Synthesis,characterization and magnetic properties of polythiophene/SrFe12-x(ZrZn)0.5xO19 nanocomposite as a microwave absorber

Ferrites are an important group of magnetic materials which are used as absorbers. The incorporation of ferrite and conducting polymer achieves great enhancement in microwave absorption properties. The nanocomposites of hexagonal ferrites embedded by conducting polymers such as polypyrrole, polyaniline and polythiophene (PTH) have been paid much attention. In the present study, strontium hexagonal ferrite doped by Zr and Zn with the final formula of SrFe_12-x(ZrZn)_0.5xO19 considering x = 0.9 and embedded by PTH was produced to achieve a nanocomposite with the highest microwave absorbing ability. In this study, after synthesis of SrFe₁₂O₁₉(ZrZn)_0.5xO19 and PTH, the nanocomposite was prepared by in situ polymerization. Wrapping the ferrite particles and PTH chains could form nanocomposite properly, and therefore acceptable interactions were observable between SrFe_12-x(ZrZn)_0.5xO19ferrite particles and PTH polymer chains in the composites. Assessing the X-ray diffraction (XRD) patterns of SrFe_12-x(ZrZn)_0.5xO19, PTH, and PTH/SrFe_12-x(ZrZn)_0.5xO19 nanocomposite indicated that the PTH characteristic peak shifts slightly and its peak intensity reduces, which may be attribute to the coating of PTH polymer chains onto SrFe_12-x(ZrZn)_0.5xO19 particles. We revealed also lower magnetic properties in the obtained nanocomposite. The morphological assessment also suggested that PTH could effectively coat the SrFe_12-x(ZrZn)_0.5xO19 particles. The synergistic effect of SrFe_12-x(ZrZn)_0.5xO19 particle plus PTH leads to microwave absorption percentage higher than 95% by PTH/SrFe_12-x(ZrZn)_0.5xO19 nanocomposite. Overall, nanocomposite creating by coupling interaction between SrFe_12-x(ZrZn)_0.5xO19 particles (x = 0.9) and PTH can effectively lead to achieve the highest rate of absorption of electromagnetic waves.     

CaDHN3, a Pepper (Capsicum annuum L.) Dehydrin Gene Enhances the Tolerance against Salt and Drought Stresses by Reducing ROS Accumulation

Dehydrins (DHNs) play an important role in abiotic stress tolerance in a large number of plants, but very little is known about the function of DHNs in pepper plants. Here, we isolated a Y₁SK₂-type DHN gene “CaDHN3” from pepper. To authenticate the function of CaDHN3 in salt and drought stresses, it was overexpressed in Arabidopsis and silenced in pepper through virus-induced gene silencing (VIGS). Sub-cellular localization showed that CaDHN3 was located in the nucleus and cell membrane. It was found that CaDHN3-overexpressed (OE) in Arabidopsis plants showed salt and drought tolerance phenotypic characteristics, i.e., increased the initial rooting length and germination rate, enhanced chlorophyll content, lowered the relative electrolyte leakage (REL) and malondialdehyde (MDA) content than the wild-type (WT) plants. Moreover, a substantial increase in the activities of antioxidant enzymes; including the superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), ascorbate peroxidase (APX), and lower hydrogen peroxide (H₂O₂) contents and higher O₂^•− contents in the transgenic Arabidopsis plants. Silencing of CaDHN3 in pepper decreased the salt- and drought-stress tolerance, through a higher REL and MDA content, and there was more accumulation of reactive oxygen species (ROS) in the CaDHN3-silenced pepper plants than the control plants. Based on the yeast two-hybrid (Y2H) screening and Bimolecular Fluorescence Complementation (BiFC) results, we found that CaDHN3 interacts with CaHIRD11 protein in the plasma membrane. Correspondingly, the expressions of four osmotic-related genes were significantly up-regulated in the CaDHN3-overexpressed lines. In brief, our results manifested that CaDHN3 may play an important role in regulating the relative osmotic stress responses in plants through the ROS signaling pathway. The results of this study will provide a basis for further analyses of the function of DHN genes in pepper.     

Effect of apple cider vinegar agent on the microstructure,phase evolution,and magnetic properties of CoFe2O4 magnetic nanoparticles

In the present study, spinel structure CoFe₂O₄ nanoparticles were successfully synthesized by the sol-gel auto-combustion technique. The effect of apple cider vinegar (ACV) addition as an organic biocompatible agent on the size, morphology, and magnetic properties of CoFe₂O₄ nanoparticles was investigated in detail. The phase evolution, particle size, and lattice parameter changes of the synthesized phase have been estimated by using Rietveld structure refinement analysis of X-ray powder diffraction data. Also, Fourier transform infrared spectra (FT-IR) of the samples verified the presence of two expected bands correspond to tetrahedral and octahedral metal-oxygen complexes within the spinel structure. Furthermore, microstructural observations revealed that ultrafine particles have a semi-spherical morphology. It was shown that the particles size decreased from ~45 to ~17 nm with an increase in the amount of ACV. Magnetic properties were carried out by vibrating sample magnetometer (VSM) at room temperature. Both the saturation magnetization (M_s) and coercivity (H_c) were found to be significantly dependent on the crystallite size and the amount of ACV.     

