Stress, Strain, or Energy: Which One Is the Superior Parameter to Estimate Fatigue Life of Notched Components? An Answer by a Novel Machine Learning-Based Framework

This paper introduces a simple framework for accurately predicting the fatigue lifetime of notched components by employing various machine learning algorithms applied to a wide range of materials, loading conditions, notch geometries, and fatigue lives. Traditional approaches for this task have relied on empirical relationships involving one of the mechanical properties, such as stress, strain, or energy. This study goes further by exploring which mechanical property serves as a better measure. The key idea of the framework is to use the gradient of the mechanical properties (stress, strain, and energy) to distinguish between different notch geometries. To demonstrate the accuracy and broad applicability of the framework, it is initially validated using isotropic materials, subsequently applied to samples produced through additive manufacturing techniques, and ultimately tested on carbon fiber laminated composites. The research demonstrates that the gradient of all three measures can be effectively employed to estimate fatigue lifetime, with stress-based predictions exhibiting the highest accuracy. Among the machine learning algorithms investigated, Gradient Boosting and Random Forest yield the most successful results. A noteworthy finding is the significant improvement in prediction accuracy achieved by incorporating new data generated based on the Basquin equation

Development of a Hierarchical Zinc Oxide Photocatalyst for the Removal of Emerging Contaminants from Water

The presence of emerging contaminants (ECs) such as antibiotics in water bodies has raised increasing concern since they are continuously introduced in aquatic ecosystem and may cause unpredictable environmental hazards and risks, even at trace concentrations. Conventional water treatment processes are known to be generally inadequate for the elimination of these persistent contaminants. As an alternative to conventional biological water treatment processes, photocatalytic degradation of antibiotics has been identified as a promising technique, as it may lead to the mineralization of contaminants into carbon dioxide, water and mineral acid. However, high energy consumption, fast recombination of photo-generated charges, low stability and difficulty in the separation of photocatalysts from treated solution are the main limitations of this process. Herein, to address these obstacles, a low energy consuming photoreactor was designed and built. Besides, the efficiency of process was improved by fluorination and exfoliation of synthesized hierarchical photocatalysts with magnetic properties based on ZnO and graphitic carbon nitride (g-C3N4) as a wide and narrow band gap photocatalyst, respectively. The synthesized photocatalysts were characterized by several characterization tests. The effect of operating parameters such as catalyst dosage, solution pH and airflow rate on the antibiotics removal efficiency and the optimization of process was studied by response surface methodology (RSM). Under the optimum conditions, the photocatalytic removal performance was examined in terms of sulfamethoxazole (SMX), ampicillin (AMP) and amoxicillin (AMX) removal and mineralization as well as detoxification of the solution and by-product formation. Moreover, the reaction kinetics, energy consumption, stability and reusability of photocatalysts were evaluated. Based on the LC-HR-MS/MS method, the formation of several by-products during the degradation of antibiotics was evaluated and a degradation pathway for SMX and AMX was proposed. The results showed that in comparison with a 500 W visible lamp, using a UV lamp (10 W) was considerably more effective for AMX removal, its mineralization and detoxification of the solution. Compared to reported values in the literature, the removal efficiency, mineralization, detoxification and energy consumption for the removal of examined antibiotics were improved in this study

Development of a complementary fuzzy decision support system for employees’ performance evaluation

This study aims to improve employee evaluation system in one of the leading automobile manufacturers in Iran by designing a fuzzy decision support system (F.D.S.S.). Since this manufacturer is a large-sized company with over 35,000 employees, the number of managers regularly evaluated requires too much capacity from the human resource team and hence increases the rate of possible misjudgements. However, the proposed F.D.S.S. can reduce the rate of unfair or inconsistent assessments by converting qualitative assessments of the panel to linguistic variables. This action increases the precision of assessment and improves the quality of evaluations. The proposed F.D.S.S. is compared with a fuzzy TOPSIS method to confirm its reliability and validity in which the results show consistency with fuzzy TOPSIS. As a result, the F.D.S.S. is implemented for evaluation of managers in this auto-mobile company instead of the traditional method

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster

A New Optimal Operation Structure For Renewable- Based Microgrid Operation based On Teaching Learning Based Optimization Algorithm

