Effects of Fe/SDS and Au nanoparticles on P. aeruginosa bacterial growth and biosurfactant production

The aim of this study was to evaluate the effects of iron (Fe)/SDS and gold (Au) nanoparticles on growth and biosurfactant production of Pseudomonas aeruginosa PBCC5. The concentrations of the nanoparticles used were 1, 500 and 1000 mg/l. In this research, the surface tension of biosurfactant, dry weight of biosurfactant and biomass, emulsification indexes (E₂₄) were measured and transmission electron microscopy analysis was used to monitor the nanoparticles. The test results showed that the effect of nanoparticles on the bacterial growth and biosurfactant production varied corresponding to the type and concentration of nanoparticles. Fe/SDS nanoparticles showed no bacterial toxicity when the concentration of nanoparticles was 1 mg/ml and increased the growth and biosurfactant production, 23.21 and 20.73%, respectively. While at higher concentrations (500, 1000 mg/l), the nanoparticles suppressed bacterial growth as well as biosurfactant production. Similarly, Au nanoparticles had no bacterial toxicity and also increased bacterial growth and biosurfactant production. The surface tensions of all samples decreased from 72 of distiled water to 32–35 mN/m.Inspec keywords: nanoparticles, iron, gold, nanofabrication, nanomedicine, surfactants, biomedical materials, surface tension, renewable materials, transmission electron microscopy, microorganismsOther keywords: Au nanoparticles, P. aeruginosa bacterial growth, biosurfactant production, Pseudomonas aeruginosa PBCC5, surface tension, biomass, emulsification indexes, dry weight, transmission electron microscopy, Fe‐SDS nanoparticles, distiled water, Fe, Au     

Fuzzy Bayesian system reliability assessment based on Pascal distribution

The main purpose of this paper is to provide a methodology for discussing the fuzzy. Bayesian system reliability from the fuzzy component reliabilities, actually we discuss on the Fuzzy Bayesian system reliability assessment based on Pascal distribution, because the data sometimes cannot be measured and recorded precisely. In order to apply the Bayesian approach, the fuzzy parameters are assumed as fuzzy random variables with fuzzy prior distributions. The (conventional) Bayes estimation method will be used to create the fuzzy Bayes point estimator of system reliability by invoking the well-known theorem called ‘Resolution Identity’ in fuzzy sets theory. On the other hand, we also provide the computational procedures to evaluate the membership degree of any given Bayes point estimate of system reliability. In order to achieve this purpose, we transform the original problem into a nonlinear programming problem. This nonlinear programming problem is then divided into four sub-problems for the purpose of simplifying computation. Finally, the sub problems can be solved by using any commercial optimizers, e.g. GAMS or LINGO.     

Vocal-based emotion recognition using random forests and decision tree

This paper proposes a new vocal-based emotion recognition method using random forests, where pairs of the features on the whole speech signal, namely, pitch, intensity, the first four formants, the first four formants bandwidths, mean autocorrelation, mean noise-to-harmonics ratio and standard deviation, are used in order to recognize the emotional state of a speaker. The proposed technique adopts random forests to represent the speech signals, along with the decision-trees approach, in order to classify them into different categories. The emotions are broadly categorised into the six groups, which are happiness, fear, sadness, neutral, surprise, and disgust. The Surrey Audio-Visual Expressed Emotion database is used. According to the experimental results using leave-one-out cross-validation, by means of combining the most significant prosodic features, the proposed method has an average recognition rate of \(66.28\%\), and at the highest level, the recognition rate of \(78\%\) has been obtained, which belongs to the happiness voice signals. The proposed method has \(13.78\%\) higher average recognition rate and \(28.1\%\) higher best recognition rate compared to the linear discriminant analysis as well as \(6.58\%\) higher average recognition rate than the deep neural networks results, both of which have been implemented on the same database.     

A novel method for voice conversion based on non-parallel corpus

This article puts forward a new algorithm for voice conversion which not only removes the necessity of parallel corpus in the training phase but also resolves the issue of insufficiency of the target speaker’s corpus. The proposed approach is based on one of the new voice conversion models utilizing classical LPC analysis-synthesis model combined with GMM. Through this algorithm, the conversion functions among vowels and demi-syllables are derived. We assumed that these functions are rather the same for different speakers if their genders, accents, and languages are alike. Therefore, we will be able to produce the demi-syllables with just having access to few sentences from the target speaker and forming the GMM for one of his/her vowels. The results from the appraisal of the proposed method for voice conversion clarifies that this method has the ability to efficiently realize the speech features of the target speaker. It can also provide results comparable to the ones obtained through the parallel-corpus-based approaches.     

