Search results

Artificial intelligence (AI) methods, such as genetic algorithm (GA) and adaptive neuro-fuzzy inference system (ANFIS), are capable of providing superior solutions for the simulation and the modeling of complex problems. The purpose of this study is to estimate the dye and the silver nanoparticle (AgNP) concentrations of silver nanoparticle-treated silk fabrics by the aforementioned methods.

In this study, the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics were matched by using the GA technique based on spectrophotometric color matching. The ANFIS method was also used; this method is based on the grid partitioning algorithm across four different methods. The first and second methods are provided for dye concentration prediction, and the third and the fourth methods are given for AgNP concentration prediction.

The mean of absolute error and root mean square (RMS) of the best dye concentration prediction by the ANFIS method based on the second method are 0.087 and 0.103, respectively. In addition, the mean of the absolute error and the RMS of the best results for AgNP concentration prediction by the ANFIS method by using the third method is 0.002 and 0.003, respectively. The obtained results indicate that the performance of the ANFIS method is better than the GA method.

The simultaneous prediction of the color and the antimicrobial properties of silver nanoparticle-treated silk fabrics was performed by using the GA and the ANFIS. The suggested method led to acceptable accuracy for color and antibacterial matching.

This paper aims to investigate using scanner-based adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANNs) and polynomial regression methods for prediction of silver nanoparticles (AgNPs) and dye concentrations on AgNP-treated silk fabrics.

For estimation of the dye and AgNPs concentration using image processing, the silk fabrics were scanned under the condition of 200 pixels per inch. The red green blue (RGB) values of scanned images were obtained after applying the median filter. Then, the relationship between scanner RGB values and dye and AgNPs concentrations were obtained by using artificial intelligence methods such as ANFIS and ANNs.

The best result was achieved by the ANFIS system for calculation concentration of dye with 0.07% error and concentration of AgNPs with 0.008 (gr/l) error. The obtained results indicate that the performance of the ANFIS system method is better than the other methods.

Using a scanner-based artificial intelligence technique for prediction of nanosilver and dye content on silk fabric.

This study aimed to Simultaneous matching of color and antimicrobial properties of silk fabric treated with silver nanoparticle. The antimicrobial finishing using silver nanoparticles (AgNPs) is one of the most important finishing processes in the textile industry. Color matching is widely applied in the textile industry, but there has been a need for the prediction of AgNPs concentration for the matching of dyed silver-treated samples.

In this research, the silk fabrics were dyed with various concentrations of C.I. Acid Red 359 dye at 0.5, 1, 1.5 and 2 per cent (w/w). The dyed fabrics were then coated with AgNPs in several concentrations at 0.015, 0.030, 0.050, 0.100 and 0.250 ml/l. The prediction of dye and AgNPs concentrations were evaluated using single constant color matching and artificial neural network techniques.

The obtained results indicate that the accuracy of dye concentration prediction, as well as AgNPs concentration prediction, was improved by using a neural network method. Also, the correlation between actual and predicted dye and AgNPs concentrations in the best neural networks is more than the single constant color matching method.

Simultaneous antibacterial and color matching of nanosilver-treated fabric is novel. This method achieved acceptable accuracy for antibacterial and color matching.

This study aimed to use a scanner as a low-cost method for measuring the opacity of textile fabric. Textile fabrics must have specific ranges of opacity according to their uses for shirting, curtaining, etc. In this way, opacity is an important property in the textile industry. Conventionally, textile opacity is estimated using a spectrophotometer, which is an expensive method.

In this study a scanner was used as a low-cost method for measuring the opacity of textile fabric. The opacity was estimated by using red, green and blue (RGB) parameters of images of fabric against white and black background.

The accuracy of opacity estimation was improved by converting RGB into several color spaces. The best opacity estimation was obtained by using the XYZ color space. In addition, using a regression method, the best estimation was obtained by using a fourth-order polynomial regression with the LSLM color space.

The opacity of fabric has been measured by spectrophotometer, but in this study, the opacity of fabric was measured by scanner as a low cost device and also with novel and simple method. This method achieved acceptable accuracy for opacity estimation. The obtained result is comparable with spectrophotometer results.

The purpose of this paper is to control flow separation over a NACA 4415 airfoil by applying unsteady forces to the separated shear layers using dielectric barrier discharge (DBD) plasma actuators. This novel flow control method is studied under conditions which the airfoil angle of attack is 18°, and Reynolds number based on chord length is 5.5 × 10⁵.

Large eddy simulation of the turbulent flow is used to capture vortical structures through the airfoil wake. Power spectral density analysis of the baseline flow indicates dominant natural frequencies associated with “shear layer mode” and “wake mode.” The wake mode frequency is used simultaneously to excite separated shear layers at both the upper surface and the trailing edge of the airfoil (dual-position excitation), and it is also used singly to excite the upper surface shear layer (single-position excitation).

Based on the results, actuations manipulate the shear layers instabilities and change the wake patterns considerably. It is revealed that in the single-position excitation case, the vortices shed from the upper surface shear layer are more coherent than the dual-position excitation case. The maximum value of lift coefficient and lift-to-drag ratio is achieved, respectively, by single-position excitation as well as dual-position excitation.

The paper contributes to the understanding and progress of DBD plasma actuators for flow control applications. Further, this research could be a beneficial solution for the promising design of advanced low speed flying vehicles.

Purpose: Since its inception in 1987, the literature on the VUCA that represents the volatile, uncertain, complex, and ambiguous nature of the business environment has progressively increased. This study systematically evaluates the vast literature on the VUCA world. Since review-based studies have been criticised as biased, this study uniquely amalgamates bibliometric analysis with content analysis, thereby taking a research triangulation discourse.

