A Hydrogeologic Evaluation of the Waterloo Area in the Upper Jefferson River Valley, Montana

The Upper Jefferson River is one of the most dewatered rivers in Montana. The river exists in an intermontane basin filled with sediment transported from the Highland Mountains to the west, the Tobacco Root Mountains to the east, and the Jefferson River from the south. The Upper Jefferson River Valley is highly dependent on the Jefferson River as the main industry in the valley is agriculture. A majority of the valley is irrigated and used to grow crops, and a good portion is also used for cattle grazing. The residents of the Upper Jefferson River Valley use the aquifer as the main source of potable water. The Jefferson River is also widely used for recreation. This study took place in the Waterloo area of the Upper Jefferson River Valley, approximately 20 miles south of Whitehall, Montana. The Waterloo area provides significant groundwater base flow to the Jefferson River, which is particularly important during the late irrigation season when the river is severely dewatered, and elevated surface-water temperatures occur, creating irrigation water shortages and poor trout habitat. This area contains two springfed streams, Willow Springs and Parson’s Slough, which discharge to the Jefferson River providing cool water in the late season as well as providing the most important trout spawning habitat in the valley. The area is bordered on both the east and west by irrigation ditches, and about 60% of the study area is irrigated. Tile drains were installed in the study area in close proximity to Parsons Slough causing some concern by neighboring residents. This study evaluated relationships between surface water, groundwater, and irrigation practices so that water managers and others can make informed management decisions about the Upper Jefferson River. Data was collected via a network of groundwater wells and surface-water sites. Additionally, water-quality samples were taken and an aquifer test was conducted to determine aquifer properties. The field data were analyzed and a groundwater budget was created in order to evaluate the aquifer. Results of the groundwater budget show that seepage from the irrigation canals and irrigation recharge have the biggest influence on recharge of the aquifer. There is significant groundwater outflow from the aquifer in the spring-fed streams as well as discharge to the Jefferson River. In comparing previous study results to this study’s results, there is no evidence of the water table decreasing due to irrigation practice changes or tile drain installation. However, given the amount of recharge irrigation practices contribute to the aquifer, if significant changes were made, they may affect groundwater elevations. Also lining the irrigation ditches would have a significant impact on the aquifer, as the amount of seepage would be greatly reduced.

Determing the Effect of Thermal Treatment Timing on Ultra-High Performance Concrete Beams

The loss of prestressing force over time influences the long-term deflection of the prestressed concrete element. Prestress losses are inherently complex due to the interaction of concrete creep, concrete shrinkage, and steel relaxation. Implementing advanced materials such as ultra-high performance concrete (UHPC) further complicates the estimation of prestress losses because of the changes in material models dependent on curing regime. Past research shows compressive creep is "locked in" when UHPC cylinders are subjected to thermal treatment before being loaded in compression. However, the current precasting manufacturing process would typically load the element (through prestressing strand release from the prestressing bed) before the element would be taken to the curing facility. Members of many ages are stored until curing could be applied to all of them at once. This research was conducted to determine the impact of variable curing times for UHPC on the prestress losses, and hence deflections. Three UHPC beams, a rectangular section, a modified bulb tee section, and a pi-girder, were assessed for losses and deflections using an incremental time step approach and material models specific to UHPC based on compressive creep and shrinkage testing. Results show that although it is important for prestressed UHPC beams to be thermally treated, to "lock in" material properties, the timing of thermal treatment leads to negligible differences in long-term deflections. Results also show that for UHPC elements that are thermally treated, changes in deflection are caused only by external loads because prestress losses are "locked-in" following thermal treatment.

