Statistical designs are a way to layout experiments before conducting your experiment. A well-chosen experimental design allows one to get more information out of the experiment. Regular fractional factorial designs are types of experimental designs that are used for studying effects of two or more constraints simultaneously, but leave large gaps in run size. Run sizes are the amount of experiments an experimenter has to conduct. Non-regular fractional factorial designs can be constructed for every run size that is a multiple of four, which allows run size flexibility and economy. My research focuses on construction of optimal designs of size 32 runs using graphic processing unit (GPU) technology. I have been working on creating a design table of non-regular fractional factorial of size 32 runs. Creating design tables make it possible for engineers and scientist to plan experiments for any combination of runs and number of variables to be studied.

Prior to about 650 million years ago, the Earth's surface is believed to have completely frozen and thawed multiple times, each frozen period lasting for millions of years. The term "Snowball Earth" describes the runaway effect of ice expanding equatorward, with its brightly reflecting surface causing reduced absorption of sunlight, increased cooling, and further ice expansion. We used Global Climates Models to better understand the conditions necessary for other planets to be trapped in a Snowball state. We modeled planets that are Earth-like but with ocean-only surfaces. Our object is to investigate and further define the inner edge of the habitable zone - the theoretical region around a star where liquid water can exist on the surface. Our research has big implications for habitability, as liquid water is required for life to exist as we know it. The obliquity of a planet is also very important, as it controls the relative proportion of sunlight received at each latitude. Even within our own solar system there is a wide range. The Earth is tilted 23 degrees, Uranus 97, and Venus 177. We present results for an ensemble of planets with differing obliquities to demonstrate its effects. Furthermore, we also present results for planets around G-type stars, like our Sun, and M-type stars, also known as red dwarfs. Red dwarfs are by far the most common type of star in the Milky Way Galaxy. It is easier to detect orbiting extrasolar planets around M-stars. M-stars tend to have a relatively high abundance of Earth-sized and smaller planets, which are more likely to be rocky - a necessary condition for habitability. The effect of an M star’s solar spectrum on a planet’s energy balance is significantly different than a G star’s, which changes the location of the habitable zone. Our model study provides exciting new insights into the differing dynamics.

In aquariums and zoos across the world marine mammals are among the most popular exhibits. Caretakers strive to provide the least stressful and natural environments for the animals. Analysis of (steroid) hormones in excrement has proven to be an effective, non-invasive method. It allows animal keepers to monitor physiological health associated with reproduction and the stress response in captive animals. Prolactin (PRL) is a non-steroidal, non-tropic peptide hormone most commonly associated with milk production in nursing females. However, it has a multitude of normal physiological functions such as: regulation of the immune system, osmotic balance, angiogenesis, an indicator of parental care (in both males and females), sexual maturity in males, and near-term pregnancy in females. Chronically high or low levels of PRL have been associated with a number of ailments. For instance, impotence reduces the secretion of estrogens and testosterone. To date, no studies have been documented on prolactin PRL levels in captive northern fur seals, (Callorhinus ursinus), harbor seals (Phoca vitulina), or sea otters (Enhydra lutris). Using an enzyme-immunoassay (EIA) kit, previously tested only in human and elephant serum analysis, we investigated PRL levels of several samples collected from captive marine mammals (Northern fur seal blood serum, harbor seal saliva, and sea otter urine/fecal samples). Due to the helical structure of PRL, fecal samples were not expected to survive intact after transit through the digestive tract; therefore, were not expected to produce positive results. Here we report preliminary results of the EIA PRL assay on several different types of samples, as well as several longitudinal samples from the same individual, to show significant changes in this hormone over time. If detectable in urine, fecal matter, and/or saliva, this assay could prove to be a useful non-invasive method for determining health in captive marine mammals.

