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Lightweight, open construction cotton knitted fabrics generally do not impart good protection from solar ultraviolet radiation (UVR). As lightweight 100% cotton single jersey is highly cherished for summerwear, it is sine qua non to understand the structural parameters that effectively strike a good balance between UV protection and thermophysiological comfort of the wearer. Relatively heavy fabrics protect from UVR, but comfort is compromised because of waning porosity, increase in thickness and thermal insulation. The purpose of this paper is to engineer knits that will bestow maximum UV protection while preserving the thermophysiological comfort of the wearer.

In total, 27 cotton single jersey fabrics with different areal densities and yarn counts were selected. Ultraviolet protection factor (UPF) was calculated based on the work of Imrith (2022). To précis, the authors constructed a UV box to measure the UPF of fabrics, denoted as UPF_B. UPF_B data were correlated with AATCC 183-2004 and yielded high correlation, R² 0.977. It was concluded that UPF 50 corresponds to UPF_B 94.3. Thermal comfort properties were measured on the Alambeta and water-vapour resistance on the Permetest. Linear programming (LP) was used to optimize UPF_B and comfort. Linear optimization focused on maximizing UPF_B while keeping the thermophysiological comfort and areal density as constraints.

The resulting linear geometrical and sensitivity analyses generated multiple technically feasible solutions of fabrics thickness and porosity that gave valid UPF_B, thermal absorptivity and water-vapour and thermal resistance. Subsequently, an interactive optimization software was developed to predict the stitch length, tightness factor and yarn count for optimum UPF_B from a given areal density. The predicted values were then used to knit seven 100% cotton single jersey fabrics and were tested for UV protection. All seven fabrics gave UPF_B above the threshold, that is, higher than 94.3. The mathematical model demonstrated good correlations with the optimized parameters and experimental values.

The optimization software predicted the optimum UPF_B reasonably well, starting from the fabric structural and constructional parameters. In addition, the models were developed as interactive user interfaces, which can be used by knitted fabric developers to engineer cotton knits for maximizing UV protection without compromising thermophysiological comfort. It has been demonstrated that LP is an efficient tool for the optimization and prediction of targeted knitted fabrics parameters.

The thermophysiological comfort of fabrics is prerequisite as customers covet adequate moisture, heat management-supported and UV protective clothing that measure up to their levels of activities and environmental conditions. Hitherto, scant tasks have been reported with the purpose of engineering both comfort and UV protection simultaneously. From that vantage point, the objective of this work is to develop a model for optimum UPF, air permeability, water-vapour resistance, thermal resistance, thermal absorptivity and areal density of knitted fabrics.

Weft knitted fabrics of various compositions were investigated. UPF was tested using the Labsphere UV transmittance analyser. The FX 3300 (Textest instruments) air permeability tester was used to test air permeability. Thermal comfort and water-vapour resistance were evaluated using the Alambeta and Permetest instruments, respectively. Based on image processing, the porosity was measured. Fabrics thickness and areal density were measured according to standard methods. Furthermore, parametric and non-parametric statistical test methods were applied to the data for analysis.

Linear regression was substantiated by Kolmogorov-Smirnov test. Then multiple linear regression of porosity and thickness together on UPF and comfort parameters were visually depicted by virtue of 3D linear plots. Residual analysis with quantile-quantile and probability plots, advocated the tests using the Shapiro-Wilk test. The result was validated by comparison with experimental data tested. The samples gave satisfactory relative errors and were supported by the z-test method. All tests indicated failure to reject the null hypothesis.

The predictive models were embedded into an interactive computer program. Fabric thickness and porosity are the inputs needed to run the program. It will predict the optimum UPF, areal density and thermophysiological comfort parameters. In a nutshell, knitters may use the program to determine optimum structural parameters for diverse permutations of UPF and thermophysiological comfort parameters; scilicet high UV protection together with low thermal insulation combined with low water-vapour resistance and high air permeability.

The age of artificial intelligence (AI) is already upon us. The rapid development of AI tools is facilitating sustainable development and its corollary social good. For AI dedicated to social good to be impactful, it has to be human-centred, striving to achieve inclusiveness, sustainable livelihoods and community well-being. In short, it offers major opportunities to holistically enhance peoples' lives in diverse areas: education, health care, food security, disaster reduction, smart cities, etc. However, ethical, unbiased and ‘secure-by-design’ algorithms that power AI are crucial to building trust in this technology. Civil society's engagement can hopefully drive the features and values that should be embedded in AI.

This chapter focuses on the societal benefits that AI can deliver. Our initiatives and decisions of today will fashion the ‘Social Good’ AI applications of tomorrow. Sustainable Development Goals (SDGs) being addressed are 2–4 and 10–11.

Manufacturing in Mauritius is mostly export-oriented. Any supply chain (SC) failure or resilience deficit may result in cancellation of orders and loss of customers, market share and revenue and reduce capability to compete globally. Addressing this challenge is complex, although digital technologies and artificial intelligence (AI) models can improve resilience by assisting decision-making and mitigate risks, thus infusing greater predictability across the SC.

Supply chains are facing increasing disruptions and uncertainties owing to extreme weather events, the war in Ukraine, market volatility and the ongoing COVID-19 pandemic, among other factors. Manufacturing industries and their supply chains essentially create thousands of jobs that enable economic growth and sustain export capability. In addition, they need to maintain or increase both productivity and efficiency and recover quickly from unforeseen or unexpected challenges – that is they need to be resilient. Transformation initiatives, whether in production or supply chain management (SCM), are never easy. Process changes not supported by data or hurried human decisions can sometimes have unintended consequences, mainly adverse. However, in times of greater uncertainty (war and pandemic), setbacks can have greater consequences on the business. Manufacturers are already apprehensive and report slowing exports as recession concerns have caused consumers and businesses to pull back on spending. There is therefore a need to reduce uncertainty and augment resilience by unlocking and synthesising insights that emanate from the power of data analytics, AI and machine learning to improve the resilience efficiency balance.

This chapter will discuss the opportunities arising from the adoption and implementation of digital technologies and AI in SCM, leading to better value creation, less greenhouse gas emissions and resilience. The hurdles that enterprises are facing to integrate AI in their logistics and SCs will also be highlighted. This work comments on initiatives that uphold the objectives of SDG 8 – decent work and economic growth, SDG 9 – industry, innovation & infrastructure and SDG 13 – climate action.