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Intrinsic 2D ferromagnetic semiconductors are an important class of materials for spin‐charge conversion applications. Cr2Ge2Te6 retains long‐range magnetic order in the bilayer at cryogenic temperatures and shows complex magnetic interactions with considerable magnetic anisotropy. Here, a series of structural, magnetic, X‐ray scattering, electronic, thermal transport and first‐principles calculation...
Walk-through metal detectors (WTMDs) are increasingly utilized as a security measure at large events held at stadiums. As this is a different environment than standard use cases (e.g. airports, prisons), work has been done to understand how to best integrate this technology into an outdoor high throughput screening environment. We have performed several experiments on these WTMDs to understand their...
Classification is a popular problem within machine learning, and increasing the effectiveness of classification algorithms has many significant applications within industry and academia. In particular, focus will be given to Higher-Order Naive Bayes (HONB), a relational variant of the famed Naive Bayes (NB) statistical classification algorithm that has been shown to outperform Naive Bayes in many...
When utilizing metal detectors at a large venue such as a sports stadium, there are the competing objectives of accuracy of the patron screening and the speed of throughput. This research, carried out in collaboration with the security staff at MetLife Stadium in New Jersey as well as other stadiums, analyzed two patron screening methods: handheld metal detectors (“wands”) and walk-through metal detectors...
We present a model and discrete event simulation of USCG Air Stations, accounting for the mission demands and maintenance procedures pertaining to USCG aircraft. The simulation provides aircraft availability distributions and mission performance metrics based on varying input scenarios, including changes in the number of stationed aircraft and maintenance targets. The Air Station model is novel in...
In real-time emergency response an accurate picture of the situation is needed quickly. Often during large-scale disasters, cell towers become overloaded, and the only way of communication is through text messages. It becomes important to gather information from text messages sent to emergency numbers in order to respond quickly and efficiently with life-saving efforts. In addition, responders are...
In this paper, a novel approach to topic modeling based on the Higher Order Learning framework, Higher-Order Latent Dirichlet Allocation (HO-LDA), is applied to a critical issue in homeland security, nuclear detection. In addition, this research strives to improve topic models in the ‘real time’ environment of online learning. In total, seventeen different nuclear radioisotopes are classified, and...
The detection of potentially threatening nuclear materials is a challenging homeland security problem. This research reports on the application of a novel statistical relational learning algorithm, Higher Order Naïve Bayes (HONB), to improve the detection and identification of nuclear isotopes. When classifying nuclear detection data, distinguishing potentially threatening from harmless radioisotopes...
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