Growth and Characterization of Zn-Incorporated Copper Oxide Films

In this work, undoped and Zn-doped copper oxide films were deposited on glass substrates at a substrate temperature of 250 ± 5°C by using an ultrasonic spray pyrolysis technique. Electrical, optical, and structural properties of the films were investigated, and the effect of Zn incorporation on these properties are presented. The variations of electrical conductivities and electrical conduction mechanisms of all films were investigated in the dark and in the light. Optical properties of the produced films were analyzed by transmission, linear absorption coefficient, and reflection spectra. The band gaps of the films were determined by an optical method. The film structures were studied by x-ray diffraction. To obtain information about structural properties in detail, the grain size (D), dislocation density (δ), and lattice parameters for preferential orientations were calculated. The elemental analyses were performed using energy-dispersive x-ray spectroscopy. It was concluded that Zn has a strong effect, especially on the electrical and structural properties, and the undoped and Zn-doped copper oxide (at 3%) films may be used as absorbing layers in solar cells due to their low resistivities and suitable linear absorption coefficient values.     

Nafion-clay hybrids with a network structure

Nafion-clay hybrid membranes with a unique microstructure were synthesized using a fundamentally new approach. The new approach is based on depletion aggregation of suspended particles - a well-known phenomenon in colloids. For certain concentrations of clay and polymer, addition of Nafion solution to clay suspensions in water leads to a gel. Using Cryo-TEM we show that the clay particles in the hybrid gels form a network structure with an average cell size in the order of 500 nm. The hybrid gels are subsequently cast to produce hybrid Nafion-clay membranes. Compared to pure Nafion the swelling of the hybrid membranes in water and methanol is dramatically reduced while their selectivity (ratio of conductivity over permeability) increases. The small decrease of ionic conductivity for the hybrid membranes is more than compensated by the large decrease in methanol permeability. Lastly the hybrid membranes are much stiffer and can withstand higher temperatures compared to pure Nafion. Both of these characteristics are highly desirable for use in fuel cell applications, since a) they will allow the use of a thinner membrane circumventing problems associated with the membrane resistance and b) enable high temperature applications.     

Control of a 3-phase 4-leg active power filter under non-ideal mains voltage condition

In this paper, instantaneous reactive power theory (IRP), also known as p–q theory based a new control algorithm is proposed for 3-phase 4-wire and 4-leg shunt active power filter (APF) to suppress harmonic currents, compensate reactive power and neutral line current and balance the load currents under unbalanced non-linear load and non-ideal mains voltage conditions. The APF is composed from 4-leg voltage source inverter (VSI) with a common DC-link capacitor and hysteresis–band PWM current controller. In order to show validity of the proposed control algorithm, compared conventional p–q and p–q–r theory, four different cases such as ideal and unbalanced and balanced-distorted and unbalanced-distorted mains voltage conditions are considered and then simulated. All simulations are performed by using Matlab-Simulink Power System Blockset. The performance of the 4-leg APF with the proposed control algorithm is found considerably effective and adequate to compensate harmonics, reactive power and neutral current and balance load currents under all non-ideal mains voltage scenarios.     

A Bi-Enzymatic Cascade Pathway towards Optically Pure FAHFAs**

Recently discovered endogenous mammalian lipids, fatty acid esters of hydroxy fatty acids (FAHFAs), have been proved to have anti-inflammatory and anti-diabetic effects. Due to their extremely low abundancies in vivo, forging a feasible scenario for FAHFA synthesis is critical for their use in uncovering biological mechanisms or in clinical trials. Here, we showcase a fully enzymatic approach, a novel in vitro bi-enzymatic cascade system, enabling an effective conversion of nature-abundant fatty acids into FAHFAs. Two hydratases from Lactobacillus acidophilus were used for converting unsaturated fatty acids to various enantiomeric hydroxy fatty acids, followed by esterification with another fatty acid catalyzed by Candida antarctica lipase A (CALA). Various FAHFAs were synthesized in a semi-preparative scale using this bi-enzymatic approach in a one-pot two-step operation mode. In all, we demonstrate that the hydratase-CALA system offers a promising route for the synthesis of optically pure structure-diverse FAHFAs.     

Using hybridized ANN-GA prediction method for DOE performed drying experiments

Abstract

Coal is an important component in the energy industry and plays a key role in energy-producing facilities. Moisture is a common condition that has a considerable impact on coal. Coal drying has long been a question of great interest in a wide range of fields. Defining parameters in the coal drying is obtained by experiments. High costs, time constraints, and repetition of an experiment are one of the most frequently stated problems with experimental works. Using qualitative methods with experiments can be more useful for identifying and characterizing the coal drying process. The purpose of this article is finding the effective parameters in the coal drying process by using a hybridized prediction method. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are hybridized with each other to identify and characterize the coal drying process. GA-ANN algorithm is applied to the coal drying process to predict the moisture of coal, but it does not provide a decent result at first. Later, the Design of Experiment (DoE) methodology is performed to determine the main effects of six parameters. Two scenarios are generated because two parameters are not statistically significant. The first scenario excludes the air relative humidity parameter, and the second scenario excludes the air relative humidity and the velocity of air parameters. Following the application of the DoE method, GA-ANN reaches decent results in scenario-2.     

新颖型非整体膜结构硅加速度计的研制

简要介绍了利用硅的各向异性腐蚀工艺研制的非整体膜结构的敏感硅芯片，较详细地报告了用此硅芯片封装成的加速度敏感元件，最后介绍这种加速度计的优良性能。     

水质自动监测记录仪的研制

指出了国内环保部门在水质监测方面存在的问题，阐述了研制水质自动监测仪的必要性。文中对该仪器的硬件和软件组成做了详细的介绍。同时对它的技术创新之处做了较详细的说明。     

Passenger Flow Prediction Based on Newly Adopted Algorithms

Passenger flow forecasting is an essential part of transportation systems. Neural networks in the transportation field have been applied to passenger demand prediction. In this paper, we developed two hybrid methods, known as parlimentary optimization algorithm-artificial neural network (POA-ANN), and intelligent water drops algorithm-ANN (IWD algorithm-ANN). In addition, we applied the proposed algorithms to illustrate the effect of precise prediction for passenger queues. We mainly focus on predicting passenger demand by comparing the genetic algorithm-ANN (GA-ANN) with POA-ANN and IWD-ANN. The results of prediction methods suggest that both POA-ANN and IWD-ANN provide a better forecasting performance, which is obtained via mean square error (MSE), than GA-ANN in the field of passenger flow prediction. This study illustrates that the newly adopted algorithms exhibit good performance for passenger prediction.     

An intelligent process planning system for prismatic parts using STEP features

This paper presents an intelligent process planning system using STEP features (ST-FeatCAPP) for prismatic parts. The system maps a STEP AP224 XML data file, without using a complex feature recognition process, and produces the corresponding machining operations to generate the process plan and corresponding STEP-NC in XML format. It carries out several stages of process planning such as operations selection, tool selection, machining parameters determination, machine tools selection and setup planning. A hybrid approach of most recent techniques (neural networks, fuzzy logic and rule-based) of artificial intelligence is used as the inference engine of the developed system. An object-oriented approach is used in the definition and implementation of the system. An example part is tested and the corresponding process plan is presented to demonstrate and verify the proposed CAPP system. The paper thus suggests a new feature-based intelligent CAPP system for avoiding complex feature recognition and knowledge acquisition problems.