Nowadays, composites made of plant-based fibers may be used to polymer resin synthetic fiber reinforced composites since they are less expensive, renewable, abundant, less abrasive, and lightweight. Echinatus fiber obtained from the stem of the echinatus plant in an abundant amount. The main objective of this study was to develop echinatus polyester-resin fiber reinforced composite material and investigate its flexural strength, hardness and tensile strength. Echinatus plant was collected and echinatus fiber was extracted by the decortication process from the echinatus plants manually, and treated with 5% NaOH for the improvement of bond and interfacial shear strength. And then, the test specimens were manufactured using a mass fraction with 0 , 45 and 90 orientations using technique of hand lay-up. The tensile strength, hardness, and flexural strength were investigated using samples that were prepared according to the ASTM standards. 70% echinatus fiber to 30% polyester composite material with 0 orientation was found as the material with maximum tensile strength of 60.60 MPa, flexural strength of 96.8 MPa, and hardness values of 44.54 HRA. Considering the mechanical properties’ experimental results, echinatus fiber-reinforced composite with 70% fiber at 0 orientation can be good substitutes for synthetic materials.

Oxo derivatives of pyrazolines and pyrazolidines are important heterocyclic compounds due to their unique biological activities and have been widely applied in pharmaceutical and agromedical fields. In this review, we provide an account of some recent advances in the field of pyrazolone chemistry, specifically on the reported synthesis methods of pyrazolinone (3-oxo-1,2-dihydropyrazole) and 3,5-pyrzolidinediones (3,5-dioxotetrahydropyrazoles) derivatives.

Forecasting is an integral approach due to its ability to make informed act decisions and develop data-driven strategies. It's also used to make decisions related to current circumstances and predictions on future conditions. An integral part has been developed using visibility analysis for COVID-19 Outbreak, a lesson from Indonesia. The author identified that its topic has limited attention, especially in assessing the forecasting models. The issue comes from predicted results that are questionable or cannot be trusted without applying the visibility analysis in the forecasting model. The visibility analysis is required to assess the model's ability to forecast future events. In conjunction with the issue, this paper introduces the analysis of visibility error with the different concepts during model development for the transmission prevention measures in making the decision. This study applied a statistical approach to assess the visibility error of forecasting performance in determining how long periods of forecasting and deciding for transmission prevention measures COVID-19 pandemics. Also, we developed the visibility error of time-variant using inductive logic. The result indicated that the number of data required to perform forecasting work on the basis of forecasting model specifications. In conclusion, this study has been completed to develop the statistical formula for identifying the largest time horizon in forecasting model N = V + 2. Also, this developed model can assist the stakeholder in forecasting the number of transmission prevention and making the decision in case of COVID-19 pandemic.

Asphaltic concrete mixtures are among the most common construction materials for the pavement of roads. As a multi-phase composite mixture with randomly distributed aggregates inside the mastic part, the mechanical properties of such materials can be influenced by different factors. Cracking and induced fracture is among the common degradation and failure modes in these construction mixtures that often takes place in cold regions. In this research, the effects of some influencing parameters including temperature, air void percentage and loading rate are investigated experimentally on the fracture toughness (K_Ic) and work of fracture (W_Ic) of hot mix asphalt material. Edge notched semi-circular bend (SCB) specimen was employed to conduct mode I fracture experiments. The thickness of SCB samples were considered as variable and the HMA mixtures were tested with two SCB thicknesses of 30 and 60 mm. The experimental results showed that both fracture toughness and fracture work are increased by increasing the thickness. However, the effect of thickness on the fracture work was much more significant than the K_Ic value. Also, the fracture and cracking resistance parameters were increased by decreasing the temperature and air void content. Both K_Ic and W_Ic values were also increased by increasing the loading rate in the investigated range of 1 to 8 mm/min. The most influencing parameters on the change of fracture resistance parameters were the temperature, loading rate, air void content and thickness, respectively.

The intention of this paper is to propose some similarity measures between Fermatean fuzzy sets (FFSs). Firstly, we propose some score based similarity measures for finding similarity measures of FFSs and also propose score based cosine similarity measures between FFSs. Furthermore, we introduce three newly scored functions for effective uses of Fermatean fuzzy sets and discuss some relevant properties of cosine similarity measure. Fermatean fuzzy sets introduced by Senapati and Yager can manipulate uncertain information more easily in the process of multi-criteria decision making (MCDM) and group decision making. Here, we investigate score based similarity measures of Fermatean fuzzy sets and scout the uses of FFSs in pattern recognition. Based on different types of similarity measures a pattern recognition problem viz. personnel appointment is presented to describe the use of FFSs and its similarity measure as well as scores. The counterfeit results show that the proposed method is more malleable than the existing method(s). Finally, concluding remarks and the scope of future research of the proposed approach are given.

