The development of environmentally benign silicone composites from sugar palm fibre and silicone rubber was carried out in this study. The mechanical, physical, and morphological properties of the composites with sugar palm (SP) filler contents ranging from 0% to 16% by weight (wt%) were investigated. Based on the uniaxial tensile tests, it was found that the increment in filler content led to higher stiffness. Via dynamic mechanical analysis (DMA), the viscoelastic properties of the silicone biocomposite showed that the storage modulus and loss modulus increased with the increment in filler content. The physical properties also revealed that the density and moisture absorption rate increased as the filler content increased. Inversely, the swelling effect of the highest filler content (16 wt%) revealed that its swelling ratio possessed the lowest rate as compared to the lower filler addition and pure silicone rubber. The morphological analysis via scanning electron microscopy (SEM) showed that the sugar palm filler was evenly dispersed and no agglomeration could be seen. Thus, it can be concluded that the addition of sugar palm filler enhanced the stiffness property of silicone rubber. These new findings could contribute positively to the employment of natural fibres as reinforcements for greener biocomposite materials.

Materials that exhibit highly nonlinear behaviour are intricate to study. This is due to their physical properties, as they possess a very large deformation. Silicone rubber is among the materials that can be classified as possessing such characteristics, despite their being soft and frequently applied in medical applications. Due to their low mechanical properties, however, it is believed that a filler addition could enhance them. This study, therefore, aims to investigate the effect of the addition of bamboo cellulosic filler to silicone rubber in terms of its compressive properties in order to quantify its material constants using the hyperelastic theory, specifically the Neo-Hookean and Mooney–Rivlin models. The specimens’ compressive properties were also compared between specimens immersed in seawater and those not immersed in seawater. The findings showed that the compressive properties, stiffness, and compressive strength of the bamboo cellulosic fibre reinforced the silicone rubber biocomposites, improved with higher bamboo filler addition. Specimens immersed in seawater showed that they can withstand a compressive load of up to 83.16 kPa in comparison to specimens not immersed in seawater (up to 79.8 kPa). Using the hyperelastic constitutive models, the Mooney–Rivlin model displayed the most accurate performance curve fit with the experimental compression data with an R² of up to 0.9999. The material constant values also revealed that the specimens immersed in seawater improved in stiffness property, as the C₁ material constant values are higher than for the specimens not immersed in seawater. From these findings, this study has shown that bamboo cellulosic filler added into silicone rubber enhances the material’s compressive properties and that the rubber further improves with immersion in seawater. Thus, these findings contribute significantly towards knowledge of bamboo cellulosic fibre–reinforced silicone rubber biocomposite materials.

As energy systems become increasingly complex, there is a growing need for sustainable and efficient energy management strategies that reduce greenhouse gas emissions. In this paper, multi-energy systems (MES) have emerged as a promising solution that integrates various energy sources and enables energy sharing between different sectors. The proposed model is based on using an Attractive Repulsive Shuffled Frog-Leaping (ARSFL) algorithm that optimizes the scheduling of energy resources, taking into account constraints such as capacity limitations and environmental regulations. The model considers different energy sources, including renewable energy and a power-to-gas (P2G) network with power grid, and incorporates a demand–response mechanism that allows consumers to adjust their energy consumption patterns in response to price signals and other incentives. The ARSFL algorithm demonstrates superior performance in managing and minimizing energy purchase uncertainty compared to the particle swarm optimization (PSO) and genetic algorithm (GA). It also exhibits significantly reduced execution time, saving approximately 1.59% compared to PSO and 2.7% compared to GA.

This article outlines the creation of a health detection system designed to identify rest tremors in Parkinson's disease (PD). The system leverages internet of things (IoT) technology to measure frequencies derived from human activities, excluding other symptoms such as heartbeat and voice recording. The core components include the Arduino Nano microcontroller and the Node ESP MCU 8266 V3 for data processing. The system employs an accelerometer sensor positioned at the body's center axis to gauge the frequency of motor symptoms associated with resting tremors, particularly when the hands are at rest in the lap. The findings indicate that 9 samples displayed symptoms of rest tremor. The recorded p-value, standing at 0.884, signifies a robust correlation between the two variables at a significant threshold of 0.01 or 1%.

Fused Deposition Modeling (FDM) is classified as the most commonly used 3D printing process due to its low cost, wide range of material selection, and high accuracy. As an additive manufacturing method, FDM selectively deposits a melted plastic material layer by layer to produce a 3D object according to a geometry defined by a CAD model. The 3D printing process parameters, including infill density, printing speed, and printing orientation, have a huge effect on the mechanical properties of the 3D printed parts. Thus, finding the optimum 3D printing parameters is a very significant task that enriches the FDM 3D printing process, resulting in 3D printed parts with augmented mechanical performance. The present study investigates the effects of the FDM injector’s nozzle diameter and printing path direction (raster angle) on the mechanical properties of the nylon/carbon fiber composite 3D printed parts. The two targeted parameters are optimized through experimental tests on the elastic and flexural strength. Their impact on the nylon/carbon fiber composites’ microstructure is also explored deploying Scanning Electron Microscopy (SEM). The findings provide a comprehensive understanding of the mechanical performance of nylon/carbon fiber composite 3D printed parts. In addition, inspecting the internal microstructure of the materials, especially at the interface zone between the nylon and carbon fiber, provides an explanation of the material composites’ failure mechanism under various loads.