THE DYEING PROCESS OF KNITTED FABRICS AT DIFFERENT TEMPERATURES USING ULTRASOUND

The dyeing of knitted fabrics made from 100 % cellulose using on-line procedure vinyl sulfonic reactive dye, with or without ultrasound energy, is carried out in this paper. The impact of temperature has been observed. The dye exhaustion is monitored using the method of absorption spectrophotometry, and the quality control of the coloration is monitored using color measurements. The acting of ultrasound on coloration consistency, as well as on some mechanical characteristics has also been examined. All examples of the ultrasound dyeing process show greater dye exhaustion in comparison to the conventional procedure. In addition, all the samples, which have been dyed with the ultrasound energy at 40°C, are significantly darker and have deeper color in comparison with the referent sample. The temperature has a great influence on kinetic energy of the dye molecules, and therefore on the diffusion processes in the dyeing system. The exhaustion chart indicates that when the temperature is lower the exhaustion degree drops. However, all the samples dyed with the ultrasound energy have bigger exhaustion. Besides that, ultrasound energy contributes to warming up the processing environment, so the additional warm up with the electricity is unnecessary, unlike the conventional way of dyeing. Since the reactive dyes chemically connect themselves with the cellulose substrate and in that way form covalent connection, the dyed fabrics have good washing consistency. Analysis results indicate that the consistencies are identical regardless the applied dyeing procedure. In other words, the dyeing method using the ultrasound energy produces the dyed fabric of the same quality. After analyzing the results of breaking force and elongation at break of knitted fabrics, it is noticeable that there is no degradation of previously mentioned knitted fabrics features (horizontally and vertically) during the ultrasound wave’s activity

DYEING OF KNITTED MICRO-VISCOSE IN THE PRESENCE OF ULTRASOUND WITH DIFFERENT FREQUENCIES

In dyeing process, the object is to transport or diffuse dyes and chemicals into the fibre. Various novel processes, including ultrasound, are being introduced and studied as more environmentally friendly alternatives. Encouraging results have been reported for the use of ultrasound energy in dyeing processes of micro-viscose. The recent studies revealed major ultrasound applications advances: savings of processing time, energy, chemicals, as well as environmental protection. Influence of various ultrasound frequencies (40, 200 and 400 kHz) on dyeing of micro-viscose knitted fabrics, by a reactive dye has been reported in this work. A method of reflection spectrophotometry has been employed to record reemission curves of the colored compounds. A software packet has been employed to calculate CIELab colored coordinates. Then, a comparison has been made with samples colored by conventional procedure according to CIELab76 and CMC (2:1) criteria. The use ultrasound in textile dyeing processing offers many potential advantages. The results prove better dye exhaustion by ultrasound and consequently the better fixing. The exhaustion for the bifunctional dye (containing two vinylsulphone groups) reaches 71.75 % without ultrasound, and 83.69 % with 400 kHz ultrasound. The 40 kHz, 150 W ultrasound causes a cavitation of higher intensity, compared to 200 and 400 kHz ultrasounds. In this particular case, destruction of cavitation bubbles is very intensive. That is why a large amount of cavitation energy is being transformed into a heat, yielding the additional bath heating. The ultrasounds with higher frequencies (200 and 400 kHz) cannot use such a strong power. The applied powering this case reaches 0.6 W. The cavitation bubbles are now smaller the cavitation disintegration is not so strong, and the energy loss is much smaller, i.e. a smaller amount of energy has been transformed into a heat. An ultrasound of an equal power, but of higher frequency contributes to the somewhat higher exhaustion and fixing. The ultrasound dyeing produces much obscured colours, compared the standard. The differences are evident and not negligible. The comparison of the samples treated ultrasound of different frequencies during dyeing revealed the higher coulours intensities with the increase of ultrasound frequencies of the equal power (200 and 400 kHz). However, the increase is not so expressed

LOW COST REMOVAL OF DIRECT DYE FROM AQUEOUS SOLUTION USING WASTE ASHES

The paper deals with the adsorption of direct dye from aqueous solution onto waste ashes prepared from the heating station of Leskovac (Serbia). Waste ashes are used for the reduction of coloration, for example, of textile wastewater. Based on the results, it can be said that the waste ashes are an efficient adsorbent for the removal of direct dye from aqueous solution. Prolonged contact time means a greater amount of color on the adsorbent, i.e. the dye concentration in the solution decreases with the adsorption duration. The typical graphical representation obtained from the Freundlich equation shows that this model provides a sufficiently accurate description of the experimental data of dye adsorption on waste ashes as opposed to the Langmuir model

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables

Selection of the optimal technology for surface mining by multi-criteria analysis

The selection of optimal technology for surface mining of mineral deposits is a standarddecision-making problem. The aim of this paper is to demonstrate the potential of thecombined AHP and ELECTRE methods in selecting the optimal technology usingthe open pit coal mine “Ugljevik East” (Ugljevik Istok) as an example. In order toresolve the problems encountered, the three types of technologies were taken intoconsideration with regards to the eight criteria for selecting the optimal solution. Thecriteria include the most important aspects of selecting the optimal technologies, suchas geology and geotechnical engineering, ecology, economy, etc. In addition, AHP isused to analyse the structure of the technology selection process and to determine thesignificance and impact of certain criteria in the selection process, while ELECTREmethod is used for the final ranking of alternatives. The obtained results indicate thatthe proposed combined method provides extraordinary results and that it can be used toresolve various, even the most complex problems that occur in mining engineering.</p

The MCDM Model for Personnel Selection Based on SWARA and ARAS Methods

Competent employees are the key resource in an organization for achieving success and, therefore, competitiveness on the market. The aim of the recruitment and selection process is to acquire personnel with certain competencies required for a particular position, i.e.,a position within the company. Bearing in mind the fact that in the process of decision making decision-makers have underused the methods of making decisions, this paper aims to establish an MCDM model for the evaluation and selection of candidates in the process of the recruitment and selection of personnel based on the SWARA and the ARAS methods. Apart from providing an MCDM model, the paper will additionally provide a set of evaluation criteria for the position of a sales manager (the middle management) in the telecommunication industry which will also be used in the numerical example. On the basis of a numerical example, in the process of employment, theproposed MCDMmodel can be successfully usedin selecting candidates

Selection of candidates in the mining industry based on the application of the SWARA and the MULTIMOORA methods

The goal of this manuscript is to provide an efficient approach to the process of the recruitment and selection of candidates in the mining industry. The proposed approach is based on the use of MCDM models for personnel selection in the mining industry; the approach will provide an MCDM model for the personnel selection based on the SWARA ant the MULTIMOORA methods. The efficiency and usability of the proposed approach are considered on the numerical example of the selection of a candidate for the position of the mining engineer for underground minin