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In this work, BiFeO3 and BiCo 0. 0 8 Fe 0. 9 2 O3 nanoparticles prepared by the solution method were further investigated the magnetic properties. At room temperature, BiFeO3 reveals the antiferromagnetic order. However, Co doping achieves ferromagnetic enhancement. The room-temperature ferromagnetism and the causes are ulteriorly studied with the help of ABK plots. The stronger coercivity is due to the oxygen vacancies and stronger magnetic anisotropy caused by Co substitution. Under variable temperatures, the bifurcate curves of zero-field-cooled and field-cooled indicate the existence of spin-glass state for both samples. The weak ferromagnetism at low temperature and the spin-state transition of cobalt ions at 270 K are also found. BiFeO3 owns the perovskite structure with rhombohedral R3C space group. When doped by Co, part of the Fe3+ is replaced by substituted ions. Co doping achieves significant ferromagnetic and magnetization enhancement due to oxygen vacancies and other reasons. Cobalt ions also experience spin transition at 270 K. [ABSTRACT FROM AUTHOR]

In this work, BiFe 1 − x Co x O 3 (x = 0, 0.08) nanoparticles were synthesized by the solution method and their structural differences were studied. X-ray diffraction results show that the rhombohedral R3c space group and perovskite structures are detected in both samples, accompanied by an impurity phase. The (104) and (110) peaks merge when cobalt ions are doped. The decrease in lattice parameters indicates that the microstructure of the nanoparticles becomes gradually distorted. Mössbauer spectroscopy analysis at room temperature reveals an additional doublet due to the oxygen vacancies in BiFe 0. 9 2 Co 0. 0 8 O 3 . Hyperfine interactions, spatial spin-modulated structures and oxygen deficiencies around iron ions are also reflected in the observed spectra and variations in hyperfine parameters. [ABSTRACT FROM AUTHOR]

Traditional Chinese Medicine (TCM) is a practical medicine based on thousands of years of medical practice in China. Arsenic dispensing powder (ADP) has been used as a treatment for MDS patients with a superior efficacy on anemia at Xiyuan Hospital of China Academy of Chinese Medical Sciences. In this study, we retrospectively analyzed MDS patients that received ADP treatment in the past 9 years and confirmed that ADP improves patients' anemia and prolongs overall survival in intermediate-risk MDS patients. Then, we used the MDS transgenic mice model and cell line to explore the drug mechanism. In normal and MDS cells, ADP does not show cellular toxicity but promotes differentiation. In mouse MDS models, we observed that ADP showed significant efficacy on promoting erythropoiesis. In the BFU-E and CFU-E assays, ADP could promote erythropoiesis not only in normal clones but also in MDS clones. Mechanistically, we found that ADP could downregulate HIF1A in MDS clones through upregulation of VHL, P53 and MDM2, which is involved in two parallel pathways to downregulate HIF1A. We also confirmed that ADP upregulates GATA factors in normal clones. Thus, our clinical and experimental studies indicate that ADP is a promising drug to promote erythropoiesis in both MDS and normal clones with a superior outcome than current regular therapies. ADP promotes erythropoiesis in myelodysplastic syndromes via downregulation of HIF1A and upregulation of GATA factors. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) is the well-accepted framework for representing and inferring uncertain knowledge. To learn the BN-based uncertain knowledge incrementally in response to the new data is useful for analysis, prediction, decision making, etc. In this paper, we propose an approach for incremental learning of BNs by focusing on the incremental revision of BN's graphical structures. First, we give the concept of influence degree to describe the influence of new data on the existing BN by measuring the variation of BN's probability parameters w.r.t. the likelihood of the new data. Then, for the nodes ordered decreasingly by their influence degrees, we give the scoring-based algorithm for revising BN's subgraphs iteratively by hill-climbing search for reversing, adding or deleting edges. In the incremental revision, we emphasize the preservation of probabilistic conditional independencies implied in the BN based on the concept and properties of Markov equivalence. Experimental results show the correctness, precision and efficiency of our approach. [ABSTRACT FROM AUTHOR]

