A Review on the Effect of Metal Oxide Nanoparticles on Tribological Properties of Biolubricants.

This review provides a comprehensive and accessible literature review on the integration of nanoparticles into biolubricants to enhance wear and friction regulation, thus improving the overall lubricated system performance. Nanotechnology has significantly impacted various industries, particularly in lubrication. Nanobiolubricants offer promising avenues for enhancing tribological properties. This review focuses on oxide nanoparticles, such as zinc oxide (ZnO), aluminum oxide (Al2O3), copper oxide (CuO), titanium dioxide (TiO2), zirconium dioxide (ZrO2), and graphene oxide (GO) nanoparticles, for their ability to enhance lubricant performance. The impact of nanoparticle concentration on biolubricant properties, including viscosity, viscosity index, flash point temperature, and pour point temperature, is analyzed. The review also addresses potential obstacles and limitations in nanoparticle incorporation, aiming to propose effective strategies for maximizing their benefits. The findings underscore the potential of nanobiolubricants to improve operational efficiency and component lifespan. This review aims to provide valuable insights for researchers, engineers, and professionals in exploring and leveraging nanotechnology's potential in the lubrication industry. This review paper explores the basics of tribology along with its significance, green principles, mechanisms, and energy savings because of friction, wear, and lubrication. Condition monitoring techniques are also explored to achieve brief knowledge about maintaining reliability and safety of the industrial components. Recent advances in tribology including superconductivity, biotribology, high-temperature tribology, tribological simulation, hybrid polymer composite's tribology, and cryogenic tribology are investigated, which gives a thorough idea about the subject.

Thermal Properties and Drying Shrinkage Performance of Palm Kernel Shell Ash and Rice Husk Ash-Based Geopolymer Concrete.

This study aims to develop suitable formulations of geopolymer concrete (GPC) by varying the percentages of the geopolymer with aggregates and evaluating the performances in thermal and mechanical properties of palm kernel shell ash (PKSA)-GPC compared to rice husk ash (RHA)-GPC and ordinary Portland cement concrete (OPCC). Preliminary tests were conducted to select the best mix design ratios before casting the specimens. Then, the performance of the PKSA-GPC, RHA-GPC and OPCC specimens was evaluated based on their thermal performance and drying shrinkage. The mix designs of PKSA-GPC 70:30, PKSA-GPC 60:40, PKSA-GPC 50:50 and PKSA-GPC 66.6:33.3 were found to produce an acceptable consistency, rheological and thixotropic behaviour for the development of the GPC. PKSA-GPC showed a better thermal performance than the RHA-GPC and OPCC due to their strong and dense intumescent layers and slow temperature increment upon exposure to a high flame temperature from ambient temperature to 169 °C. The low molar ratio of the Si/Al present in the PKSA-GPC created a thermally stable intumescent layer. In the drying shrinkage test, PKSA-GPC 60:40 and RHA-GPC 60:40 shared an equal drying shrinkage performance (5.040%) compared to the OPCC (8.996%). It was observed that microcrack formation could significantly contribute to the high shrinkage in the PKSA-GPC 50:50 and RHA-GPC 70:30 specimens. The findings of this study show that PKSA could be incorporated into GPC as a fire-retardant material due to its capability of prolonging the spread of fire upon ignition and acting as an alternative to the conventional OPCC.

Optimization of Printing Process Variables and the Effect of Post-Heat Treatments on the Mechanical Properties of Extruded Polylactic Acid-Aluminum Composites.

Polymer extrusions are employed in the fabrication of crucial parts for automotive, aerospace, and other mechanical applications. The use of fillers and microfibers is contributing to the advancement of material extrusion polymers. In order to enhance their mechanical characteristics, printed materials undergo a post-heating process utilizing microwaves. Specimens were fabricated using polylactic acid filaments containing 2 wt% aluminum. Two sets of specimens were fabricated and subjected to testing in order to evaluate the features of extruded specimens and specimens that underwent post-heating. In terms of mechanical performance, specimens subjected to post-heating exhibited superior results compared to specimens merely subjected to extrusion. The tensile, flexural, and Shore D hardness properties of the specimens exhibited improvements of 5.07, 6.16, and 1.32%, respectively, after being subjected to heating. Furthermore, the wear rate decreased by 13.58 percent. The results of the regression analysis indicate that the print angle and the air gap exhibit the greatest influence on the observed responses. The validation test outcomes exhibit a high level of concordance with the predicted findings. The mechanical and surface properties of components extruded with filler-added material are enhanced by subsequent heating.

