Synthesis and Physical Properties of Mn Doped ZnO Dilute Magnetic Semiconductor Nanostructures

Mn doped ZnO nanostructures have been prepared using low temperature simple, quick, and versatile synthesis approach. The structural, microstructural, and vibrational investigations reveal that as prepared nanostructures with low Mn doping concentration have single hexagonal phase and are grown along the preferred c-axis. The X-rays photoelectron spectroscopy demonstrates that the Mn ions are in mixed oxidation states for high doping concentration of Mn, while are in 2+ oxidation state for low concentration into ZnO lattice. The photoluminescence spectrum (PL) exhibits a significant red-shift of 22 nm in the optical band gap of doped ZnO and shows the improved luminescence properties, which makes it potential for its use in the photocatalyst, optoelectronics and solar cell nanodevices. Furthermore, the magnetic measurement of Mn doped ZnO nanostructures exhibits the ferromagnetism at room temperature.     

A Study of Web Services Projects in the Financial Services Industry

This study examines the significance of technological, methodological, and business factors in contributing to the success of initial Web Services projects. Focusing on four case studies from the financial services sector, the authors' findings suggest that a strong focus on business factors is associated with successful Web Services strategies.     

Prediction of human breast and colon cancers from imbalanced data using nearest neighbor and support vector machines

This study proposes a novel prediction approach for human breast and colon cancers using different feature spaces. The proposed scheme consists of two stages: the preprocessor and the predictor. In the preprocessor stage, the mega-trend diffusion (MTD) technique is employed to increase the samples of the minority class, thereby balancing the dataset. In the predictor stage, machine-learning approaches of K-nearest neighbor (KNN) and support vector machines (SVM) are used to develop hybrid MTD-SVM and MTD-KNN prediction models. MTD-SVM model has provided the best values of accuracy, G-mean and Matthew's correlation coefficient of 96.71%, 96.70% and 71.98% for cancer/non-cancer dataset, breast/non-breast cancer dataset and colon/non-colon cancer dataset, respectively. We found that hybrid MTD-SVM is the best with respect to prediction performance and computational cost. MTD-KNN model has achieved moderately better prediction as compared to hybrid MTD-NB (Naïve Bayes) but at the expense of higher computing cost. MTD-KNN model is faster than MTD-RF (random forest) but its prediction is not better than MTD-RF. To the best of our knowledge, the reported results are the best results, so far, for these datasets. The proposed scheme indicates that the developed models can be used as a tool for the prediction of cancer. This scheme may be useful for study of any sequential information such as protein sequence or any nucleic acid sequence.     

A data mining approach for machine fault diagnosis based on associated frequency patterns

Bearings play a crucial role in rotational machines and their failure is one of the foremost causes of breakdowns in rotary machinery. Their functionality is directly relevant to the operational performance, service life and efficiency of these machines. Therefore, bearing fault identification is very significant. The accuracy of fault or anomaly detection by the current techniques is not adequate. We propose a data mining-based framework for fault identification and anomaly detection from machine vibration data. In this framework, to capture the useful knowledge from the vibration data stream (VDS), we first pre-process the data using Fast Fourier Transform (FFT) to extract the frequency signature and then build a compact tree called SAFP-tree (sliding window associated frequency pattern tree), and propose a mining algorithm called SAFP. Our SAFP algorithm can mine associated frequency patterns (i.e., fault frequency signatures) in the current window of VDS and use them to identify faults in the bearing data. Finally, SAFP is further enhanced to SAFP-AD for anomaly detection by determining the normal behavior measure (NBM) from the extracted frequency patterns. The results show that our technique is very efficient in identifying faults and detecting anomalies over VDS and can be used for remote machine health diagnosis.     

Chemical Coupled PEDOT:PSS/Si Electrode: Suppressed Electrolyte Consumption Enables Long‑Term Stability

Huge volume changes of Si during lithiation/delithiation lead to regeneration of solid-electrolyte interphase(SEI)and consume electrolyte.In this article,γ-glycidoxypropyl trimethoxysilane(GOPS)was incorporated in Si/PEDOT:PSS electrodes to construct a flexible and conductive artificial SEI,effectively suppressing the consumption of electrolyte.The optimized electrode can maintain 1000 mAh g^−1 for nearly 800 cycles under limited electrolyte compared with 40 cycles of the electrodes without GOPS.Also,the optimized electrode exhibits excellent rate capability.The use of GOPS greatly improves the interface compatibility between Si and PEDOT:PSS.XPS Ar+etching depth analysis proved that the addition of GOPS is conducive to forming a more stable SEI.A full battery assembled with NCM 523 cathode delivers a high energy density of 520 Wh kg^−1,offering good stability.     

Asymptotic stability, ℓ2 gain,boundness analysis,and control synthesis for switched systems: a switching frequency approach

In this paper, several concepts of switching frequency are introduced to analyze the properties and performance of switched systems in infinite as well as finite‐time intervals. The observation is very motivating that different system properties and performances depend on different switching frequencies. Sufficient conditions ensuring asymptotic stability, ?₂ gain performance, and state boundness are derived on the basis of the notions of switching frequency, respectively. Then, on the basis of the analysis results, the control synthesis problems are addressed. LMI‐based design algorithms are proposed to meet different control synthesis requirements. Numerical design examples are provided to demonstrate our results. Copyright © 2011 John Wiley & Sons, Ltd.     

Micromechanical Investigation of the Fatigue Crack Propagation and Damage Development in Al/Al2O3/SiC Hybrid Metal Matrix Composite

Russian Journal of Non-Ferrous Metals - In this study, the micro-mechanisms involved in fatigue crack propagation are investigated qualitatively in a Al/Al2O3/SiC hybrid metal matrix composite...     

Mathematical Model for Isothermal Wire-Coating From a Bath of Giesekus Viscoelastic Fluid

A mathematical model of wire-coating based on Giesekus constitutive equation is analyzed under isothermal conditions. It is desired to see the functional dependence of Giesekus model parameters on the important operating variables in the wire-coating process, which include volumetric flow rate (later referred to as flow rate), shear stress at the wire surface (later referred to as shear stress), force required for pulling the wire (later referred to as force), and radius of the coated wire (later referred to as coated wire thickness). To this end, the equation governing the laminar, incompressible, and rectilinear flow is first derived and then solved analytically for the case of vanishing axial pressure gradient. A numerical procedure is described to obtain the solution for the case of nonvanishing pressure gradient. Our results indicate that the magnitude of shear stress and force follow a decreasing trend with increasing Giesekus model parameters in both cases. The flow rate and coated wire thickness decrease on increasing the Giesekus model parameters when there is no imposed pressure gradient. However, in the presence of pressure gradient these variables first decrease with increasing Giesekus model parameters and then follow an increasing trend.