Structure-property relations for a phase-pure,nanograined tetragonal zirconia ceramic stabilized with minimum CaO doping

Dense (~97%) CaO-stabilized ZrO₂ ceramic was stabilized with minimum (3 mol%) doping (reported to date) and processed via conventional sintering at a low temperature (~1200°C); compositional analysis via X-ray florescence confirmed the CaO doping accuracy. Phase-pure tetragonal structure (characterized via both X-ray diffraction and Raman spectroscopy) along with uniform nanograins (90 nm) of the ceramic ensured the evolution of no monoclinic phase even after vigorous low-temperature degradation experiments (both thermal and hydrothermal aging for 80-100 h). The sintered ceramic recorded a high hardness (~15 GPa); the indentation toughness value was also comparable to a 3 mol% yttria-stabilized zirconia system. The remarkable structure–property correlations in the 3 mol% CaO-stabilized ZrO₂ ceramic suggests that the same may be worth examining for suitable future applications (e.g., in dental ceramics).     

Hydrothermal synthesis of multiwalled TiO2 nanotubes and its photocatalytic activities for Orange II removal

The solvothermal synthesis of multiwalled TiO₂ nanotubes (MWTNTs) with the reaction of titanium dioxide (TiO₂) nanoparticles and sodium hydroxide aqueous solution at moderate temperature is presented. The kinetic and isotherm studies were investigated for Orange II removal mechanism. MWTNTs were prepared in length 70–200 nm, average diameter 4–10 nm, and inter-shell spacing 0.78 nm. Different characterizations were performed to confirm anatase and mesoporous structure of MWTNTs. An improvement in properties as compared to commercial TiO₂ was observed; specific surface area 244.81 m²/g and band gap 3.0 eV. An excellent photocatalytic activity for Orange II removal was exhibited using synthesized MWTNTs.     

Intensification of Freeze-Drying Rate of Bacillus subtilis MTCC 2396 Using Tungsten Halogen Radiation: Optimization of Moisture Content and α-Amylase Activity

A novel freeze-drying protocol has been explored to render fast and cost-effective freeze drying of hyperamylase producing Bacillus subtilis MTCC2396 employing a tungsten halogen lamp radiator (THLR) as a heat source. Response surface methodology assessed the maximum reduction in moisture content (96.07%) and minimum reduction in α-amylase (EC 3.2.1.1) activity (1.02%) in 4 h drying time at 42.5°C radiation temperature. α-amylase activity (0.046 U) and final moisture content (3.93%) of the optimally freeze-dried bacterial strain appeared satisfactory. The freeze-drying time using THLR (4 h) is remarkably lower compared to that under a conventional conductive plate heater (CPH) (10 h) at otherwise identical conditions. The higher effective moisture diffusivity of 0.0052 to 0.0078 m ²/s under THLR compared to 0.00084 to 0.0015 m ²/s under CPH (corresponding to 20–50°C) advocated the superiority of the THLR heating protocol. The higher efficacy of THLR was also evidenced through lower activation energy (8.42 kJ/mol) of moisture diffusion compared to that (12.051 kJ/mol) of CPH. The optimally freeze-dried bacteria demonstrated the same growth rate in addition to exhibiting excellent retention of bioremedial (Hg²⁺ removal) activity to that of the control.     

An adaptive hybrid surrogate model

The determination of complex underlying relationships between system parameters from simulated and/or recorded data requires advanced interpolating functions, also known as surrogates. The development of surrogates for such complex relationships often requires the modeling of high dimensional and non-smooth functions using limited information. To this end, the hybrid surrogate modeling paradigm, where different surrogate models are combined, offers an effective solution. In this paper, we develop a new high fidelity surrogate modeling technique that we call the Adaptive Hybrid Functions (AHF). The AHF formulates a reliable Crowding Distance-Based Trust Region (CD-TR), and adaptively combines the favorable characteristics of different surrogate models. The weight of each contributing surrogate model is determined based on the local measure of accuracy for that surrogate model in the pertinent trust region. Such an approach is intended to exploit the advantages of each component surrogate. This approach seeks to simultaneously capture the global trend of the function as well as the local deviations. In this paper, the AHF combines four component surrogate models: (i) the Quadratic Response Surface Model (QRSM), (ii) the Radial Basis Functions (RBF), (iii) the Extended Radial Basis Functions (E-RBF), and (iv) the Kriging model. The AHF is applied to standard test problems and to a complex engineering design problem. Subsequent evaluations of the Root Mean Squared Error (RMSE) and the Maximum Absolute Error (MAE) illustrate the promising potential of this hybrid surrogate modeling approach.     

