Photoelectrochemistry with quinone radical anions: Kinetics of homogeneous redox catalysis

Cyclic voltammograms of quinones were recorded in acetonitrile in the presence of various substrates: carbonyl compounds, halobenzenes, Methyl Viologen and Neutral Red. When illuminated with light of λ >410 nm, catalytic waves were observed. From the ratio of the catalysed to uncatalysed peak current, electron transfer rate constants were calculated using the working curves of Saveant and coworkers. The values of these rate constants were compared with the values obtained by Shukla and Rusling for different systems using a similar method and with quenching rate constants calculated using Rehm-Weller-Marcus theory.

Combining Bio- and Chemo-Catalysis for the Conversion of Bio-Renewable Alcohols: Homogeneous Iridium Catalysed Hydrogen Transfer Initiated Dehydration of 1,3-Propanediol to Aldehydes

Combining whole cell biocatalysis and chemocatalysis in a single reaction sequence avoids unnecessary separations, and the associated waste and energy consumption. Bacterial fermentation has been employed to convert waste glycerol from biodiesel production into 1,3-propanediol. This 1,3-propanediol can be extracted selectively from the aqueous fermentation broth using ionic liquids. 1,3-propanediol in ionic liquid solution was converted to propanal by hydrogen transfer initiated dehydration (HTID) catalysed by a Cp*IrCl2(NHC) (Cp* = pentamethylcyclopentadienyl; NHC = carbene ligand) complex. The use of an ionic liquid solvent enabled the reaction to be performed under reduced pressure, facilitating the isolation of the product, and improving the reaction selectivity. The Ir(III) catalyst in ionic liquid was found to be highly recyclable.

Detecting localized homogeneous anomalies over spatio-temporal data

The last decade has witnessed an unprecedented growth in availability of data having spatio-temporal characteristics. Given the scale and richness of such data, finding spatio-temporal patterns that demonstrate significantly different behavior from their neighbors could be of interest for various application scenarios such as – weather modeling, analyzing spread of disease outbreaks, monitoring traffic congestions, and so on. In this paper, we propose an automated approach of exploring and discovering such anomalous patterns irrespective of the underlying domain from which the data is recovered. Our approach differs significantly from traditional methods of spatial outlier detection, and employs two phases – i) discovering homogeneous regions, and ii) evaluating these regions as anomalies based on their statistical difference from a generalized neighborhood. We evaluate the quality of our approach and distinguish it from existing techniques via an extensive experimental evaluation.

Experimental Saltwater Intrusion in Coastal aquifers Using Automated Image Analysis: Applications to Homogeneous Aquifers.

This paper presents the applications of a novel methodology to quantify saltwater intrusion parameters in laboratory-scale experiments. The methodology uses an automated image analysis procedure, minimizing manual inputs and the subsequent systematic errors that can be introduced. This allowed the quantification of the width of the mixing zone which is difficult to measure in experimental methods that are based on visual observations. Glass beads of different grain sizes were tested for both steady-state and transient conditions. The transient results showed good correlation between experimental and numerical intrusion rates. The experimental intrusion rates revealed that the saltwater wedge reached a steady state condition sooner while receding than advancing. The hydrodynamics of the experimental mixing zone exhibited similar
traits; a greater increase in the width of the mixing zone was observed in the receding saltwater wedge, which indicates faster fluid velocities and higher dispersion. The angle of intrusion analysis revealed the formation of a volume of diluted saltwater at the toe position when the saltwater wedge is prompted to recede. In addition, results of different physical repeats of the experiment produced an average coefficient of variation less than 0.18 of the measured toe length and width of the mixing zone.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

Temperature modelling of an homogeneous medium using genetically selected RBF (LIC)

Temperature modelling of human tissue subjected to ultrasound for therapeutic use is essencial for an accurate instrumental assessment and calibration. In this paper punctual temperature modeling of a homogeneous medium, radiated by therapeutic ultrasound, is presented.

Transverse Vibrations in Beams Supported by a Piece-Wise Homogeneous Visco- Elastic Foundation

Transversal vibrations induced by a load moving uniformly along an infinite beam resting on a piece-wise homogeneous visco-elastic foundation are studied. Special attention is paid to the additional vibrations, conventionally referred to as transition radiations, which arise as the point load traverses the place of foundation discontinuity. The governing equations of the problem are solved by the normalmode analysis. The solution is expressed in a form of infinite sum of orthogonal natural modes multiplied by the generalized coordinate of displacement. The natural frequencies are obtained numerically exploiting the concept of the global dynamic stiffness matrix. This ensures that the frequencies obtained are exact. The methodology has restrictions neither on velocity nor on damping. The approach looks simple, though, the numerical expression of the results is not straightforward. A general procedure for numerical implementation is presented and verified. To illustrate the utility of the methodology parametric optimization is presented and influence of the load mass is studied. The results obtained have direct application in analysis of railway track vibrations induced by high-speed trains when passing regions with significantly different foundation stiffness.

