Elder women often suffer from chest pain and pressure discomfort due to breast displacement during exercise. To design functional sports bras that will mitigate chest pain and pressure discomfort under dynamic conditions, a survey was completed to find out the bra design special requirements and simulation experiments were carried out to investigate breast displacement rules. A standard sized running woman was selected as a subject to determine the typical breast movement positions, and an elder woman avatar was used to simulate the breast displacement. The results showed that by dividing the bra into different functional areas, the breast movement postures can be generated on the virtual model and this method could be applied to develop ergonomic sports bras that can limit the breast movement for elder females. Approaches involved in this research are also suitable to design sportswear with specific functional requirements for various age women.

Manufacturers aim to achieve the optimal quality, therefore, the evaluation of yarn parameters and the determination of factors that influence yarn quality is of great importance. The yarn coefficient of mass variation (CVm%) reflects the irregularity of the yarn which reflects the yarns’ quality. This study investigates the parameters affecting the CVm% that was previously estimated using image processing and artificial neural networks. Yarn images and data were used as inputs into neural networks and CVm% was evaluated. In addition, two statistical methods were used which were: correlation and ANOVA to research the effect of yarn count, twist factor, blend ratio, and cotton type on CVm%. It was found that the yarn count and twist factor were the highest correlated parameters regarding CVm%.

Now-a-days, researchers focus on the use of smart materials due to their multiple functional capabilities. A “smart material” is one having a nano level structure that can responds in a specialised and controlled way to influence on its sensing mechanisms. Due to the multiple properties of smart materials, they have a great influence on current analytical methods and diagnostic strategies by reorganizing the sensing modules for nano-sized objects (protein biomarkers and viruses) and biomolecules detection. Incontestably, current sensing mechanisms need a continuous update for addressing the growing challenges in the field of diagnosis for viruses because these viruses altered and spread rapidly from person-to-persons. It becomes critical to take into consideration several factors for viral diagnosis ranging from the type and quality of specimen collected, mode of transport, time of specimen collection, level of accuracy or specificity, viral detection sensitivity, and the type of diagnostic method used. In this review, we briefly explained the principle and different types of smart materials being used for diagnosing infectious viruses. The development in the field of smart material based nano sensors with resource-scarce settings is further discussed and elaborated the pros and cons of current methods for viral detection as a conclusion and future perspective.

At present, the intelligent design of clothing patterns still needs to rely on the experience of the pattern maker, and it cannot liberate the pattern maker from the complicated design work. The efficiency and flexibility of the design of clothing patterns are not high. In order to realize the intelligent pattern design of design knowledge automation, a directed graph method is proposed to construct a clothing pattern design model, and the model is used to analyze the clothing pattern. In view of the characteristics of clothing pattern design, the directed graph method is used to record the pattern design process, and the model of clothing pattern design is constructed. The model is accurately analyzed by decomposing the original model of clothing pattern design into sub-models of each structural line. Application examples verify the feasibility of constructing a pattern design model and application by the directed graph method. The research results show that the pattern design model can clearly express the pattern design process and the valuable experience involved, and can accurately and quickly analyze the pattern. This research can provide a way to extract, collect, process, share and reuse pattern design knowledge for intelligent clothing design, and improve the efficiency of clothing pattern design.

This review considers the current literature that is focused on the interface nanostructure/cell-wall microorganism to understand the annihilation mechanism. In this report, photocatalysis is discussed for viral disinfection including TiO₂ photocatalysis and other metal-containing photocatalysis. TiO₂ based materials and its composites, metal-TiO₂ systems, TiO₂ heterojunction systems with other semi-conductors, and TiO₂ systems with graphene and other carbonaceous materials are discussed in detail. Some practical uses of titanium dioxide for photocatalytic disinfection processes for the effective prevention/eradication of microorganisms, considering the resistance that the microorganism could develop without the appropriate regulatory aspects for human and ecosystem safety are also discussed.