New terpenoid dyes, 3-arylazocamphor derivatives: Dyeing performance and fastness properties

The 3-arylazocamphor (I–VI) and 3-arylazocamphor-2-oxime (VII–XI) dyes were prepared, for use as azodisperse dyes by coupling camphor or camphoroxime respectively with the appropriate diazonium salt in cold ethanol containing sodium acetate solution (10%). The structure of these compounds (I–XI) was established by studying their elemental analysis, i.r. and u.v. spectra. The effect of the nature and position of substituent in the arylazo moiety on the colour, dyeing behaviour, rate of exhaustion and fastness properties when applied to protein and man-made fibres were discussed. The effect of after-treatment with different metal salts on the dyed woollen fibre was also investigated.     

Monte Carlo Estimation of a Joint Density Using Malliavin Calculus, and Application to American Options

We use the Malliavin integration by parts formula in order to provide a family of representations of the joint density (which does not involve Dirac measures) of (X_θ, X _{θ + δ}), where X is a d-dimensional Markov diffusion (d ≥ 1), θ > 0 and δ > 0. Following Bouchard et al. (2004), the different representations are determined by a pair of localizing functions. We discuss the problem of variance reduction within the family of separable localizing functions: We characterize a pair of exponential functions as the unique integrated-variance minimizer among this class of separable localizing functions. We test our method on the d-dimensional Brownian motion and provide an application to the problem of American options valuation by the quantization tree method introduced by Bally et al. (2002). MSC 1991 Subject Classifications: Primary 60H07 65C05, secondary 49-00     

System Design of Fast Pet Scanners Utilizing Time-of-Flight

Recent advances in PET designs have shown that a gain in signal-to-noise ratio can be expected by incorporating time-of-flight data in positron emission tomography over the conventional PET mode. It has also been shown that cesium fluoride (CsF) offers the potential of faster timing and high detection efficiency which would be required for a clinical scanner utilizing time-of-flight information. Our research with CsF and the results of a feasibility study of time-off-light positron emission tomography reconstruction have shown that, indeed, a significant improvement in image quality results from such an approach and that coincidence resolving times of less than 500 psec FWHM are easily achievable with CsF detectors. However, the design of fast tomographic systems with multiple detectors which maintain this fast coincidence timing poses a challenging technical problem. The solution to this problem requires a departure from the conventional mode of PET designs to a fast on-line microprocessor based system which is capable of compressing and correcting the data for timing differences, normalization and image function. Such a system is described in this paper and its advantages and disadvantages are discussed.     

Multi-objective optimization of a flexible slider-crank mechanism synthesis,based on dynamic responses

This work deals with a multi-body system synthesis. A flexible slider crank mechanism has been investigated as an illustrative application. The main interest is focused on the mechanism design variables’ identification based on its dynamic responses. Three responses have been involved such as the slider velocity, the slider acceleration and the mid-point transversal deflection of the flexible connecting rod. Each of these responses has been embroiled separately in a mono-objective optimization. Subsequently, the multi-objective optimization subsuming these responses has been established. Two different optimization methods have been studied namely the genetic algorithm (GA) and the particle swarm optimization (PSO) technique. It has been proved that the multi-objective optimization presents more accurate results beside the mono-objective optimization. Compared to the GA, the PSO is more powerful and is able to identify the mechanism design variable with better accuracy, in spite of the affordable computational time allowed with the GA optimization.     

Modelling the Psychological Impact of COVID-19 in Saudi Arabia Using Machine Learning

This article aims to assess health habits, safety behaviors, and anxiety factors in the community during the novel coronavirus disease (COVID-19) pandemic in Saudi Arabia based on primary data collected through a questionnaire with 320 respondents. In other words, this paper aims to provide empirical insights into the correlation and the correspondence between socio-demographic factors (gender, nationality, age, citizenship factors, income, and education), and psycho-behavioral effects on individuals in response to the emergence of this new pandemic. To focus on the interaction between these variables and their effects, we suggest different methods of analysis, comprising regression trees and support vector machine regression (SVMR) algorithms. According to the regression tree results, the age variable plays a predominant role in health habits, safety behaviors, and anxiety. The health habit index, which focuses on the extent of behavioral change toward the commitment to use the health and protection methods, is highly affected by gender and age factors. The average monthly income is also a relevant factor but has contrasting effects during the COVID-19 pandemic period. The results of the SVMR model reveal a strong positive effect of income, with R² values of 99.59%, 99.93% and 99.88% corresponding to health habits, safety behaviors, and anxiety.     

Mixture‐based clustering for count data using approximated Fisher Scoring and Minorization–Maximization approaches

The multinomial distribution has been widely used to model count data. To increase clustering efficiency, we use an approximation to the Fisher scoring algorithm, which is more robust regarding the choice of initial parameter values. Then, we use a novel approach to estimate the optimal number of components, based on minimum message length criterion. Moreover, we consider a generalization of the multinomial model obtained by introducing the Dirichlet as prior, yielding the Dirichlet Compound Multinomial (DCM). Even though DCM can address the burstiness phenomenon of count data, the presence of Gamma function in its density function usually leads to undesired complications. In this article, we use two alternative representations of DCM distribution to perform clustering based on finite mixture models, where the mixture parameters are estimated using the minorization–maximization framework. To evaluate and compare the performance of our proposed models, we have considered three challenging real‐world applications that involve high‐dimensional count vectors, namely, sentiment analysis, facial expression recognition, and human action recognition. The results show that the proposed algorithms increase the clustering efficiency of their respective models remarkably, and the best results are achieved by the second parametrization of DCM, which can accommodate over‐dispersed count data.     

Variational learning of hierarchical infinite generalized Dirichlet mixture models and applications

Data clustering is a fundamental unsupervised learning task in several domains such as data mining, computer vision, information retrieval, and pattern recognition. In this paper, we propose and analyze a new clustering approach based on both hierarchical Dirichlet processes and the generalized Dirichlet distribution, which leads to an interesting statistical framework for data analysis and modelling. Our approach can be viewed as a hierarchical extension of the infinite generalized Dirichlet mixture model previously proposed in Bouguila and Ziou (IEEE Trans Neural Netw 21(1):107–122, 2010). The proposed clustering approach tackles the problem of modelling grouped data where observations are organized into groups that we allow to remain statistically linked by sharing mixture components. The resulting clustering model is learned using a principled variational Bayes inference-based algorithm that we have developed. Extensive experiments and simulations, based on two challenging applications namely images categorization and web service intrusion detection, demonstrate our model usefulness and merits.