes. Future extensions of the procedure are also outlined here

OPTIMIZATION OF TEST/DIAGNOSIS/REWORK LOCATION(S) AND CHARACTERISTICS IN ELECTRONIC SYSTEMS ASSEMBLY

ABSTRACT Title of Dissertation: OPTIMIZATION OF TEST/DIAGNOSIS/REWORK LOCATION(S) AND CHARACTERISTICS IN ELECTRONIC SYSTEMS ASSEMBLY Zhen Shi, Doctor of Philosophy, 2004 Dissertation directed by: Associate Professor Peter A. Sandborn Department of Mechanical Engineering For electronic systems it is not uncommon for 60% or more of the recurring cost to be associated with testing. Performing tradeoffs associated with where in a process to test and what level of test, diagnosis and rework to perform are key to optimizing the cost and yield of an electronic system's assembly. In this dissertation, a methodology that uses a real-coded genetic algorithm has been developed to minimize the yielded cost of electronic products by optimizing the locations of test, diagnosis and rework operations and their characteristics. This dissertation presents a test, diagnosis, and rework analysis model for use in electronic systems assembly. The approach includes a model of functional test operations characterized by fault coverage, false positives, and defects introduced in test; in addition, rework and diagnosis operations (diagnostic test) have variable success rates and their own defect introduction mechanisms. The model accommodates multiple rework attempts on a product instance. For use in practical assembly processes, the model has been extended by defining a general form of the relationship between test cost and fault coverage. The model is applied within a framework for optimizing the location(s) and characteristics (fault coverage/test cost and rework attempts) of Test/Diagnosis/Rework (TDR) operations in a general assembly process. A new search algorithm called Waiting Sequence Search (WSS) is applied to traverse a general process flow to perform the cumulative calculation of a yielded cost objective function. Real-Coded Genetic Algorithms (RCGAs) are used to perform a multi-variable optimization that minimizes yielded cost. Several simple cases are analyzed for validation and general complex process flows are used to demonstrate the applicability of the algorithm. A real multichip module (MCM) manufacturing and assembly process is used to demonstrate that the optimization methodology developed in this dissertation can find test and rework solutions that have lower yielded cost than solutions calculated by manually choosing the test strategies and characteristics. The optimization methodology with Monte Carlo methods included for the process flow under uncertain inputs is also addressed in this dissertation. It is anticipated that this research will improve the ability of manufacturing engineers to place TDR operations in a process flow. The ability to optimize the TDR operations can also be used as a feedback to a Design for Test (DFT) analysis of the electronic systems showing which portion of the system should be redesigned to accommodate testing for a higher level of fault coverage, and where there is less need for test

The doctoral research abstracts. Vol:6 2014 / Institute of Graduate Studies, UiTM

Congratulations to Institute of Graduate Studies on the continuous efforts to publish the 6th issue of the Doctoral Research Abstracts which ranged from the discipline of science and technology, business and administration to social science and humanities. This issue captures the novelty of research from 52 PhD doctorates receiving their scrolls in the UiTM’s 81st Convocation. This convocation is very significant especially for UiTM since we are celebrating the success of 52 PhD graduands – the highest number ever conferred at any one time. To the 52 doctorates, I would like it to be known that you have most certainly done UiTM proud by journeying through the scholastic path with its endless challenges and impediments, and by persevering right till the very end. This convocation should not be regarded as the end of your highest scholarly achievement and contribution to the body of knowledge but rather as the beginning of embarking into more innovative research from knowledge gained during this academic journey, for the community and country. As alumni of UiTM, we hold you dear to our hearts. The relationship that was once between a student and supervisor has now matured into comrades, forging and exploring together beyond the frontier of knowledge. We wish you all the best in your endeavour and may I offer my congratulations to all the graduands. ‘UiTM sentiasa dihati ku’ Tan Sri Dato’ Sri Prof Ir Dr Sahol Hamid Abu Bakar , FASc, PEng Vice Chancellor Universiti Teknologi MAR