The Zinc doped calcium borate (Zn-CaB) was synthesized by the co-precipitation method. Zinc was known to exhibit a good antimicrobial effect without causing any environmental issues. However, the antimicrobial activity of zinc is relatively low as compared to silver or copper analogues. In the present investigation, an attempt was made to incorporate zinc in the matrix of calcium borates to enhance the antibacterial effect. Cosmetic products were made using the zinc-calcium-borates and the antibacterial/antifungal activity was evaluated and compared with the commercial products. The studies revealed that the antibacterial activity of Zn-CaB was equivalent to that commercially available product with a maximum of 99% reduction in bacterial strains and above 95% reduction in fungal strains. An attempt was made to incorporate a bacteriostatic effect on fabric by adding zinc-calcium-borates to detergent which exhibited prolonged antibacterial activity, even after 3 washing cycles. The cosmetic products were also evaluated for their thermal and photostability.

Most job shops in practice are constrained by both machine and labor availability. Worker assignment in these so-called Dual Resource Constrained (DRC) job shops is typically solved in the literature via the use of meta-heuristics, i.e. “when” and “where” rules, or heuristic assignment rules. While the former does not necessarily lead to optimal results, the latter suffers from high computational time and complexity, especially when there is a large number of workstations. This paper uses game theory to propose a new worker assignment rule for DRC job shops. The Gale-Shapley model (also known as the stable marriage problem) forms a ‘couple’ made up of a worker and machine following a periodic review strategy. Simulation is used to evaluate and compare the proposed model to “when” and “where” rules previously proposed in the literature. Simulation experiments under different conditions demonstrate that the Gale-Shapley model provides better results for worker assignments in complex DRC systems, particularly when the workers have different efficiency levels. The implications of the findings for research and practice are outlined.

Gas Metal Arc Welding (GMAW) is an extensively implemented arc welding process through the control of input process parameters and the metal from the filler wire. Despite its popular use in various industries, the complex interrelationship between the actual bead and the varying welding parameters makes it challenging to predict appropriate bead geometries via mathematical modeling in a continually changing welding process. In this study, the Regression Learner App was used to compare the performance of supervised Machine Learning (ML) predictive models comprising the Linear Regression (LR), Regression Tree (RT), Support Vector Machine (SVM), Ensembles of Tree (ET), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) using GMAW dataset. The dataset was scaled and normalized at a range of -1 to +1 to facilitate the visualization of the variation effect. The wire feed speed, voltage, weld velocity, unmelted wire length, and melted wire volume were considered as the input parameters to predict the bead geometry. In addition, the five-fold cross-validation was employed to avoid overfitting and poor generalization. Finally, statistical indicators, namely the Coefficient of Determination (R²), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE), were performed on all developed models to evaluate their performance. Thus, the fine tree and ANN models achieved the highest prediction accuracies of 88–91%, signifying their potential use in future research. In short, the present study demonstrated the performance of various supervised ML algorithms for bead geometry prediction, which would assist the selection of appropriately supervised ML algorithms in future studies.

Multicriteria Decision Making (MCDM) is one the most important branches of decision theory. Due to the fact that MCDM methods have the utmost significance in management, scholars try to develop more MCDM methods. Since calculating the weights of criteria is an important step in any MCDM method, increasing the accuracy of weight calculating methods can highly affect these methods. This accuracy can be improved by less pairwise comparison between criteria. To this end, the present study seeks to make a comparison between two new weight calculating techniques, namely BWM and FUCOM in a fuzzy environment using a real-world case study Results of this study shows that FUCOM-F provides more reliable results compared to FBWM since its consistency is less than FBWM by a great amount.

The FT-IR spectrum of salicylic acid (SA) was studied in the region 10-4000 cm-1. The optimized molecular geometry and fundamental vibration frequencies are interpreted using structural optimizations based on the Functional Density Theory (DFT) method. Additionally, we used B3LYP / 6-311G (d, p) to determine the chemical descriptor, the ionization potential (I), the electron affinity (A), the chemical potential (μ), the chemical hardness (η). Nonlinear optical descriptors (NLO) such as dipole moment (μ), polarizability (α), first hyperpolarizability (β) and second hyperpolarizability (γ), 3D maps of HOMO and LUMO orbitals, lengths and Bond angles of salicylic acid isomers are also determined by both DFT and MP2 (The Møller-Plesset theory of order 2 perturbation) methods. The overall descriptors and the non-linear optical properties confirmed that salicylic acid is the reactive molecule and para-hydroxybenzoic acid is the stable molecule. An electrostatic molecular potential (MEP) study was also performed to determine the reactivity of molecules.