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With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

While aiming to create methods for fibre recycling, the question of colours in waste textiles is also in focus; whether the colour should be kept or should be removed while recycling textile fibre. More knowledge is needed for colour management in a circular economy approach.

The research included the use of different dye types in a cotton dyeing process, the process for decolourizing and the results. Two reactive dyes, two direct dyes and one vat dye were used in the study. Four chemical treatment sequences were used to evaluate colour removal from the dyed cotton fabrics, namely, HCE-A, HCE-P-A, HCE-Z-P-A and HCE-Y-A.

The objective was to evaluate how different chemical refining sequences remove colour from direct, reactive and vat dyed cotton fabrics, and how they influence the specific cellulose properties. Dyeing methods and the used refining sequences influence the degree of colour removal. The highest achieved final brightness of refined cotton materials were between 71 and 91 per cent ISO brightness, depending on the dyeing method used.

Only cotton fibre and three different colour types were tested.

With cotton waste, it appears to be easier to remove the colour than to retain it, especially if the textile contains polyester residues, which are desired to be removed in the textile refining stage.

Colour management in the CE context is an important new track to study in the context of the increasing amount of textile waste used as a raw material.

The paper aims to investigate the comfort-related performances of an innovative solar shading solution based on a new composite patented material that consists of a cement-based matrix coupled with a stretchable three-dimensional textile. The paper’s aim is, through a performance-based generative design approach, to develop a high-performance static shading system able to guarantee adequate daylit spaces, a connection with the outdoors and a glare-free environment in the view of a holistic and occupant-centric daylight assessment.

The paper describes the design and simulation process of a complex static shading system for digital manufacturing purposes. Initially, the optical material properties were characterized to calibrate radiance-based simulations. The developed models were then implemented in a multi-objective genetic optimization algorithm to improve the shading geometries, and their performance was assessed and compared with traditional external louvres and overhangs.

The system developed demonstrates, for a reference office space located in Milan (Italy), the potential of increasing useful daylight illuminance by 35% with a reduced glare of up to 70%–80% while providing better uniformity and connection with the outdoors as a result of a topological optimization of the shape and position of the openings.

The paper presents the innovative nature of a new composite material that, coupled with the proposed performance-based optimization process, enables the fabrication of optimized shading/cladding surfaces with complex geometries whose formability does not require ad hoc formworks, making the process fast and economic.

The purpose of this paper is to study the effects of sports sponsorship on brand equity using two managerially related outcomes: price premium and market share.

This study uses a best–worst discrete choice experiment (BWDCE) and compares the outcome with that of the purchase intention scale, an established probabilistic measure of purchase intention. The total sample consists of 409 fans of three soccer teams sponsored by three different competing brands: Nike, Adidas and Puma.

With sports sponsorship, fans were willing to pay more for the sponsor’s product, with the sponsoring brand obtaining the highest market share. Prominent brands generally performed better than less prominent brands. The best–worst scaling method was also 35% more accurate in predicting brand choice than a purchase intention scale.

Future research could use the same method to study other types of sponsors, such as title sponsors or other product categories.

Sponsorship managers can use this methodology to assess the return on investment in sponsorship engagement.

Prior sponsorship studies on brand equity tend to ignore market share or fans’ willingness to pay a price premium for a sponsor’s goods and services. However, these two measures are crucial in assessing the effectiveness of sponsorship. This study demonstrates how to conduct such an assessment using the BWDCE method. It provides a clearer picture of sponsorship in terms of its economic value, which is more managerially useful.

During the last 20 years trade between Korea and Mexico has increased steadily, reaching $2.8 billion in 2003. This trade expansion will be further accelerated with the increase in intra-industry trade in the future. Mexico's chronic trade deficit against Korea, which is attributed to Korea's increasing investment in Mexico, has rather contributed to Mexico's exports to the American region.

As Mexico expands its FTA networks, Korean companies are having difficulties in accessing the Mexican market. Considering this situation and the economic benefits of a Korea-Mexico FTA for both countries, we should first expand cooperation between private sectors, which is expected to facilitate a formal discussion on the Korea-Mexico FTA.