Discovery of biological networks from diverse functional genomic data

We have developed a general probabilistic system for query-based discovery of pathway-specific networks through integration of diverse genome-wide data. This framework was validated by accurately recovering known networks for 31 biological processes in Saccharomyces cerevisiae and experimentally verifying predictions for the process of chromosomal segregation. Our system, bioPIXIE, a public, comprehensive system for integration, analysis, and visualization of biological network predictions for S. cerevisiae, is freely accessible over the worldwide web

Technology 2002: the Third National Technology Transfer Conference and Exposition, Volume 1

The proceedings from the conference are presented. The topics covered include the following: computer technology, advanced manufacturing, materials science, biotechnology, and electronics

Modeling Small Molecule Metabolism in Human Liver Microsome to Better Predict Toxicity Risk

Adverse drug reactions (ADRs) are a serious problem with increasing morbidity, mortality, and health care costs worldwide. In the U.S., ADRs are responsible for more than 50% of acute liver failure cases and are the fourth most common cause of death, costing 100,000 lives annually.Idiosyncratic adverse drug reactions (IADRs) are immune-mediated hypersensitivity ADRs that are difficult to foresee during drug development. IADRs are often caused by reactive metabolites produced during drug metabolism. These reactive metabolites covalently attach to cellular components, and the resulting conjugates may provoke toxic immune response. Because reactive metabolites are short-lived, they can be difficult to detect. Tools to reliably predict whether a compound forms reactive metabolites would enable us to avoid drug candidates prone to causing IADRs and make new medicines safer. Unfortunately, due to inadequate modeling of metabolism, current experimental and computational approaches do not reliably identify drug candidates that form reactive metabolites. Bioactivation pathways leading to reactive metabolite formations often are composed of multiple steps. To accurately predict reactive metabolite formation, we must explicitly model metabolic steps of bioactivation pathways. Therefore, we built models to predict specific metabolic transformations such as hydroxylation, epoxidation, dehydrogenation, quinonation, hydrolysis, reduction, glucuronidation, sulfuration, acetylation, and methylation. Using machine learning and literature-derived data, we trained models that can predict both the likelihood that a molecules undergoes a certain chemical transformation and the specific site(s) within the molecule where this transformation happens. Together, our metabolism models cover ∼ 95% of enzymatically-driven chemical reactions in human. Our models achieve high area under the receiver operating characteristic curve scores (AUCs) of ∼ 90% in cross-validated tests. Our mechanistic approach outperformed structural alerts—a common tool used to screen out candidate compounds during drug development. Structural alerts are chemical moieties that were frequently observed to give rise to reactive metabolite upon bioactivation. However, many safe drugs also contain structural alerts which are not bioactivated and, conversely, many toxic drugs contain no structural alert. We combined models of metabolism, metabolite structure prediction, and reactivity to offer a better prediction of reactive metabolite formation in the context of structural alerts. Based on the known bioactivation pathway(s) of each structural alert, appropriate metabolism models were applied to evaluate whether drugs containing the structural alert actually form reactive metabolites. Our study focused on the furan, phenol, nitroaromatic, and thiophene alerts. Specifically, we used models of epoxidation, quinone formation, reduction, and sulfur-oxidation to predict the bioactivation of furan-, phenol-, nitroaromatic-, and thiophene-containing drugs. Our models separated bioactivated and not-bioactivated furan-, phenol-, nitroaromatic-, and thiophene-containing drugs with AUC performances of 100%, 73%, 93%, and 88%, respectively. In addition, we used our models to uncover bioactivation mechanisms that were previously under-appreciated. For example, N-dealkylation is the oxidation of an alkylated amine at the nitrogen-carbon bond, cleaving the parent compound into an amine and an aldehyde. Even though aldehydes can be toxic, metabolic studies usually neglect to report or investigate them because they are assumed to be efficiently detoxified into carboxylic acids and alcohols. Applying the N-dealkylation model to approved and withdrawn medicines, we found that aldehyde metabolites produced from N-dealkylation may explain the hepatotoxicity of several drugs: indinavir, piperacillin, verapamil, and ziprasidone. These results demonstrated the utility of comprehensive bioactivation models that systematically consider constituent metabolic steps in gauging toxicity risks