21:I[432972,["/_next/static/chunks/bb923814e1f4d2e9.js","/_next/static/chunks/dd3ee9f5b7716ca6.js","/_next/static/chunks/4204a0ae82542ca8.js","/_next/static/chunks/1270dead247da766.js","/_next/static/chunks/6e1aa89a642f40be.js","/_next/static/chunks/2d7aa753e81324a8.js","/_next/static/chunks/8ffd0b71567920d8.js","/_next/static/chunks/67ea6dfd21f16c5f.js","/_next/static/chunks/24eb4379f68013fd.js","/_next/static/chunks/b8cd775ffdf935bd.js","/_next/static/chunks/983103cd9eb7b428.js","/_next/static/chunks/263f2cdcb9aa2341.js","/_next/static/chunks/294b9525248c8e5f.js"],"default"] 22:T4e6,Three kinds of magnesium-based spinel nanoferrites with the chemical formulas of MgFe₂O₄ (Mg ferrite), Mg_0.9Mn_0.1Fe₂O₄ (Mg-Mn ferrite), and Mg_0.9Mn_0.1In_0.1Fe_1.9O₄ (Mg-Mn-In ferrite) were synthesized by hydrothermal route. We report the composition-dependent magnetic parameters and microwave properties of Mg-based ferrite nanoparticles. XRD results revealed that the Mg-based ferrite nanoparticles exhibited a cubic spinel structure and had an average nanocrystallite size in the range of 5.8⁻2.6 nm. Raman spectroscopy analysis confirmed the formation of cubic-spinel phase Mg-based nanoferrites. The room-temperature magnetization measurements indicated that the Mg ferrite nanoparticles had superparamagnetic behavior; whereas the Mg-Mn and Mg-Mn-In ferrite nanoparticles exhibited a paramagnetic nature. The microwave properties of obtained ferrite nanoparticles were studied by alternating current (AC) magnetic susceptibility measurement and electron paramagnetic resonance (EPR) spectroscopy. It was found that the un-substituted Mg ferrite sample exhibited microwave characteristics better than those of the Mn substituted and Mn-In co-substituted Mg ferrite samples.23:T52d,Entity resolution (ER), which detects records referring to the same entity across data sources, is a long-lasting challenge in database management research. The sheer volume of data collections today calls for the need of a blocking-based ER algorithm using the MapReduce framework for cloud computing. Most studies on blocking-based ER assume that only one blocking key is associated with an entity. An entity in reality may have multiple blocking keys in some applications. When the entities have a number of blocking keys, ER can be more efficient since two entities can form a similar pair only if they share several common keys. Therefore, we propose a MapReduce algorithm to solve the ER problem for a huge collection of entities with multiple keys. The algorithm is characterized in the combination-based blocking and the load-balanced matching. The combination-based blocking utilizes the multiple keys to sort out necessary entity pairs for future matching. The load-balanced matching evenly distributes the required similarity computations to all the reducers in the matching step so as to remove the bottleneck of skewed matching computations for a single node in a MapReduce framework. Our experiments using the well-known CiteSeerX digital library show that the proposed algorithm is both efficient and scalable.

#	Concept	H-Index	Publications	Citations
1	Engineering	2	2	92
2	Management	1	1	29
3	Natural Sciences	1	1	63
4	Chemistry	1	1	63
5	Computer Science	1	1	29

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