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Magnetic nanoparticles have had a significant impact on a wide range of advanced applications in the academic and industrial fields. In particular, in nanomedicine, the nanoparticles require specific properties, including hydrophilic behavior, uniform and tunable dimensions, and good magnetic properties, which are still challenging to achieve by industrial-scale synthesis. Here, we report a gram-scale synthesis of hydrophilic magnetic nanoclusters based on a one-pot solvothermal system. Using this approach, we achieved the nanoclusters with controlled size composed of magnetite nanocrystals in close-packed superstructures that exhibited hydrophilicity, superparamagnetism, high magnetization, and colloidal stability. The proposed solvothermal method is found to be highly suitable for synthesizing industrial quantities (gram-per-batch level) of magnetic spheres with unchanged structural and magnetic properties. Furthermore, coating the magnetic spheres with an additional silica layer provided further stability and specific functionalities favorable for biological applications. Using in vitro and in vivo studies, we successfully demonstrated both positive and negative separation and the use of the magnetic nanoclusters as a theragnostic nanoprobe. This scalable synthetic procedure is expected to be highly suitable for widespread use in biomedical, energy storage, photonics, and catalysis fields, among others.

The retrieval models based on the extended boolean retrieval framework, e.g., the fuzzy set model and the extended boolean model have been proposed in the past to provide the conventional boolean retrievat system with the document ranking facility.However, due to undesirable properties of evaluation formulas for the AND and OR operations, the former generates incorrect ranked output in certain cases and the latter suffers from the complexity of computation. There have been a variety of fuzzy operators to replace the evaluation formulas. In this paper we first investigate the behavioral aspects of the fuzzy operators and address important issues to affect retrieval effectiveness. We then defiie an operator class called positively compensatory operators giving high retrieval effectiveness, and present a pair of positively compensatory operators providing high retrieval efficiency as well as high retrieval effectiveness. All the claims are justifkxt through experiments.

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In this paper we investigate document ranking methods in thesaurus-based boolean retrieval systems, and propose a new thesaurus-based ranking algorithm called the Extended Relevance (E-Relevance) algorithm. The E-Relevance algorithm integrates the extended boolean model and the thesaurus-based relevance algorithm. Since the E-Relevance algorithm has all the desirable properties of the extended boolean model, it avoids the various problems of previous thesaurus-based ranking algorithms. The E-Relevance algorithm also ranks documents effectively by using term dependence information from the thesaurus. We have shown through performance comparison that the proposed algorithm achieves higher retrieval effectiveness than the others proposed earlier.

In this paper we present data distribution methods for parallel pro cessing environment The primary objective is to process partial match retrieval type queries for parallel devices.The main contribution of this paper is the development of a new approach called FX (Fieldwise exclusive) distribution for maximizing data access concurrency. An algebraic property of exclusive-or operation, and field transformation techniques are fundamental to this data distribution techniques. We have shown through theorems and corollaries that this FX distribution approach performs better than other methods proposed earlier.We have also shown, by computing probability of optimal distribution and query response time, that FX distribution gives better performance than others over a large class of partial match queries. This approach presents a new basis in which optimal data distribution for more general type of queries can be formulated.

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