Characteristics of Carbon-Fiber-Reinforced Polymer Face Sheet and Glass-Fiber-Reinforced Rigid Polyurethane Foam Sandwich Structures under Flexural and Compression Tests.

Composite sandwich structures are extensively used in aircraft applications. Aircraft components are required to be robust and lightweight. Sandwich structures made of carbon-fiber-reinforced polymer as the facing sheets and milled-glass-fiber-reinforced rigid polyurethane foam with a different glass fiber content as the core structure were prepared. The influence of glass fiber content in the foam on the sandwich structure's mechanical properties was investigated. Flexural and compression tests were performed to assess the mechanical properties of the sandwich structures. Visual inspection and an optical microscope were used to observe the morphology of the polyurethane composite foams at different contents. From the flexural test, the force, facing stress and core shear stress improved with the increase in the milled fiber loading with the maximum increase at 10 wt.% loading and then a drop. Meanwhile, the compression modulus and strength increased up to 20 wt.% loadings and then dropped subsequently. The increase in the polyurethane composite foam's compression strength shifted the bending load's failure type from facing crack failure into core shear failure. The loadings range of 8-10 wt.% showed a transitional of the bending loading failure type. The density of the foams increased with the increase in milled glass fiber loading. At 10 wt.% loading, the density increased by 20%, and it increased by 47% at 30 wt.% loading. At 30 wt% loading, the optical microscope images of the foam showed wall thinning and broken walls that were responsible for the drop in the mechanical properties of the sandwich.

Effect of Cymbopogan citratus Fibre on Physical and Impact Properties of Thermoplastic Cassava Starch/Palm Wax Composites.

Cymbopogan citratus fibre (CCF) is an agricultural waste plant derived from a natural cellulosic source of fibre that can be used in various bio-material applications. This paper beneficially prepared thermoplastic cassava starch/palm wax blends incorporated with Cymbopogan citratus fibre (TCPS/PW/CCF) bio-composites at different CCF concentrations of 0, 10, 20, 30, 40, 50 and 60 wt%. In contrast, palm wax loading remained constant at 5 wt% concentration using the hot moulding compression method. TCPS/PW/CCF bio-composites were characterised in the present paper via their physical and impact properties. The addition of CCF significantly improved impact strength by 50.65% until 50 wt% CCF loading. Furthermore, it was observed that the inclusion of CCF resulted in a little decrement in biocomposite solubility compared to neat TPCS/PW biocomposite from 28.68% to 16.76%. Water absorption showed higher water resistance in the composites incorporating 60 wt.% fibre loading. The TPCS/PW/CCF biocomposites with different fibre contents had 11.04-5.65% moisture content, which was lower than the control biocomposite. The thickness of all samples decreased gradually with increasing fibre content. Overall, these findings provide evidence that CCF waste can be utilised as a high-quality filler in biocomposites due to its diverse characteristics, including improving the properties of biocomposites and strengthening their structural integrity.

Creep Properties and Analysis of Cross Arms' Materials and Structures in Latticed Transmission Towers: Current Progress and Future Perspectives.

Fibre-reinforced polymer (FRP) composites have been selected as an alternative to conventional wooden timber cross arms. The advantages of FRP composites include a high strength-to-weight ratio, lightweight, ease of production, as well as optimal mechanical performance. Since a non-conductive cross arm structure is exposed to constant loading for a very long time, creep is one of the main factors that cause structural failure. In this state, the structure experiences creep deformation, which can result in serviceability problems, stress redistribution, pre-stress loss, and the failure of structural elements. These issues can be resolved by assessing the creep trends and properties of the structure, which can forecast its serviceability and long-term mechanical performance. Hence, the principles, approaches, and characteristics of creep are used to comprehend and analyse the behaviour of wood and composite cantilever structures under long-term loads. The development of appropriate creep methods and approaches to non-conductive cross arm construction is given particular attention in this literature review, including suitable mitigation strategies such as sleeve installation, the addition of bracing systems, and the inclusion of cross arm beams in the core structure. Thus, this article delivers a state-of-the-art review of creep properties, as well as an analysis of non-conductive cross arm structures using experimental approaches. Additionally, this review highlights future developments and progress in cross arm studies.

A Hybrid Machine Learning Approach to Screen Optimal Predictors for the Classification of Primary Breast Tumors from Gene Expression Microarray Data.

