Mechanical and optical properties in precipitated regions of alumina‐rich magnesium aluminate spinel

The toughening and strengthening of transparent ceramics is challenging because microstructural alterations typically lead to light scattering. Here, controlled precipitation of α‐Al₂O₃ from nonstoichiometric spinel is explored to demonstrate unique control over the evolution of second phase Al₂O₃ and how the microstructure might be altered to enhance fracture toughness while minimizing light scatter. Alumina‐rich magnesium aluminate spinel, MgO·nAl₂O₃, where n=2, was hot pressed and HIPed to produce fully dense, single‐phase material. The material was then heat treated in air at 1573 K for up to 20 hours to create a two‐phase spinel‐Al₂O₃ composite. The fracture toughness varies from 0.88 to 2.47 MPa√m depending on the microstructure; enhanced toughness at the surface was due to increased crack tortuosity at phase boundaries, but residual tensile stresses were observed in the interior of the material. Precipitation causes local volume contraction and the formation of porosity, decreasing optical transmission, especially for heat treatment times longer than 5 hours.     

Photoinduced operation and diagnostic of superconductivity in the MgB2 films

The occurrence of the optical second harmonic generation (SHG) at fundamental wavelength of Er:glass nanosecond 1,540 nm stimulated by two coherent CO₂ microsecond laser beams at wavelengths 10.6 and 5.3 μm was observed for the MgB₂ films. The SHG signal has shown its sensitivity to the occurrence of the superconducting (SC) phase transition. The SHG achieves its maximum at temperatures higher with respect to the SC transition. The effect is reversible with respect to the photo induced treatment. After the switching off of the external photoinducing treatment the effect almost disappears. However, this still strongly affects the SC properties of the MgB₂ thin layers, as the critical current is increased by 50 % after illumination.     

Fabrication of High Performance PM Nanocrystalline Bulk AA2124

AA2124 nanopowders <100 nm in particle size and 20 nm internal structure produced by high energy ball milling of gas-atomized micronpowders ~45 μm in particle size and 700 nm internal structure were processed in to bulk rods. The micro- and nanopowders were hot compacted using uniaxial pressing for preliminary densification at 0.7T _m of the alloy. Selected intact hot compacts (HCs) were promoted for warm severe plastic deformation via equal channel angular pressing (ECAP) at the minimum possible deforming temperature for final densification. Effect of the fabrication method of the consolidated powders was investigated. A combined processing via HC/ECAP produced bulk nanostructured rods 2.5 μm and 50-60 nm in grain size for the micro- and nanopowder consolidates, respectively. The powder properties controlled the degree of densification and mechanical behavior during the hot compaction stage, which influenced strongly the deformation behavior during subsequent ECAP. At the end of HC/ECAP one pass, the HC stage was responsible for about 83 and 95% of the total grain coarsening encountered for the micro- and nanopowder HCs, respectively. Throughout the various consolidation stages employed, the ball-milled (BM) nanopowder consolidates exhibited 2/3 the grain growth and displayed almost twice the hardness and compressive strength values of the gas-atomized micronpowder ones. Influence of BM and HC on the Al₂O₃ layer formed around the individual powder particles was also investigated.     

SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters

Clustering is one of the fundamental data mining tasks. Many different clustering paradigms have been developed over the years, which include partitional, hierarchical, mixture model based, density-based, spectral, subspace, and so on. The focus of this paper is on full-dimensional, arbitrary shaped clusters. Existing methods for this problem suffer either in terms of the memory or time complexity (quadratic or even cubic). This shortcoming has restricted these algorithms to datasets of moderate sizes. In this paper we propose SPARCL, a simple and scalable algorithm for finding clusters with arbitrary shapes and sizes, and it has linear space and time complexity. SPARCL consists of two stages—the first stage runs a carefully initialized version of the Kmeans algorithm to generate many small seed clusters. The second stage iteratively merges the generated clusters to obtain the final shape-based clusters. Experiments were conducted on a variety of datasets to highlight the effectiveness, efficiency, and scalability of our approach. On the large datasets SPARCL is an order of magnitude faster than the best existing approaches.     

