Polyacrylonitrile nanofiber (PANFS), a well-known polymers, has been extensively employed in the manufacturing of carbon nanofibers (CNFS), which have recently gained substantial attention due to their excellent features, such as spinnability, environmental friendliness, and commercial feasibility. Because of their high carbon yield and versatility in tailoring the final CNFS structure, In addition to the simple formation of ladder structures through nitrile polymerization to yield stable products, CNFS and PAN have been the focus of extensive research as potential production precursors. For instance, the development of biomedical and high-performance composites has now become achievable. PAN homopolymer or PAN-based precursor copolymer can be employed to make CNFS. Water gets polluted because it throws industrial waste bodies of water, especially those containing dyes, heavy metals, and inorganic and organic wastes. Adsorbents, which are cheap and readily available, can be used to address the issue of water deterioration. According to this review, numerous PAN variations are being employed in scientific and technological settings. Nanocomposite fibers need extensive research efforts to advance technology and bring them to commercialization
In this study, composite materials were prepared using unsaturated polyester resin as binder with two types of fillers (sawdust and chopped reeds). The molding method is used to prepare sheets of UPE / sawdust composite and UPE / chopped reeds composite. The mechanical properties were studied including flexural strength and Young's modulus for the samples at normal conditions (N.C). The Commercial wood, UPE and its composite samples were immersed in water for about 30 days to find the weight gain (Mt%) of water for the samples, also to find the effect of water on their flexural strength and Young's modulus. The results showed that the samples of UPE / chopped reeds composite gained highest values of flexural strength (24.
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The bandwidth requirements of the telecommunication network users increased rapidly during the last decades. Optical access technologies must provide the bandwidth demand for each user.The passive optical
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In this paper, three approximate methods namely the Bernoulli, the Bernstein, and the shifted Legendre polynomials operational matrices are presented to solve two important nonlinear ordinary differential equations that appeared in engineering and applied science. The Riccati and the Darcy-Brinkman-Forchheimer moment equations are solved and the approximate solutions are obtained. The methods are summarized by converting the nonlinear differential equations into a nonlinear system of algebraic equations that is solved using Mathematica®12. The efficiency of these methods was investigated by calculating the root mean square error (RMS) and the maximum error remainder (𝑀𝐸𝑅n) and it was found that the accuracy increases with increasi
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In many applications such as production, planning, the decision maker is important in optimizing an objective function that has fuzzy ratio two functions which can be handed using fuzzy fractional programming problem technique. A special class of optimization technique named fuzzy fractional programming problem is considered in this work when the coefficients of objective function are fuzzy. New ranking function is proposed and used to convert the data of the fuzzy fractional programming problem from fuzzy number to crisp number so that the shortcoming when treating the original fuzzy problem can be avoided. Here a novel ranking function approach of ordinary fuzzy numbers is adopted for ranking of triangular fuzzy numbers with simpler an
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Wildfire risk has globally increased during the past few years due to several factors. An efficient and fast response to wildfires is extremely important to reduce the damaging effect on humans and wildlife. This work introduces a methodology for designing an efficient machine learning system to detect wildfires using satellite imagery. A convolutional neural network (CNN) model is optimized to reduce the required computational resources. Due to the limitations of images containing fire and seasonal variations, an image augmentation process is used to develop adequate training samples for the change in the forest’s visual features and the seasonal wind direction at the study area during the fire season. The selected CNN model (Mob
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