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This chapter investigates a new human-centric perspective oriented to consider society as a whole, by following a systemic vision, where social well-being is important: Industry 5.0. The analysis is based on the great advantages for the ecosystem, going beyond the custom of affirming technological development and managerial views, favoring the dialog between the actors of the system, the adaptation of technologies to the context in which one operates, choosing the most appropriate business strategies, sustaining smart, sustainable and inclusive solutions in the innovation process. In this sense, big data, internet of things, augmented realities, or robotics are all technologies to increase productivity and improve working conditions but, at the same time, they have to improve social life. To overcome the main related challenges and support the transition, new insights to design innovative strategies urge. Our research aims to explore the scientific literature about Industry 5.0 and its development in the European Union. We carried out a systematic review and a bibliometric analysis of the scientific production through the Web of Science (WoS) and SCOPUS databases. In addition, our study analyzes how the proposed topic has evolved over time by collecting contributions from different countries and authors. We adopted an innovative tool that goes beyond the traditional statistical methods generally used in bibliometric research, and it allowed us to obtain more advanced results. Our software permitted us to join the findings of two databases simultaneously, such as WoS and SCOPUS, to generate a bibliometric analysis and identify new paradigm trends.

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.

Peanut skins are an agro-waste product with no commercial value. The purpose of this paper is to evaluate peanut skin as a natural dyestuff and to determine if this natural dye could be used in the dyeing of some Egyptian cotton cultivars.

The methodology consists of several steps; dye extraction procedure from peanut skin through aqueous extraction, then dyeing optimized using simultaneous mordanting using alum. Finally, dyed cotton has been subjected to different textile laboratory tests, for example, color measurements and mechanical properties. Color-fastness was determined on Egyptian cotton fabric. The peanut skin as a source of natural dye and the dyed cotton sample were characterized by fourier transform infrared spectroscopy (FTIR) analysis.

It was found that the natural dye extracted from peanut skin has an affinity for cotton samples and showed high dyeability with a unique color shade, good color strength and very good fastness.

The novelty of this paper is the extraction of color from the peanut's outer skin which is discarded as waste such as agro-waste of the agricultural process which can be used as a natural dye in the textile industry and applied to dyeing some Egyptian cotton fibers from different genotypes.

This study aims to identify the bioactive compounds in the Onion (Allium burdickii [A.B]) bulb using Raman and Fourier transform infrared spectroscopy (FTIR) spectroscopy. It assessed the extraction conditions of bioactive compounds from A.B. while evaluating the best extraction conditions.

The research opted for an experimental qualitative approach. It examined the extraction conditions of A.B., namely, temperature (°C), time (min) and mass-to-liquor ratio (M:R) using ultraviolet-visible spectrophotometry. Identification of bioactive compounds present in the dye was performed using Raman spectroscopy and the validation of the results was done by FTIR spectroscopy.

The study determined the best extraction conditions (time, temperature, M:R) for A.B bulb. The study confirmed the presence of bioactive compounds.

The limitation was quantification of bioactive compounds in A.B bulb.

The findings prove that the A.B. bulb can provide a sustainable source of bioactive compounds (functionalized compounds). The study provides suitable extraction conditions for A.B. and further elaborates on the techniques for identifying bioactive compounds in A.B. bulb extracts.

The study provides A.B. as a source of bioactive compounds and a clean dye for textile coloration.

To the best of the authors’ knowledge, there is no documented study on the qualitative analysis of bioactive compounds in A.B using Raman and FTIR. Therefore, the study fulfils the identified need to ascertain alternative procedures for the analysis of bioactive compounds.

Solar cells could make textile-based wearable systems energy independent without the need for battery replacement or recharging; however, their laundry resistance, which is prerequisite for the product acceptance of e-textiles, has been rarely examined. This paper aims to report a systematic study of the laundry durability of solar cells embedded in textiles.

This research included small commercial monocrystalline silicon solar cells which were encapsulated with functional synthetic textile materials using an industrially relevant textile lamination process and found them to reliably endure laundry washing (ISO 6330:2012). The energy harvesting capability of eight textile laminated solar cells was measured after 10–50 cycles of laundry at 40 °C and compared with light transmittance spectroscopy and visual inspection.

Five of the eight textile solar cell samples fully maintained their efficiency over the 50 laundry cycles, whereas the other three showed a 20%–27% decrease. The cells did not cause any visual damage to the fabric. The result indicates that the textile encapsulated solar cell module provides sufficient protection for the solar cells against water, washing agents and mechanical stress to endure repetitive domestic laundry.

This study used rigid monocrystalline silicon solar cells. Flexible amorphous silicon cells were excluded because of low durability in preliminary tests. Other types of solar cells were not tested.

A review of literature reveals the tendency of researchers to avoid standardized textile washing resistance testing. This study removes the most critical obstacle of textile integrated solar energy harvesting, the washing resistance.