Search

Synthesis of silver nanoparticles (Ag-NPs) using green nanotechnology has gained enormous attention due to their extensive range of antibacterial applications such as in the food packaging industry. In this study, Ag-NPs were successfully synthesized using Goji Berry (a fruit of wolfberry) extract. The basic aim of this study is to analyze the antibacterial response of Ag-NPs in gelatin scaffolds. Positively, the reducing agents that are already present in the Goji Berry extract behave as capping and stabilizing agents, so there is no need to add additional constituents from outside. This was then followed by the characterization of samples. The crystallinity of Ag-NPs was determined by X-ray Diffractometer (XRD) that revealed the FCC structure of the sample. The formation of silver particles was confirmed by Ultraviolet–Visible (UV–Vis) spectroscopy. The surface morphology of Ag-NPs was found to be almost spherical, which was determined by a transmission electron microscope and showed spherical particles having an average diameter of 22 nm. Moreover, Fourier transform infrared spectroscopy showed the presence of hydrocarbon groups bonded with Ag-NPs. The antibacterial properties of samples were analyzed by the gram-positive disk diffusion method. It was enhanced when Ag-NPs concentration in gelatin scaffolds increased, thereby producing an 18 mm zone of inhibition.

Cupola slag is a waste material of the steel and iron industries. Its composition is determined by the cupola furnace and other elements used in steel and iron manufacturing. This paper investigates the characterization behavior of various cupola slag materials. As a result, x-ray fluorescence (XRF), x-ray diffraction (XRD), thermogravimetry differential thermal analysis, and scanning electron microscopy (SEM) methods were used to characterize three cupola slag samples from distinct origins. In addition, various physical properties were used to compare different cupola slags. The specific gravity values of CS-1 (cupola slag-1 sample), CS-2 (cupola slag-2 sample), and CS-3 (cupola slag-3 sample) are 1.36, 2.5, and 2.917, respectively. The density and water absorption for CS-1, CS-2, and CS-3 are 1414.86, 1477.71, and 1796 kg/m3, and 0.37%, 0.32%, and 0.26%, respectively. Cupola slag also includes a larger percentage of lime, according to XRF data, which contributes to its improved binding characteristics. A higher calcium oxide content in CS-3 could facilitate the pozzolanic process. The presence of angular particles that aid in material binding is seen in the SEM image. Compounds with a nanostructure are then flawlessly blended into the mixture and grouped with calcium alumina silicates formed by cement hydration. The XRD pattern of cupola slag exhibits high peaks, indicating that the material is crystalline in character and can be utilized as sand. It also shows the presence of other chemical compounds, such as silica, which ranges from 30% to 45%. CS-1 and CS-2 have comparable XRD patterns. However, CS-3 has a somewhat different pattern because of the greater CaO content. Weight loss begins at higher temperatures, which shows that the material is stable at higher temperatures, according to a thermo-gravimetric study. The differential thermal analysis curve of CS-3 indicates that the material remains stable up to a temperature of 600 °C. The physical characteristics of all cupola slag samples show that cupola slag may be utilized to make sustainable building materials because of its lower specific gravity, density, and water absorption.

Waspaloy, a nickel-based super alloy, has much potential in gas turbine component production. Machining this type of material using the traditional manufacturing method is very hard. Hence, electrical discharge machining (EDM) was selected for the investigation of the machining characteristics of Waspaloy by choosing the control parameters, namely, current, voltage, pulse on time, pulse off time, and spark gap. Gray relational analysis is carried out using 18 experiments according to an orthogonal array (L18) to determine the gray relation grade for this machining process with multiple machining characteristics such as material removal rate (MR), tool wear rate (TW), and surface roughness (SR). The significant objective of the multi objective optimization to attain high MR, low TW, and low SR is found out. From gray relation optimization, the optimum machining conditions for EDM, namely, current: 26 A, voltage: 200 V, pulse on time: 120 μs, pulse on time: 10 μs, and spark gap: 0.15 m, have been identified. The foremost parameter affecting the responses was found to be peak current.

Offering intended products at an affordable price is a highly challenging task in a business environment. To meet such challenges, industrial experts and researchers have different approaches, such as shifting the purchase of materials from import mode to local vendors and optimizing machining cost by optimizing process parameters, waste reduction, rework reduction, and technological improvements. The novelty of this study lies in reducing material cost by accurate materials forecasting to minimize storage cost, ablation cost, average annual consumption cost, etc. This study aims to compare the efficacy of the Seasonal Decomposition Method (SDM) against Multi-Echelon Inventory Optimization (MEIO) for minimizing inventory through accurate materials forecasting. Through rigorous evaluation and analysis, this research seeks to clarify the strengths and weaknesses of each approach, thereby providing insights into their applicability and effectiveness in addressing inventory management challenges across diverse seasonal demand patterns. The sample size was taken as 50 per group. The G-power applied is about 80%. The significance value obtained is 0.001 (p < 0.05), indicating a statistically significant difference between the two algorithms used for inventory reduction and materials forecasting. SDM (63.43) outperforms MEIO (48.57) in terms of accuracy. SDM yielded a better accuracy when compared to MEIO for inventory minimization and materials forecasting.

Poor planning and scheduling increase buying, storage, and obsolescence expenses. Material shortages increase labor, machine optimum time, etc. Industrial raw materials, semi-finished items, spares, and consumables have distinct consumption patterns, reorder points, purchase lead times, quantity limits, discounts, etc. To save money, machine learning predicts demand and prepares materials. This study employs ARIMA or Support Vector Machine (SVM) machine learning-based forecasting approaches to forecast materials for less inventory. Feature engineering eliminates seasonality, time series, and external demand and ignores data irregularities, missing figures, and disparities. This approach needs to adapt traits to factors, separate test and training data, and consider many future models to represent the best forecasts. Forecast reliability and consistency were examined for each model. Inventory management systems were evaluated for computational complexity and installation ease and found implementation issues. Both models’ input data and resilience were examined using sensitivity analysis. Accurate prediction SVM and ARIMA predict material demand differently. Meaningful statistics show the optimal model. Performance differences between SVM and ARIMA enhance model selection. Thinking about the execution of high inventory system integration and computational complexity, response surface methodology chooses factorial variables with the highest or lowest responses. Analysis of variance, factor analysis, and effect modeling expansions demonstrated for the response.