Using Novel Approaches for Navigating Complex Energy Landscapes: Ion Channel Conductance using Hyperdynamics and Human-Guided Global Optimization of Lennard-Jones Clusters

Molecular dynamics (MD) is a widely used tool to study molecular systems on atomic level. However, the timescale of a traditional MD simulation is typically limited to nanoseconds. Thus many interesting processes that occur on microseconds or larger timescale can\u27t be studied. Hyperdynamics provides a way to extend the timescale of MD simulation. In hyperdynamics, MD is performed on a biased potential then corrected to get true dynamics provided certain conditions are met. Here, we tried to study potassium channel conductance using the hyperdynamics method with a bias potential constructed based on the potential of mean force of ion translocation through the selective filter of a potassium ion channel. However, when MD was performed on this biased potential, no ion translocation events were observed. Although some new insights were gained into the rate-limiting steps for ion mobility in this system from these negative results, no further studies are planned with this project. The second project is based on the assumption that hybrid human{computational algorithm is more efficient than purely computational algorithm itself. Such ideas have already been studied by many \crowd-sourcing games, such as Foldit [1] for the protein structure prediction problem, and QuantumMoves [2] for quantum physics. Here, the same idea is applied to cluster structure optimization. A virtual reality android cellphone app was developed to study global optimization of Lennard-Jones clusters with both computational algorithm and hybrid human{computational algorithm. Using linear mixed model analysis, we found statistically significant differences between the expected runtime of both methods, at least for cluster of certain sizes. Further analysis of the data showing human intelligence weakened the strong dependence of the efficiency of the computational method on cluster sizes. We hypothesis that this is due to that humans are able to make large moves that allows the algorithm to cover a large region in the potential energy surface faster. Further studies with more cluster sizes are needed to draw a more complete conclusion. Human intelligence can potentially be integrated into more advanced optimization technique and applied to more complicated optimization problems in the future. Patterns analysis of human behaviors during the optimization process can be conducted to gain insights of mechanisms and strategies of optimization process

Bayesian Nonparametric Dirichlet Process Mixture Modeling in Transportation Safety Studies

In transportation safety studies, it is often necessary to account for unobserved heterogeneity and multimodality in data. The commonly used standard generalized linear models (e.g., Poisson-gamma models) do not fully address unobserved heterogeneity, assuming unimodal exponential families of distributions. This thesis illustrates how restrictive assumptions (e.g., unimodality) common to most road safety studies can be relaxed employing Bayesian nonparametric Dirichlet process mixture models. We use a truncated Dirichlet process, so that our models reduce to the form of finite mixture (latent class) models, which can be estimated employing standard Markov chain Monte Carlo methods, emphasizing computational simplicity. Interestingly, our approach estimates the number of latent subpopulations as part of its analysis algorithm using an elegant mathematical framework. We use pseudo Bayes factors for model selection, showing how the predictive capability of models can be affected by different assumptions. In univariate settings, we extend standard generalized linear models to a Dirichlet process mixture generalized linear model in which the random intercepts density is modeled nonparametrically, thereby adding flexibility to the model. We examine the performance of the proposed approach using both simulated and real data. We also examine the performance of the proposed model in terms of replicating datasets with high proportions of zero crashes. In terms of engineering insights, we provide a policy example related to the identification of high-crash locations, a critical component of the transportation safety management process. With respect to multilevel settings, this thesis introduces a flexible latent class multilevel model for analyzing crash data that are of hierarchical nature. We extend the standard multilevel model by accounting for unobserved cross-group heterogeneity through multimodal intercepts (group effects). The proposed method allows identifying latent subpopulations (and consequently outliers) at the highest level of the hierarchy (e.g., geographic areas). We evaluate our method on two recent railway grade crossing crash datasets from Canada. This research confirms the need for a multilevel approach for both datasets due to the presence of spatial dependencies among crossings nested within the same region. We provide a novel approach to benchmark different regions based on their safety performance measures. To this end, we identify latent clusters among different regions that share similar unidentified features, stimulating further investigations to explore reasons behind such similarities and dissimilarities. This could have important policy implications for various safety management programs. This thesis also investigates inference for multivariate crash data by introducing two flexible Bayesian multivariate models: a multivariate mixture of points and a mixture of multivariate normal densities. We use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. We allow for interdependence between outcomes through a Dirichlet process prior on the random intercepts density. The resulting models collapse into a form of latent class multivariate model, an appealing way to address unobserved heterogeneity in multivariate settings. Therefore, the multivariate models that we derive in this thesis account for correlation among crash types through a heterogeneous correlation structure, which better captures the complex structure of correlated data. To our knowledge, this is the first study to propose and apply such a model in the transportation literature. Using a highway injury-severity dataset, we illustrate how the robustness to homogeneous correlation structures can be examined using a multivariate mixture of points model that relaxes the homogeneity assumption with respect to the location of the dependence structure. We then use the mixture of multivariate normal densities model‒relaxing the homogeneity assumption with respect to both the location and the covariance matrix‒to investigate the effects of various factors on pedestrian and cyclist safety in an urban setting, modeling both outcomes simultaneously. To our knowledge, this is the first study to conduct a joint safety analysis of active modes at an intersection level, a micro-level, which is expected to provide more detailed insights. We show how spurious assumptions affect predictive performance of the multivariate model and the interpretation of the explanatory variables using marginal effects. The results show that our flexible model specification better captures the underlying structure of pedestrian/cyclist crash data, resulting in a more accurate model that contributes to a better understanding of safety correlates of non-motorist road users. This in turn helps decision-makers in selecting more appropriate countermeasures targeting vulnerable road users, promoting the mobility and safety of active modes of transportation

