与旧城区和谐共生的建筑设计初探——以伊朗科曼莎市为例

近年来,伊朗许多旧城区的建筑都变得破旧不堪,探索针对旧城区的改造方法对建筑师、规划师和当局政府来说都是一项巨大的挑战。该文通过对伊朗旧城区建筑改造的3种方法的比较分析,认为伊朗传统建筑不仅包括建筑的审美和结构性因素,还体现了对环境和文化因素的极大尊重,如果能够充分重视传统建筑中的这些特点,并将它们有意识地应用到新建筑设计与旧建筑立面改造中,将有助于建筑师设计出与环境、文化和谐共生的建筑。     

Robust adaptive vibration control of a flexible structure

Different types of L1 adaptive control systems show that using robust theories with adaptive control approaches has produced high performance controllers. In this study, a model reference adaptive control scheme considering robust theories is used to propose a practical control system for vibration suppression of a flexible launch vehicle (FLV). In this method, control input of the system is shaped from the dynamic model of the vehicle and components of the control input are adaptively constructed by estimating the undesirable vibration frequencies. Robust stability of the adaptive vibration control system is guaranteed by using the L1 small gain theorem. Simulation results of the robust adaptive vibration control strategy confirm that the effects of vibration on the vehicle performance considerably decrease without the loss of the phase margin of the system.     

Magnetic Actuation Methods in Bio/Soft Robotics

In recent years, magnetism has gained an enormous amount of interest among researchers for actuating different sizes and types of bio/soft robots, which can be via an electromagnetic-coil system, or a system of moving permanent magnets. Different actuation strategies are used in robots with magnetic actuation having a number of advantages in possible realization of microscale robots such as bioinspired microrobots, tetherless microrobots, cellular microrobots, or even normal size soft robots such as electromagnetic soft robots and medical robots. This review provides a summary of recent research in magnetically actuated bio/soft robots, discussing fabrication processes and actuation methods together with relevant applications in biomedical area and discusses future prospects of this way of actuation for possible improvements in performance of different types of bio/soft robots.     

Highly conductive supercapacitor based on laser-induced graphene and silver nanowires

One of the foremost necessary desires of energy systems has been the existence of efficient, flexible, transportable, and eco-friendly devices. Among all the energy storage systems, supercapacitors have attracted plenty of attention thanks to their distinctive properties. Among all capacitor technologies, laser-induced graphene (LIG)-based capacitors are within the spotlight nowadays due to their high flexibility and simple manufacture. The most downside with LIG-based capacitors is their low conductivity and low charge capacity. During this work, to overcome this problem, the surface of LIG is covered with silver nanowires (AgNWs) and LIG/AgNWs composite is employed to form supercapacitor. In this study, all the electrochemical properties of the prepared composite were investigated, and therefore the results showed that AgNWs could increase the electrical conductivity of LIG by about 2.25 times, improve electrode–electrolyte interaction, and increase areal capacitance by 1.3 times. Additionally, the synthesized supercapacitor shows stable cyclic behavior and retention capacity equal to 78% after 1000 charge–discharge cycles. A singular increase in LIG conductivity and improved in its cyclic performance. Furthermore, galvanostatic charge/discharge curves indicated acceptable charge capacity of the LIG/AgNWs supercapacitor.

     

Co-electrophoretic deposition of Mn2O3/activated carbon on CuO nanowire array growth on copper foam as a binder-free electrode for high-performance supercapacitors

Journal of Materials Science: Materials in Electronics - The development of new materials with complex structures has proved to be an effective approach to improve their capabilities in a variety...     

Fabrication of poly-Ge-based thermopiles on plastic

Fabrication of Ge-based thermocouple on polyethylene-terephthalate (PET) plastic substrates is reported. The amorphous Ge film, deposited using electron beam evaporation, is post treated to form a polycrystalline film. The annealing process has been performed at temperatures ranging from 120/spl deg/C to 175/spl deg/C and study of physical characteristics of Ge films using XRD and SEM confirms its crystallinity. A value of 100 /spl mu/V//spl deg/C is extracted for the Ge-Al junctions. The thermocouple fabrication and its response to flow are reported. A novel approach is described to perform the micromachining of PET substrates for the formation of craters and membranes. Di-methyl-formamide (DMF) is used as the solvent of the PET substrate, masked with a Ge/Cu multilayer. An average chemical etch rate of 12 /spl mu/m/h is achieved in the presence of 6.5 mW/cm/sup 2/ of 360-nm UV at ambient temperature.     

Application of MLP-ANN as novel tool for estimation of effect of inhibitors on asphaltene precipitation reduction

Sedimentation of heavy fractions of oil such as asphaltene is the main concern in different parts of petroleum industries like production and transportation. Due to this fact, the inhibition of asphaltene precipitation becomes one of the great interests in the petroleum industry. In the present investigation, multi-layer perceptron artificial neural network (MLP-ANN) was developed to estimate asphaltene precipitation reduction as a function of concentration and kind of inhibitors and oil properties. To this end, a total number of 75 data points were extracted from reliable source for training and validation of predicting algorithm. The outputs of MLP-ANN were compared with experimental data graphically and statistically, the determined coefficients of determination (R²) for training and testing are 0.996522 and 0.995239 respectively. These comparisons expressed the high quality of this algorithm in the prediction of asphaltene precipitation reduction. so the MLP-ANN can be used as a powerful machine for estimation of different processes in petroleum industries.     

Modelling of LDMOS transistor using artificial neural networks: DC and RF performances

This article presents an accurate method based on artificial neural networks (ANNs) for DC and RF modelling of laterally diffused metal oxide semiconductor (LDMOS) transistors, under various temperature conditions. In LDMOS transistors, temperature is an effective factor, so the proposed models include this parameter. Two neural networks‐based procedures have been proposed for LDMOS transistor modelling, first for DC and second for RF modelling. In each case, two kinds of neural networks have been used, multilayer perceptron and radial basis function neural networks. Two models are compared to each other in terms of accuracy, and for both of them, an excellent agreement between modelled and measured data is obtained. The ANN model is developed and trained with the help of data obtained by simulation of a Si‐LDMOS transistor using ADS software.     

Texture development and residual stress in UHV evaporated silver films on glass substrates as a function of substrate temperature

Pole figures were collected for silver films of 140 nm thickness deposited on glass substrates with deposition rate of 0.076 nm s⁻¹, for substrate temperatures between 300 and 600 K covering all three zones in the structure zone model (SZM), using an X-ray diffractometer in texture mode. Additional information for determination of residual stress in these films was obtained by the technique. The components of the stress tensor were obtained using measurements at three different φ angles of 0°, 45° and 90°. The crystallite sizes as a function of substrate temperature and ψ angle were also obtained, and showed, an increase with substrate temperature in agreement with the SZM predictions, and a decrease with ψ angle, possibly due to some correlation between the preferred orientation and grain size. The relation between stress in these films and the processes of film growth in the SZM is discussed.