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The purpose of this paper is to evaluate the effect of titanium oxide (TiO₂) and yttrium oxide (Y₂O₃) nanoparticles-reinforced pure aluminium (Al) on the mechanical properties of hybrid aluminium matrix nanocomposites (HAMNCs).

The HAMNCs were fabricated via a vacuum die-assisted stir casting route by a two-step feeding method. The varying weight percentages of TiO₂ and Y₂O₃ nanoparticles were added as 2.5, 5, 7.5 and 10 Wt.%.

Scanning electron microscope images showed the homogenous dispersion of nanoparticles in Al matrix. The tensile strength by 28.97%, yield strength by 50.60%, compression strength by 104.6% and micro-hardness by 50.90% were improved in HAMNC1 when compared to the base matrix. The highest values impact strength of 36.3 J was observed for HAMNC1. The elongation % was decreased by increasing the weight percentage of the nanoparticles. HAMNC1 improved the wear resistance by 23.68%, while increasing the coefficient of friction by 14.18%. Field emission scanning electron microscope analysis of the fractured surfaces of tensile samples revealed microcracks and the debonding of nanoparticles.

The combined effect of TiO₂ and Y₂O₃ nanoparticles with pure Al on mechanical properties has been studied. The composites were fabricated with two-step feeding vacuum-assisted stir casting.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Several graphs, streamlines, isotherms and 3D plots are illustrated to enlighten the noteworthy fallouts of the investigation. Embedding flow factors for velocity, induced magnetic field and temperature have been determined using parametric analysis.

Ternary hybrid nanofluids has outstanding hydrothermal performance compared to classical mono nanofluids and hybrid nanofluids owing to the presence of triple tiny metallic particles. Ternary hybrid nanofluids are considered as most promising candidates in solar energy, heat exchangers, electronics cooling, automotive cooling, nuclear reactors, automobile, aerospace, biomedical devices, food processing etc. In this work, a ternary hybrid nanofluid flow that contains metallic nanoparticles over a wedge under the prevalence of solar radiating heat, induced magnetic field and the shape factor of nanoparticles is considered. A ternary hybrid nanofluid is synthesized by dispersing iron oxide (Fe3O4), silver (Ag) and magnesium oxide (MgO) nanoparticles in a water (H₂O) base fluid. By employing similarity transformations, we can convert the governing equations into ordinary differential equations and then solve numerically by using the Runge–Kutta–Fehlberg approach.

There is no fund for the research work.

This kind of study may be used to improve the performance of solar collectors, solar energy and solar cells.

This investigation unfolds the hydrothermal changes of radiative water-based Fe₃O₄-Ag-MgO-H₂O ternary hybrid nanofluidic transport past a static and moving wedge in the presence of solar radiating heating and induced magnetic fields. The shape factor of nanoparticles has been considered in this study.

Advancement in optimization of VLSI circuits involves reduction in chip size from micrometer to nanometer level as well as fabrication of a billions of transistors in a single die where global routing problem remains significant with a trade-off of power dissipation and interconnect delay. This paper aims to solve the increased complexity in VLSI chip by minimization of the wire length in VLSI circuits using a new approach based on nature-inspired meta-heuristic, invasive weed optimization (IWO). Further, this paper aims to achieve maximum circuit optimization using IWO hybridized with particle swarm optimization (PSO).

This paper projects the complexities of global routing process of VLSI circuit design in mapping it with a well-known NP-complete problem, the minimum rectilinear Steiner tree (MRST) problem. IWO meta-heuristic algorithm is proposed to meet the MRST problem more efficiently and thereby reducing the overall wire-length of interconnected nodes. Further, the proposed approach is hybridized with PSO, and a comparative analysis is performed with geosteiner 5.0.1 and existing PSO technique over minimization, consistency and convergence against available benchmark.

This paper provides high performance–enhanced IWO algorithm, which keeps in generating low MRST value, thereby successful wire length reduction of VLSI circuits is significantly achieved as evident from the experimental results as compared to PSO algorithm and also generates value nearer to geosteiner 5.0.1 benchmark. Even with big VLSI instances, hybrid IWO with PSO establishes its robustness over achieving improved optimization of overall wire length of VLSI circuits.

This paper includes implications in the areas of optimization of VLSI circuit design specifically in the arena of VLSI routing and the recent developments in routing optimization using meta-heuristic algorithms.

This paper fulfills an identified need to study optimization of VLSI circuits where minimization of overall interconnected wire length in global routing plays a significant role. Use of nature-based meta-heuristics in solving the global routing problem is projected to be an alternative approach other than conventional method.

Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.

This study aims to produce a superhydrophobic fabric surface with a layered rough structure and which are resistant to droplet adhesion. Polydimethylsiloxane (PDMS) systems doped with stearic acid modified titanium dioxide (SA-TiO₂) nanoparticles was sprayed onto the surface of cotton fabric.

