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Thinking about life after graduation? Industry Night is your chance to talk to industry professionals about what a career in chemistry is really like and how to get your foot in the door. Refreshments will be served along with a cash bar. All attendees receive one complimentary beverage. Please direct all your questions to chemstdn ucalgary.

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Chemistry Students’ Chapter: Industry Night 2020

The purpose of our social club is to provide safe, comfortable settings for active single men and women over the age of 50 to meet others, make friends, and have fun! In addition to the monthly breakfast, many club members volunteer to plan, organize, and host activities that occur weekly, monthly, or only occasionally. TBC-CSC understands that this club may not be right for everyone, but we ask that you give us a chance to impress you. We’re sure prospective members will find it is the BEST fee they will every pay!

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Established in , Lengerich Cement Plant produces eleven types of cement for all major applications in civil engineering and general construction, plus a range of special-purpose binders, masonry cement and lime. And since Dyckerhoff is always on the lookout for young engineers, the company was immediately amenable to hosting the ZKG field trip. The crushed limestone is mixed with sand, gypsum, clay or iron ore and pulverized in a raw mill. After calcining in a six-stage heat exchanger preheater tower , the material is transferred to a rotary kiln for firing into cement clinker and subsequent transfer to clinker silos.

Oil-well cement is filled into 1. Having taken in all the details of the process, the students then had a chance to test their knowledge. Split up into several groups, they were tasked with arranging the procedural diagrams in the proper order, preferably more quickly than the other groups. In the end, two groups proved to be not only really fast, but also very accurate.

Finally, with a little help from Ingo Sosna, all of them managed to correctly place the rotary kiln downstream of the raw mill. Then, two groups were formed for a tour of the plant with experienced guides.

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Speed dating is a relative new concept that allows researchers to study various theories related to mate selection. A problem with current research is that it focuses on finding general trends and relationships between the attributes. This report explores the use of machine learning techniques to predict whether an individual will want to meet his partner again after the 4-minute meeting based on their attributes that were known before they met.

It is shown that Random Forests perform better than Support Vector Machines and that extended attributes give better result for both classifiers. Furthermore, it is observed that the more information is known about the individuals, the better a classifier performs. Clubbing preferences of the partner stands out as an important attribute, followed by the same preference for the individual. The definition of short names is found in Appendix A. This gives researchers the ability to study and confirm various theories related to mate selection.

The problem with current research is that it focuses on finding general correlation between a set of attributes and the decision to prefer a partner. This is problematic because it does not consider the individual and his specific preferences. Thus, we want to investigate if we can develop a model that can predict whether a person will want to meet the partner again after their first 4-minute meeting based on data from one speed-dating experiment. Our idea is to use machine learning techniques and a priori knowledge about the candidates.

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This was not an easy decision to make or taken lightly, but we felt this was the necessary action to take for the health, safety and well being of all our members. The decision was made in accordance with the guidelines set forth by Bishop Thomas J. We are considering other options to maintain community engagement and the fellowship portion of our ministry available to you during these unprecedented times.

Stay tuned as we will be sharing more information on these future opportunities in the coming weeks.

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In recent years there has been a trend of leaving the strict molecular clock in order to infer dating of speciations and other evolutionary events. Explicit modeling of substitution rates and divergence times makes formulation of informative prior distributions for branch lengths possible. Models with birth-death priors on tree branching and auto-correlated or iid substitution rates among lineages have been proposed, enabling simultaneous inference of substitution rates and divergence times.

This problem has, however, mainly been analysed in the Markov chain Monte Carlo MCMC framework, an approach requiring computation times of hours or days when applied to large phylogenies. We demonstrate that a hill-climbing maximum a posteriori MAP adaptation of the MCMC scheme results in considerable gain in computational efficiency. We demonstrate also that a novel dynamic programming DP algorithm for branch length factorization, useful both in the hill-climbing and in the MCMC setting, further reduces computation time.

For the problem of inferring rates and times parameters on a fixed tree, we perform simulations, comparisons between hill-climbing and MCMC on a plant rbcL gene dataset, and dating analysis on an animal mtDNA dataset, showing that our methodology enables efficient, highly accurate analysis of very large trees. Datasets requiring a computation time of several days with MCMC can with our MAP algorithm be accurately analysed in less than a minute.

From the results of our example analyses, we conclude that our methodology generally avoids getting trapped early in local optima. For the cases where this nevertheless can be a problem, for instance when we in addition to the parameters also infer the tree topology, we show that the problem can be evaded by using a simulated-annealing like SAL method in which we favour tree swaps early in the inference while biasing our focus towards rate and time parameter changes later on.

Our contribution leaves the field open for fast and accurate dating analysis of nucleotide sequence data.

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