Ag2O-石墨混合物非等温分解的动力学分析

根据模型拟合Malek法、积分主曲线法和无模型的先进等转化率法,采用差示扫描量热仪系统研究了Ag2O-石墨混合物热分解的动力学。结果表明,热分解包含两个阶段,在第一阶段,Ag2O分解形成多孔银颗粒,该过程复杂,至少存在两种机制;在第二阶段,多孔粒子发生结构变化而形成大块银晶体,此过程为单步过程。     

Electrocatalytic and simultaneous determination of isoproterenol, uric acid and folic acid at molybdenum (VI) complex-carbon nanotube paste electrode

This paper describes the development, electrochemical characterization and utilization of a novel modified molybdenum (VI) complex-carbon nanotube paste electrode for the electrocatalytic determination of isoproterenol (IP). The electrochemical profile of the proposed modified electrode was analyzed by cyclic voltammetry (CV) that showed a shift of the oxidation peak potential of IP at 175 mV to less positive value, compared with an unmodified carbon paste electrode. Differential pulse voltammetry (DPV) in 0.1 M phosphate buffer solution (PBS) at pH 7.0 was performed to determine IP in the range from 0.7 to 600.0 μM, with a detection limit of 35.0 nM. Then the modified electrode was used to determine IP in an excess of uric acid (UA) and folic acid (FA) by DPV. Finally, this method was used for the determination of IP in some real samples.     

Iron oxide/gold nanoparticles‐decorated reduced graphene oxide nanohybrid as the thermo‐radiotherapy agent

The main focus of the current study is the fabrication of a multifunctional nanohybrid based on graphene oxide (GO)/iron oxide/gold nanoparticles (NPs) as the combinatorial cancer treatment agent. Gold and iron oxide NPs formed on the GONPs via the in situ synthesis approach. The characterisations showed that gold and iron oxide NPs formed onto the GO. Cell toxicity assessment revealed that the fabricated nanohybrid exhibited negligible toxicity against MCF‐7 cells in low doses (<50 ppm). Temperature measurement showed a time and dose‐dependent heat elevation under the interaction of the nanohybrid with the radio frequency (RF) wave. The highest temperature was recorded using 200 ppm concentration nanohybrid during 40 min exposure. The combinatorial treatments demonstrated that the maximum cell death (average of 53%) was induced with the combination of the nanohybrid with RF waves and radiotherapy (RT). The mechanistic study using the flow cytometry technique illustrated that early apoptosis was the main underlying cell death. Moreover, the dose enhancement factor of 1.63 and 2.63 were obtained from RT and RF, respectively. To sum up, the authors’ findings indicated that the prepared nanohybrid could be considered as multifunctional and combinatorial cancer therapy agents.Inspec keywords: radiation therapy, toxicology, gold, biomedical materials, nanofabrication, nanoparticles, iron compounds, cancer, nanomedicine, cellular biophysics, tumours, graphene compounds, biothermicsOther keywords: graphene oxide nanohybrid, combinatorial cancer treatment agent, cell toxicity assessment, MCF‐7 cells, dose‐dependent heat elevation, multifunctional cancer therapy agents, thermoradiotherapy agent, graphene oxide‐iron oxide‐gold nanoparticles, temperature measurement, radiofrequency wave, flow cytometry, time 40.0 min, CO‐FeO‐Au     

Numerical analysis of plastic deformation of a circular sheet metal subjected to transverse impact loading

Based on the concept of Tensor Code a numerical method is presented for analysis of the plastic deformation process of circular sheet metals subjected to transverse impact loading. In the solution process, the equation of motion is solved explicitly with finite difference method in a series of small time steps over a Lagrangian mesh of zones. Using this method, a code has been developed and utilized for investigation of the deformation behavior of an explosively loaded circular sheet metal under various conditions. Deformation characteristics of a sheet under rectangular and triangular pressure distributions are discussed. It is shown that when these simple distributions are combined with each other, their individual effects on the deformation behavior are also combined in the deformation process. Effects of shape and duration of pressure pulse as well as boundary conditions have been explored. Moreover, results of the numerical simulation have been compared with those of theoretical solution and experiments reported by other researchers. Good agreements between them show the validity of the developed code.     

On Density-Based Data Streams Clustering Algorithms: A Survey简

Clustering data streams has drawn lots of attention in the last few years due to their ever-growing presence. Data streams put additional challenges on clustering such as limited time and memory and one pass clustering. Furthermore, discovering clusters with arbitrary shapes is very important in data stream applications. Data streams are infinite and evolving over time, and we do not have any knowledge about the number of clusters. In a data stream environment due to various factors, some noise appears occasionally. Density-based method is a remarkable class in clustering data streams, which has the ability to discover arbitrary shape clusters and to detect noise. Furthermore, it does not need the nmnber of clusters in advance. Due to data stream characteristics, the traditional density-based clustering is not applicable. Recently, a lot of density-based clustering algorithms are extended for data streams. The main idea in these algorithms is using density- based methods in the clustering process and at the same time overcoming the constraints, which are put out by data streanFs nature. The purpose of this paper is to shed light on some algorithms in the literature on density-based clustering over data streams. We not only summarize the main density-based clustering algorithms on data streams, discuss their uniqueness and limitations, but also explain how they address the challenges in clustering data streams. Moreover, we investigate the evaluation metrics used in validating cluster quality and measuring algorithms＇ performance. It is hoped that this survey will serve as a steppingstone for researchers studying data streams clustering, particularly density-based algorithms.     

