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Eva Obodo's recent art exhibition at the kó gallery in Lagos showcased his works in textile art, which explore various aspects of social life in Nigeria. Obodo, a contemporary Nigerian artist and sculptor, incorporates techniques such as tying, stretching, and twisting materials to create extraordinary pieces. His works address social and political issues in postcolonial Africa, using materials like jute, fabric, and charcoal. The exhibition reflects the artist's sensitivity to the socio-political climate in Nigeria, particularly during the COVID-19 pandemic. Obodo's works challenge viewers to reflect on leadership challenges and the need for credibility, competence, and courage in society. [Extracted from the article]

Yarn engineering is a long-standing problem for the cotton spinning industry as the functional relationship between fiber and yarn properties is quite complex. The objective of this research is to develop a hybrid machine learning-based prescriptive yarn engineering system that can foretell the properties of cotton fiber for achieving desired yarn properties. Artificial neural network (ANN) and genetic algorithm (GA) were used to develop the predictive model for cotton yarn properties and optimization of cotton fiber properties, respectively. Two separate ANN models were developed for predicting yarn tenacity and yarn unevenness. The functional relationships approximated by the ANN models were used to formulate the fitness function for GA. The validation of the ANN-GA system demonstrated good accuracy as cotton fiber strength, length and length uniformity were predicted with very good accuracy (mean error < 5%). The developed machine learning system can supplant the intuition-based decision making in textile spinning industry and pave the way for yarn engineering. [ABSTRACT FROM AUTHOR]

Keywords: Spinning waste; cotton fibers; web; nonwoven; tear strength; burst strength; ; ; ; ; ; EN Spinning waste cotton fibers web nonwoven tear strength burst strength ZH 9064 9073 10 11/21/22 20221220 NES 221220 Introduction The raw material is considered as one of the most important factors affecting product costs in the textile industry based on its type, processing, and end-use. In this work, the webs of cotton fibers from spinning soft and hard wastes were formed after separating the short fibers from impurities by trash analyzer and carding fibers to form a web. Thus, textile material wastes can be classified as pre-consumer textile waste (soft and hard waste) and post-consumer textile waste. Following the procedures of staple length measurement mentioned above, the mean of fibers length from spinning waste is; I Mean fibers length (cm) = 1.34 ± 0.53 i The standard classification indicates that those fibers with length of 1 in. or less are categorized under short-staple fibers (Klein [11]). [Extracted from the article]

This paper reported the process of extraction and characterization of the properties of natural fiber from the leaves of the Furcraea foetida plant. The water retting method was used to extract the fiber. The fiber properties obtained were used to analyze the ability of fiber to be spun into yarn for textile application. A bundle of Furcraea foetida fibers consist of many individual fibers. The longitudinal view of the fiber cells show up as long cylindrical tubes and the cross-section of fibers were an irregular, polygonal shape. The tensile strength of the Furcraea foetida fibers was 3 g/denier. The elongation at break of the Furcraea foetida fibers was 8.61%. The fineness of the Furcraea foetida fibers was 8.3 tex. The average fiber length was 70.8 cm. The Furcraea foetida fibers were hygroscopic fibers with 8.48% moisture regain. The coefficient of friction of the Furcraea Foetida fibers was 0.053. The properties of the fiber showed that the Furcraea foetida fiber has potential to be spun into yarn. Natural fibers from the leaves of the Furcraea foetida plant could be used as an alternative material for textile application. [ABSTRACT FROM AUTHOR]