Rapidly Solidified Sm–Co–V Nanocomposite Permanent Magnets

Alloys around the Sm–Co eutectic composition provide an opportunity to form two-phase nanocomposite permanent magnets consisting of nanoscale Co fibers embedded in Sm2Co17.While ternary alloying elements may refine the scale of the structure, they may also disrupt the eutectic growth and lead to the formation of primary Co. Thus, microstructural selection maps were constructed for conventionally solidified Sm–Co–V alloys. It was found that V additions enlarged the primary Sm2Co17-forming region and, at (Sm0.09Co0.91)97 V3, resulted in a eutectic structure. Upon rapid solidification, this alloy was determined to have a coercivity of 5 kOe with a high remanent ratio. However, the V addition reduced the magnetization, which limited the energy product to 4.3 MG Oe. The rapidly solidified structure consisted of primary SmCo7 dendrites along with an intergranular Co region, suggesting that eutectic structure formation is skewed by underlying metastable phase relationships

Rapidly Solidified Sm–Co–V Nanocomposite Permanent Magnets

Alloys around the Sm–Co eutectic composition provide an opportunity to form two-phase nanocomposite permanent magnets consisting of nanoscale Co fibers embedded in Sm2Co17.While ternary alloying elements may refine the scale of the structure, they may also disrupt the eutectic growth and lead to the formation of primary Co. Thus, microstructural selection maps were constructed for conventionally solidified Sm–Co–V alloys. It was found that V additions enlarged the primary Sm2Co17-forming region and, at (Sm0.09Co0.91)97 V3, resulted in a eutectic structure. Upon rapid solidification, this alloy was determined to have a coercivity of 5 kOe with a high remanent ratio. However, the V addition reduced the magnetization, which limited the energy product to 4.3 MG Oe. The rapidly solidified structure consisted of primary SmCo7 dendrites along with an intergranular Co region, suggesting that eutectic structure formation is skewed by underlying metastable phase relationships

The Flavonoid Metabolite 2,4,6-Trihydroxybenzoic Acid Is a CDK Inhibitor and an Anti-Proliferative Agent: A Potential Role in Cancer Prevention

Flavonoids have emerged as promising compounds capable of preventing colorectal cancer (CRC) due to their anti-oxidant and anti-inflammatory properties. It is hypothesized that the metabolites of flavonoids are primarily responsible for the observed anti-cancer effects owing to the unstable nature of the parent compounds and their degradation by colonic microflora. In this study, we investigated the ability of one metabolite, 2,4,6-trihydroxybenzoic acid (2,4,6-THBA) to inhibit Cyclin Dependent Kinase (CDK) activity and cancer cell proliferation. Using in vitro kinase assays, we demonstrated that 2,4,6-THBA dose-dependently inhibited CDKs 1, 2 and 4 and in silico studies identified key amino acids involved in these interactions. Interestingly, no significant CDK inhibition was observed with the structurally related compounds 3,4,5-trihydroxybenzoic acid (3,4,5-THBA) and phloroglucinol, suggesting that orientation of the functional groups and specific amino acid interactions may play a role in inhibition. We showed that cellular uptake of 2,4,6-THBA required the expression of functional SLC5A8, a monocarboxylic acid transporter. Consistent with this, in cells expressing functional SLC5A8, 2,4,6-THBA induced CDK inhibitory proteins p21Cip1 and p27Kip1 and inhibited cell proliferation. These findings, for the first time, suggest that the flavonoid metabolite 2,4,6-THBA may mediate its effects through a CDK- and SLC5A8-dependent pathway contributing to the prevention of CRC

Self-Organizing doubly-linked lists

In this paper, we study the problem of maintaining a doubly-linked list (DLL) in approximately optimal order, with respect to the mean search time. We study two types of DLL reorganization strategies. Move-To-End (MTE) [12] and SWAP [14] are two memoryless DLL heuristics obtained from natural extensions of the well-known singly-linked-list (SLL) heuristics, move-to-front and transposition, respectively. We first derive a general sufficient condition which permits comparison of any two DLL heuristics. We use this condition as a guideline to identify families of access distributions for which SWAP yields a lower expected cost than the MTE. We have also presented an absorbing DLL heuristic. The strategy requires one additional memory location and is analogous to the scheme presented in [15]. The reorganization is achieved by moving each element exactly once to its final position in the reorganized list. The scheme is stochastically absorbing and it is shown to be optimal for a restricted family of distributions. Thus, for these distributions, the probability of the scheme converging to the optimal list order can be made as close to unity as desired

On using the chi-squared metric for determining stochastic dependence

In several pattern recognition applications it is often necessary to approximate probability distributions/densities. In estimating this density function, it is either assumed that the form of the density function is known and that parameters that characterize the distribution are merely estimated, or it is assumed that no information about the density function is available. The latter formulation is considered with the additional constraint that even the stochastic dependence is unknown. For the case of discrete-valued features the well-known method due to Chow and Liu (IEEE Trans. Inf. Theory 14, 462-467 (May 1968)) which uses dependence trees, can be used to approximate the underlying probability distribution. This method determines the best dependence-tree based on the well-acclaimed expected mutual information measure (EMIM) metric. The suitability of a chi-squared metric is studied for the same purpose. For a restricted class of distributions, both these metrics are shown to be equivalent and stochastically optimal. For more general cases, the latter metric is almost as efficient as the optimal one, and in all cases the technique presented here is computationally almost an order of magnitude faster than the EMIM based method

Recognizing Sources of Random Strings

Let us assume that we have a number of independent sources where each source generates random strings of fixed length M, composed of symbols drawn from an alphabet R. Each source generates these random strings according to its own distribution. The problem we consider in this correspondence is one of identifying the source given a sequence of random strings. Two modes of random string generation are analyzed. In the first mode, arbitrary strings are generated in which the individual symbols can occur many times in the strings. In the second mode the individual symbols occur exactly once in each random string. The latter case corresponds to the situation in which the sources generate random permutations. In both these cases, the best match to the distribution being used by each source can be obtained by maintaining an exponential number of statistics. This being infeasible, we propose a simple parametrization of the distributions. For arbitrary strings, the simple unigram based model (U-model) has been proposed. For the case of permutations, we have proposed a new model called the S-model and employed it to analyze and/or approximate unknown distributions of permutations. The relevant estimation procedures together with the applications to source recognition have been presented. Considering the fact that the symbolic data is processed, and statistically analyzed, our method clearly presents a unique blend of syntactic and statistical pattern recognition