This experiment therefore uses a simple method to prepare superhydrophobic textiles by spraying SA-TiO₂ particles mixed with PDMS onto the surface of cotton fabrics. The effects of the ratio of stearic acid to TiO₂, spraying times and tension on the apparent morphological structure and hydrophobic properties of the cotton fabric were investigated.

The results showed that the stearic acid-modified TiO₂ nanoparticles were hydrophobic and more uniformly dispersed in the PDMS solution. When the modification ratio of stearic acid to TiO₂ was 3:5, the water contact angle of cotton fabric was 155.48° and sliding angle was 6.67° under the applied tension for three times of spraying, showing superhydrophobicity. The fabric shows super hydrophobic and anti-adhesive properties to a wide range of liquids such as cola, dyeing liquids, tea, milk and simulated blood. The surface tension of the liquid shows a negative correlation with its adhesion to the fabric.

The SA-TiO₂ and PDMS were applied to the fabric surface by spraying, which not only gave the fabric superhydrophobic properties, but also created anti-adhesion to a wide range of droplets.

The superhydrophobic cotton fabrics prepared by this method showed good anti-adhesive behavior to common stains and simulated blood and can be used in the development of medical protective textiles.

Modification of TiO₂ with stearic acid to prepare SA-TiO₂ with excellent hydrophobic properties, which was mixed with PDMS to make suspensions. Fluorine-free superhydrophobic fabrics were prepared by spraying method. It also exhibited excellent anti-adhesive properties against blood, providing a reference for the preparation of self-cleaning and anti-adhesive surgical gowns.

Renewable energy alternatives and nanoscale materials have gained huge attention in recent years due to the problems associated with fossil fuels. The recyclable battery is one of the recent developments to address the energy requirement issues. In this work, the development of nanoscale materials is focused on using green synthesis methods to address the energy requirements of hybrid electric vehicles.

The current research focuses on developing metal oxide nanoscale materials (NANO-SMs). The Zno-Aloe vera NANO-SM is prepared using the green synthesis method. The developed nanoscale materials are characterized using analysis methods like FESEM, TEM, XRD and FTIR.

The average size of ZnO-Aloe vera mono-crystalline was recorded as 60–70 nm/Hexagonal shape. The nanoscale materials are used for the detection of LPG gases. The sensitivity observed was 48%. The response time and recovery time were recorded as 8–10 s and 230–250 s, respectively. The average size of SnO₂-green papaya leaves poly-crystalline was recorded as 10–20 nm/powder form.

Nanoscale materials are developed using green synthesis methods for hybrid vehicle applications. The nanoscale materials are used for the detection of harmful gases in hybrid vehicles.

This study aims to perform the surface treatment of synthetic α-Fe₂O₃ red iron oxide pigment with hydrolysate 3-aminopropyl silane (A) and colloidal silica (CS) and investigate the effects of surface-treated pigment on the styrene acrylic (SA) emulsion and polyurethane (PU) dispersion.

For this purpose, firstly red iron oxide particles were modified with A and CS separately in an aqueous medium. After isolation of the modified iron oxide were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS) and scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS). Moreover, the degree of the dispersion stability of the modified pigment in coatings with SA emulsion and PU dispersion was investigated by using an oscillation rheometer. Loss (G''), storage (G') modulus, loss factor [tan(δ)] and yield stress (τ⁰) values were determined by performing amplitude and frequency sweep tests.

The τ⁰ in SA coatings decreases with the amount of used A and increases with the amount of used CS. The τ⁰ decreases as the amount of used A and CS in PU coatings increases. The use of CS on red iron oxide pigments causes storage modulus to increase in SA coatings at low angular frequencies, while it causes a decrease in PU coatings.

To the best of the authors’ knowledge, for the first time, the suspended state of the iron oxide hybrid pigment formed with CS in the coating was investigated rheologically in this study.

Toner is a crucial dry colorant composite used in printing based on the electrophotographic process. The quality of printed images is greatly influenced by the toner production method and material formulation. Chemically in situ polymerization methods are currently preferred. This paper aims to optimize the characteristics of a composite produced through emulsion polymerization using common raw materials for electrophotographic toner production.

Emulsion polymerization provides the possibility to optimize the physical and color properties of the final products. Response surface methodology (RSM) was used to optimize variables affecting particle size (PS), PS distribution (PSD), glass transition temperature (T_g°C), color properties (ΔE) and monomer conversion. Box–Behnken experimental design with three levels of styrene and butyl acrylate monomer ratios, carbon black pigment and sodium dodecyl sulfate surfactant was used for RSM optimization. Additionally, thermogravimetric analysis and surface morphology of composite particles were examined.

The results indicated that colorants with small PS, narrow PSDs, spherical shape morphology, acceptable thermal and color properties and a high percentage of conversion could be easily prepared by optimization of material parameters in this method. The anticipated outcome of the present inquiry holds promise as a guiding beacon toward the realization of electrographic toner of superior quality and exceptional efficacy, a vital factor for streamlined mass production.

To the best of the authors’ knowledge, for the first time, material parameters were evaluated to determine their impact on the characteristics of emulsion polymerized toner composites.