The sedimentology of the Bloomington fan complex: an element of the Oak Ridges Moraine, Southern Ontario

The Oak Ridges Moraine is a major physiographic feature of south-central Ontario, extending from Rice Lake westward to the Niagara Escarpment. While much previous work has largely postulated a relatively simple the origin of the moraine, recent investigations have concentrated on delineating the discernible glacigenic deposits (or landform architectural elements) which comprise the complex mosaic of the Oak Ridges Moraine. This study investigates the sedimentology of the Bloomington fan complex, one of the oldest elements of the Oak Ridges Moraine. The main sediment body of the Bloomington fan complex was deposited during early stages of the formation of the Oak Ridges Moraine, when the ice subdivided, and formed a confined, interlobate lake basin between the northern and southern lobes. Deposition from several conduits produced a fan complex characterized by multiple, laterally overlapping, fan bodies. It appears that the fans were active sequentially in an eastward direction, until the formation of the Bloomington fan complex was dominated by the largest fan fed by a conduit near the northeastern margin of the deposit. Following deposition of the fan complex, the northern and southern ice margins continued to retreat, opening drainage outlets to the west and causing water levels to drop in the lake basin. Glaciofluvial sediment was deposited at this time, cutting into the underlying fan complex. Re-advancing northern ice then closed westerly outlets, and caused water levels to increase, initiating the re-advance of the southern ice. As the southern ice approached the Bloomington fan, it deposited an ice-marginal sediment complex consisting of glacigenic sediment gravity flows, and glaciolacustrine and glaciofluvial sediments exhibiting north and northwesterly paleocurrents. Continued advance of the southern ice, overriding the fan complex, ii produced large-scale glaciotectonic deformation structures, and deposited the Halton Till. The subaqueous fan depositional model that is postulated for the Bloomington fan complex differs from published models due to the complex facies associations produced by the multiple conduit sources of sediment feeding the fans. The fluctuating northern and southern ice margins, which moved across the study area in opposite directions, controlled the water level in the interlobate basin and caused major changes in depositional environments. The influence of these two lobes also caused deposition from two distinct source directions. Finally, erosion, deposition, and deformation of the deposit with the readvance of the southern ice contributed further to the complexity of the Bloomington fan complex.

Effects of large anphiphilic ligands upon the spectra and kinetics of cytochrome C oxidase

Cytoch ro me c oxidase (ferrocytochrome c : 02 oxidoreductase ; EC 1.9. 3.1) is the terminal enzyme in the mitochondrial electron transport chain, catalyzing the transfer of electrons from ferrocytochrome c to molecular oxygen. The effects of two large amphiphilic molecules - valinomycin and dibucaine upon the spectra of the isolated enzyme and upon the activity of both isolated enzyme and enzyme in membrane systems are investigated by using spectrophotometric and oxygen electrode techniques. The results show that both valinomycin and dibucaine change the Soret region of the speetrum and cause a partial inhibition in a concentration range higher than that in which they act as ionophores. It is concluded that both valinomycin and dibucaine binding induce a conformational change of the protein structure which modifies the spectrum of the a3 CUB centre and diminishes the rate of electron transfer between cytochrome a and the binuclear centre.

