The hygiene area is one of the most important facilities in a space station. If its environmental lighting is appropriately designed, it can significantly reduce the psychological pressure on astronauts. This study investigates the effect of correlated colour temperature (CCT) on heart rate, galvanic skin response, emotion and satisfaction in the hygiene area of a space station. Forty subjects participated in experiments in a hygiene area simulator with a controlled lighting environment. The lighting conditions included 2700 K, 3300 K, 3600 K, 5000 K and 6300 K; physiological responses (heart rate, galvanic skin response), as well as emotion and satisfaction, were recorded. The results showed that CCT significantly influenced the participants’ physiological and subjective responses in the space station hygiene area. 6300 K led to the best emotion and satisfaction levels, the highest galvanic skin response and the lowest heart rate. The opposite was true for 2700 K.

During space missions, astronauts live in a confined technological environment, completely isolated and deprived of the variety and variation found in the environment on Earth. This circumstance has a strong impact on the psycho-physiological states of the crew. Particularly in light of the plans for long-duration missions, new research needs to be carried out. The goal of this study, conducted at Xiangtan Central Hospital in China, was to test whether multicolour lighting can improve people’s psychological state in an isolated and confined environment over a period of seven days. Twenty participants (10 male and 10 female) were randomly divided into two groups: one group that was exposed to multicolour lighting and a control group, which was exposed to a static, monotonous white interior. The participants’ psychological state was recorded on the first day, the fourth day, and the seventh day. The results of the control group showed that the participants’ negative emotions and anxiety continued to increase over time, whereas the group randomly exposed to multicolour lighting that changed every three hours did not show any significant increase in negative emotions and anxiety. Moreover, the random change of light colour in the isolated environment appeared to help the participants increase their sense of surprise, thereby counteracting monotony. Finally, during this experiment, it was observed that when people who are accustomed to being connected to social networks were deprived of this, they experienced insomnia and unaccustomed reactions, in particular on the first days of deprivation. This article contributes to future space exploration and to social and psychological support of life in isolated and confined environments.

Fashion Trends and Forecasting explores how designers, merchandisers, buyers, marketers and strategists use fashion trend forecasting to predict upcoming trends and understand past or current ones. It explains the importance of trend forecasting for businesses and how technology such as artificial intelligence can help to advance it for the future. Addressing both macro- and micro-trends, including fashion styles, textiles and colour trends as well as the impact of technology such as AI and the metaverse, the book offers guidance on how fashion students can address fast-changing consumer needs and demands and identify wider shifts in the cultural, environmental, political and technological worlds. Case studies from globally recognised organisations feature in every chapter, highlighting and contextualising theory for students. Discussion questions, further reading and exercises help facilitate class discussion and deeper understanding. Designed to equip the reader with Fashion Forecasting tools, methods and principles in an accessible and clear way, this text is recommended and core reading for students studying fashion forecasting, fashion buying and fashion marketing. Supplementary online resources can be found at www.routledge.com/9781032541860 and include a short instructor’s manual of points to be used for seminar discussions and chapter-by-chapter PowerPoint slides for instructors. <!----> <!----> <!----> <!---->

Based on previous visual assessments of 440 color pairs of 3D-printed samples, we tested the performance of eight color-difference formulas (CIELAB, CIEDE2000, CAM02-LCD, CAM02-SCD, CAM02-UCS, CAM16-LCD, CAM16-SCD, and CAM16-UCS) using the standardized residual sum of squares (STRESS) index. For the whole set of 440 color pairs, the introduction of k_L (lightness parametric factor), b (exponent in total color difference), and k_L + b produced an average STRESS decrease of 2.6%, 26.9%, and 29.6%, respectively. In most cases, the CIELAB formula was significantly worse statistically than the remaining seven formulas, for which no statistically significant differences were found. Therefore, based on visual results using 3D-object colors with the specific shape, size, gloss, and magnitude of color differences considered here, we concluded that the CIEDE2000, CAM02-, and CAM16-based formulas were equivalent and thus cannot recommend only one of them. Disregarding CIELAB, the average STRESS decreases in the k_L + b-optimized formulas from changes in each one of the four analyzed parametric factors were not statistically significant and had the following values: 6.2 units changing from color pairs with less to more than 5.0 CIELAB units; 2.9 units changing the shape of the samples (lowest STRESS values for cylinders); 0.7 units changing from nearly-matte to high-gloss samples; and 0.5 units changing from 4 cm to 2 cm samples.