Experiential Cyber Education

There is a gap between available cyber professionals with necessary skills and experience to meet industry requirements. Institutions of higher education (IHE)—as well as other programs—have begun increasing course and degree offerings to help educate, train, and even retrain working professionals to close this gap. Of growing importance are tools and techniques to supplement theoretical development with accelerated experiential cyber training. Fortunately, there has been an increase in providers offering these services, although they vary substantially in features, costs, and opportunities. The purpose of this research is to identify a current spectrum of vendors and opportunities providing hands-on cyber training. The authors of this paper include cyber faculty at a university offering undergraduate and master's cybersecurity degrees. Both degrees are offered to resident as well as online students.

A Novel Fault Detection and Identification Framework for Rotating Machinery Using Residual Current Spectrum

A novel framework of model-based fault detection and identification (MFDI) for induction motor (IM)-driven rotating machinery (RM) is proposed in this study. A data-driven subspace identification (SID) algorithm is employed to obtain the IM state-space model from the voltage and current signals in a quasi-steady-state condition. This study aims to improve the frequency–domain fault detection and identification (FDI) by replacing the current signal with a residual signal where a thresholding method is applied to the residual signal. Through the residual spectrum and threshold comparison, a binary decision is made to find fault signatures in the spectrum. The statistical Q-function is used to generate the fault frequency band to distinguish between the fault signature and the noise signature. The experiment in this study is performed on a wastewater pump in an existing industrial facility to verify the proposed FDI. Two faulty conditions with mathematically known and mathematically unknown faulty signatures are experimented with and diagnosed. The study results present that the residual spectrum demonstrated to be more sensitive to fault signatures compare to the current spectrum. The proposed FDI has successfully shown to identify the fault signatures even for the mathematically unknown faulty signatures.

On ‘Nostalgia for the Empire: The Politics of Neo-Ottomanism’ by M. Hakan Yavuz

Summary Four international scholars have individually reflected critically on M. Hakan Yavuz’s new book Nostalgia for the Empire: The Politics of Neo-Ottomanism. The book recognizes nostalgia as a major variable in articulating and analyzing the current spectrum of Turkish politics by exploring neo-Ottomanism which has, in many respects, become an instrumental frontal display for Islam and Islamism.

EXPRESS: Baseline Correction Based on a Search Algorithm from Artificial Intelligence

Many spectra have a polynomial-like baseline. Iterative polynomial fitting (IPF) is one of the most popular methods for baseline correction of these spectra. However, the baseline estimated by IPF may have substantially error when the spectrum contains significantly strong peaks or have strong peaks located at the endpoints. First, IPF uses temporary baseline estimated from the current spectrum to identify peak data points. If the current spectrum contains strong peaks, then the temporary baseline substantially deviates from the true baseline. Some good baseline data points of the spectrum might be mistakenly identified as peak data points and are artificially re-assigned with a low value. Second, if a strong peak is located at the endpoint of the spectrum, then the endpoint region of the estimated baseline might have significant error due to overfitting. This study proposes a search algorithm-based baseline correction method (SA) that aims to compress sample the raw spectrum to a dataset with small number of data points and then convert the peak removal process into solving a search problem in artificial intelligence (AI) to minimize an objective function by deleting peak data points. First, the raw spectrum is smoothened out by the moving average method to reduce noise and then divided into dozens of unequally spaced sections on the basis of Chebyshev nodes. Finally, the minimal points of each section are collected to form a dataset for peak removal through search algorithm. SA selects the mean absolute error (MAE) as the objective function because of its sensitivity to overfitting and rapid calculation. The baseline correction performance of SA is compared with those of three baseline correction methods: Lieber and Mahadevan–Jansen method, adaptive iteratively reweighted penalized least squares method, and improved asymmetric least squares method. Simulated and real FTIR and Raman spectra with polynomial-like baselines are employed in the experiments. Results show that for these spectra, the baseline estimated by SA has fewer error than those by the three other methods.