Urban image classification with semisupervised multiscale cluster kernels

This paper presents a semisupervised support vector machine (SVM) that integrates the information of both labeled and unlabeled pixels efficiently. Method's performance is illustrated in the relevant problem of very high resolution image classification of urban areas. The SVM is trained with the linear combination of two kernels: a base kernel working only with labeled examples is deformed by a likelihood kernel encoding similarities between labeled and unlabeled examples. Results obtained on very high resolution (VHR) multispectral and hyperspectral images show the relevance of the method in the context of urban image classification. Also, its simplicity and the few parameters involved make the method versatile and workable by unexperienced users.

Unveiling Shi’i Religious Identities: Case Studies of Hijab in Culturally Homogeneous Canadian Schools

This qualitative case study explored 10 young female Shi’i Muslim Arabic-Canadian students’ experiences associated with wearing the Hijab (headscarf) within their home, community, and predominantly White Canadian public elementary school environments. The study integrated several bodies of scholarly theories in order to examine the data under a set of comprehensive lenses that more fully articulates and theorizes on the diversity of female Shi’i Muslim Canadian students’ experiences. These theories are: identity theories with a focus on religious identity and negative stereotypes associated with Muslims; feminism and the Hijab discourses; research pertaining to Muslims in school settings; and critical race theory. In order to readdress the dearth of information about Shi’is’ experiences in schools, this study provides an in-depth case study analysis in which the methodology strategies included 10 semi-structured in-depth interviews, 2 focus-group meetings, and the incorporation of the researcher’s fieldnotes. Data analysis revealed the following themes corresponding to participants’ experiences and values in their social worlds of home, community, and schools: (a) martyrdom and self-sacrifice as a means for social justice; (b) transformational meaning of the Hijab; (c) intersectionality between culture, religion, and gender; and (d) effects of visits “back home” on participants’ religious identities. Additional themes related to participants’ school experiences included: (a) “us versus them” mentality; (b) religious and complex secular dialogues; (c) absence of Muslim representations in monocultural schools; (d) discrimination; (e) remaining silent versus speaking out; and (f) participants’ strategies for preserving their identities. Recommendations are made to integrate Shi’i Muslim females’ identity within the context of Islam and the West, most notably in relation to: (a) the role of Muslim community in nondiverse settings as a space that advances and nurtures Shi’i Muslim identity; and (b) holistic and culturally responsive teaching that fosters respect of others’ religiosity and spirituality. This study makes new inroads into feminist theorizing by drawing conceptual links between these previously unknown connections such as the impact of the historical female exemplary role model and the ritual stories on the experiences of Muslim females wearing the Hijab.

Changes in Protein, Carbohydrate and lipid content of cashew kernels during storage under two humidity conditions

The changes occuring to cashew kernels during storage at two humidity levels - 80% to 20% with respect to organoleptic characteristics, protein content, carbohydrate content, oil content, iodine and peroxide values were studied. From the present study it is concluded that organoleptic characteristics of cashew kernels deteriorates with increase in humidity. Decrease in protein and carbohydrate content of stored cashew kernel is dependent on humidity. Humidity increased oxidative rancidification.

Effect of Antioxidants on the Shelf Life of Cashew Kernels

Cashew kernels have high nutritive value. Upon exposure to air kernels turn rancid and their nutritive value decreases. From this study it is concluded that chemical treatment using antioxidants reduced oxidative rancidity but failed to prevent deterioration in organoleptic characteristics and decrease in protein and carbohydrate content of stored kernels.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

A Homogeneous Set-Theoretical Frame for Clustering Fuzzy Relational Data

Our purpose is to provide a set-theoretical frame to clustering fuzzy relational data basically based on cardinality of the fuzzy subsets that represent objects and their complementaries, without applying any crisp property. From this perspective we define a family of fuzzy similarity indexes which includes a set of fuzzy indexes introduced by Tolias et al, and we analyze under which conditions it is defined a fuzzy proximity relation. Following an original idea due to S. Miyamoto we evaluate the similarity between objects and features by means the same mathematical procedure. Joining these concepts and methods we establish an algorithm to clustering fuzzy relational data. Finally, we present an example to make clear all the process

Fronts from complex two-dimensional dispersal kernels: theory and application to Reid's paradox

Bimodal dispersal probability distributions with characteristic distances differing by several orders of magnitude have been derived and favorably compared to observations by Nathan [Nature (London) 418, 409 (2002)]. For such bimodal kernels, we show that two-dimensional molecular dynamics computer simulations are unable to yield accurate front speeds. Analytically, the usual continuous-space random walks (CSRWs) are applied to two dimensions. We also introduce discrete-space random walks and use them to check the CSRW results (because of the inefficiency of the numerical simulations). The physical results reported are shown to predict front speeds high enough to possibly explain Reid's paradox of rapid tree migration. We also show that, for a time-ordered evolution equation, fronts are always slower in two dimensions than in one dimension and that this difference is important both for unimodal and for bimodal kernels