With any particle physics experiment, precision is paramount. In experiments with electromagnetic shields, the inherent magnetic field noise from the shield itself can impede the accuracy of high precision experiments. For this research project I designed a model of an electromagnetic shield to find the magnetic field noise of the shield; the results of which can be used to increase the precision of a particle physics experiment being conducted at the University of Washington, searching for a potential source of time reversal symmetry by looking for a permanent electric dipole moment of a specific isotope of mercury (the existence of which would suggest a source of CP-violation). The cylindrical electromagnetic shield was modeled in the finite element analysis application, COMSOL Mulitphysics. The model is initially configured with a single magnetic dipole arranged on the axis of symmetry of the magnetic shield. The accuracy of the model is then compared to an analytic solution found by S.-K. Lee and M. V. Romalis at Princeton University. After refining the model to reflect the analytic solution, the number and orientation of magnetic dipoles is changed, producing the first, second and third derivatives of the magnetic field noise of the shield being used. Accounting for the magnetic field noise will increase the sensitivity of the experiment being conducted.

Everyday, people's lives are affected by the weather; whether sunshine or rain, weather is a part of daily life. Extreme weather events can bring normal activities to a halt. Weather events known as "blocking" involve a pause in the of the typical passage of storms in the jet stream, causing weather systems to stay in place for lengthened periods of time. Wintertime blocking events often cause extreme cold winter temperatures over land, and data show that blocking frequency is increasing. A potential cause for this is due to sea ice loss and the warming of the Atlantic Ocean. Our investigation is of the extraordinary January 2012 blocking event over Europe. The nature of this extreme event was tested using the Weather and Research Model applied to the domain including the Western Arctic Ocean, the North Atlantic Ocean, and Europe, with a resolution of 40 kilometers. The event was simulated over the period from January 15, 2012 to February 6, 2012, with horizontal boundaries and initial conditions constrained by atmospheric observations. In a series of model integrations, we manipulated the thickness of sea ice and sea surface temperature in high altitudes and found that there is a link between thinner sea ice and warmer ocean temperatures and an increased intensity of the blocking event over Europe. This discovery allows for a better understanding of the potential impacts of melting sea ice as the climate changes. 

Although every cell in the human body has nearly the same DNA sequence, there are many different cell types performing different functions; for example, lymphoblast cells which differentiate into white blood cells or endothelial cells which provide a lining throughout the human vascular system. But cells do not always function as they should, for example, overproduction of white blood cells can be caused by lymphoblastic leukemia, a type of cancer. I intend to use data on the sensitivity of parts of the genome to deoxyribonuclease (DNase) digestion to explain the differences in cell behavior. Regions of the genome structure that are hypersensitive to DNase have already been shown to affect gene expression. I want to determine if portions of the genome which are mildly sensitive, or mesosensitive, to DNase play a role in cell differentiation. My hypothesis is that DNase can identify large scale regions, on the order of thousands of base pairs, that harbor cell type specific genes. I used Segway, software which uses a dynamic Bayesian network, to segment the genome based on DNase sensitivity data into regions of low sensitivity, mesosensitivity, and hypersensitivity on six different cell types. From my segmentations I removed the hypersensitive regions from considerations, and compared the low and mesosensitive regions to gene expression data. I found that regions Segway labels mesosensitive had higher levels of expression than low sensitivity regions. I expect to find that many of the regions responsible for cell type specific behavior are mesosensitive. If this is the case, I hope the results will be robust enough such that anyone with access to DNase data can determine what genes are active in a population of cells, enabling them to better predict the behavior of the cells. In furthering our understanding of gene regulation, we may better understand diseases such as leukemia.

The purpose of this study is to determine fundamental structure-solvent-property relationships in organic photovoltaic polymers (OPV’s), with the overarching goal of increasing the efficiency of OPV’s. A typical silicon-based photovoltaic plant outputs approximately 88,000 m2 of photovoltaic cells each year- about the size of 16 football fields. On the other hand, an organic photovoltaic printer will produce the same area in up to 10 hours. OPV’s are incredibly fast, cheap, and easy to produce relative to their inorganic counterparts, but fall short within the realm of efficiency; the 10% efficiency of OPV’s can hardly rival the ~40% efficiency of inorganic photovoltaic technology. If the efficiency of OPV’s can be increased, this will have a drastic impact on the competitive marketability of photovoltaic technology. This project aims at developing computational models to study the role of solvents in the self-assembly of polythiophenes during the printing process. These models are generated in GROMACS, a program that uses classical forces such as Coulombic or Van Der Waals forces, to predict how molecules will behave with time. This is problematic, however, for OPV’s, as key interactions occur on the quantum level, and new classical force fields, which are computationally very efficient, need to be altered in order to accurately calculate these interactions. A force field is being developed and refined by comparing our computational results with experimental results from the Pozzo Research Group within the Department of Chemical Engineering. Once these results are reliably consistent with each other, we can begin running simulations and gathering data regarding how OPV’s assemble in various environments. In particular, this poster explains a systematic methodology I have developed to use the GROMACS program to perform molecular dynamics (MD) simulations of single chain OPV’s in various solvents as well as simulations of bulk OPV’s in the melt state.

