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.     

A novel driver assist stability system for all-wheel-drive electric vehicles

A novel driver-assist stability system for all-wheel-drive electric vehicles is introduced. The system helps drivers maintain control in the event of a driving emergency, including heavy braking or obstacle avoidance. The system comprises a fuzzy logic system that independently controls wheel torque to prevent vehicle spin. Another fuzzy wheel slip controller is used to enhance vehicle stability and safety. A neural network is trained to generate the required reference for yaw rate. Vehicle true speed is estimated by a sensor data fusion method. The intrinsic robustness of fuzzy controllers allows the system to operate in different road conditions successfully. Moreover, the ease of implementing fuzzy controllers gives a potential for vehicle stability enhancement.     

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.     

Reliability assessment of an automated distribution system

Automation can greatly enhance distribution-network reliability by speeding up service restoration and thus significantly reduce customer-outage time. The paper presents an approach to assess quantitatively the adequacy of a particular automated distribution scheme designated as the `low interruption system' (LIS). Owing to the use of a high-speed communication system and line sensors, this automated scheme can reduce drastically the number of interruptions, the service interruption time and also the area affected by the fault. This scheme provides a simple and cost-effective way to automate distribution systems in which the remotely controlled switches speed up isolation of faulted sections and the restoration of healthy sections through alternative routes. The step-by-step calculation procedure is presented using a typical small automated distribution system. The proposed technique is then applied to a larger distribution system to examine the effectiveness of the technique and also to examine the level of reliability improvement achieved by automation     

A hermetic glass-silicon micropackage with high-density on-chipfeedthroughs for sensors and actuators

This paper describes the development of a hermetic micropackage with high-density on-chip feedthroughs for sensor and actuator applications. The packaging technique uses low-temperature (320°C) electrostatic bonding of a custom-made glass capsule (Corning 7740, 2×2×8 mm³) to fine grain polysilicon in order to form a hermetically sealed cavity. High-density on-chip multiple polysilicon feedthroughs (200 per millimeter) are used for connecting external sensors and actuators to the electronic circuitry inside the package. A high degree of planarity over feedthrough areas is obtained by using grid-shaped polysilicon feedthrough lines that are covered with phosphosilicate glass (PSG), which is subsequently reflown at 1100°C in steam for 2 h. Saline and DI water soak tests at elevated temperatures (85 and 95°C) were performed to determine the reliability of the package. Preliminary results have shown a mean time to failure (MTTF) of 284 days and 118 days at 85 and 95°C, respectively, in DI water. An Arrhenius diffusion model for moisture penetration yields an expected lifetime of 116 years at body temperature (37°C) for these packages. In vivo tests in guinea pigs and rats for periods ranging from one to two months have shown no sign of infection, inflammation, or tissue abnormality around the implanted package     

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.