This paper proposes a new optimization framework for the optimal power dispatch in both grid-connected and islanded microgrid modes. Solving the microgrid operation by the evolutionary algorithms can be faster than analytical models due to the complexity of the problem. To demonstrate the efficiency and high performance of the proposed technique, it is applied on the IEEE 33 bus test network. Also, the proposed technique is compared with the analytical model, and also well-known heuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA)

A New Optimal Operation Structure For Renewable- Based Microgrid Operation based On Teaching Learning Based Optimization Algorithm

This paper proposes a new optimization framework for the optimal power dispatch in both grid-connected and islanded microgrid modes. Solving the microgrid operation by the evolutionary algorithms can be faster than analytical models due to the complexity of the problem. To demonstrate the efficiency and high performance of the proposed technique, it is applied on the IEEE 33 bus test network. Also, the proposed technique is compared with the analytical model, and also well-known heuristic methods such as particle swarm optimization (PSO), genetic algorithm (GA)

Experimental Study of a Rotating Electrode Plasma Reactor for Hydrogen Production from Liquid Petroleum Gas Conversion

In this work, a new plasma reactor operating with a butane/propane (C4H10/C3H8) gaseous mixture, designed for hydrogen molecule production, was experimentally studied. This reactor is based on a rotating electrode, biased by an AC high voltage. The plasma discharge was investigated for different AC voltages, rotational frequencies, and gas flow rates. A discharge in the filamentary mode was produced as proved by the electrical characterization. Gas Chromatography (GC) was applied to study the LPG remediation. The maximum conversion factors of 70% and 60% were found for the C3H8 and C4H10, respectively, with an H2 selectivity of 98%. Hydrogen atomic lines from the Balmer series and various molecular bands were detected by optical emission spectroscopy (OES). The stark broadening of the Hα Balmer line was used for the determination of the electron density. The spectra simulation of the C2 band was permitted to obtain the gas temperature while the first five lines of hydrogen atoms were used to calculate the electron temperature. A non-equilibrium plasma with two very different temperatures for electrons and heavy particles was found. The spectroscopic study allowed us to explain the experimental results of the LPG conversion and its dependence on the plasma conditions, resulting in optimizing the H2 formation

Evaluation of Sesquiterpenes from Ferula assa-foetida on inflamatory parameters and study of biding modes using computational methods

introduction: Ferula assafoetida is a source of sesquiterpenes [1]. According to an investigation, phenolic compounds at physiological concentration can inhibit inflammatory enzymes [2]. These enzymes digest the extracellular matrix and provide the conditions for activation and migration and proliferation of endothelial cells. Reported studies on medicinal plants for their inhibitory effect on MMP are very limited. Methods and Results: Acetone extract of plant was prepared and Sesquiterpenes were purified using HPLC preparative analyses and their structures were elucidated. After culturing the cell at confluence, cells were isolated and the supernatant was removed. The pure substances were applied on cell lines U87MG and Wehi activities. Besides the structure has been docked in the active site of metalloproteinase, and significant interactions were determined.Subsequently, ligand-protein complexes were subjected to molecular dynamics simulation in water and thermodynamic properties were calculated. In the phytochemistry field galbanic acid, mogoltadone, kellerin, polyanthin and polyanthininwere produced from F. assafoetida. The results of celluar toxicity study shows that IC50 of Galbanic acid, Mogoltadone and Polyanthin in Wehi cell line were 925.2703, 721.86, and 680.3 µg/ml in U87MG cell line were 952.193, 752.352, 678.742. Galbanic acid, mogoltadone, kellerin, polyanthin and polyanthininwere solated from F. assafoetida. The results of celluar toxicity study show that IC50 of Galbanic acid, Mogoltadone and Polyanthin in Wehi cell line were 925.2703, 721.86, and 680.3 µg/ml in U87MG cell line were 952.193, 752.352, 678.742 Conclusion: Investigation revealed that the coumarins have inhibitory effects on the content and activity of MMP 2.9 and showed anti-angiogenetic effect. So, they can be potentially effective in the treatment of cancer. Interactive and competitive binding between MMP-9 and Galbanic acid were studied with FT-IR, UV-Vis and fluorescence methods and MMP-9 structure was changed in these interactions