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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.

In this research project, electrical conductive yarns were knitted together with 100 per cent cotton yarns to create knitted fabrics that would be used as electromagnetic (EM) shielding materials. The paper aims to discuss these issues.

1×1 plain fabrics knitted on double-bed hand knitting machines of five and seven gauges. Several strands of the cotton yarns were used together in order to knit samples with good handling properties. The electrical conductive yarn has six plies and each ply has 29 filaments with Naño-coating of silver and having an electrical resistance of 4 Ohms per 100 mm and a count of 96 Tex. The knitted fabrics have similar texture but vary in term of specific weight, fabric density, loop length, Tex, tightness factor, thickness and electrical conductivity. These variations affected the properties of the fabrics, determining factors of a good shielding or not. A special designed Faraday cage was built to measure the EMSE of each knitted fabrics. The EM waves were sent through the signal generator at different frequencies as from 400 to 1,100 MHz and with three different power inputs of 10, 20 and 30 dBm. EMSE measurements were also carried out after the knitted samples were rotated clockwise.

Good EMSE shielding results were achieved with the knitted samples, however in this study it was found that different knitted fabrics shielded better at specific frequencies and power inputs.

Knitted fabrics can be used to develop comfortable garments that can be used to shield EM waves and protect the wearer.

The choice of using the conductive yarns is exclusive. In addition the EMSE were measured with fabrics knitted in the same structure but on different knitting machine gauge. Three different power inputs were considered and EMSE measurements were taken using frequencies as from 400 to 1,100 MHz. A new method for measuring the electrical resistance on the knitted fabrics and the method used for measuring the EMSE for each knitted fabric were considered.

In this chapter, a general introduction on artificial intelligence (AI) is given as well as an overview of the advances of AI in different engineering disciplines, including its effectiveness in driving the United Nations Sustainable Development Goals (UN SDGs). This chapter begins with some fundamental definitions and concepts on AI and machine learning (ML) followed by a classification of the different categories of ML algorithms. After that, a general overview of the impact which different engineering disciplines such as Civil, Chemical, Mechanical, Electrical and Telecommunications Engineering have on the UN SDGs is given. The application of AI and ML to enhance the processes in these different engineering disciplines is also briefly explained. This chapter concludes with a brief description of the UN SDGs and how AI can positively impact the attainment of these goals by the target year of 2030.

The tourist sector in Mauritius is aiming at welcoming 2 million tourists by the year 2015 and coming up with creative ideas and products branded with ‘Made in Mauritius’ to boost the country’s craft sector. This project looks into the manufacture of paper-yarn. Different types of paper were cut into ribbons of varying widths and converted into yarn by two methods: twisting and folding. The folds were made along the axis of a ribbon while twisting required prior moistening and was carried out on a modified yarn-twist measuring device. The relationship between the number of folds/twists and the strength of the resulting paper yarn was investigated. Yarn of high quality can be obtained by twisting ribbons of relatively smaller widths or folding relatively wider ribbons. The yarn can be used for manufacturing craft products.

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.

There is an urgent need to develop climate-smart agrosystems capable of mitigating climate change and adapting to its effects. Conventional agricultural practices prevail in Mauritius, whereby synthetic chemical fertilizers, pesticides and insecticides are used. It should be noted that Mauritius remains a net-food importing developing country of staple food such as cereals and products, roots and tubers, pulses, oil crops, vegetables, fruits and meat (FAO, 2011). In Mauritius, the agricultural sector faces extreme weather conditions like drought or heavy rainfall. Moreover, to increase the crop yields, farmers tend to use 2.5 times the prescribed amount of fertilizers in their fields. These excess fertilizers are washed away during heavy rainfall and contaminate lakes and river waters. By using smart irrigation and fertilization system, a better management of soil water reserves for improved agricultural production can be implemented. Soil Nitrogen, Phosphorus and Potassium (NPK) content, humidity, pH, conductivity and moisture data can be monitored through the cloud platform. The data will be processed at the level of the cloud and an appropriate mix of NPK and irrigation will be used to optimise the growth of the crops. Machine learning algorithms will be used for the control of the land drainage, fertilization and irrigation systems and real time data will be available through a mobile application for the whole system. This will contribute towards the Sustainable Development Goals (SDGs): 2 (Zero Hunger), 11 (Sustainable cities and communities), 12 (Responsible consumption and production) and 15 (Life on Land). With this project, the yield of crops will be boosted, thus reducing the hunger rate (SDG 2). On top of that, this will encourage farmers to collect the waters and reduce fertilizer consumption thereafter sustaining the quality of the soil on which they are cultivating the crops, thereby increasing their yields (SDG 15).

Adsorption parameters (e.g. Langmuir constant, mass transfer coefficient and Thomas rate constant) are involved in the design of aqueous-media adsorption treatment units. However, the classic approach to estimating such parameters is perceived to be imprecise. Herein, the essential features and performances of the ant colony, bee colony and elephant herd optimisation approaches are introduced to the experimental chemist and chemical engineer engaged in adsorption research for aqueous systems. Key research and development directions, believed to harness these algorithms for real-scale water treatment (which falls within the wide-ranging coverage of the Sustainable Development Goal 6 (SDG 6) ‘Clean Water and Sanitation for All’), are also proposed. The ant colony, bee colony and elephant herd optimisations have higher precision and accuracy, and are particularly efficient in finding the global optimum solution. It is hoped that the discussions can stimulate both the experimental chemist and chemical engineer to delineate the progress achieved so far and collaborate further to devise strategies for integrating these intelligent optimisations in the design and operation of real multicomponent multi-complexity adsorption systems for water purification.