Multifunctional fabrics using conventional processes have piqued increasing global interest. The focus of this experiment was to assess the modification of the cotton fabric surface by utilizing silver nanoparticles (AgNPs) and introducing functional properties along with sustainable dyeing performance. A single-jersey knitted fabric composed of cellulose-enriched 100% natural fiber (cotton) with an areal density of 172 GSM was used in this study. The standard recipe and test methods were employed. FTIR-ATR spectra were used to determine the fixing of AgNPs onto the fiber surface. A comparative assessment was conducted in response to the distribution of color, color fastness to wash, water, perspiration, rubbing, and light. Scanning electron microscopy (SEM) was used to characterize the surface of nano-Ag-deposited specimens. In terms of functional properties, antimicrobial activity was scrutinized. Our findings reveal that the nanoparticles impart remarkable antibacterial effects to cellulose-enriched fabric against S. aureus (Gram-positive) and E. coli (Gram-negative). Direct dyes were used for dyeing the proposed samples, resulting in enhanced dyeing performance. Except for light fastness, the samples dipped with AgNPs showed outstanding color levelness and color durability characteristics. The developed fabrics can be applied in a wide range of functions, including protective clothing, packaging materials, and healthcare, among others.

Consumers, manufacturers, and retailers worldwide are becoming conscious about high quality products at minimum cost. But plenty of apparels are becoming waste which increases the cost of production. As resources are decreasing but increasing costs of the products. Effective apparel waste management is needed to ensure the profit. Reducing such waste can be profitable options for the manufacturers as well as the buyers. Considering this matter, a project work is done in a ready-made garment manufacturing industry to improve the quality of the products through using a traffic light system. A traffic light system was implemented to minimize the defect rates of production. The study shows that the average defect rates were dropped from 4.13 to 1.25 pieces of a line for daily eight hours of production. By implementing this system, the defect rates are minimized and the monthly production is also increased and it clearly depicts that the monthly capacity before implementing the traffic light system was equivalent but the defective production was more whereas, after implementation, the defective production was negligible.

No abstract

2D pose estimation is a general problem in computer vision, where the main objective is to detect a person’s body key-points and estimate a 2D skeletonized pose of a person.Skeleton estimation is outbound as an essential part of body parts detection in many fields, such as healthcare, rehabilitation, sports and fitness, animation, gaming, augmented reality, robotics. These systems are based on neural network applications and able to give reliable, objective and cost-effective benefits. Various methods are available based on this topic and used to update existing systems. In this regard, in this work, we have proposed a method for skeleton-based angle detection where we have used MobileNet model. This model is developed based on the convolution neural network (CNN). At first, 18 key-points of the human body parts were generated through the model. After that, by using the extracted key-points the skeleton of the human body parts is generated by estimating key-points according to the body part pairs. Furthermore, based on the generated skeletons, different skeleton joint angles at different key-points are estimated. To evaluate the performance of the proposed model at different environmental conditions, a customized dataset was utilized. This approach shows 95.37% accuracy for key-points detection, for joint angle estimation the accuracy is 96.11%, and shows 96.667% accuracy for body part length measurement.