The average mixing matrix signature

Laplacian-based descriptors, such as the Heat Kernel Signature and the Wave Kernel Signature, allow one to embed the vertices of a graph onto a vectorial space, and have been successfully used to find the optimal matching between a pair of input graphs. While the HKS uses a heat di↵usion process to probe the local structure of a graph, the WKS attempts to do the same through wave propagation. In this paper, we propose an alternative structural descriptor that is based on continuoustime quantum walks. More specifically, we characterise the structure of a graph using its average mixing matrix. The average mixing matrix is a doubly-stochastic matrix that encodes the time-averaged behaviour of a continuous-time quantum walk on the graph. We propose to use the rows of the average mixing matrix for increasing stopping times to develop a novel signature, the Average Mixing Matrix Signature (AMMS). We perform an extensive range of experiments and we show that the proposed signature is robust under structural perturbations of the original graphs and it outperforms both the HKS and WKS when used as a node descriptor in a graph matching task.

Quantum kernels for unattributed graphs using discrete-time quantum walks

In this paper, we develop a new family of graph kernels where the graph structure is probed by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk evolve on each graph and compute a density matrix with each walk. With the density matrices for the pair of graphs to hand, the kernel between the graphs is defined as the negative exponential of the quantum Jensen–Shannon divergence between their density matrices. In order to cope with large graph structures, we propose to construct a sparser version of the original graphs using the simplification method introduced in Qiu and Hancock (2007). To this end, we compute the minimum spanning tree over the commute time matrix of a graph. This spanning tree representation minimizes the number of edges of the original graph while preserving most of its structural information. The kernel between two graphs is then computed on their respective minimum spanning trees. We evaluate the performance of the proposed kernels on several standard graph datasets and we demonstrate their effectiveness and efficiency.

Role of the Tryptophan Residues in the Specific interaction of the Sea Anemone Stichodacty la helianthus’s Actinoporin Sticholysin II with Biological Membranes

Actinoporins are pore-forming toxins from sea anemones. Upon interaction with sphingomyelin-containing bilayers, they become integral oligomeric membrane structures that form a pore. Sticholysin II from Stichodactyla helianthus contains five tryptophans located at strategic positions; its role has now been studied using different mutants. Results show that W43 and W115 play a eterminant role in maintaining the high thermostability of the protein, while W146 provides specific interactions for protomer−protomer assembly. W110 and W114 sustain the hydrophobic effect, which is one of the major driving forces for membrane binding in the presence of Chol. However, in its absence, additional interactions with sphingomyelin are required. These conclusions were confirmed with two sphingomyelin analogues, one of which had impaired hydrogen bonding properties. The results obtained support actinoporins’ Trp residues playing a major role in membrane recognition and binding, but their residues have an only minor influence on the diffusion and oligomerization steps needed to assemble a functional pore.

Predicting the concentration of residual methanol in industrial formalin using machine learning

In this thesis, a machine learning approach was used to develop a predictive model for residual methanol concentration in industrial formalin produced at the Akzo Nobel factory in Kristinehamn, Sweden. The MATLABTM computational environment supplemented with the Statistics and Machine LearningTM toolbox from the MathWorks were used to test various machine learning algorithms on the formalin production data from Akzo Nobel. As a result, the Gaussian Process Regression algorithm was found to provide the best results and was used to create the predictive model. The model was compiled to a stand-alone application with a graphical user interface using the MATLAB CompilerTM.

Improving Bangla ocr output through correction algorithms

Bangla OCR (Optical Character Recognition) is a long deserving software for Bengali community all over the world. Numerous e efforts suggest that due to the inherent complex nature of Bangla alphabet and its word formation process development of high fidelity OCR producing a reasonably acceptable output still remains a challenge. One possible way of improvement is by using post processing of OCR’s output; algorithms such as Edit Distance and the use of n-grams statistical information have been used to rectify misspelled words in language processing. This work presents the first known approach to use these algorithms to replace misrecognized words produced by Bangla OCR. The assessment is made on a set of fifty documents written in Bangla script and uses a dictionary of 541,167 words. The proposed correction model can correct several words lowering the recognition error rate by 2.87% and 3.18% for the character based n- gram and edit distance algorithms respectively. The developed system suggests a list of 5 (five) alternatives for a misspelled word. It is found that in 33.82% cases, the correct word is the topmost suggestion of 5 words list for n-gram algorithm while using Edit distance algorithm the first word in the suggestion properly matches 36.31% of the cases. This work will ignite rooms of thoughts for possible improvements in character recognition endeavour.