A participative end-user method for multi-perspective business process elicitation and improvement

A business process can be characterized by multiple perspectives (intentional, organizational, operational, functional, interactional, informational, etc). Business process modeling must allow different stakeholders to analyze and represent process models according to these different perspectives. This representation is traditionally built using classical data acquisition methods together with a process representation language such as BPMN or UML. These techniques and specialized languages can easily become hard, complex and time consuming. In this paper, we propose ISEA, a participative end-user modeling approach that allows the stakeholders in a business process to collaborate together in a simple way to communicate and improve the business process elicitation in an accurate and understandable manner. Our approach covers the organizational perspective of business processes, exploits the information compiled during the elicitation of the organizational perspective and touches lightly an interactional perspective allowing users to create customized interface sketches to test the user interface navigability and the coherence within the processes. Thus, ISEA can be seen as a participative end-user modeling approach for business process elicitation and improvement.     

Experimental investigation on corrosion inhibition of thiourea on aluminum surface in 0.5 M H2SO4 acid solution

Effect of thiourea on corrosion resistance of aluminum in 0.5 M H₂SO₄ solution is experimentally investigated. Results show that the addition of thiourea inhibits the sulfuric acid corrosion of aluminum. The inhibition efficiency can be enhanced with an increase in thiourea concentration. It is found that Rp values increases and C _dl values decreases with increasing inhibitor concentration. The inhibition happens through adsorption of thiourea on the metal surface without modifying the mechanism of corrosion process. Inhibition efficiency obtained from all methods is in good agreement. The adsorption of inhibitor on aluminum surface from 0.5 M H₂SO₄ acid solution obeys Langmuir adsorption isotherm.

     

Mathematical Modeling and Genetic Algorithm Optimization of Reactive Absorption of H2S

A rate‐based mathematical model was developed for the reactive absorption of H₂S in NaOH, with NaOCl or H₂O₂ as the chemical oxidant solutions in a packed column. A modified mass transfer coefficient in the gas phase was obtained by genetic algorithm and implemented in the model to correct the assumption of instantaneous reactions. The effects of different operating variables including the inlet H₂S concentration, inlet gas mass flux, initial NaOH, concentrations of the chemical oxidants in the scrubbing solutions, and liquid‐to‐gas ratio on the H₂S removal efficiency were studied. A genetic algorithm was employed to optimize the operating variables in order to obtain maximum removal efficiency of H₂S. The model results were in good agreement with the experimental data.     

Superior Tribological Properties of Particulate Aluminum Matrix Nano Composites

Aluminum is the best metal for producing metal matrix composites which are known as one of the most useful and high-tech composites in our world. Combining aluminum and nano Al₂O₃ particles will yield a material with high mechanical properties. Characterization of tribological properties revealed that the presence of nano particles significantly increased wear resistance of the composite. In case of unreinforced Al alloy, the depth of penetration is governed by the hardness of the specimen surface and applied load. But, in case of Al matrix composite, the depth of penetration of the harder asperities of hardened steel disk is primarily governed by the protruded hard ceramic reinforcement. The hard Al₂O₃ particles act as a protrusion over the matrix, carries a major portion of the applied load and protect the abrasives from penetration into the specimen surface.     

Multidimensional appraisal of customer relationship management: integrating balanced scorecard and multi criteria decision making approaches

This paper presents a comprehensive integrative framework for measuring the performance of customer relationship management (CRM) system based on a detailed empirical study of 32 Iranian internet service provider (ISP) firms. At first, by an extensive literature review and experts who have real practical experiences in the field of CRM, appropriate key performance indicators (KPIs) based on four perspectives of balanced scorecard have been extracted. Then, due to the interdependency and feedback among these KPIs, multiple criteria decision making techniques are used to evaluate CRM performance. Specifically, this study first applies the decision making trial and evaluation laboratory approach to determine the interrelated relationships among criteria, and to find the crucial central and influential factors. Then, the analytical network process method is used to obtain the criterion weights. Finally, according to these previous results, the technique for order of preference by similarity to ideal solution method is adopted to analyze the CRM performance of 32 Iranian ISP firms. The results of this study illustrated that learning and growth is the most crucial influential perspective and it would influence on the other perspectives much more. Furthermore, indicators including organization capital, human capital, customer retention process, customer perceived value, and customer expansion process play an essential role in succeeding of CRM. The results of this study can provide a comprehensive insight for managers into discerning how and through which mechanisms CRM can create merits for the organizations.