Exploratory analysis of brain connectivity with ICA.

Covariance-based methods of exploration of functional connectivity of the brain from functional magnetic resonance imaging (fMRI) experiments, such as principal component analysis (PCA) and structural equation modeling (SEM), require a priori knowledge such as an anatomical model to infer functional connectivity. In this research, a hybrid method, combining independent component analysis (ICA) and SEM, which is capable of deriving functional connectivity in an exploratory manner without the need of a prior model is introduced. The spatial ICA (SICA) derives independent neural systems or sources involved in task-related brain activation, while an automated method based on the SEM finds the structure of the connectivity among the elements in independent neural systems. Unlike second-order approaches used in earlier studies, the task-related neural systems derived from the ICA provide brain connectivity in the complete statistical sense. The use and efficacy of this approach is illustrated on two fMRI datasets obtained from a visual task and a language reading task.     

Realization of a temperature sensor using both two- and three-dimensional photonic structures through a machine learning technique

A temperature sensor based on photonic crystal structures with two- and three-dimensional geometries is proposed, and its measurement performance is estimated using a machine learning technique. The temperature characteristics of the photonic crystal structures are studied by mathematical modeling. The physics of the structure is investigated based on the effective electrical permittivity of the substrate (silicon) and column (air) materials for a signal at 1200 nm, whereas the mathematical principle of its operation is studied using the plane-wave expansion method. Moreover, the intrinsic characteristics are investigated based on the absorption and reflection losses as frequently considered for such photonic structures. The output signal (transmitted energy) passing through the structures determines the magnitude of the corresponding temperature variation. Furthermore, the numerical interpretation indicates that the output signal varies nonlinearly with temperature for both the two- and three-dimensional photonic structures. The relation between the transmitted energy and the temperature is found through polynomial-regression-based machine learning techniques. Moreover, rigorous mathematical computations indicate that a second-order polynomial regression could be an appropriate candidate to establish this relation. Polynomial regression is implemented using the Numpy and Scikit-learn library on the Google Colab platform.

     

Synthesis, Characterization and Catalytic Properties of Mesoporous Cobalt Aluminophosphate Molecular Sieves

Isomorphously substituted cobalt(II) hexagonal mesoporous aluminophosphate (CoHMA) molecular sieves were synthesized hydrothermally and characterized by various analytical and spectroscopic techniques. It was deduced that cobalt ions exhibit a divalent oxidation state in tetrahedral coordination in mesoporous aluminophosphates. Further, unlike cobalt-containing microporous aluminophosphate molecular sieves, Co(II) ions remain in a tetrahedral geometry even after calcination. The catalytic activity of CoHMA was tested for the cyclohexane oxidation reaction under mild conditions. Remarkable substrate conversion and product (cyclohexanol) selectivity were obtained compared to several previously reported heterogeneous catalysts.     

Studies on the Reduction of Coefficient of Variation: A Case Study

In a spinning mill, yarn is the final product. Linear density expressed in terms of count is one of the important characteristics of yarn. Because variability of textile strands increases as the linear density increases, the variability in the count is often measured in terms of coefficient of variation (CV)%. The yarn with a high CV% of count leads to a higher end breakage rate during the spinning and subsequent weaving/knitting operations, and consequently, results in lesser productivity and poorer appearance quality of the woven/knitted fabric. When this woven/knitted fabric is dyed, uneven shades are generated. Because the production of yarn involves processing of raw cotton in multimachines at multistages, possible sources that lead to a high CV% of count are many. Enrick's (1960) analysis procedure, which is based on the modification of the range method for analysis of variance, is used conventionally for detecting the stages where excessive “between-machine” differences are present. When the CV% of count is inflated due to the generation of systematic variation in any machine or introduction of high variability by any machine, this inflation remains undetected when using Enrick's procedure. The case study presented here demonstrates that a step-by-step analysis of linear densities of different stage-outputs starting from yarn to card sliver, using appropriate nested design models along with Duncan's multiple range test, is very useful in detecting all possible sources of a high CV% of count of yarn.     

Independent component analysis based on nonparametric density estimation

In this paper, we introduce a novel independent component analysis (ICA) algorithm, which is truly blind to the particular underlying distribution of the mixed signals. Using a nonparametric kernel density estimation technique, the algorithm performs simultaneously the estimation of the unknown probability density functions of the source signals and the estimation of the unmixing matrix. Following the proposed approach, the blind signal separation framework can be posed as a nonlinear optimization problem, where a closed form expression of the cost function is available, and only the elements of the unmixing matrix appear as unknowns. We conducted a series of Monte Carlo simulations, involving linear mixtures of various source signals with different statistical characteristics and sample sizes. The new algorithm not only consistently outperformed all state-of-the-art ICA methods, but also demonstrated the following properties: 1) Only a flexible model, capable of learning the source statistics, can consistently achieve an accurate separation of all the mixed signals. 2) Adopting a suitably designed optimization framework, it is possible to derive a flexible ICA algorithm that matches the stability and convergence properties of conventional algorithms. 3) A nonparametric approach does not necessarily require large sample sizes in order to outperform methods with fixed or partially adaptive contrast functions.     