 Understanding the heterogeneity in neural activity will help develop theories in neural dynamics and coding - how sensory information is encoded via spike times and patterns. For example, the correlation structure of spiking activity in neural populations, which affects the collective coding capacity, is heavily dependent on the heterogeneous distribution of firing rates across neural networks. A natural cause of heterogeneity in a network arises from the non-regular connectivity between neurons. To capture this natural heterogeneity, we used an adapted Erdos-Renyi node-probability model to construct random architectures for both purely excitatory and excitatory-inhibitory networks of different sizes. The Erdos-Renyi model flips a weighted coin defined by a probability parameter to determine whether a path between nodes exists. However, we need to construct directed graphs, so we adapted the Erdos-Renyi model to construct directed paths based on the probability parameter. For each randomly generated network we examined the coherent structure of the steady-states that persist as the connection strengths between neurons are uniformly increased. In purely excitatory networks, we saw that the steady-state growth is very linear with respect to the connection strength of the network. As a result, stronger connection strengths lead to stronger heterogeneity between the firing rates within the network. We are using the coefficients in the linear approximations to steady-state growth to define the coherent structure of each network. We are continuing towards finding a relationship between the frequencies of three-cell connectivity motifs and this coherent structure since realistic neural network connectivity patterns are not purely random, but have significantly prevalent motifs that define neural architecture. We are specifically looking at how different frequencies of chain, divergent and convergent three-cell motifs affect the first and second order moments of the coherent structure that represents the heterogeneity.

 For our research project, we asked the question: 'Is there some way to automate the redrawing of district lines in each state mandated by the constitution after every decennial census?'  This presentation will consist of a brief overview of the factors considered in redistricting (population, compactness, alignment with city and county lines, etc.) followed by a demonstration and discussion of the software application that we developed to address this problem.  The presentation will conclude with a mention of potential political and social drawbacks of automated redistricting.

We estimate a Gaussian graphical model corresponding to a covariance matrix using machine learning techniques. A motivating application is to identify genes associated with cancer in the human genome. Suppose we have genetic data from two people, one of whom is healthy, while the other has cancer. Our goal is to identify gene expressions that differ between datasets and contribute to cancer. We model each dataset as a Gaussian graphical model, which is a network of edges and nodes. Each node represents a random variable, or gene. Each edge represents the dependence between the two variables it connects. An edge with zero value indicates the two variables are independent when conditioned on the rest of the variables. Such networks are analogously represented as inverse covariance matrices. Our goal is to use empirical genetic information from cancer patients to recover the true inverse covariance matrices. We suppose that the inverse covariance matrices have a certain structure and we set up a problem to encourage that structure. Structure refers to the relative magnitudes of matrix elements. A structure of interest is a hub node, which is highly connected to other nodes. These nodes play a regulatory role in gene expression and may be related to cancer. Our goal is to identify hub nodes in the difference between two inverse covariance matrices. We formulate this question as a convex optimization problem. We encourage the solution to have a certain structure by adding regularizer terms to the objective function. We recover the overall structure of the inverse matrices, while simultaneously encouraging the solution to have specific structures of interest. An alternating directions method of multipliers (ADMM) algorithm is used to solve the optimization problem. We run the algorithm on synthetic genetic data, and on real gene expression data to identify cancer related genes.

