Search

Tri-material and tri-layered composites find numerous applications in the biomedical, aerospace, and automotive industries. However, such composites face challenges when fabricated through conventional methods (Electrospinning, hand lay-up, Film Casting, In-situ polymerization), such as interface bond strength, strength-to-mass ratio, etc. Additive manufacturing can be used to fabricate such composites to overcome these challenges. In this study, tensile samples of tri-material based 3D-printed (TM3DP) polymer composite of polylactic acid (PLA), polyethylene terephthalate glycol (PETG), and acrylonitrile butadiene styrene (ABS) were successfully processed using fused filament fabrication (FFF) for the first time. The composite is fabricated such that 33.333% PLA was printed first, followed by 33.333% PETG, and, in the end, 33.333% ABS so that PETG is sandwiched between PLA and ABS. The effect of FFF processing parameters on the tensile properties of the printed composites was investigated. After preliminary experiments and a literature review, infill density (ID), printing speed (PS), and layer thickness (LT) were selected as the main processing parameters. The tensile strength (TS) and tensile strain (ℇ) were selected as the outputs (responses) of this study. Tensile testing was performed after printing composite tensile samples on Instron Universal Testing Machine (5 KN). An analysis of variance (ANOVA) was also performed to check the significance of FFF process parameters. The results concluded that the selected FFF process parameters were significant in the interaction state of both tensile properties. Scanning electron microscopy (SEM) and optical microscopy were also performed, which indicated that various defects, including micropores, voids, and micro / major delamination occurred in conditions of high LT, high PS, and low ID. This resulted in a low TS of 27.1 MPa and ℇ of 0.5 mm/mm. No major defects were observed under high LT, high PS, and high ID, which resulted in the highest TS of 39.5 MPa and ℇ of 0.95. Finally, optimum conditions were suggested for fabricating the thick TM3DP composite samples, believed to enhance the TS-to-mass ratio by 16.4% compared to the single solid 3D-printed base materials.

Among the three main divergent lineages of gray wolf (Canis lupus), the Holarctic lineage is the most widespread and best studied, particularly in North America and Europe. Less is known about Tibetan (also called Himalayan) and Indian wolf lineages in southern Asia, especially in areas surrounding Pakistan where all three lineages are thought to meet. Given the endangered status of the Indian wolf in neighboring India and unclear southwestern boundary of the Tibetan wolf range, we conducted mitochondrial and genome-wide sequencing of wolves from Pakistan and Kyrgyzstan. Sequences of the mitochondrial D-loop region of 81 wolves from Pakistan indicated contact zones between Holarctic and Indian lineages across the northern and western mountains of Pakistan. Reduced-representation genome sequencing of eight wolves indicated an east-to-west cline of Indian to Holarctic ancestry, consistent with a contact zone between these two lineages in Pakistan. The western boundary of the Tibetan lineage corresponded to the Ladakh region of India's Himalayas with a narrow zone of admixture spanning this boundary from the Karakoram Mountains of northern Pakistan into Ladakh, India. Our results highlight the conservation significance of Pakistan's wolf populations, especially the remaining populations in Sindh and Southern Punjab that represent the highly endangered Indian lineage. [ABSTRACT FROM AUTHOR]

Flexural properties calculation helps in designing structural elements like beam, cantilever and shafts. Moreover, the flexural properties are of vital importance in engineering and industrial applications such as joints replacements. The purpose of this investigation is to study for the first time, how the friction stir processing (FSP) parameters affects the flexural properties of UHMW-PE composites reinforced with nano particles. The tool rotational speed ( ψ ), tool feed rate ( ƒ ), volume percentage ( ν ) of nano powder and tool shoulder temperature ( τ ) are selected as the process parameters. The ultimate flexural strength (UFS) and flexural yield strength (FYS) are calculated from the flexural test stress-strain diagrams. The analysis of variance is conducted which reveals that the selected parameters are significant for both UFS and FYS. Macroscopic and microscopic study shows that the FSP parameters affects the mixing of the strengthening particles and hence the flexural properties of the composite. The combinations of low level of ν with medium level values of other parameters results in the highest flexural properties. Moreover, the combinations of higher levels of τ and ψ results in material degradation. At the end, optimum conditions for the highest flexural properties are sorted out and the effect of increasing the number of passes has been investigated which significantly improve the flexural properties of the composite material.

One of the most difficult challenges in medicine is predicting heart disease at an early stage. In this study, six machine learning (ML) algorithms, viz., logistic regression, K-nearest neighbor, support vector machine, decision tree, random forest classifier, and extreme gradient boosting, were used to analyze two heart disease datasets. One dataset was UCI Kaggle Cleveland and the other was the comprehensive UCI Kaggle Cleveland, Hungary, Switzerland, and Long Beach V. The performance results of the machine learning techniques were obtained. The support vector machine with tuned hyperparameters achieved the highest testing accuracy of 87.91% for dataset-I and the extreme gradient boosting classifier with tuned hyperparameters achieved the highest testing accuracy of 99.03% for the comprehensive dataset-II. The novelty of this work was the use of grid search cross-validation to enhance the performance in the form of training and testing. The ideal parameters for predicting heart disease were identified through experimental results. Comparative studies were also carried out with the existing studies focusing on the prediction of heart disease, where the approach used in this work significantly outperformed their results. [ABSTRACT FROM AUTHOR]