The potential applications and challenges of uncertain data mining have recently attracted interests from researchers. Most uncertain data mining algorithms consider aleatory (random) uncertainty of data, i.e. these algorithms require that exact probability distributions or confidence values are attached to uncertain data. However, knowledge about uncertainty may be incomplete in the case of epistemic (incomplete) uncertainty of data, i.e. probabilities of uncertain data may be imprecise, coarse, or missing in some applications. The paper focuses on uncertain data which miss probabilities, specially, value-uncertain discrete objects which miss probabilities (for short uncertain objects). On the other hand, classification is one of the most important tasks in data mining. But, to the best of our knowledge, there is no method to learn Naïve Bayesian classifier from uncertain objects. So the paper studies Naïve Bayesian classification of uncertain objects. Firstly, the paper defines interval probabilities of uncertain objects from probabilistic cardinality point of view, and bridges the gap between uncertain objects and the theory of interval probability by proving that interval probabilities are F-probabilities. Secondly, based on the theory of interval probability, the paper defines conditional interval probabilities including the intuitive concept and the canonical concept, and the conditional independence of the intuitive concept. Further, the paper gives a formula to effectively compute the intuitive concept. Thirdly, the paper presents a Naïve Bayesian classifier with interval probability parameters which can handle both uncertain objects and certain objects. Finally, experiments with uncertain objects based on UCI data show satisfactory performances. [ABSTRACT FROM AUTHOR]

Discovering different groups, or called classes, is useful for pattern recognition, data preprocessing, association analysis, query optimization, etc. To make every object satisfied as much as possible, the groups are generated by the associations or behaviors among participating objects other than the attributes owned by themselves. By mainly considering the mutual associations among the given objects and based on the game theory, in this paper we study the multi-objective oriented categorization. Based on the idea of Shapley value in the coalitional game, we first propose the concept of priority groups and give the efficient algorithm for computing the satisfaction degree of players in a group. Based on the idea of strategic games and Nash equilibrium, we then give the algorithm for computing an approximate equilibrium to solve the conflicts between the strategies of players, and consequently achieve the ultimate multi-objective oriented groups. Preliminary experiments and performance studies verify the efficiency and effectiveness of our methods. [ABSTRACT FROM AUTHOR]

In multiple attribute decision making (MADM) problems, it is usual that no single alternative works best for all performance attributes, so it is difficult to select the best from among multiple available alternatives, especially in the situation that the attribute weights are completely unknown. This research propose a game theory-based approach (GMADM) which incorporates static strategic game theory into MADM problems to derive the attribute weights, and then utilize the weight arithmetic average (WAA) operator to aggregate the attribute values corresponding to each alternative and rank alternatives by means of aggregated information. In GMADM, each attribute is regarded as a player taking part in the game, and the player's strategy is to select a value from interval [0,1] to assign corresponding attribute weight, and the player's utility is defined as the agreement between the ranking of alternatives determined by the aggregated information and the one determined by the attribute values. When the game is in equilibrium status, the strategy profile is the best attribute weights which make each player have good utilities. Moreover, the equilibrium solution of game and the resolution method for the MADM problem without weight information have also been developed. Finally, the result of proposed approach for a practical MADM problem and its comparisons with one of other methods are given. [ABSTRACT FROM AUTHOR]

Bayesian networks are powerful tools for common knowledge representation and reasoning of partial beliefs under uncertainty. In the last decade, Bayesian networks have been successfully applied to a variety of problem domains and many Bayesian networks have been established. Confronted with many real-world applications, each Bayesian network established may be a local model of the whole knowledge domain. It is desirable to combine all local models of the whole domain into a global and more general representation. It is not realistic to expect the domain experts to construct the global model manually due to the too broad domain. As well, it is not feasible to relearn the model since the dataset may have been discarded or the whole domain may be distributed. Thus, constructing the global model by combining local models is doomed to be an alternative solution. This paper concentrates on finding a method of combination without loss of any information and free of datasets by capturing the graphical characterizations of global models. From the graphical perspective, this paper first captures two graphical characterizations to determine the skeleton and V-structure of global models. Moreover, a simple algorithm is elicited from these graphical characterizations to recover the global underlying model from multiple local models. A preliminary experiment demonstrates empirically that our algorithm is feasible. [ABSTRACT FROM AUTHOR]