Deterring violent extremism in America by utilizing good counter-radicalization practices from abroad: a policy perspective

The problem of violent extremism is approaching a critical point in America. American government and community leaders must find an effective approach to deterring violent extremism immediately. A national and comprehensive approach to deter violent extremism in America is currently lacking. This comparative policy perspective seeks to determine whether the United Kingdom and Australia have good practices to deter violent extremism that can be useful in America. Secondly, this thesis also seeks to determine whether the United States can implement potential good practices to deter violent extremism. After a thorough review of the issue of violent extremism in America, this thesis studies similar background, research, and violent extremism issues in the United Kingdom and Australia. This thesis concludes that a variety of good practices in the United Kingdom and Australia can counter radicalize and deter violent extremists in America. The findings and recommendations from this research include challenges and a way forward to implement certain potential best practices for the United States. The arguments offer viable options and alternatives that the United States should consider when creating a national deterring-violent-extremism strategy. The details and information regarding understanding and implementing good practices to deter violent extremism in America are contained in this thesis.http://archive.org/details/deterringviolent1094552953Major, New York Army National Guard, Latham, New YorkApproved for public release; distribution is unlimited

Identifying Student Discussion in Computer-Mediated Problem Solving Chat

The COMPS project employs computer chat for students working in small groups solving classroom problems. This summer’s project aims to build computer classifiers that could effectively “look over the shoulders” of the students while working, to approximately recognize whether the students are engaging in productive discussion. Research questions are: can we write machine classifiers that can recognize reasoning, agreement, and disagreement in student discussions? Can we achieve this using only a common English vocabulary? Several thousand lines of COMPS transcripts were manually annotated. A topic modelling program was used to determine 10 main topics which appeared in the transcripts and the words in those topics. A Linear Classifier and a Support Vector Machine Classifier used the topic model to predict the annotation of each line of dialogue. To address the common English vocabulary research question, an intersection of many transcripts from different sources was combined with Google word lists and modified to accommodate text-chat conventions. In the normal vocabulary, we found f1 scores of 0.7 and above for reasoning. Using only common vocabulary, the scores were slightly lower. The next step is to train our topic model on a combination of transcripts and apply it to other transcripts from different student discussions

Concentration dependent aerosol substrates: UV-vis attenuation measurement

Ultraviolet and visible (UV/vis) light were used to determine the composition of aerosol samples taken from several military bases located in the Middle East. The aerosols were collected using a cascade impactor placing time resolved aerosols on strips of Mylar. These strips were then fed into a fiber optic UV/vis spectrometer which passes light through the Mylar strip and detects the amount of transmitted light relative to a blank standard. By measuring the light transmitted, the amount of aerosol on the Mylar strip was determined proportional to a calibration curve of standard mass depositions. The UV/vis tests were then compared to results from β-gauge analysis performed on the same samples to determine the validity of optical transparency as a substitute for electron attenuation studies. It was determined that the UV/vis data is largely comparable to the β -gauge data showing that UV/vis is a viable alternative to the β -gauge method as well as being more convenient, expedient, and easier to perform

How BRCA1 deficiency affects emergency granulopoeisis in cells

BRCA1 mutation carriers are predisposed to breast and ovarian cancer. Chemotherapy is a common treatment used in breast cancer patients. However, chemotherapy can cause damage to bone marrow. Bone marrow is responsible for the production of white blood cells, namely neutrophils, which are the first line of defense in the innate immune system2. When an infectious or inflammatory challenge presents itself, neutrophils are used up in large quantities, and the hematopoietic system in the body has to rapidly adapt to increased demands by switching from the process of steady-state granulopoeisis to emergency granulopoeisis3. Evidence has shown that BRCA1 mutation carriers who have undergone chemotherapy treatment experience low counts of neutrophils1. Additional evidence has shown that the Fanconi gene pathway contributes to genomic stability during emergency granulopoeisis, and increased Fanconi C (Fancc) gene expression contributes to emergency granulopoeisis4. Since the BRCA1 gene is downstream of the FANCC gene, a myeloid leukemia cell line (U937) was tested to determine whether BRCA1 deficiency contributes to emergency granulopoeisis as well. Different concentrations of the protein IL-1Beta was added to the cells in order to mimic the emergency granulopoeisis response, and both FANCC and BRCA1 gene expression was measured. The general trend for the expression of both genes was found to be different than has previously been reported4. Shanley S et al. Clin Cancer Res. 2006; 12(23): 7033-7038. Kolaczkowzka E et al. Nat Rev Immunol. 2013; 13(3): 159-175. Manz M et al. Nature Reviews Immunology. 2014; 14: 302-314. Hu L et al. J Clin Invest. 2013; 123(9): 3952-3966

Gas emissions, minerals, and tars associated with three coal fires, Powder River Basin, USA.