Heuristics for the Critical Node Detection Problem in Large Complex Networks

Complex networks have recently attracted a significant amount of research attention due to their ability to model real world phenomena. One important problem often encountered is to limit diffusive processes spread over the network, for example mitigating pandemic disease or computer virus spread. A number of problem formulations have been proposed that aim to solve such problems based on desired network characteristics, such as maintaining the largest network component after node removal. The recently formulated critical node detection problem aims to remove a small subset of vertices from the network such that the residual network has minimum pairwise connectivity. Unfortunately, the problem is NP-hard and also the number of constraints is cubic in number of vertices, making very large scale problems impossible to solve with traditional mathematical programming techniques. Even many approximation algorithm strategies such as dynamic programming, evolutionary algorithms, etc. all are unusable for networks that contain thousands to millions of vertices. A computationally efficient and simple approach is required in such circumstances, but none currently exist. In this thesis, such an algorithm is proposed. The methodology is based on a depth-first search traversal of the network, and a specially designed ranking function that considers information local to each vertex. Due to the variety of network structures, a number of characteristics must be taken into consideration and combined into a single rank that measures the utility of removing each vertex. Since removing a vertex in sequential fashion impacts the network structure, an efficient post-processing algorithm is also proposed to quickly re-rank vertices. Experiments on a range of common complex network models with varying number of vertices are considered, in addition to real world networks. The proposed algorithm, DFSH, is shown to be highly competitive and often outperforms existing strategies such as Google PageRank for minimizing pairwise connectivity.

Canadian Niagara Power Glass Slide - Large Group in Tunnel

A large group of men in hard hats in the tunnel. The names of the men are unknown.

Canadian Niagara Power Company - Large group photo in tunnel

Large group of men standing in the tunnel wearing boots and hard hats.

Photograph - Woman Standing by a Large Tree Trunk

A woman wearing a hat and holding a small purse. She is standing next to a large tree trunk and there are benches behind her.

Safe Handling of Large Animals

The article discusses improving welfare by reducing fear by studying: Animal Sensory Perception, Animal Behavior Patterns, Animal Habituation and Temperament, Effects of Previous Handling, Training Animals, Training Time and Temperament, Genetic Effects on Handling, Handling of escaped Animals, Facilities, Aggression in Grazing Animals, Inherent Danger of Large Animals, Cattle and Car Accidents.

Dynamics in large scale networks

In this thesis we study the properties of two large dynamic networks, the competition network of advertisers on the Google and Bing search engines and the dynamic network of friend relationships among avatars in the massively multiplayer online game (MMOG) Planetside 2. We are particularly interested in removal patterns in these networks. Our main finding is that in both of these networks the nodes which are most commonly removed are minor near isolated nodes. We also investigate the process of merging of two large networks using data captured during the merger of servers of Planetside 2. We found that the original network structures do not really merge but rather they get gradually replaced by newcomers not associated with the original structures. In the final part of the thesis we investigate the concept of motifs in the Barabási-Albert random graph. We establish some bounds on the number of motifs in this graph.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

Simulation-Based Finite and Large Sample Tests in Multivariate Regressions.

In the context of multivariate linear regression (MLR) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. In this paper, we propose a general method for constructing exact tests of possibly nonlinear hypotheses on the coefficients of MLR systems. For the case of uniform linear hypotheses, we present exact distributional invariance results concerning several standard test criteria. These include Wilks' likelihood ratio (LR) criterion as well as trace and maximum root criteria. The normality assumption is not necessary for most of the results to hold. Implications for inference are two-fold. First, invariance to nuisance parameters entails that the technique of Monte Carlo tests can be applied on all these statistics to obtain exact tests of uniform linear hypotheses. Second, the invariance property of the latter statistic is exploited to derive general nuisance-parameter-free bounds on the distribution of the LR statistic for arbitrary hypotheses. Even though it may be difficult to compute these bounds analytically, they can easily be simulated, hence yielding exact bounds Monte Carlo tests. Illustrative simulation experiments show that the bounds are sufficiently tight to provide conclusive results with a high probability. Our findings illustrate the value of the bounds as a tool to be used in conjunction with more traditional simulation-based test methods (e.g., the parametric bootstrap) which may be applied when the bounds are not conclusive.

Simulation-Based Finite-and Large-sample Inference Methods in Multivariate Regressions and Seemingly Unrelated Regressions

In the context of multivariate regression (MLR) and seemingly unrelated regressions (SURE) models, it is well known that commonly employed asymptotic test criteria are seriously biased towards overrejection. in this paper, we propose finite-and large-sample likelihood-based test procedures for possibly non-linear hypotheses on the coefficients of MLR and SURE systems.