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In this chapter the potential to use water-based Trombe walls to provide heated water for building applications during the summer months is investigated. Design Builder software is used to model a simple single-story building with a south-facing Trombe wall. The effects of using different thermal storage mediums within the Trombe wall on building heating loads during the winter and building cooling loads during the summer are modeled. The amount of thermal energy stored and temperature of water within the thermal storage medium during hot weather conditions were also simulated. On a sunny day on Toronto, Canada, the average temperature of the water in a Trombe wall integrated into a single-story building can reach ∼57°C, which is high enough to provide for the main hot water usages in buildings. Furthermore, the amount of water heated is three times greater than that required in an average household in Canada. The results from this work suggest that water-based Trombe walls have great potential to enhance the flexibility and utility of Trombe walls by providing heated water for building applications during summer months, without compromising performance during winter months.

Purpose – This purpose of the research is to investigate the process of manufacturing LDPE recycle thermoplastic composites with reinforcement oil palm empty fruit bunch (OPEFB) biomass microfillers.

Design/Methodology/Approach – Methods of physical and chemical modification of OPEFB fibers into the LDPE matrix and the addition of some compatibilizer such as MAPE and xylene process through melt blending can improve mechanical properties, electrical properties, biodegradability, and improve the morphology of composites.

Research Limitations/Implications – These composites are prepared by the following matrix ratio: filler (70:30)% and filler size (63, 75, 90, and 106) μm. The LDPE plastic is crushed to a size of 0.5–1 cm, then pressed with hot press free heating for 5 min and with a pressure of 10 min at 145 °C. Based on the characterization obtained, the tensile strength and the high impact on the use of 106 μm filler is 13.86 MPa and 3,542.6 J/m², and thermal stability indicates the degradation temperature (T₀) 497.83 °C. FT-IR analysis shows the presence of functional groups of cellulose and lignin molecules derived from TKKS collected in the composite.

Practical Implications – Based on the characterization obtained, this composite can be applied as furniture material and vehicle dashboard.

Originality/Value – Composites obtained from recycle of LDPPE plastics waste has some advantages such as good compatibility and high tensile strength. This composite used the OPEFB filler whose size is in micrometer, and so this product is different from other products.

Composites based on fiber are commonly used in high-performance building materials. The composites mostly use petrochemically derived fibers like polyester and e-glass, due to their advantageous material features like high stiffness and strength. All the same, these fibers also have important shortcomings related to energy consumption, recyclability, initial processing expense, resulting health hazards, and sustainability. Increasing environmental awareness and new sustainable building technologies are driving the research, development, and usage of “green” building materials, especially the development of biomaterials.

In this chapter, the natural fiber evaluation approach is applied, which covers a diverse set of criteria. Consequently, the comparative assessment of diverse natural fiber types is applied through the use of an expert decision system approach. The best performing fiber choice is made by comparatively evaluating the materials related to green building. The proposed fiber can be used and applied by green building material manufacturing companies in various countries or locations as a reference when selecting the fiber with the best performance.

This chapter offers an overview of the applications of artificial intelligence (AI) in the textile industry and in particular, the textile colouration and finishing industry. The advent of new technologies such as AI and the Internet of Things (IoT) has changed many businesses and one area AI is seeing growth in is the textile industry. It is estimated that the AI software market shall reach a new high of over US$60 billion by 2022, and the largest increase is projected to be in the area of machine learning (ML). This is the area of AI where machines process and analyse vast amount of data they collect to perform tasks and processes. In the textile manufacturing industry, AI is applied to various areas such as colour matching, colour recipe formulation, pattern recognition, garment manufacture, process optimisation, quality control and supply chain management for enhanced productivity, product quality and competitiveness, reduced environmental impact and overall improved customer experience. The importance and success of AI is set to grow as ML algorithms become more sophisticated and smarter, and computing power increases.

Purpose – This paper proposes a new procedure for measuring affective responses during social interaction using facial thermographic imaging.

Methodology – We first describe the results of several small pilot experiments designed to develop and refine this new measure that reveal some of the methodological advantages and challenges offered by this measurement approach. We then demonstrate the potential utility of this measure using data from a laboratory experiment (N=114) in which we used performance feedback to manipulate identity deflection and measured several types of affective responses – including self-impressions and emotions.

Findings – We find warming of the brow (near the corrugator muscle) and cheek (near the zygomatic major muscle) related most strongly to emotion valence and self-potency, with those whose brows and cheeks warmed the most feeling less positive emotion and less potent self-impressions. Warming in the eye area (near the orbicularis oculi) related most closely to undirected identity deflection and to positive self-sentiments. Positive self-views and strong identity disruptions both contributed to warming of the eyes.

Implications – The rigor of contemporary sociological theories of emotion exceeds our current ability to empirically test these theories. Facial thermographic imaging may offer sociologists new assessments of affect and emotion that are ecologically valid, socially unreactive, temporally sensitive, and accurate. This could dramatically improve our ability to test and develop affect based theories of social interaction.

Currently, Mauritius is adopting landfilling as the main waste management method, which makes the waste sector the second biggest emitter of greenhouse gas (GHG) in the country. This presents a challenge for the island to attain its commitments to reduce its GHG emissions to 30% by 2030 to cater for SDG 13 (Climate Action). Moreover, issues like eyesores caused by littering and overflowing of bins and low recycling rates due to low levels of waste segregation are adding to the obstacles for Mauritius to attain other SDGs like SDG 11 (Make Cities & Human Settlements Inclusive, Safe, Resilient & Sustainable) and SDG 12 (Guarantee Sustainable Consumption & Production Patterns). Therefore, together with an optimisation of waste collection, transportation and sorting processes, it is important to establish a solid waste characterisation to determine more sustainable waste management options for Mauritius to divert waste from the landfill. However, traditional waste characterisation is time consuming and costly. Thus, this chapter consists of looking at the feasibility of adopting machine learning to forecast the solid waste characteristics and to improve the solid waste management processes as per the concept of smart waste management for the island of Mauritius in line with reducing the current challenges being faced to attain SDGs 11, 12 and 13.

The Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4) (Intergovernmental Panel on Climate Change, 2007) of 2007 concluded that most of the warming of the climate is very likely driven by human activities that increase greenhouse gas (GHG) concentrations in the atmosphere. Activities such as burning of fossil fuels for power generation and in vehicles, as well as increasing deforestation, result in emissions of four long-lived GHGs: carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and halocarbons (a group of gases containing fluorine, chlorine, or bromine). The report projects that by the end of the 21st century global temperatures could rise by 1.1–6.4°C over 1990 levels, while global mean sea levels could rise by 18–59cm, depending on future scenarios of varying global emission levels. This is likely to adversely impact ecosystem resilience, putting many plant and animal species at the risk of extinction. Sea level rise and coastal erosion coupled with temperature extremes, heat waves, and heavy precipitation events that are projected to become more frequent will affect the health and well-being of millions of people around the world.