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This paper proposes a Sine Cosine hybrid optimization algorithm with Modified Whale Optimization Algorithm (SCMWOA). The goal is to leverage the strengths of WOA and SCA to solve problems with continuous and binary decision variables. The SCMWOA algorithm is first tested on nineteen datasets from the UCI Machine Learning Repository with different number attributes, instances, and classes for feature selection. It is then employed to solve several benchmark functions and classical engineering case studies. The SCMWOA algorithm is applied for solving constrained optimization problems. The two tested examples are the welded beam design and the tension/compression spring design. The results emphasize that the SCMWOA algorithm outperforms several comparative optimization algorithms and provides better accuracy compared to other algorithms. The statistical analysis tests, including one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum, confirm that the SCMWOA algorithm performs better.

Additive manufacturing technology can help us to produce complex components/parts easily. It can be used to develop the parts with multi-material structures consisting of thermoplastic, thermosetting plastic, and ceramic fibers. This multi-material structure enhances the performance of lightweight polymer-based components. In this research work, poly-lactic acid (PLA) lattice (mono-material structure), PLA lattice filled with epoxy (bi-material structure), and PLA lattice incorporated with embedded milled glass fibers (MGFs) in an epoxy matrix (tri-material structure, TMS) were designed and developed by fused filament fabrication and solution casting methods. PLA lattice of 50% volume was fixed in all structures and 50% volume was filled with epoxy and MGFs. The dispersed MGFs in epoxy matrix were varied from 0, 2.5, 5, and 7.5 vol%. The mechanical properties were carried out by compression test, three-point bending test, and tensile test. The results revealed that 5 vol% of MGFs in the epoxy sample (TMS) exhibited improved mechanical performances compared to other samples. The cone-beam CT scan results confirmed the voids/porous free surfaces in the developed materials. The high-resolution scanning electron microscope microstructural evolutions in-terms of topography and fractured regions were also examined and reported.

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We investigated how the geometrical and mechanical properties of eggshell of Japanese quail are affected by strain and flock age. Two strains of quail (white and gray) were used in the current experiment. The results showed that there was no significant difference for all geometric measurements due to strain effect. Eggs produced from the older birds showed significantly higher (P < 0.01) values compared with younger age for all studied traits. Eggs produced from quails at 22 weeks had a significantly (P < 0.01) darker yolk color than that of the younger age. Superiority in shell thickness, shell weight, and breaking force was detected in eggs of gray quails compared with white quails. On the other hand, the eggs from white quails had significantly higher values for static stiffness and Young's modulus as compared with those of gray counterparts. A significant decrease (P < 0.01) was found for fracture toughness and Young's modulus in eggs of aged birds. A significant negative relationship was found between the breaking force and both static stiffness and Young's modulus. A significant positive relationship was observed between breaking force and both shell thickness and shell percentage. The phenotypic correlation between eggshell breaking force and toughness was relatively high.

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The sample's hemoglobin and glucose levels can be determined by obtaining a blood sample from the human body using a needle and analyzing it. Hemoglobin (HGB) is a critical component of the human body because it transports oxygen from the lungs to the body's tissues and returns carbon dioxide from the tissues to the lungs. Calculating the HGB level is a critical step in any blood analysis job. The HGB levels often indicate whether a person is anemic or polycythemia vera. Constructing ensemble models by combining two or more base machine learning (ML) models can help create a more improved model. The purpose of this work is to present a weighted average ensemble model for predicting hemoglobin levels. An optimization method is utilized to get the ensemble's optimum weights. The optimum weight for this work is determined using a sine cosine algorithm based on stochastic fractal search (SCSFS). The proposed SCSFS ensemble is compared to Decision Tree, Multilayer perceptron (MLP), Support Vector Regression (SVR) and Random Forest Regressors as model-based approaches and the average ensemble model. The SCSFS results indicate that the proposed model outperforms existing models and provides an almost accurate hemoglobin estimate.