Need of the Study: This study was conducted to consolidate the literature about the VUCA environment and uncover the foundational and emerging themes for future research agendas.

Design/Methodology/Approach: This study conducts a performance analysis and science mapping of 193 carefully selected articles (using PRISMA) published in various sources during 2012–2023. Articles for the study were procured from the SCOPUS database. While performance analysis focused on analysing publication and citation evolution, thematic evolution, leading publications, country publication analysis, and most relevant authors and sources. On the other hand, science mapping revealed conceptual structures (keyword plus co-occurrence analysis and thematic maps) and intellectual structures (co-authorship analysis). VOSviewer and Biblioshiny (R-tool for comprehensive science mapping) were used for this study’s choice of application.

Findings: This chapter concludes with future research agendas using content analysis of the ‘scope for the future research’ section of selected publications and bibliographic coupling (to unearth emerging themes).

Practical Implications: The work presented in this chapter will help the researchers gain a structured conceptual, intellectual, and social understanding of the vast literature on the VUCA environment.

The purpose of this paper is to examine the influence of entrepreneurial orientation on the network and exhibitor’s performance. The entrepreneurial orientation is seen as a highly competitive factor for the company, which can foster its trade fair business.

A survey-based quantitative approach was adopted, including a questionnaire (n = 362) applied to companies participating in trade fairs. To arrive at results, the study developed structural equations modeling techniques, using SPSS 24 and AMOS 20 software.

The study demonstrates positive impacts of entrepreneurial orientation on network capability and consequent exhibitor’s non-sales performance and exhibitor’s sales performance. A conceptual model is presented.

The study was carried out mainly on Portuguese companies, restricting its generalization. In addition, the exhibitor’s performance was measured based on the exhibitors’ level of satisfaction and not on real sales results.

The study offers a process which the results highlight such as innovativeness, proactivity, risk-taking, competitiveness and autonomy (dimensions of entrepreneurial orientation) as a mix of important ingredients for the exhibitor’s networking. The networking promotes intangible results (non-sales performance) that can generate sales (sales performance).

The study is the first research to apply entrepreneurial orientation in the trade fair context and it also presents a relationship between non-sales performance and sales performance.

The purpose of this study is to investigate the post–enterprise resource planning (ERP) issues and challenges in the context of Saudi Arabia. There is a lack of research in the context of developing countries regarding post-ERP implementation issues and challenges. The high failure rate of the ERP system is a reflection of many management issues that occurred at different phases of ERP implementation. Previous research indicated that even after a successful implementation, the ERP system was unable to sustain itself in the organizations.

An exploratory case study design was adopted to proceed with this research process at two organizations in Saudi Arabia. The interviews of the top and middle management are conducted and transcribed. These case studies were further analyzed using the Creswell approach to generate several themes, and descriptions provided a deeper understanding of the post ERP implementation issues and challenges.

Research findings show that for successful ERP implementation, identified factors are top management support, integration, strategy, employee resistance, BPR, change management, vendor selection, team formation and culture. Moreover, factors for the post-ERP implementation that led to sustainability are top management support, training, system adoption, system testing, data migration, cost overrun, employee retention and post-implementation support.

This study is unique in its type to examine the issues and challenges organizations face after deploying ERP initiatives. This research's findings were useful and supportive for the senior management interested in successfully sustaining such an initiative in the organization.

The primary aim of the current study is to validate the theoretical model presented by Demir (2021a, b, c) via empirical evidence. In this respect, the study intends to offer a holistic pathway for obtaining both external and internal advantages from the implementation of ISO 9001, with a particular emphasis on developing countries.

The research utilized an empirical methodology to investigate the research objective. Data were collected from various sources, including employees and managers of the various companies. In this context, a model was developed, and 170 data points were collected from businesses in the Kurdistan region of Iraq in order to test the hypotheses in this model.

After the collected data passed the reliability and validity test, the hypotheses designed were tested by applying the partial least squares method. When the results were examined, it was observed that organizational culture and organizational learning variables were the two key antecedents that these factors would emerge after applying ISO 9001 practices with discipline for a certain period of time and that organizational benefits could be obtained only as a result of them. Detailed results and suggestions were given to managers and theorists as a result of the analysis.

This study contributes to the literature as an empirical application of the theoretical work written by Demir in 2021. Demir's work, which produced a theoretical concept based on many empirical and theoretical studies done before, still needs much more empirical work in developing countries.

This study aims to comprehend the application of analytics in the supply chain during the ongoing COVID-19 crisis and identify the emerging themes.

The author downloaded a list of research articles on the application of analytics to the supply chain from SCOPUS, conducted a systematic literature review for exploratory analysis and proposed a framework. Notably, the author used the topic modeling technique to identify research themes published during the ongoing COVID-19 crisis and thereby underscore some future research directions.

The author found that artificial intelligence, machine learning, internet of thing and blockchain are trending topics. Additionally, the author identified five themes by topic modeling, including the theme “Social Media information in Supply chain.”

The results were derived from a data set extracted from SCOPUS. Thus, the author excluded all studies not listed in SCOPUS from the analysis. Future research with articles indexed in other databases should be investigated to get a more holistic perspective of specific themes.

This study provides a deeper understanding and proposes a framework for applications of analytics in the supply chain that researchers could use for future research and industry practitioners to implement in their organizations to make a more sustainable and resilient supply chain.

This study provides exploratory information from published articles on the use of analytics in the supply chain during the COVID-19 crisis and generates themes that help understand the emerging and underpinned area of research.