The high dimensionality and sparsity of the microarray gene expression data make it challenging to analyze and screen the optimal subset of genes as predictors of breast cancer (BC). The authors in the present study propose a novel hybrid Feature Selection (FS) sequential framework involving minimum Redundancy-Maximum Relevance (mRMR), a two-tailed unpaired t-test, and meta-heuristics to screen the most optimal set of gene biomarkers as predictors for BC. The proposed framework identified a set of three most optimal gene biomarkers, namely, MAPK 1, APOBEC3B, and ENAH. In addition, the state-of-the-art supervised Machine Learning (ML) algorithms, namely Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Neural Net (NN), Naïve Bayes (NB), Decision Tree (DT), eXtreme Gradient Boosting (XGBoost), and Logistic Regression (LR) were used to test the predictive capability of the selected gene biomarkers and select the most effective breast cancer diagnostic model with higher values of performance matrices. Our study found that the XGBoost-based model was the superior performer with an accuracy of 0.976 ± 0.027, an F1-Score of 0.974 ± 0.030, and an AUC value of 0.961 ± 0.035 when tested on an independent test dataset. The screened gene biomarkers-based classification system efficiently detects primary breast tumors from normal breast samples.

Analysis of Parkinson's Disease Using an Imbalanced-Speech Dataset by Employing Decision Tree Ensemble Methods.

Parkinson's disease (PD) currently affects approximately 10 million people worldwide. The detection of PD positive subjects is vital in terms of disease prognostics, diagnostics, management and treatment. Different types of early symptoms, such as speech impairment and changes in writing, are associated with Parkinson disease. To classify potential patients of PD, many researchers used machine learning algorithms in various datasets related to this disease. In our research, we study the dataset of the PD vocal impairment feature, which is an imbalanced dataset. We propose comparative performance evaluation using various decision tree ensemble methods, with or without oversampling techniques. In addition, we compare the performance of classifiers with different sizes of ensembles and various ratios of the minority class and the majority class with oversampling and undersampling. Finally, we combine feature selection with best-performing ensemble classifiers. The result shows that AdaBoost, random forest, and decision tree developed for the RUSBoost imbalanced dataset perform well in performance metrics such as precision, recall, F1-score, area under the receiver operating characteristic curve (AUROC) and the geometric mean. Further, feature selection methods, namely lasso and information gain, were used to screen the 10 best features using the best ensemble classifiers. AdaBoost with information gain feature selection method is the best performing ensemble method with an F1-score of 0.903.

Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis.

Objective: In recent years, among the available tools, the concurrent application of Artificial Intelligence (AI) has improved the diagnostic performance of breast cancer screening. In this context, the present study intends to provide a comprehensive overview of the evolution of AI for breast cancer diagnosis and prognosis research using bibliometric analysis. Methodology: Therefore, in the present study, relevant peer-reviewed research articles published from 2000 to 2021 were downloaded from the Scopus and Web of Science (WOS) databases and later quantitatively analyzed and visualized using Bibliometrix (R package). Finally, open challenges areas were identified for future research work. Results: The present study revealed that the number of literature studies published in AI for breast cancer detection and survival prediction has increased from 12 to 546 between the years 2000 to 2021. The United States of America (USA), the Republic of China, and India are the most productive publication-wise in this field. Furthermore, the USA leads in terms of the total citations; however, hungry and Holland take the lead positions in average citations per year. Wang J is the most productive author, and Zhan J is the most relevant author in this field. Stanford University in the USA is the most relevant affiliation by the number of published articles. The top 10 most relevant sources are Q1 journals with PLOS ONE and computer in Biology and Medicine are the leading journals in this field. The most trending topics related to our study, transfer learning and deep learning, were identified. Conclusion: The present findings provide insight and research directions for policymakers and academic researchers for future collaboration and research in AI for breast cancer patients.

On the Residual Stresses and Fracture Toughness of Glass/Carbon Epoxy Composites.

The resistance to delamination in polymer composite depends on their constituents, manufacturing process, environmental factors, specimen geometry, and loading conditions. The manufacturing of laminated composites is usually carried out at an elevated temperature, which induces thermal stresses in composites mainly due to a mismatch in the coefficient of thermal expansion (CTE) of fiber and matrix. This work aims to investigate the effect of these process-induced stresses on mode-I interlaminar fracture toughness (GI) of Glass-Carbon-Epoxy (GCE) and Glass-Epoxy (GE) composites. These composites are prepared using a manual layup technique and cured under room temperature, followed by post-curing using different curing conditions. Double cantilever beam (DCB) specimens were used to determine GI experimentally. The slitting technique was used to estimate residual stresses (longitudinal and transverse direction of crack growth) inherited in cured composites and the impact of these stresses on GI was investigated. Delaminated surfaces of composites were examined using a scanning electron microscopy (SEM) to investigate the effect of post-curing on the mode-I failure mechanism. It was found that GI of both GE and GEC composites are sensitive to the state of residual stress in the laminas. The increase in the GI of laminates can also be attributed to an increase in matrix deformation and fiber-matrix interfacial bond with the increase in post-curing temperature.