Simulated Annealing

The book contains 15 chapters presenting recent contributions of top researchers working with Simulated Annealing (SA). Although it represents a small sample of the research activity on SA, the book will certainly serve as a valuable tool for researchers interested in getting involved in this multidisciplinary field. In fact, one of the salient features is that the book is highly multidisciplinary in terms of application areas since it assembles experts from the fields of Biology, Telecommunications, Geology, Electronics and Medicine

Robust condition monitoring for modern power conversion

The entire US electrical grid contains assets valued at approximately $800 billion, and many of these assets are nearing the end of their design lifetimes. In addition, there is a growing dependence upon power electronics in mission-critical assets (i.e. for drives in power plants and naval ships, wind farms, and within the oil and natural-gas industries). These assets must be monitored. Diagnostic algorithms have been developed to use certain key performance indicators (KPI) to detect incipient failures in electric machines and drives. This work was designed to be operated in real-time on operational machines and drives. For example the technique can detect impending failures in both mechanical and electrical components of a motor as well as semiconductor switches in power electronic drives. When monitoring power electronic drives, one is typically interested in the failure of power semiconductors and capacitors. To detect incipient faults in IGBTs, for instance, one must be able to track KPIs such as the on-state voltage and gate charge. This is particularly challenging in drives where one must measure voltages on the order of one or two volts in the presence of significant EMI. Sensing techniques have been developed to allow these signals to be reliably acquired and transmitted to the controller. This dissertation proposes a conservative approach for condition monitoring that uses communications and cloud-based analytics for condition monitoring of power conversion assets. Some of the potential benefits include lifetime extension of assets, improved efficiency and controllability, and reductions in operating costs especially with remotely located equipment

VLSI Design

This book provides some recent advances in design nanometer VLSI chips. The selected topics try to present some open problems and challenges with important topics ranging from design tools, new post-silicon devices, GPU-based parallel computing, emerging 3D integration, and antenna design. The book consists of two parts, with chapters such as: VLSI design for multi-sensor smart systems on a chip, Three-dimensional integrated circuits design for thousand-core processors, Parallel symbolic analysis of large analog circuits on GPU platforms, Algorithms for CAD tools VLSI design, A multilevel memetic algorithm for large SAT-encoded problems, etc

Integrated platform to assess seismic resilience at the community level

Due to the increasing frequency of disastrous events, the challenge of creating large-scale simulation models has become of major significance. Indeed, several simulation strategies and methodologies have been recently developed to explore the response of communities to natural disasters. Such models can support decision-makers during emergency operations allowing to create a global view of the emergency identifying consequences. An integrated platform that implements a community hybrid model with real-time simulation capabilities is presented in this paper. The platform's goal is to assess seismic resilience and vulnerability of critical infrastructures (e.g., built environment, power grid, socio-technical network) at the urban level, taking into account their interdependencies. Finally, different seismic scenarios have been applied to a large-scale virtual city model. The platform proved to be effective to analyze the emergency and could be used to implement countermeasures that improve community response and overall resilience