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Study the Effect of Different Percentages of Natural (Orange Peels and Date Seeds) and Industrial Materials (Carbon and Silica) on the Mechanical and Thermal Properties of Polymeric Reinforced Composites
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Mechanical and thermal properties of composites, consisted of unsaturated polyester resin, reinforced by different kinds of natural materials (Orange peels and Date seeds) and industrial materials (carbon and silica) with particle size 98 µm were studied. Various weight ratios, 5, 10, and 15 wt. % of natural and industrial materials have been infused into polyester. Tensile, three-point bending and thermal conductivity tests were conducted for the unfilled polyester, natural and industrial composite to identify the weight ratio effect on the properties of materials. The results indicated that when the weight ratio for polyester with date seeds increased from 10% to 15%, the maximum Young’s modulus decreased by 54%. When the weight ratio was 5%, the maximum Young’s modulus, yield stress and ultimate tensile stress occurred in the polyester with date seeds. The results of tensile and flexural tests showed that the natural composite material has a higher strength than the industrial material. While the results of flexural tests manifested that the maximum improvement in the flexural strength is obtained for orange peels at 5 wt. %, where the maximum increasing percentage is 153.4% than pure polyester. The thermal conductivity of orange peels decreased to the half value when the weight ratio increased from 10% to 15%. The thermal conductivity for polyester with orange peels was greater than the thermal conductivity of polyester with date seeds with maximum percentage occurred at weight ratio 10% is 14.4%, but the thermal conductivity of the industrial composite material was higher than the natural composite material. Finally, the date seeds composite was a good insulator and it had a reduced heat transfer rate in comparison to the rest of the samples, also the maximum variation of temperature with time occurred in date seeds composite.

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Publication Date
Thu Jul 02 2026
Journal Name
Innovative Construction And Petrochemical Technologies
Improving Arabic Text Classification Accuracy Using Lightweight NLP Techniques
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Arabic text classification is a challenging task because of the complex morphology of the language, the existence of different writing forms and a multitude of dialects, which can result in sparser common text representations. While transformer models such as AraBERT have obtained superior results on many Arabic NLP tasks, their high computational requirements make them difficult to deploy in environments with limited hardware resources. In some cases this can also make the model less practical for researchers working with basic computer systems. This study focuses on a more practical issue: how much accuracy a simple classifier may lose when the amount of required computation is reduced. We use a combined TF-IDF representation based on bo

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