Dwarf Kingfisher-Inspired Bionic Flapping Wing and Its Aerodynamic Performance at Lowest Flight Speed.

This paper aims to understand the aerodynamic performance of a bio-inspired flapping-wing model using the dwarf Kingfisher wing as the bionic reference. The paper demonstrates the numerical investigation of the Kingfisher-inspired flapping-wing followed by experimental validation to comprehend the results fully and examine the aerodynamic characteristics at a flight velocity of 4.4 m/s, with wingbeat frequencies of 11 Hz, 16 Hz, and 21 Hz, at various angles of rotation ranging from 0° to 20° for each stroke cycle. The motivation to study the performance at low speed is based on lift generation as a challenge at low speed as per quasi-steady theory. The temporal evolution of the mean force coefficients has been plotted for various angles of rotation. The results show amplification of the maximum value for the cycle average lift and drag coefficient as the rotation angle increases. The history of vertical force and the flow patterns around the wing is captured in a full cycle with asymmetric lift development in a single stroke cycle. It is observed from the results that the downstroke generates more lift force in magnitude compared to the upstroke. In addition to the rotation angle, lift asymmetry is also affected by wing-wake interaction. Experimental results reveal that there is a stable leading-edge vortex developed in the downstroke, which sheds during the upstroke. An optimum lift and thrust flapping flight can be achieved, with a lift coefficient of 3.45 at 12°. The experimental and parametric study results also reveal the importance of passive rotation in wings for aerodynamic performance and wing flexibility as an important factor for lift generation.

Selection of Natural Fibre for Pultruded Hybrid Synthetic/Natural Fibre Reinforced Polymer Composites Using Analytical Hierarchy Process for Structural Applications.

Application of synthetic fibres in composites has been raising environmental issues due to carbon emissions from the production site and reliability on non-renewable resources upon production. Hence, this research sets as a preliminary study to select suitable natural fibres to be hybridized with glass fibres for the development of sustainable and high-performance hybrid composites as potential alternative to conventional pultruded fibreglass composites in structural profile applications. In this study, analytical hierarchy process (AHP) was conducted to select the ideal natural fibre as reinforcement in the hybrid pultruded FRP composites suitable for structural applications. Hence, 13 natural fibre candidates were selected as alternatives and six criteria were chosen and analysed to select the best candidate for pultruded hybrid FRP. Criteria such as tensile strength, tensile modulus, density, cellulose content, elongation, and availability of fibres were assigned as the standard of selecting natural fibres for the application intended in this study. Among the 13 alternatives, kenaf was found to be the most suitable reinforcement for the application as it yielded the highest priority vector at 0.1. The results were then validated by carrying out sensitivity analysis to ensure kenaf is the most suitable material for the research.

A Hybrid Feature Selection Approach to Screen a Novel Set of Blood Biomarkers for Early COVID-19 Mortality Prediction.

The increase in coronavirus disease 2019 (COVID-19) infection caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has placed pressure on healthcare services worldwide. Therefore, it is crucial to identify critical factors for the assessment of the severity of COVID-19 infection and the optimization of an individual treatment strategy. In this regard, the present study leverages a dataset of blood samples from 485 COVID-19 individuals in the region of Wuhan, China to identify essential blood biomarkers that predict the mortality of COVID-19 individuals. For this purpose, a hybrid of filter, statistical, and heuristic-based feature selection approach was used to select the best subset of informative features. As a result, minimum redundancy maximum relevance (mRMR), a two-tailed unpaired t-test, and whale optimization algorithm (WOA) were eventually selected as the three most informative blood biomarkers: International normalized ratio (INR), platelet large cell ratio (P-LCR), and D-dimer. In addition, various machine learning (ML) algorithms (random forest (RF), support vector machine (SVM), extreme gradient boosting (EGB), naïve Bayes (NB), logistic regression (LR), and k-nearest neighbor (KNN)) were trained. The performance of the trained models was compared to determine the model that assist in predicting the mortality of COVID-19 individuals with higher accuracy, F1 score, and area under the curve (AUC) values. In this paper, the best performing RF-based model built using the three most informative blood parameters predicts the mortality of COVID-19 individuals with an accuracy of 0.96 ± 0.062, F1 score of 0.96 ± 0.099, and AUC value of 0.98 ± 0.024, respectively on the independent test data. Furthermore, the performance of our proposed RF-based model in terms of accuracy, F1 score, and AUC was significantly better than the known blood biomarkers-based ML models built using the Pre_Surv_COVID_19 data. Therefore, the present study provides a novel hybrid approach to screen the most informative blood biomarkers to develop an RF-based model, which accurately and reliably predicts in-hospital mortality of confirmed COVID-19 individuals, during surge periods. An application based on our proposed model was implemented and deployed at Heroku.