Synthesis of nanocrystalline hydroxyapatite using surfactant template systems: Role of templates in controlling morphology

Hydroxyapatite (HA) nanopowder was synthesized by reverse microemulsion technique using calcium nitrate and phosphoric acid as starting materials in aqueous phase. Cyclohexane, hexane, and isooctane were used as organic solvents, and Dioctyl sulfosuccinate sodium salt (AOT), dodecyl phosphate (DP), NP5 (poly(oxyethylene)₅ nonylphenol ether), and NP12 (poly(oxyethylene)₁₂ nonylphenol ether) as surfactants to make the emulsion. Effect of synthesis parameters, such as type of surfactant, aqueous to organic ratio (A/O), pH and temperature on powder characteristics were studied. It was found that the surfactant templates played a significant role in regulating the morphology of the nanoparticle. Hydroxyapatite nanoparticle of different morphologies such as spherical, needle shape or rod-like were obtained by adjusting the conditions of the emulsion system. Synthesized powder was characterized using X-ray diffraction (XRD), BET surface area and transmission electron microscopy (TEM). Phase pure HA nanopowder with highest surface area of 121 m²/g were prepared by this technique using NP5 as a surfactant. Densification studies showed that this nanoparticle can give about 98% of their theoretical density. In vitro bioactivity of the dense HA compacts was confirmed by excellent apatite layer formation after 21 days in SBF solution. Cell material interaction study showed good cell attachment and after 5 days cells were proliferated on HA compacts in OPC1 cell culture medium. The results imply this to be a versatile approach for making hydroxyapatite nanocrystals with controlled morphology and excellent biocompatibility.     

Comparison of performances of five prospective approaches for the multi-response optimization

The Taguchi method of experimental design is widely used for optimization of process performance. However, this method has been developed to optimize single-response processes. But, in many situations, the engineers are required to determine the process settings that can simultaneously optimize multiple responses. In the recent past, researchers have proposed several systematic procedures for multi-response optimization. Most of these methods use complicated statistical/mathematical models and are, therefore, not easily comprehendible to the engineers who do not have a strong background in mathematics. Only a few methods, e.g. weighted signal-to-noise (WSN) ratio, Grey relational analysis, multiple-response signal-to-noise ratio, VIKOR (VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian), and weighted principal component methods, use relatively simpler procedures. In this paper, the computational procedures for these five methods are standardized. Three sets of experimental data are analyzed using these standardized procedures and the predicted optimization performances of the five methods are compared. The results show that no method can give better optimization than the WSN method.     

An alternative characterization of fuzzy complement functional

In this short note we propose a novel fuzzy complement functional. This functional is different from other functionals known in the literature. However, it turns out to be an alternative characterization of the well-known negation function. We sincerely thank the anonymous reviewer whose insightful suggestions have significantly improved the paper.     

The effect of blanching on quality attributes of dehydrated carrots during long-term storage

 The effect of blanching on the retention of β-carotene and ascorbic acid, and non-enzymatic browning (NEB) during storage of dehydrated carrot slices was studied. Blanched carrots contained higher β-carotene but lower ascorbic acid than their unblanched counterpart just after drying, whereas NEB was unaffected by blanching. During storage of dehydrated carrots a decrease in β-carotene and ascorbic acid content with an increase in NEB values was observed. Blanching was helpful in limiting the loss of quality parameters irrespective of storage and packaging conditions. Received: 22 May 2000 / Revised version: 11 October 2000     

RF/wireless interconnect for inter- and intra-chip communications

Recent studies showed that conventional approaches being used to solve problems imposed by hard-wired metal interconnects will eventually encounter fundamental limits and may impede the advance of future ultralarge-scale integrated circuits (ULSls). To surpass these fundamental limits, we introduce a novel RF/wireless interconnect concept for future inter- and intra-ULSI communications. Unlike the traditional “passive” metal interconnect, the “active” RF/wireless interconnect is based on low loss and dispersion-free microwave signal transmission, near-field capacitive coupling, and modem multiple-access algorithms. In this paper we address issues relevant to the signal channeling of the RF/wireless interconnect and discuss its advantages in speed, signal integrity, and channel reconfiguration. The electronic overhead required in the RF/wireless-interconnect system and its compatibility with the future ULSI and MCM (multi-chip-module) will be discussed as well