Concurrent surrogate model selection (COSMOS): optimizing model type,kernel function,and hyper-parameters

This paper presents an automated surrogate model selection framework called the Concurrent Surrogate Model Selection or COSMOS. Unlike most existing techniques, COSMOS coherently operates at three levels, namely: 1) selecting the model type (e.g., RBF or Kriging), 2) selecting the kernel function type (e.g., cubic or multiquadric kernel in RBF), and 3) determining the optimal values of the typically user-prescribed hyper-parameters (e.g., shape parameter in RBF). The quality of the models is determined and compared using measures of median and maximum error, given by the Predictive Estimation of Model Fidelity (PEMF) method. PEMF is a robust implementation of sequential k-fold cross-validation. The selection process undertakes either a cascaded approach over the three levels or a more computationally-efficient one-step approach that solves a mixed-integer nonlinear programming problem. Genetic algorithms are used to perform the optimal selection. Application of COSMOS to benchmark test functions resulted in optimal model choices that agree well with those given by analyzing the model errors on a large set of additional test points. For the four analytical benchmark problems and three practical engineering applications – airfoil design, window heat transfer modeling, and building energy modeling – diverse forms of models/kernels are observed to be selected as optimal choices. These observations further establish the need for automated multi-level model selection that is also guided by dependable measures of model fidelity.     

Fabrication and measurements of dynamic response of an SOI based non-planar CMUT array

An SOI based hyperbolic paraboloid geometry capacitive micromachined ultrasonic transducer (CMUT) array has been fabricated to realize a frequency independent constant beamwidth broadband beamforming capability. Detailed fabrication and packaging techniques are presented along with measurement results on individual CMUT cells for transient and steady-state response using a Polytec laser Doppler vibrometer. The measurement results are compared with analytical and 3-D electromechanical finite element analysis using IntelliSuite?. The measured results are in excellent agreement with analytical and FEA results. Measurement results also show that the CMUTs exhibit a flat bandwidth within the specified frequency range. SEM inspection also shows very close agreement between mask features and fabricated geometry that ensures reproducibility of the device with a high degree of fidelity. The CMUT array has been designed for automotive blindspot detection application and works in the 113–167?kHz range.     

Colonic fold detection from computed tomographic colonography images using diffusion-FCM and level sets

Colon cancer is the second major cause of cancer related deaths in industrial nations. Computed tomographic colonography (CTC) has emerged in the last decade as a new less invasive colon diagnostic alternative to the usually practiced optical colonoscopy. The overall goal is to increase the effectiveness of virtual endoscopic navigation of the existing computer-aided detection (CAD) system. The colonic/haustral folds serve as important landmarks for various associated tasks in the virtual endoscopic navigation like prone–supine registration, colonic polyp detection and tenia coli extraction. In this paper, we present two different techniques, first in isolation and then in synergism, for the detection of haustral folds. Our input is volumetric computed tomographic colonography (CTC) images. The first method, which uses a combination of heat diffusion and fuzzy c-means algorithm (FCM), has a tendency of over-segmentation. The second method, which employs level sets, suffers from under-segmentation. A synergistic combination, where the output of the first is used as an input for the second, is shown to improve the segmentation quality. Experimental results are presented on digital colon phantoms as well as real patient scans. The combined method has a total erroneous (over-segmentation plus under-segmentation) detection of (6.5 ± 2)% of the total number of folds per colon as compared to (12.5 ± 5)% for the diffusion-FCM-based method and (11.5 ± 3)% for the level set-based method. The p-values obtained from the associated ANOVA tests indicate that the performance improvements are statistically significant.     

Fuzzy hypersemigroups

We introduce the notions of fuzzy hypersemigroup, fuzzy hypergroup, fuzzy hyperideal, homomorphism, hyper congruence, fuzzy homomorphism, fuzzy hypercongruence. The purpose of this note is the study of some characterization of fuzzy hypersemigroup, fuzzy hyperideal of a fuzzy hypersemigroup and homomorphism and hypercongruence on a hypersemigroup.     

A penalized batch-Bayesian approach to informative path planning for decentralized swarm robotic search

Autonomous Robots - Swarm-robotic approaches to search and target localization, where target sources emit a spatially varying signal, promise unparalleled time efficiency and robustness. With most...     

An effective method for classification with missing values

Classification is one of the most important tasks in machine learning with a huge number of real-life applications. In many practical classification problems, the available information for making object classification is partial or incomplete because some attribute values can be missing due to various reasons. These missing values can significantly affect the efficacy of the classification model. So it is crucial to develop effective techniques to impute these missing values. A number of methods have been introduced for solving classification problem with missing values. However they have various problems. So, we introduce an effective method for imputing missing values using the correlation among the attributes. Other methods which consider correlation for imputing missing values works better either for categorical or numeric data, or designed for a particular application only. Moreover they will not work if all the records have at least one missing attribute. Our method, Model based Missing value Imputation using Correlation (MMIC), can effectively impute both categorical and numeric data. It uses an effective model based technique for filling the missing values attribute wise and reusing then effectively using the model. Extensive performance analyzes show that our proposed approach achieves high performance in imputing missing values and thus increases the efficacy of the classifier. The experimental results also show that our method outperforms various existing methods for handling missing data in classification.