Ramsey Theory is an intricate field of pure mathematics, but it also has applications in computer science, where graphs are commonlu used as data structures. Every pair of "forbidden" graphs has a Ramsey number, which indicates when one of those graphs must occur. A Ramsey number for a pair of forbidden graphs is the smallest number of points R for which every edge-coloring of the complete graph on R points includes one of the forbidden graphs in its respective color. In this paper we use two original methods to find and prove new Ramsey number formulas for certain tadpole graphs based upon known Ramsey number formulas for arbitrary cycle graphs.  A tadpole graph Qn,t is formed by connecting one end of a path graph on t points (called the “tail”) to a cycle of size n (called the “head”). A pan graph Qn is defined as a tadpole graph with t=1. We first use an intuitive counting method to prove exact Ramsey number formulas for all “near-diagonal” pan graphs, i.e. those pairs of pan graphs where the maximum cycle size of the pair is no more than 5/2 times the minimum cycle size. Then, we demonstrate a number-theoretic method to find exact Ramsey numbers for arbitrarily far-from-diagonal tadpole pairs with arbitrarily long tails. However, the second method requires that the tadpoles meet number-theoretic constraints (rather than size constraints). For example, in the diagonal cases, n-1 must be relatively prime to n+t, where either n-1 has an odd order (mod n+t) or the additive inverse of n-1 has an even index relative to n-1 (mod n+t). These “scattered” on- and off-diagonal results provide upper bounds on the Ramsey numbers for other tadpole pairs that fail to meet the number-theoretic constraints.

Computer-generated transportation models are used by State Departments of Transportation to simulate flows of goods and people on roadways. Infrastructure investments and policy decisions can be supported by results of these models. Passenger travel is well developed and uses activity-based models using characteristics of the household to more accurately model travel behavior. Freight, on the other hand, is not as developed, and uses simple algorithms to predict goods movement. This research focuses on characterizing freight travel in the context of supply chains to more appropriately model frieght behavior. A survey was conducted of motor carriers in Oregon and Washington to capture fleet statistics, carrier services, travel distances, time of day travel patterns, and company characteristics to find which factors differentiate motor carriers. The results revealed a key distinction between "Supply Chain Node Carriers" and "Only Transportation Carriers," with "Supply Chain Node Carriers" being split further into those companies linked to raw materials, manufacturing facilities, storage facilities, distribution centers, and retail outlets. Suggestions are made on how to implement these findings into state transportation model developments to enhance them. These more precise models allow for evidence-backed infrastructure investments and policy decisions.

Can a group of men and women be paired together to guarantee no divorces or scandals? In 1962, two economists posed this question mathematically and offered a solution. A 2012 Nobel Prize went to two of the key researchers that worked on this problem. Their solution took the form of an algorithm, a systematic procedure that could be performed by a computer. The solution to the "stable marriage problem" has both interesting theoretical properties and real-world applications, such as the assignment of medical students to hospitals. In the original version of the problem, each man and woman know their preferences over all their potential partners. We are considering a variant in which they only know some of their preferences. The algorithm not only needs to figure out whom to match with whom, but also what information to gather on preferences. We present our preliminary results on efficient algorithms for this problem. This is joint work with Anna Karlin and Jamie Morgenstern.

Governments can manipulate reports of the inflation rate to hide economic problems, and to generate revenues from the hidden depreciation of their liabilities valued in domestic currency. However, such practices are unpopular and generally short-lived because citizens soon recognize the loss in their purchasing power. Previously, I constructed an alternative price index for 2006 to 2008 and found that the National Statistics and Censuses Institute underreported the official inflation rate since February 2007. My data, used in conjunction with other alternative indexes, showed that the reported Consumer Price Index (CPI) only accounted for between a half and a third of the real inflation rate. This project explores effects of underreporting inflation in Argentina, and provides further evidence to support the existence of manipulation in the official statistics reports. Using the alternative price indexes, I estimated government gains from domestic bonds and the social security system due to the misreported inflation rates since the manipulation began. By reporting a CPI that is lower than the real one, the government was able to finance its expenditures at a decreasing real cost, at the expense of private investors and citizens. My preliminary results show that the Argentinian government profited in real terms from issuing bonds, and maintained a moderately low budget deficit despite increasing the number of social security beneficiaries. I also studied the national accounts and central bank publications of the last decade and observed a surprisingly high rate of nominal growth beginning in 2007. I will demonstrate that this unexpected rise can be explained by the discrepancy between the reported and the real inflation rate. Since 2009, the Argentinian government has actively censored independent domestic studies on the official CPI, so for the time being, it is essential for foreign scholars to research and keep track of Argentina’s deteriorating statistics.