Effect of Different Pre-Treatment on the Microstructure and Intumescent Properties of Rice Husk Ash-Based Geopolymer Hybrid Coating.

Despite the growing popularity of rice husk ash (RHA) in various applications, limited research has been devoted to identify the influence of silica content in RHA on the intumescent properties. The present work aims to introduce a novel and economical geopolymer hybrid fire retardant coating by utilizing the use of RHA. The silica from Rice husk (RH) was extracted using distilled water and hydrochloric acid as leaching agents and subjected to pyrolysis treatment. X-ray fluorescence (XRF) analysis indicated that RH that underwent HCl pre-treatment at 600 °C for one hour produced a high purity amorphous silica content of 93.92%. XRD measurements revealed that HCl pretreatment increased the crystallization temperature of RHA to 1000 °C and retained the amorphous state of silica for 2 h. In a fire resistance test, temperature at the equilibrium and time taken to reach 200 °C for sample S3 (93.92% wt. silica) showed 5.83% and 3.48% improvement compared to sample S1 (87.49% wt. silica). The microstructure analysis showed that sample S1 possessed bigger pores on the coating surface while an increment in silica content in sample S3 produced a dense foam structure. Results from a fire resistance test were supported by the Energy dispersive X-ray (EDX) analysis of the sample. The oxygen-to-carbon ratio of S1 and S3 coating samples were 1.695 and 1.622 respectively, which indicated that lower oxygen-to-carbon ratio in sample S3 coating resulted in better anti-oxidant properties. Interestingly, the increment of SiO2 content in RHA efficiently improved the compactness of the char layer, which resulted in a relatively higher fire-retardant efficiency. RHA proved to be a promising environmentally friendly strategy to replace halogenated fire retardant materials.

A Review on the Effect of Fabric Reinforcement on Strength Enhancement of Natural Fiber Composites.

The main objective of this study is to examine the impact of reinforcements on the strength of natural fiber composites. Recent advancements in natural fiber composites have minimized the usage of man-made fibers, especially in the field of structural applications such as aircraft stiffeners and rotor blades. However, large variations in the strength and modulus of natural fiber degrade the properties of the composites and lower the safety level of the structures under dynamic load. Without compromising the safety of the composite structure, it is significant to enrich the strength and modulus of natural fiber reinforcement for real-time applications. The strength and durability of natural fiber can be enriched by reinforcing natural fiber. The reinforcement effect on natural fiber in their woven, braided, and knit forms enhances their structural properties. It improves the properties of natural fiber composites related to reinforcement with short and random-orientation fibers. The article also reviews the effect of the hybridization of natural fiber with cellulosic fiber, synthetic fiber, and intra-ply hybridization on its mechanical properties, dynamic mechanical properties, and free vibration characteristics, which are important for predicting the life and performance of natural fiber composites for weight-sensitive applications under dynamic load.

Effect of Prosopis Juliflora Thorns on Mechanical Properties of Plastic Waste Reinforced Epoxy Composites.

Plastics are unavoidable at this times, putting our planet in danger. The Prosopis juliflora (PJ) thorns are collected, processed, and powdered. The mechanical characteristics of these powders are examined when combined with polymer composites. Pores are the main cause of moisture input, hence using powder filler materials reduces the number of pores in the composite, increasing water resistance. The composites are made by altering three parameters: waste plastic content, filler powder composition, and chemical treatment. It was discovered that the integration of thorn powder increased the wear resistance. The composites were tested in accordance with ASTM standards, and the results were optimized. Based on the results, composite specimens were created and tested for validation.

Corrigendum: Evolution of research trends in artificial intelligence for breast cancer diagnosis and prognosis over the past two decades: A bibliometric analysis.

[This corrects the article DOI: 10.3389/fonc.2022.854927.].