Missouri University of Science and Technology

About: Missouri University of Science and Technology is a education organization based out in Rolla, Missouri, United States. It is known for research contribution in the topics: Control theory & Artificial neural network. The organization has 9380 authors who have published 21161 publications receiving 462544 citations. The organization is also known as: Missouri S&T & University of Missouri–Rolla.

Clustering Algorithms in Biomedical Research: A Review

Abstract: Applications of clustering algorithms in biomedical research are ubiquitous, with typical examples including gene expression data analysis, genomic sequence analysis, biomedical document mining, and MRI image analysis However, due to the diversity of cluster analysis, the differing terminologies, goals, and assumptions underlying different clustering algorithms can be daunting Thus, determining the right match between clustering algorithms and biomedical applications has become particularly important This paper is presented to provide biomedical researchers with an overview of the status quo of clustering algorithms, to illustrate examples of biomedical applications based on cluster analysis, and to help biomedical researchers select the most suitable clustering algorithms for their own applications

Pressureless Sintering of Zirconium Diboride

Abstract: Zirconium diboride (ZrB 2 ) ceramics were sintered to a relative density of ∼ 98% without applied external pressure. Densification studies were performed in the temperature range of 1900 -2150 C. Examination of bulk density as a function of temperature revealed that shrinkage started at ∼2100°C, with significant densification occurring at only 2150°C. At 2150°C, isothermal holds were used to determine the effect of time on relative density and microstructure. For a hold time of 540 min at 2150 C, ZrB 2 pellets reached an average density of 6.02±0.04 g/cm 3 (98% of theoretical) with an average grain size of 9.0 ±5.6 μm. Four-point bend strength, elastic modulus, and Vickers' hardness were measured for sintered ZrB 2 and compared with values reported for hot-pressed materials. Vickers' hardness of sintered ZrB 2 was 14.5±2.6 GPa, which was significantly lower when compared with 23 GPa for hot-pressed ZrB 2 . Strength and elastic modulus of the ZrB 2 were 444±30 MPa and 454 GPa, which were comparable with values reported for hot-pressed ZrB 2 . The ability to densify ZrB 2 ceramics without hot pressing should enable near-net shape processing, which would significantly reduce the cost of fabricating ZrB 2 components compared with conventional hot pressing and machining.

Hydrogen storage and carbon dioxide capture in an iron-based sodalite-type metal–organic framework (Fe-BTT) discovered via high-throughput methods

Abstract: Using high-throughput instrumentation to screen conditions, the reaction between FeCl2 and H3BTT·2HCl (BTT3− = 1,3,5-benzenetristetrazolate) in a mixture of DMF and DMSO was found to afford Fe3[(Fe4Cl)3(BTT)8]2·22DMF·32DMSO·11H2O. This compound adopts a porous three-dimensional framework structure consisting of square [Fe4Cl]7+ units linked via triangular BTT3− bridging ligands to give an anionic 3,8-net. Mossbauer spectroscopy carried out on a DMF-solvated version of the material indicated the framework to contain high-spin Fe2+ with a distribution of local environments and confirmed the presence of extra-framework iron cations. Upon soaking the compound in methanol and heating at 135 °C for 24 h under dynamic vacuum, most of the solvent is removed to yield Fe3[(Fe4Cl)3(BTT)8(MeOH)4]2 (Fe-BTT), a microporous solid with a BET surface area of 2010 m2 g−1 and open Fe2+ coordination sites. Hydrogen adsorption data collected at 77 K show a steep rise in the isotherm, associated with an initial isosteric heat of adsorption of 11.9 kJ mol−1, leading to a total storage capacity of 1.1 wt% and 8.4 g L−1 at 100 bar and 298 K. Powder neutron diffraction experiments performed at 4 K under various D2 loadings enabled identification of ten different adsorption sites, with the strongest binding site residing just 2.17(5) A from the framework Fe2+ cation. Inelastic neutron scattering spectra are consistent with the strong rotational hindering of the H2 molecules at low loadings, and further reveal the catalytic conversion of ortho-H2 to para-H2 by the paramagnetic iron centers. The exposed Fe2+ cation sites within Fe-BTT also lead to the selective adsorption of CO2 over N2, with isotherms collected at 298 K indicating uptake ratios of 30.7 and 10.8 by weight at 0.1 and 1.0 bar, respectively.

Secure k-nearest neighbor query over encrypted data in outsourced environments

Abstract: For the past decade, query processing on relational data has been studied extensively, and many theoretical and practical solutions to query processing have been proposed under various scenarios. With the recent popularity of cloud computing, users now have the opportunity to outsource their data as well as the data management tasks to the cloud. However, due to the rise of various privacy issues, sensitive data (e.g., medical records) need to be encrypted before outsourcing to the cloud. In addition, query processing tasks should be handled by the cloud; otherwise, there would be no point to outsource the data at the first place. To process queries over encrypted data without the cloud ever decrypting the data is a very challenging task. In this paper, we focus on solving the k-nearest neighbor (kNN) query problem over encrypted database outsourced to a cloud: a user issues an encrypted query record to the cloud, and the cloud returns the k closest records to the user. We first present a basic scheme and demonstrate that such a naive solution is not secure. To provide better security, we propose a secure kNN protocol that protects the confidentiality of the data, user's input query, and data access patterns. Also, we empirically analyze the efficiency of our protocols through various experiments. These results indicate that our secure protocol is very efficient on the user end, and this lightweight scheme allows a user to use any mobile device to perform the kNN query.