Prediction of ultrasonic wave velocity in particleboard and fiberboard

Ultrasonic wave velocities were determined at parallel and perpendicular to manufacturing direction and at the interval angles of 15° in clockwise and counterclockwise directions of particleboard and fiberboard. The experimental results were compared with the predicted values using some empirical formulae such as Hankinson and Jacoby equations. The results showed that the ultrasonic wave velocity were the highest in parallel direction in particleboard and fiberboard and decreases with increase of angle and the lowest values occurred in perpendicular direction. The predicted ultrasonic velocity using Hankinson and Jacoby equations are in close agreement with the measured values. Relationship between ultrasonic wave velocities and particles and fibers angle could be successfully presented by cubic and quadratic regression equations as well.     

Cancer prevention--behavior changes: the short and the long of it

BACKGROUND: Behavioral factors play an important role in the adoption and maintenance of nutritional modifications, delivered either through chemopreventive regimens or through dietary adaptation. A body of research supports a protective role of fruits and vegetables, dietary fiber, and eating a low-fat diet in reducing cancer risk. There is only limited knowledge, however, about the cancer-preventing properties of specific micronutrients, apart from beta-carotene; about optimal levels of intake of differing micronutrients; and about patterns of food intake associated with reduced cancer risk. Thus, it would seem preferable, overall, to be able to recommend cancer prevention through dietary modification rather than through the administration of individual agents. METHODS: Studies of dietary adherence in cancer prevention have yielded varying success rates, but are generally quite promising. We have developed a model that encompasses the range of behavioral, psychological, social, and systemic variables thought to influence adherence to nutritional regimens. RESULTS: The model is being utilized to influence the form and content of nutritional regimens and to promote short-term change which can then be sustained as long-term lifestyle modification. The model can also be applied to evaluate adherence to nutritional or other behavioral modifications and to determine the factors predictive of success.     

A novel approach to predict shear strength of tilted angle connectors using artificial intelligence techniques

Shear connectors play a prominent role in the design of steel-concrete composite systems. The behavior of shear connectors is generally determined through conducting push-out tests. However, these tests are costly and require plenty of time. As an alternative approach, soft computing (SC) can be used to eliminate the need for conducting push-out tests. This study aims to investigate the application of artificial intelligence (AI) techniques, as sub-branches of SC methods, in the behavior prediction of an innovative type of C-shaped shear connectors, called Tilted Angle Connectors. For this purpose, several push-out tests are conducted on these connectors and the required data for the AI models are collected. Then, an adaptive neuro-fuzzy inference system (ANFIS) is developed to identify the most influencing parameters on the shear strength of the tilted angle connectors. Totally, six different models are created based on the ANFIS results. Finally, AI techniques such as an artificial neural network (ANN), an extreme learning machine (ELM), and another ANFIS are employed to predict the shear strength of the connectors in each of the six models. The results of the paper show that slip is the most influential factor in the shear strength of tilted connectors and after that, the inclination angle is the most effective one. Moreover, it is deducted that considering only four parameters in the predictive models is enough to have a very accurate prediction. It is also demonstrated that ELM needs less time and it can reach slightly better performance indices than those of ANN and ANFIS.

     

Incorporation of a Regularization Term to Control Negative Correlation in Mixture of Experts

Combining accurate neural networks (NN) in the ensemble with negative error correlation greatly improves the generalization ability. Mixture of experts (ME) is a popular combining method which employs special error function for the simultaneous training of NN experts to produce negatively correlated NN experts. Although ME can produce negatively correlated experts, it does not include a control parameter like negative correlation learning (NCL) method to adjust this parameter explicitly. In this study, an approach is proposed to introduce this advantage of NCL into the training algorithm of ME, i.e., mixture of negatively correlated experts (MNCE). In this proposed method, the capability of a control parameter for NCL is incorporated in the error function of ME, which enables its training algorithm to establish better balance in bias-variance-covariance trade-off and thus improves the generalization ability. The proposed hybrid ensemble method, MNCE, is compared with their constituent methods, ME and NCL, in solving several benchmark problems. The experimental results show that our proposed ensemble method significantly improves the performance over the original ensemble methods.     

A self-adjusting algorithm for byzantine agreement

Summary Byzantine Agreement is important both in the theory and practice of distributed computing. However, protocols to reach Byzantine Agreement are usually expensive both in the time required as well as in the number of messages exchanged. In this paper, we present a self-adjusting approach to the problem. The Mostly Byzantine Agreement is proposed as a more restrictive agreement problem that requires that in the consecutive attempts to reach agreement, the number of disagreements (i.e., failures to reach Byzantine Agreement) is finite. Fort faulty processes, we give an algorithm that has at mostt disagreements for 4t or more processes. Another algorithm is given forn3t+1 processes with the number of disagreements belowt ²/2. Both algorithms useO(n ³) message bits for binary value agreement. Yi Zhao is currently working on his Ph.D. degree in Computer Science at University of Houston. His research interests include fault tolerance, distributed computing, parallel computation and neural networks. He obtained his M.S. from University of Houston in 1988 and B.S. from Beijing University of Aeronautics and Astronautics in 1984, both in computer science. Farokh B. Bastani received the B. Tech. degree in electrical engineering from the Indian Institute of Technology, Bombay, India, and the M.S. and Ph.D. degrees in electrical engineering and computer science from the University of California, Berkeley. He joined the University of Houston in 1980, where he is currently an Associate Professor of Computer Science. His research interests include software design and validation techniques, distributed systems, and fault-tolerant systems. He is a member of the ACM and the IEEE and is on the editorial board of theIEEE Transactions on Software Engineering.