The process of retting bast fiber plants for the production of long fiber has presented major challenges. Water retting, dew retting, chemical extraction, and micro-organism (fungi, enzymes) ...techniques were applied to the extraction of natural fibers. The two nettle samples were extracted with water retting for 14 days and dew retting for 4 weeks. This research investigated the effects on the traditional retting process of nettle fiber by fungi and bacteria formation in lignocellulosic. The latter biological extraction methods successfully degraded the lignin and pectin materials of the fiber and increases the cellulose content. These extraction methods produced high quality fiber and tensile strength at a low cost. This study determined the chemical, physical, and mechanical characteristics such as fiber cellulose, non-cellulosic content, tensile strength, tenacity, and elongation break to see how treatments affected them. The treated fiber surface morphology was characterized using scanning electron microscopy. To evaluate functional group alterations, Fourier-transform infrared spectroscopy was used on the fiber specimen.
Wireless Sensor Network (WSN) is very effective for research community in recent years in demand and adaptive applications. The nodes of WSN are limited with their energy which requires the energy to ...be utilized in efficient manner. The factor of energy depletion would be reduced by grouping the nodes to support routing and data collection. Different algorithms recommended towards the energy management in different conditions. This research work proposes an Energy Efficient Cluster based Multilevel Hierarchical Routing (EECMHR) technique to support effective data collection in WSN. The method splits the entire network region into number of regions and clusters where each cluster has single Cluster Head (CH). The nodes of WSN are grouped in hierarchical level which performs routing through the CH identified which is rotated at each timestamp according to energy conditions. EECMHR approach has been simulated by using the Network Simulator 2 under different scenarios, and its performance is compared with existing clustering algorithms.
Software reliability is the important attribute for complex computing systems to provide reliability could cause series issues such as extra cost, development delay and image of the software solution ...providers. Hence, ensuring the reliability of software before deliver to the customer is essential part for the company. Finding the error in right time with reasonable degree of accuracy helps to prevent the consequences. Several software reliability growth models developed and used to measure the trustworthiness based on development and testing phases with unrealistic assumption over the environment and applied Block box methodologies while constructing model. This paper presents well established statistical time series (S)ARIMA approach for developing a forecasting model that able to provide significantly improved reliability prediction. Using real time publicly available software failure sets, the prediction of proposed model is developed and compared with previously available reliability models.
This paper explores the tribological efficiency of Musa acuminata (B - Banana)/Corchorus capsularis (J - Jute) fibers reinforced epoxy-based tribo-composites which was fabricated by varying the ...content of these fibers (MC1 - 15 wt.% of Banana and 15 wt.% of Jute, MC2 - 20 wt.% of Banana and 20 wt.% of Jute and MC3 - 25 wt.% of Banana and 25 wt.% of Jute) by means of the compression molding method. The pin-on-disk dry wear check was carried out by varying the load conditions (20, 40 and 60 N) and sliding distance (602.88, 1205.76 and 1808.64 m). The experimental outcomes disclosed that the hybrid composites have superior properties than the single fiber-reinforced composites. Moreover, the Scanning Electron Microscopy (SEM) was used to analyze the damaged surfaces of the samples.
The present research of the article is used to describe the microwave sintering of nitinol composite and its surface topography analysis by atomic microscopy (AFM). Scanning electron microscopy (SEM) ...and energy dispersive spectroscopy (EDS) technique is used to analyze the characterization and alloying composition of the composite. Silicon carbide is the reinforcing particle between nickel and titanium. Wear test is conducted to investigate the worn surface morphology of the sintered specimen. The synthesized composite has exceptional material properties such as ultimate tensile strength of 1020MPa, 16% of elongation to fracture, 7.51 g/cc of density and hardness of 310 HV. Low wear rate of 0.01 mg/m is perceived at minimum load and minimum sintering time. Surface roughness is varied from 22.87 to 54.05nm at different sintering time. Maximum roughness height of 327.6 nm is observed in the surface profile of the composite.
The combination of nickel (Ni) and titanium alloy (Ti) with silicon carbide (SiC) reinforcement offers promising avenues for research and applications due to their exceptional resistance to heat, ...oxidation, and corrosion properties. These studies investigate the influence of silicon carbide (SiC) on Ni-Ti matrix materials, focusing on Ni-Ti-SiC composites fabricated through the microwave sintering method. The microstructure analysis reveals uniform dispersion of Ni and Ti, indicating desirable properties. This study utilized the Taguchi methodology to optimize the wear rate of Ni-Ti-SiC composites by examining sliding distance, load, and sliding velocity as essential variables. The experimental findings showed a minimum wear rate of 0.014 mg/m under certain conditions and a maximum of 0.052 mg/m under other conditions. The optimal wear rate was determined using the signal-to-noise ratio (SN ratio) at a sliding distance of 1500 m, load of 20N, and sliding velocity of 1 m/s. Sliding distance was found to be the most significant factor affecting wear rate (55%), followed by load (32%) and sliding velocity (7.3%) according to the variance analysis. Scanning electron microscope (SEM) analysis showed increased wear grooves and a smoother surface in the specimen with the lower wear rate. Atomic force microscopy was used to analyze the worn surfaces, revealing detailed 3D image patterns. Surface parameters showed significant variations between surfaces with higher and lower wear rates. Surfaces with lower wear rates exhibited minimum average roughness (6.69 nm) and maximum peak-to-valley roughness (88.65 nm).
The aim of this paper is to study the effect of volume fraction on mechanical and physical properties such as tensile, flexural, impact, interlaminar shear strength, void content and water absorption ...of flax and bamboo fibers reinforced hybrid epoxy composites. Flax and bamboo fibers reinforced epoxy resin matrix hybrid composites have been fabricated by compression molding techniques. The hybrid composites were fabricated with different volume fraction of fibers. SEM analysis on the hybrid composite materials was performed to analyze the bonding behavior of materials and internal structure of the fractured surfaces. The effect of chemical treatment of flax and bamboo fibers was verified by FTIR analysis. The results showed that the tensile, impact, flexural and ILSS are maximum for 40:0 (flax: bamboo) hybrid composites. The void content decreased for 20:20 (flax:bamboo) composites due to tightly packed flax fiber and more compatibility towards epoxy resin.
Renewable energy sources are present copiously in the nature and are good for environmental conservation as they restore themselves and thus have considerable potential in the near future. It is ...hence important to concentrate on the forecast of these energy sources in order to make effective use of them as soon as possible. This paper is focused primarily on solar energy. There are many approaches that could be applied for the prediction of global solar radiation (GSR). In the field of artificial intelligence (AI), the forecasting of solar resources has moved from conventional mathematical approaches to the use of intelligent techniques. The extent to which data based decisions are made for planning such as judicious and functional for the solar energy sector has been increased to a large extent by this giant step. In modelling challenging and unpredictable connections in between a set of input data and output data along with specific patterns that occur between datasets, AI techniques have demonstrated increasing reliability. In this regard, purpose of this paper is to provide a synopsis of solar energy forecasting methods utilizing machine learning and deep learning approaches to the best of our understanding.