Eco-friendly printing of natural fabrics using natural dyes from alkanet and rhubarb

In the present work, the suitability of printing natural fabrics (wool, silk, cotton and flax) with two natural dyes (alkanet and rhubarb) using pigment-printing technique has been investigated. The effect of different factors, i.e. dye concentration, nature of thickening agent, type of fixation, concentration and type of mordant, has been studied. The printed goods were evaluated by measuring the K/S value and the overall fastness properties. Results show that the highest K/S value was obtained by using Meypro gum as a thickener. The K/S increases rapidly as the concentration of the natural dye powder in the printing paste increases from 10 to 40 g/kg printing paste. Moreover, results show that the printed goods, which were fixed via steaming, have relatively higher colour strength than their corresponding samples fixed via thermofixation. The effect of mordants on colour development was also studied and alkanet dye was chosen as an example for this investigation. The best results were obtained by using mordant at a concentration of 20 g/kg printing paste. Different colour yields could be obtained by using different mordants, and all of colour fastness results were ranging between very well and excellent.     

Artificial Neural Network modeling of a hydrogen dual fueled diesel engine characteristics: An experiment approach

The rapid growth of vehicular pollution; mostly running on the diesel engine, emissions emerging are the concerns of the day. Owing to clean burn characteristics features, Hydrogen (H₂) as a fuel is the paradigm of the researcher. Extensive research presented in the literature on H₂ dual fueled diesel engine reveals, the significant role of H₂ in reducing emissions and enhancing the performance of a dual fueled diesel engine. With meager qualitative experiment data, the feasibility to develop an efficient Artificial Neural Network (ANN) model is investigated, the developed model can be utilized as a tool to investigate the H₂ dual fueled diesel engine further. In the process of developing an ANN model, engine load and H₂ flow rate are varied to register performance and emission characteristics. The creditability of the experiment is ascertained with uncertainty analysis of measurable and computed parameters. Leave-out-one method is adopted with 16 data sets; seven training algorithms are explored with eight transfer function combinations to evolve a competent ANN model. The efficacy of the developed model is adjudged with standard benchmark statistic indices. ANN model trained with Broyden, Fletcher, Goldfarb, & Shanno (BFGS) quasi-Newton backpropagation (trainbfg) stand out the best among other algorithms with regression coefficient ranging between 0.9869 and 0.9996.     

Blind information-theoretic multiuser detection algorithms for DS-CDMA and WCDMA downlink systems

Code division multiple access (CDMA) is based on the spread-spectrum technology and is a dominant air interface for 2.5G, 3G, and future wireless networks. For the CDMA downlink, the transmitted CDMA signals from the base station (BS) propagate through a noisy multipath fading communication channel before arriving at the receiver of the user equipment/mobile station (UE/MS). Classical CDMA single-user detection (SUD) algorithms implemented in the UE/MS receiver do not provide the required performance for modern high data-rate applications. In contrast, multi-user detection (MUD) approaches require a lot of a priori information not available to the UE/MS. In this paper, three promising adaptive Riemannian contra-variant (or natural) gradient based user detection approaches, capable of handling the highly dynamic wireless environments, are proposed. The first approach, blind multiuser detection (BMUD), is the process of simultaneously estimating multiple symbol sequences associated with all the users in the downlink of a CDMA communication system using only the received wireless data and without any knowledge of the user spreading codes. This approach is applicable to CDMA systems with relatively short spreading codes but becomes impractical for systems using long spreading codes. We also propose two other adaptive approaches, namely, RAKE-blind source recovery (RAKE-BSR) and RAKE-principal component analysis (RAKE-PCA) that fuse an adaptive stage into a standard RAKE receiver. This adaptation results in robust user detection algorithms with performance exceeding the linear minimum mean squared error (LMMSE) detectors for both Direct Sequence CDMA (DS-CDMA) and wide-band CDMA (WCDMA) systems under conditions of congestion, imprecise channel estimation and unmodeled multiple access interference (MAI).     

Economic efficiency of coordinated multilateral trades in electricity markets

This paper presents economic efficiency evaluation of electricity markets operating on the basis of a coordinated multilateral trading concept. The evaluation accounts for the overall costs of power generation, network losses, and system and unit constraints. We assume a non-collusive oligopolistic competition. An iterative Cournot model is used to characterize the competitive behavior of suppliers. A supplier maximizes the profit of each of his generating units while taking rivals' generation as given. Time span is over multiple hours. This leads to a mixed integer non-linear programming problem. We use the augmented Lagrangian approach to solve iteratively for globally optimal schedules. An IEEE 24-bus, 8-supplier, and 17-customer test system is used for illustration. The results show that such a market at times of light demands exhibits little market power, and at times of large demands exhibits a great deal of market power. This contrasts with the PCMI and HHI concentration measures, which give fixed measurement values of market power. The results of two-year (730 round) market simulations show a range of deadweight efficiency loss between 0.9 and 6% compared to that of PoolCo which results in a range between 0.5 and 10% for the same test case.