Ground-based surveys of three coal fires and airborne surveys of two of the fires were conducted near Sheridan, Wyoming. The fires occur in natural outcrops and in abandoned mines, all containing Paleocene-age subbituminous coals. Diffuse (carbon dioxide (CO(2)) only) and vent (CO(2), carbon monoxide (CO), methane, hydrogen sulfide (H(2)S), and elemental mercury) emission estimates were made for each of the fires. Additionally, gas samples were collected for volatile organic compound (VOC) analysis and showed a large range in variation between vents. The fires produce locally dangerous levels of CO, CO(2), H(2)S, and benzene, among other gases. At one fire in an abandoned coal mine, trends in gas and tar composition followed a change in topography. Total CO(2) fluxes for the fires from airborne, ground-based, and rate of fire advancement estimates ranged from 0.9 to 780mg/s/m(2) and are comparable to other coal fires worldwide. Samples of tar and coal-fire minerals collected from the mouth of vents provided insight into the behavior and formation of the coal fires

Electrochemical Reduction of CO2 using Cu-Pd clusters on Graphene

In this study, five copper-palladium clusters supported on defective graphene were investigated as catalysts for the electrochemical reduction of CO2 using the first-principles approach and the computational hydrogen electrode model. The limiting potential to reduce CO2 to CH4 using five different copper-palladium catalysts was determined. Of all the catalysts studied, the best one was the Cu2Pd cluster. This cluster showed the lowest necessary overpotential (0.93 V) out of all the catalysts examined to produce CH4. Reaction pathways to produce a variety of C1 products CO, HCOOH, HCHO, CH3OH and CH4 was studied in detail for Cu2Pd. From the pathways, it was determined that it will likely produce CH4 and HCOOH

Optimal Portfolio Using a Genetic Algorithm

Distributing the amount of money to invest in each stock of a portfolio, while maximizing profit and minimizing risk is key. This project applied the method of a genetic algorithm in order to select an optimal portfolio. A genetic algorithm generates solutions to optimization problems using techniques inspired by natural evolution. A five stock, five years’ portfolio was utilized in order to demonstrate the efficiency of a genetic algorithm. The most important steps of this method were the fitness function and the crossover. The fitness function is a formula that determined the effectiveness of the portfolio distribution; it returned a value for each portfolio distribution and the higher the value the better the distribution. The fitness function allowed us to rank and sort the generated distributions. Then, the crossover was performed in order to see how the genetic algorithm converges towards the optimal solution. The best portfolio distributions, according to the fitness function, were used for the crossover in order to generate even better distributions. Crossover was executed a couple of times by generating new generations of distributions, until the best distribution was produced. The best distribution produced a twenty-five percent average return and its computing time was eleven minutes

Spectroscopic Classification of Post-AGB Candidates: (Searching for Proto-Planetary Nebulae)

We have determined the spectral classification of approximately 50 candidates for post-AGB, proto-planetary nebula (PPN) objects. The candidates were chosen based on their infrared excesses as seen in the IRAS data. The spectra are of low-resolution (R~550) and were obtained with the Kitt Peak National Observatory 2.1-m and the Steward Observatory 2.3-m telescopes. Spectroscopy has permitted us to discriminate between PPNe and other objects with dust, such as pre-main sequence stars and planetary nebulae. Most of the objects appear to be PPNe, ranging in spectral type from K to B, with some of the hotter ones showing hydrogen emission lines. A number of the objects have published classifications, which we list for comparison. Some were previously classified as PPNe but a few are new identifications. We are also carrying on a photometric monitoring program of many of these objects to study light variability. This research is supported by the NSF (most recently AST-1413660)

The Influence of pH Variation on CooA Activity

CooA, a CO-sensing heme protein, acts as a transcriptional activator of CO-metabolizing proteins in bacteria such as Rhodospirillum rubrum and Carboxydothermus hydrogenoformans through sequence-specific DNA binding. Previous research indicated a reduced iron center and CO gas were necessary for CooA to achieve its active conformation and bind DNA. To determine if other reaction conditions facilitate CooA activation, the role of pH on CooA function was tested. Specifically, a fluorescence anisotropy assay was employed to measure possible Fe(III) CooA DNA binding from pH 3 - 12. Interestingly, CooA was observed to bind DNA without CO at acidic conditions, with optimal binding observed at pH ~3. These results are discussed in light of the normal CO-dependent activation mechanism of CooA proteins

Neutral Pion Asymmetries at Intermediate Pseudorapidity in Transversely Polarized p + p Collisions at √ s = 200 GeV

Among the unanswered questions pertaining to nucleon spin physics is the origin of large azimuthal asymmetries (AN ) found in π forward pseudorapidity, η, from high-energy transversely polarized p + p collisions. One possible explanation is offered by twist-3 parton distribution and fragmentation functions. In order to test these and other mechanisms, it is important to study how the asymmetry changes over a range of pion kinematics. The STAR Endcap Electromagnetic Calorimeter (EEMC) is the only RHIC detector with the ability to study AN for π available at intermediate pseudorapidity, 0.8 ≤ η ≤ 2.0. STAR recently published the first measurement of AN for π using data collected in 2006 with collision energy √ STAR collected a high-statistics dataset with transverse beam polarization at √ s = 200 GeV. This offers over a five-fold increase in integrated luminosity relative to the 2006 dataset and a chance to enhance the precision of the previous results. The primary objective of this study is to determine the quality of the data from 2012 and to estimate the final statistical uncertainty.Preliminary results from this study indicate a significant improvement over the 2006 results