A general insight into the effect of neuron structure on classification

This paper gives a general insight into how the neuron structure in a multilayer perceptron (MLP) can affect the ability of neurons to deal with classification. Most of the common neuron structures are based on monotonic activation functions and linear input mappings. In comparison, the proposed neuron structure utilizes a nonmonotonic activation function and/or a nonlinear input mapping to increase the power of a neuron. An MLP of these high power neurons usually requires a less number of hidden nodes than conventional MLP for solving classification problems. The fewer number of neurons is equivalent to the smaller number of network weights that must be optimally determined by a learning algorithm. The performance of learning algorithm is usually improved by reducing the number of weights, i.e., the dimension of the search space. This usually helps the learning algorithm to escape local optimums, and also, the convergence speed of the algorithm is increased regardless of which algorithm is used for learning. Several 2-dimensional examples are provided manually to visualize how the number of neurons can be reduced by choosing an appropriate neuron structure. Moreover, to show the efficiency of the proposed scheme in solving real-world classification problems, the Iris data classification problem is solved using an MLP whose neurons are equipped by nonmonotonic activation functions, and the result is compared with two well-known monotonic activation functions.     

Adaptive Network-Based Fuzzy Inference Systems Coupled with Genetic Algorithms for Predicting Soil Permeability Coefficient

This paper extends hybrid-type optimization models of genetic algorithm adaptive network-based fuzzy inference system (GA-ANFIS) for predicting the soil permeability coefficient (SPC) of different types of soil. In these models, GA optimizes parameters of a subtractive clustering technique that controls the structure of the ANFIS model’s fuzzy rule base. Simultaneously, a hybrid leaning algorithm is employed in the ANFIS, as a trained fuzzy inference system (FIS), which optimally determines the parameter sets of the examined FISs in ANFIS. Using an updated large database of SPCs consisting of 338 fine-grained, 178 mixed and 94 granular soil samples, GA-ANFIS framework constructs different models of predicting the permeability coefficient of respectively fine-grained, mixed and granular soils. A fuzzy C-mean technique has been used to cluster the entire data samples of each type of soil and divide them uniformly into training and testing data sets. Different prediction models of SPC have been trained and tested for each of the three soil types, and the appropriate models have been selected. The selected models have been compared with ANN and modified-by-GA empirical prediction models. Results show that the constructed GA-ANFIS models outperform the other models in terms of the prediction accuracy and the generalization capability.     

Predicting performance in ASEAN banks: an integrated fuzzy MCDM–neural network approach

This paper presents a performance assessment of 88 Association of Southeast Asian Nations banks from 2010 to 2013, using an integrated three‐stage approach on financial criteria that emulates the CAMELS rating system. More precisely, fuzzy analytic hierarchy process is used first to assess the relative weights of a number of criteria related to capital adequacy (C), asset quality (A), management quality (M), earnings (E), liquidity (L), and sensitivity to market risk (S) based on the opinion of 88 Association of Southeast Asian Nations experts. Then, these weights are used as technique for order of preference by similarity to ideal solution inputs to assess their relative efficiency. Lastly, neural networks are combined with technique for order of preference by similarity to ideal solution results to produce a model for banking performance with effective predictive ability. The results reveal that contextual variables have a prominent impact on efficiency. Specifically, parsimony in equity leveraging derived from Islamic finance principles may be the underlying cause in explaining higher efficiency levels.     

Cold Spray Deposition of Freestanding Inconel Samples and Comparative Analysis with Selective Laser Melting

Cold spray offers the possibility of obtaining almost zero-porosity buildups with no theoretical limit to the thickness. Moreover, cold spray can eliminate particle melting, evaporation, crystallization, grain growth, unwanted oxidation, undesirable phases and thermally induced tensile residual stresses. Such characteristics can boost its potential to be used as an additive manufacturing technique. Indeed, deposition via cold spray is recently finding its path toward fabrication of freeform components since it can address the common challenges of powder-bed additive manufacturing techniques including major size constraints, deposition rate limitations and high process temperature. Herein, we prepared nickel-based superalloy Inconel 718 samples with cold spray technique and compared them with similar samples fabricated by selective laser melting method. The samples fabricated using both methods were characterized in terms of mechanical strength, microstructural and porosity characteristics, Vickers microhardness and residual stresses distribution. Different heat treatment cycles were applied to the cold-sprayed samples in order to enhance their mechanical characteristics. The obtained data confirm that cold spray technique can be used as a complementary additive manufacturing method for fabrication of high-quality freestanding components where higher deposition rate, larger final size and lower fabrication temperatures are desired.     

Design and simulation of a novel metallic microgripper using vibration to release nano objects actively

In this study, we investigate a novel metallic microgripper which is able to grasp and transport nano particles (nano tubes/wires) and release them on desirable substrate by vibrating the gripper arms. This microgripper consists of a chevron actuator to grip nano object electrothermally and interdigited comb drive systems to generate vibration at the gripper arms electrostatically. Metallic (nickel) properties enable the chevron actuator to close the gap and pick the nano particle at low voltage and temperature. In order to reduce the out of plane bending during operation and also increase the gripping force, thickness of the nickel layer must be increased, hence electroplating process is proposed for deposition of nickel layer. To generate vibration at the end effectors, comb drive systems are stimulated by applying two voltage signals at desired resonant frequency to the stators. Practically, by sweeping the frequency of these signals around the resonant frequency the end effectors start vibrating. The vibration results in overcoming the adhesion forces due to inertial effects.