Search

This study investigates the impact of sheet metal thickness and strain generated on a cold forging process. A cold forging die was manufactured based on a numerically modelled product design that considered stress concentration die profiles. Experiments involved cold forging various sheet metals (lead, brass, aluminium, and steel) with thicknesses ranging from 2mm to 6mm to assess their influence on the thickness and strain. An assumption was formulated using Solidworks to determine the appropriate sheet metal thickness based on the material type. This assumption, based on volume considerations, showed practical applicability for selecting sheet metals with thicknesses close to those designed in the numerical modelling. The results revealed that softer materials, such as lead and aluminium, closely approximated the design thickness, particularly in the central region of the die profile, with error deviations of 1.14 % and 4.36 %, respectively. In contrast, harder materials demonstrated larger deviations from the design, with minimum errors of 4.94 % for brass and 5.73 % for steel. All sheet metals underwent compressive strain in the central region of the die profile due to the stamping operation. This work demonstrates a practical approach for selecting sheet metals based on material and thickness considerations, providing insights into the behaviour of different materials during the cold forging process.

Table grape berries are classified as a perishable crop that deteriorates quickly after harvest. The application of melatonin after harvest was found to be effective for retarding senescence and slowing ripening. In the current study, we tested the influence of two melatonin concentrations (50 and 100 µmol) as a postharvest application on quality, bioactive compounds, and enzyme activities of grape berries cv “Crimson” stored at 0 ± 1 °C and 90% relative humidity (RH) for 35 days. Our results indicated that melatonin application extends the shelf-life of berries by reducing weight loss and maintaining total soluble solids (TSS), titratable acidity (TA), berry adherence strength, and firmness. Melatonin treatment also reduced pectin methyl esterase (PME) and polygalactouranase (PG) enzyme activities compared to the control. Moreover, O2•− and H2O2 rates in berries were reduced by high melatonin concentration. Moreover, peroxidase (POD) and catalase (CAT) enzyme activities were increased by melatonin application. Our findings suggested using melatonin postharvest to increase shelf life and maintain quality attributes during refrigerated storage, which could be advantageous on a large scale.

Plant growth regulators can affect the primary and secondary metabolites of various plant species. However, the effect of paclobutrazol (PBZ) on the composition of lavender oil, especially related to the terpenoid pathway, is still unclear in literatures. In this study, the effect of PBZ as a foliar spray (0.200, 400 and 600 ppm) on the vegetative growth, phytochemical content, and both antioxidant and antimicrobial properties of lavender oil were investigated. The results indicated that all examined PBZ treatments led to a significant (p ≤ 0.05) decrease in growth parameters compared to the untreated plants. Meanwhile, the yield of essential oil was significantly decreased by the treatment of PBZ at 200 ppm compared to the control. In contrast, applied-PBZ significantly enhanced the chlorophyll content and displayed a marked change in the composition of the essential oil. This change included an obvious and significant increase in 3-carene, eucalyptol, γ–terpinene, α-pinocarvone, caryophyllene, β-vetivenene, β-santalol, ledol, geranyl isovalerate, farnesol, caryophyllene oxide, and phytol percentage. Generally, the highest significant values were achieved by the treatment of 400 ppm compared to the other treatments. Furthermore, this treatment showed the highest free radical scavenging activity against DPPH (1,1-diphenyl-2-picrylhydrazyl) by 13% over the control. Additionally, to determine the antimicrobial activities of the extracted oil, each treatment was examined against two strains of Gram positive bacteria (S. aureus and B. cereus), two strains of Gram negative bacteria (S. enteritidis and E. coli), and two fungal species (C. albicans and A. niger) represent the yeast modal and filamentous fungus, respectively. The findings demonstrated that all examined species were more sensitive to the oil that was extracted from lavender plants, treated with 400 ppm PBZ, compared to the other concentrations.

The power system is one of the most susceptible systems to failures, which are most frequently caused by transmission line faults. Transmission line failures account for 85% of all power system malfunctions. However, over the last decade, numerous fault detection methods have been developed to ensure the reliability and stability of power systems. A hybrid detection method based on the idea of redundancy property is presented in this paper. Because the continuous wavelet transform itself does not extract fault features for small defects effectively, the stationary wavelet transform approach is employed to assist in their detection. As a result of its ability to decompose the signal into high- and low-frequency components, undecimated reconstruction by using the algebraic summation operation (ASO) is used. This approach creates redundancy, which is useful for the feature extraction of small defects and makes faulty parts more evident. The numerical value of the redundancy ratio’s contribution to the original signal is approximately equal to 36%. Following this method for redundant signal reconstruction, a continuous wavelet transform is used to extract the fault characteristic significantly easier in the time-scale (frequency) domain. Finally, the suggested technique has been demonstrated to be an efficient fault detection and identification tool for use in power systems. In fact, using this advanced signal processing technique will help with early fault detection, which is mainly about predictive maintenance. This application provides more reliable operation conditions.

This paper proposes an effective approach for predicting quality of life (QoL) for dependent individuals in guardianship entities. In addition, it aims to improve the QoL of people with intellectual disabilities. The proposed QoL prediction approach employs machine learning (ML) techniques to model the relationship between eight aspects of QoL and the corresponding QoL index. It determines whether or not a person needs assistance based on the index value. The proposed approach determines the priority of care (PoC) value for each aspect of a person. Based on PoC, the deficit aspect is determined, followed by the type of assistance a person requires, based on the decision priorities. It also generates a support report with suggested actions to highlight the level in that aspect. In addition, we train multiple ML models to predict the standard score (SS), which represents the support value related to the eight aspects of QoL. Finally, we use SS values to train an ML model to predict the support intensity scale (SIS). On a dataset compiled from guardianship entities, the proposed approach is validated. The QoL index, SS, and SIS prediction models achieve average R2 values of 0.9897, 0.9998, and 0.9977 with a standard deviation of 0.0051, 0.0002, and 0.0007, respectively.

In the recent years, the penetration of photovoltaics (PV) has obviously been increased in unbalanced power distribution systems. Driven by this trend, comprehensive simulation tools are required to accurately analyze large-scale distribution systems with a fast-computational speed. In this paper, we propose an efficient method for performing time-series simulations for unbalanced power distribution systems with PV. Unlike the existing iterative methods, the proposed method is based on machine learning. Specifically, we propose a fast, reliable and accurate method for determining energy losses in distribution systems with PV. The proposed method is applied to a large-scale unbalanced distribution system (the IEEE 906 Bus European LV Test Feeder) with PV grid-connected units. The method is validated using OpenDSS software. The results demonstrate the high accuracy and computational performance of the proposed method.