Adaptive neuro-fuzzy inference system multi-objective optimization using the genetic algorithm/singular value decomposition method for modelling the discharge coefficient in rectangular sharp-crested side weirs

In the present article, the adaptive neuro-fuzzy inference system (ANFIS) is employed to model the discharge coefficient in rectangular sharp-crested side weirs. The genetic algorithm (GA) is used for the optimum selection of membership functions, while the singular value decomposition (SVD) method helps in computing the linear parameters of the ANFIS results section (GA/SVD-ANFIS). The effect of each dimensionless parameter on discharge coefficient prediction is examined in five different models to conduct sensitivity analysis by applying the above-mentioned dimensionless parameters. Two different sets of experimental data are utilized to examine the models and obtain the best model. The study results indicate that the model designed through GA/SVD-ANFIS predicts the discharge coefficient with a good level of accuracy (mean absolute percentage error?=?3.362 and root mean square error?=?0.027). Moreover, comparing this method with existing equations and the multi-layer perceptron–artificial neural network (MLP-ANN) indicates that the GA/SVD-ANFIS method has superior performance in simulating the discharge coefficient of side weirs.     

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.     

Systematic Review: Targeted Molecular Imaging of Angiogenesis and Its Mediators in Rheumatoid Arthritis

Extensive angiogenesis is a characteristic feature in the synovial tissue of rheumatoid arthritis (RA) from a very early stage of the disease onward and constitutes a crucial event for the development of the proliferative synovium. This process is markedly intensified in patients with prolonged disease duration, high disease activity, disease severity, and significant inflammatory cell infiltration. Angiogenesis is therefore an interesting target for the development of new therapeutic approaches as well as disease monitoring strategies in RA. To this end, nuclear imaging modalities represent valuable non-invasive tools that can selectively target molecular markers of angiogenesis and accurately and quantitatively track molecular changes in multiple joints simultaneously. This systematic review summarizes the imaging markers used for single photon emission computed tomography (SPECT) and/or positron emission tomography (PET) approaches, targeting pathways and mediators involved in synovial neo-angiogenesis in RA.     

Light Spectral Composition Modifies Polyamine Metabolism in Young Wheat Plants

Although light-emitting diode (LED) technology has extended the research on targeted photomorphogenic, physiological, and biochemical responses in plants, there is not enough direct information about how light affects polyamine metabolism. In this study, the effect of three spectral compositions (referred to by their most typical characteristic: blue, red, and the combination of blue and red [pink] lights) on polyamine metabolism was compared to those obtained under white light conditions at the same light intensity. Although light quality induced pronounced differences in plant morphology, pigment contents, and the expression of polyamine metabolism-related genes, endogenous polyamine levels did not differ substantially. When exogenous polyamines were applied, their roborative effect were detected under all light conditions, but these beneficial changes were correlated with an increase in polyamine content and polyamine metabolism-related gene expression only under blue light. The effect of the polyamines on leaf gene expression under red light was the opposite, with a decreasing tendency. Results suggest that light quality may optimize plant growth through the adjustment of polyamine metabolism at the gene expression level. Polyamine treatments induced different strategies in fine-tuning of polyamine metabolism, which were induced for optimal plant growth and development under different spectral compositions.     

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.

     

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.