Conventional wisdom holds that interregional migration of labor is a stabilizing mechanism for unemployment and wages. For example, labor supply will increase in a high-wage region, as people migrate into that region seeking better compensation; this pushes down wages in the receiving region. However, less often noted is the idea that in-migration may also raise the demand for labor. High rates of population growth will spur demand for housing, infrastructure, services, and other durable nontradables. This increase in demand can, in the short and moderate run, be greater than the increase in supply, which spurs accelerating waves of further in-migration to fill those demands. I focus on this aspect of labor migration, and hypothesize that in the United States from 2000 onwards, both new residential construction and thus the demand for labor in rapidly growing U.S. metropolitan statistical areas (MSAs) were significantly and positively affected by in-migration; additionally, I posit that MSAs with higher in-migration during the decade were more severely impacted once migration stopped. I statistically test these hypotheses, adapting a study by Gonzalez and Ortega on the impact of international migration on Spanish regional economies over the last decade. My regression models, estimating the change in housing prices and change in residential construction in response to migration, add insights from urban economics and investment accelerator theory to the factors used in the Spanish study. I then test these models using data calculated or obtained from the U.S. Census Bureau and the Internal Revenue Service. The central question I seek to answer is whether, in the short to medium run, migration is a stabilizing or destabilizing factor in regional labor markets.

Solar technology is a potential way to help meet the growing demand for clean, renewable energy. Hybrid composites of inorganic quantum dots, with organic semiconducting polymers offer a potential means of producing low-cost, solution-processable photovoltaics. The synthesis of quantum dots typically involves the use of large surfactant molecules to facilitate particle growth and solubility. These native ligands act as electrical insulators that impede charge transport in photovoltaic devices. Therefore, it is necessary to exchange these large ligands with small molecules to achieve efficient charge carrier photogeneration and transport. In this research, we examine bulk heterojunction blends of low band gap PbS quantum dots with the conjugated polymer poly((4,8-bis(octyloxy)benzo(1,2-b:4,5-b')dithiophene-2,6-diyl)(2-((dodecyloxy)carbonyl)thieno(3,4-b)thiophenediyl)) (PTB1) treated with different ligands including cetyltrimethlammonium bromide (CTAB), tetrabutylammonium thiocyanate (TBAT), and 3-mercaptopropionic acid (MPA). We aim to elucidate how these different ligands influence the photovoltaic performance of PbS/PTB1blends by studying the differences in long-lived charge generation and recombination kinetics associated with the quantum dot ligand treatments using transient photovoltage (TPV) and photoinduced absorption (PIA) spectroscopy, respectively. We will also determine whether different ligand treatments influence film morphology using scanning electron microscopy (SEM) and transmission electron microscopy (TEM). We hope to gain a better understanding of how quantum dot ligand treatments may be used to optimize the performance of hybrid photovoltaics.

Packaging and the products associated with it produce millions of tons of waste every year - over 70 million tons in the U.S. alone in 2009. About 40% of this waste is recycled or reused, but the other 60% winds up in the landfill. According to the EPA, reducing packaging waste by only 25% would reduce carbon dioxide emissions by 20-50 million metric tons (MMTCO₂) per year. This among many other benefits is equal to reducing the environmental harms of 216,000 passenger cars not driven for one year. This study investigated sustainable packaging solutions dervied from traditional origami designs and asks the questions: can these designs reduce environmental waste and is origami design widely feasible based on current market needs for reliability of packaging and associated costs? To do this, I designed an origami-based packaging solution for emergency aid packages containing food, water and survival supplies. I used the resulting design to assess at the product's life cycle, affectability and its aftermath. What happens to a product after its life is just as important a design consideration as its lifetime requirements and attributes. The results of this research show that sustainability does not have to expensive and complicated. Rather it can be achieved through simple design and creative considerations.

