Block Spin Ground State and 3-Dimensionality of (K,Tl)Fe1.6_{1.6}Se2_2

The magnetic properties and electronic structure of (K,Tl)y Fe1.6 Se2 is studied using first-principles calculations. The ground state is checkerboard antiferromagnetically coupled blocks of the minimal Fe4 squares, with a large block spin moment ~11.2{\mu}B . The magnetic interactions could be modelled with a simple spin model involving both the inter- and intra-block, as well as the n.n. and n.n.n. couplings. The calculations also suggest a metallic ground state except for y = 0.8 where a band gap ~400 - 550 meV opens, showing an antiferromagnetic insulator ground state for (K,Tl)0.8 Fe1.6 Se2 . The electronic structure of the metallic (K,Tl)y Fe1.6 Se2 is highly 3-dimensional with unique Fermi surface structure and topology. These features indicate that the Fe-vacancy ordering is crucial to the physical properties of (K,Tl)y Fe2-x Se2 .Comment: Magnetic coupling constants double checked, journal ref. adde

Derivative Process Model of Development Power in Industry: Empirical Research and Forecast for Chinese Software Industry and US Economy

Based on concept and theory of Development Power [1], this paper analyzes the transferability and the diffusibility of industrial development power, points out that the chaos is the extreme of DP releasing and order is the highest degree of DP accumulating, puts forward A-C strength, the index of adjusting and controlling strength, and sets up the derivative process model for industrial development power on the Partial Distribution [2]-[4]. By the derivative process model, a kind of time series model, we can describe the process of industrial development effectively, and can forecast the future direction of industry or economy on using with [7]. Finally, by making use of the actual data of Chinese software industry and data of USA GDP (chained) price index, we give the examples of empirical analysis, and forecast the future of Chinese software industry and USA economic development. The conclusions in this paper are believed to be valuable and significant to guide the establishment of the industrial policy and to control the industrial development.development power (DP), partial distribution, derivative process, industry and macroeconomy, empirical research, forecast analysis

Derivative Process Model of Development Power in Industry: Empirical Research and Forecast for Chinese Software Industry and US Economy

Based on [1], this paper analyzes the transferability and the diffusibility of industrial development power, puts forward the index of management strength, and sets up the derivative process model for industrial development power on the Partial Distribution ([2]-[3]). By the derivative process model, a kind of time series model, we can describe the process of industrial development effectively, and can forecast the future direction of industry or economy on using with [6]. Finally, by making use of the actual data of Chinese software industry and data of USA GDP (chained) price index, we give the examples of empirical analysis, and forecast the future of Chinese software industry and USA economic development. The conclusions in this paper are believed to be valuable and significant to guide the establishment of the industrial policy and to control the industrial development.development power (DP), partial distribution, derivative process, industry and macroeconomy, empirical research, forecast analysis

Human platelets repurposed as vehicles for in vivo imaging of myeloma xenotransplants.

Human platelets were identified in tumors by Trousseau in 1865, although their roles in tumor microenvironments have only recently attracted the attention of cancer researchers. In this study we exploit and enhance platelet interactions in tumor microenvironments by introducing tumor-targeting and imaging functions. The first step in repurposing human platelets as vehicles for tumor-targeting was to inhibit platelet-aggregation by cytoplasmic-loading of kabiramide (KabC), a potent inhibitor of actin polymerization and membrane protrusion. KabC-Platelets can accumulate high levels of other membrane-permeable cytoxins and probes, including epidoxorubicin, carboxyfluorescein di-ester and chlorin-e6. Finally, mild reaction conditions were developed to couple tumor-targeting proteins and antibodies to KabC-platelets. Fluorescence microscopy studies showed KabC-platelets, surface-coupled with transferrin and Cy5, bind specifically to RPMI8226 and K562 cells, both of which over-express the transferrin receptor. Repurposed platelets circulate for upto 9-days a feature that increases their chance of interacting with target cells. KabC-platelets, surface-coupled with transferrin and Cy7, or chlorin-e6, and injected in immuno-compromised mice were shown to accumulate specifically in sub-cutaneous and intra-cranial myeloma xenotransplants. The high-contrast, in vivo fluorescence images recorded from repurposed platelets within early-stage myeloma is a consequence in part of their large size (φ~2µm), which allows them to transport 100 to 1000-times more targeting-protein and probe molecules respectively. Human platelets can be configured with a plurality of therapeutic and targeting antibodies to help stage tumor environments for an immunotherapy, or with combinations of therapeutic antibodies and therapeutic agents to target and treat cardiovascular and neurologic diseases

Magnetic Frustration and Iron-Vacancy Ordering in Iron-Chalcogenide

We show that the magnetic and vacancy orders in the 122 $(A_{1-y}Fe_{2-x}Se_2)$ iron-chalcogenides can be naturally derived from the $J_1-J_2-J_3$ model with $J_1$ being the ferromagnetic (FM) nearest neighbor exchange coupling and $J_{2}, J_3$ being the antiferromagnetic (AFM) next and third nearest neighbor ones respectively, previously proposed to describe the magnetism in the 11(FeTe/Se) systems. In the 11 systems, the magnetic exchange couplings are extremely frustrated in the ordered bi-collinear antiferromagnetic state so that the magnetic transition temperature is low. In the 122 systems, the formation of iron vacancy order reduces the magnetic frustration and significantly increases the magnetic transition temperature and the ordered magnetic moment. The pattern of the 245 iron-vacancy order ($\sqrt{5}\times \sqrt{5}$) observed in experiments is correlated to the maximum reduction of magnetic frustration. The nature of the iron-vacancy ordering may hence be electronically driven. We explore other possible vacancy patterns and magnetic orders associated with them. We also calculate the spin wave excitations and their novel features to test our model.Comment: Figures are modified and more discussion is adde

Positive periodic solutions generated by impulses for the delay Nicholson's blowflies model

In this paper, by using Krasnoselskii's fixed point theorem, we study the existence and multiplicity of positive periodic solutions for the delay Nicholson's blowflies model with impulsive effects. Our results show that these positive periodic solutions are generated by impulses. To the authors' knowledge, there are no papers about positive periodic solution generated by impulses for first order delay differential equation. Our results are completely new. Finally, some examples are given to illustrate our main results

The Effect of Interactivity on SNS Users\u27 Loyalty: Flow and Presence as Mediators

According to the stimulus–organism–response (S–O–R) paradigm, this study aims to understand how interactivity affects SNS users’ loyalty through user experience of presence and flow. Data collected from 242 respondents was analyzed with structural equation modeling (SEM). The results show that machine interactivity affects telepresence and flow, person interactivity affects social presence and flow, social presence affects flow and flow further affects SNS users’ loyalty. In addition, telepresence has a significant effect on social presence

A Robust Transformation-Based Learning Approach Using Ripple Down Rules for Part-of-Speech Tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new rules are only added to correct the errors of existing rules; thus allowing systematic control of the interaction between the rules. Experimental results on 13 languages show that our approach is fast in terms of training time and tagging speed. Furthermore, our approach obtains very competitive accuracy in comparison to state-of-the-art POS and morphological taggers.Comment: Version 1: 13 pages. Version 2: Submitted to AI Communications - the European Journal on Artificial Intelligence. Version 3: Resubmitted after major revisions. Version 4: Resubmitted after minor revisions. Version 5: to appear in AI Communications (accepted for publication on 3/12/2015