Comparison of rocket-borne probes for electron density measurements Quarterly status report no. 5, Aug. 1 - Oct. 31, 1965

Impedance, resonance, capacitance, electrostatic, and Langmuir probe performance for ionospheric electron density profil

A survey of new technology for cockpit application to 1990's transport aircraft simulators

Two problems were investigated: inter-equipment data transfer, both on board the aircraft and between air and ground; and crew equipment communication via the cockpit displays and controls. Inter-equipment data transfer is discussed in terms of data bus and data link requirements. Crew equipment communication is discussed regarding the availability of CRT display systems for use in research simulators to represent flat panel displays of the future, and of software controllable touch panels

Modeling Non-Linear Spatial Dynamics: A Family of Spatial STAR Models and an Application to U.S. Economic Growth

This paper investigates non-linearity in spatial processes models and allows for a gradual regime-switching structure in the form of a smooth transition autoregressive process. Until now, applications of the smooth transition autoregressive (STAR) model have been largely confined to the time series context. The paper focuses on extending the non-linear smooth transition perspective to spatial processes models, in which spatial correlation is taken into account through the use of a so-called weights matrix identifying the topology of the spatial system. We start by deriving a non-linearity test for a simple spatial model, in which spatial correlation is only included in the transition function. Next, we propose a non-linearity test for a model that includes a spatially lagged dependent variable or spatially autocorrelated innovations as well. Monte Carlo simulations of the various test statistics are performed to examine their power and size. The proposed modeling framework is then used to identify convergence clubs in the context of U.S. county-level economic growth over the period 1963–2003.spatial econometrics, non-linearity, utoregressive smooth transition, Research Methods/ Statistical Methods, C12, C21, C51, O18, R11,

Examining signals of trust in criminal markets online

This study examines the signals of trust in stolen data advertisements by analysing the structural and situational factors that influence the type of feedback sellers receive. Specifically, this article explores the factors associated with positive and negative buyer feedback from the purchase of stolen credit card data in a series of advertisements from a sample of Russian and English language forums where individuals buy and sell personal information. The results of zero-inflated Poisson regression models suggest that the sellers may influence their likelihood of receiving feedback by specifying the type of payment mechanism, choosing the advertisement language and selecting the type of market they operate within. The implications of this study for our understanding of online illicit markets, criminological theory and policy-making will be explored in depth.Department of Homeland Security (DHS) Science and Technology Directorate, Cyber Security Division (DHSSandT/ CSD) Broad Agency Announcement 11.02, the Government of Australia and SPAWAR Systems Center Pacific [N66001-13-C-0131]; National Institute of Justice, Office of Justice Programs, US Department of Justice [2010-IJ-CX-1676, 2010]This is the final version of the article. It first appeared from Oxford University Press via https://doi.org/10.1093/cybsec/tyw00

Using multiple classifiers for predicting the risk of endovascular aortic aneurysm repair re-intervention through hybrid feature selection.

Feature selection is essential in medical area; however, its process becomes complicated with the presence of censoring which is the unique character of survival analysis. Most survival feature selection methods are based on Cox's proportional hazard model, though machine learning classifiers are preferred. They are less employed in survival analysis due to censoring which prevents them from directly being used to survival data. Among the few work that employed machine learning classifiers, partial logistic artificial neural network with auto-relevance determination is a well-known method that deals with censoring and perform feature selection for survival data. However, it depends on data replication to handle censoring which leads to unbalanced and biased prediction results especially in highly censored data. Other methods cannot deal with high censoring. Therefore, in this article, a new hybrid feature selection method is proposed which presents a solution to high level censoring. It combines support vector machine, neural network, and K-nearest neighbor classifiers using simple majority voting and a new weighted majority voting method based on survival metric to construct a multiple classifier system. The new hybrid feature selection process uses multiple classifier system as a wrapper method and merges it with iterated feature ranking filter method to further reduce features. Two endovascular aortic repair datasets containing 91% censored patients collected from two centers were used to construct a multicenter study to evaluate the performance of the proposed approach. The results showed the proposed technique outperformed individual classifiers and variable selection methods based on Cox's model such as Akaike and Bayesian information criterions and least absolute shrinkage and selector operator in p values of the log-rank test, sensitivity, and concordance index. This indicates that the proposed classifier is more powerful in correctly predicting the risk of re-intervention enabling doctor in selecting patients' future follow-up plan

Fluoroquinolone-mediated inhibition of cell growth, S-G2/M cell cycle arrest, and apoptosis in canine osteosarcoma cell lines.

Despite significant advancements in osteosarcoma research, the overall survival of canine and human osteosarcoma patients has remained essentially static over the past 2 decades. Post-operative limb-spare infection has been associated with improved survival in both species, yet a mechanism for improved survival has not been clearly established. Given that the majority of canine osteosarcoma patients experiencing post-operative infections were treated with fluoroquinolone antibiotics, we hypothesized that fluoroquinolone antibiotics might directly inhibit the survival and proliferation of canine osteosarcoma cells. Ciprofloxacin or enrofloxacin were found to inhibit p21(WAF1) expression resulting in decreased proliferation and increased S-G(2)/M accumulation. Furthermore, fluoroquinolone exposure induced apoptosis of canine osteosarcoma cells as demonstrated by cleavage of caspase-3 and PARP, and activation of caspase-3/7. These results support further studies examining the potential impact of quinolones on survival and proliferation of osteosarcoma

Segregated water observed in a putative fish embryo cryopreservative

Development of new cryopreservation strategies has major potential in medicine and agriculture and is critical to the conservation of endangered species that currently cannot be preserved. A critical property of any potential cryopreservative solution is its ability to prevent cell-damaging ice formation during cooling and subsequent heating. This study focuses on the freezing behaviour of promising model cryoprotective solutions. We perform neutron scattering analysis, combined with computer modelling, of the water structure after quench cooling these solutions. It is found that water in this solution forms nano-clusters encapsulated by the surrounding matrix of cryoprotectant solute molecules. We posit that these small volumes inhibit ice formation, because water does not have space for the structural relaxation required to crystallize on the timescale of the cooling process