Cooperative interactions are observed at all levels of biological organization. Genes cooperate to form genomes, bacteria aggregate to form biofilms, and individuals organize into societies. The formation of cooperation facilitates the transition from simple forms of life into higher levels of biological complexity. In these systems a cooperator enhances the fitness of its partner at its own expense. Yet Darwinian evolution favors selfish acts which promote individual fitness, rather than selfless acts which incur a cost to the altruist. How could cooperation evolve under this paradigm which, seemingly, is its antithesis? To address this question, I have used a synthetic cooperative system, amenable to genetic and quantitative analysis, termed CoSMO (Cooperation that is Synthetic and Mutually Obligatory), as a model for incipient cooperation. CoSMO is an engineered system consisting of two reproductively isolated Saccharomyces cerevisiae strains: a red-fluorescent strain that overproduces adenine but requires lysine, and a green-fluorescent strain that overproduces lysine but requires adenine. Evolved CoSMO cocultures are able to survive significant reductions in population density. We have determined that adaptations in the lysine-requiring, adenine-overproducing cooperator are sufficient for this system level improvement. Phenotypic analyses revealed that evolved CoSMO cooperators exhibit increased tolerance to limited lysine, and increased adenine release. These evolved phenotypes are attributed to mutations in ECM21 and RSP5, genes controlling ubiquitin-mediated endocytosis of membrane transporters. These mutations facilitated adaptation to nutrient limitation by stabilizing the high-affinity lysine transporter LYP1 on the plasma membrane. Simultaneously, they increased adenine release to the partner via membrane stabilization of the purine-cytosine permease FCY2. Thus, pleiotropy, the influence of one gene over multiple phenotypes, can serve as a mechanism to stabilize incipient cooperation. Through mutations in pleiotropic genes, genetically optimizing self-interest during the cooperative interaction still allowed individuals to directly benefit their partner, thus allowing cooperation to persist under Darwinian selection.

The discovery of a new particle, which we suspect is the long-sought-after Higgs boson, has opened a wide range of opportunities to explore new physics at the Large Hadron Collider (LHC). One particularly promising direction is the search for "flavor violation" in Higgs decays, where a Higgs decays to a non-matching pair of particles. We focus particularly on decays into a tau and a mu lepton (both of which are heavier relatives of the electron). At present, indirect data from low-energy experiments allows these Higgs-to-tau-mu decays to make up up to 10% of all Higgs decays, while the current Standard Model of particle physics does not expect them to happen at all. The goal of my research is to develop a method for separating these decays out of the other billions of collisions that happen every second in the ATLAS detector, and analyzing them in such a way to make flavor violating decays stand out against background events (which look similar but have different origins). This will allow us to put much stricter limits on the rate at which these decays occur, and if any do occur we will have made a big step into previously-unexplored physics territory.

Next generation DNA sequencing, with high throughput and low costs, has already greatly benefitted life sciences and personalized medicine. Nanopore sequencing is a new single molecule technique with potential to further revolutionize DNA sequencing. In this technique DNA is electrophoretically drawn through the nanopore, an integrated membrane channel protein, while an ionic current is recorded. We use the nanopore Mycobacterium smegmatis porin A, MspA, a porin engineered to provide a robust nanopore with the necessary features for nanopore sequencing. To control DNA translocation, we use a phi29 DNA polymerase to pull single stranded DNA through MspA in single nucleotide steps. For each step, the recorded current can be mapped the four standard bases, A, C, G, and T, present in the pore’s narrow constriction. Additionally, the modified nucleobases associated with suppressed gene expression and a number of diseases, 5-methylcytosine and 5-hydroxymethylcytosine, may be directly detected with MspA. We compare two mutants of MspA and examine how different amino groups within MspA’s constriction affect the nucleotide-specific current. We find that additional modifications to MspA may be used to tailor the pore for specific sequencing applications, such as increased accuracy in detecting and, other modified DNA detection.

Most of the questionnaires used to assess college students’ thoughts about their drinking are developed by researchers and are passively responded to by participants. Such researcher-generated measures, however, may not accurately reflect college students’ thoughts about their drinking. In contrast, the current study used participants’ own words in response to an open-ended measure to evaluate advantages and disadvantages college students perceive in maintaining their current drinking behaviors. Participants were college students (N=760) at the University of Washington who reported at least one heavy drinking episode in the past thirty days. The Decisional Balance Worksheet (DBW), an open-ended measure of motivation to change, was used to record the pros and cons of current drinking behaviors. Conventional content analysis methods were used to extract common themes. Findings indicated that social, “fun,” and psychological effects of drinking were the most commonly mentioned advantages of drinking, whereas physical side effects, money, and calories/weight gain were the most commonly mentioned disadvantages of drinking. These findings show that college students use alcohol to achieve certain kinds of psychological states, enhance social interactions, and ease day-to-day stress. Further, many college students report experiencing physical side effects of drinking as well as more minor inconveniences, such as concerns about calories. In contrast, previous researcher-generated decisional balance measures have emphasized very different categories for advantages (e.g., coping, social, and excitement) and disadvantages (e.g., health effects, lack of control, and legal problems) (Migneault et al., 1997). Our findings therefore suggest that using open-ended questionnaires may result in a more accurate representation of what college students perceive as advantages and disadvantages to drinking.

A Hamiltonian cycle is a path that goes through all vertices exactly one and comes back at the starting vertex. Finding Hamiltonian cycles is a well-known problem in graph theory. While necessary and sufficient conditions for Hamiltonian cycles remain unknown, we have developed an algorithm for finding Hamiltonian cycles in polynomial time in certain types of graphs presented in the paper. The algorithm involves finding k cyclic subgraphs within the original graph by deleting edges and then reconstructing some specific edges to create one single Hamiltonian cycle. Along with several algorithms developed earlier, this can contribute to a more general solution, or solutions of the classic Travelling Salesman Problem. 

In the 20th century, mathematicians attempted to reduce mathematics to a set of axioms. One of the properties that David Hilbert, a famous mathematician, postulated was decidability. This refers to an algorithm that can determine whether or not another algorithm in the same system is actually computable. In his 1939 paper, Alan Turing proves decidability cannot occur in formal mathematical systems. Turing used an unconventional way to prove his argument—the Turing Machine. The machine is a theoretical computational device. This abstract device has revolutionized the way we think of computing and computers among other fields. It consists of four parts: a head that reads symbols, an infinite tape that stores inputs and outputs, a state register that holds the current state (or state of mind if you prefer), and a table that describes how the machine transitions from state to state. Turing machines can compute anything that is computable. Computability equates to being able to describe how to obtain the solution of some function with an algorithm. This abstract idea is interdisciplinary; which makes it a great educational tool. We have built a working Turing machine out of LEGOS. To both extend and improve functionality, we have added various components. These components cross over to other fields of study making the project great for students of various education paths to reproduce in a team setting. Our hope is that others can use our work and further devolop it. Besides providing a concrete example of an abstract idea,  building the Turing machine allows us to reaffirm findings.  It is in the spirit of scientific descovery to reproduce work. This research project allows us to follow in the foot steps of intellectual giants.

The mountain pine beetle (MPB) infestation in British Columbia (BC) forests ranks among the largest scale ecological disturbances recorded to date and thus presents a unique testbed for understanding forest dynamics and associated climatic feedbacks. Previous work has shown that BC MPB infestation affects the local surface energy balance, decreasing evapotranspiration and increasing surface temperature. Based on this study and related physics, we hypothesized that there may be greater cloud cover over affected areas of BC post-disturbance relative to pre-disturbance. To explore this hypothesis, we used forest inventory data together with data from the Geostationary Operational Environmental Satellite (GOES) imager to quantify the impact of the devastation on summertime cloud development. Using published calibrations, we transformed raw counts at visible and infrared wavelengths to reflectance and temperature, respectively. We then made cloud maps by applying threshold criteria to the derived reflectance and temperature. Visual inspection of the cloud maps compared to the forest mortality map suggests no obvious difference between the pre- and post-disturbance cloud scenes. Ongoing work is attempting to verify and quantify this qualitative result. Because cloud development processes can have an important influence on the local climate, our research will show whether climatic effects of this ecological disturbance could raise additional concerns about the MPB issue in BC.