AA3003-H14 aluminum alloy plates were welded by friction stir welding and TIG welding.
Fatigue properties of the welded joints were evaluated based on the superior tensile properties for
FSW at 1500 rpm rotational speed and 80 mm/min welding speed. However, there is not much
information available on effect of welding parameters with evolution of fatigue life of friction stir
welds. The present study experimentally analyzed fatigue properties for base, FSW, and TIG welds
of AA 3003-H14 aluminum alloy. Fatigue properties of FSW joints were slightly lower than the
base metal and higher than TIG welding.

Bacterial infections pose an ongoing challenge due to resistance developed by infectious bacteria. So much research targeting designing new antibacterials is published annually. Our goal is to synthesize compounds that have given antibacterial activity according to molecular docking against the chosen target protein and that have acceptable ADMET properties that can be synthesized and used in the future. New 2-(5-methoxy-1-(4-chlorobenzene)-2-methyl-1H-indol-3-yl)acetohydrazide derivatives’ antibacterial efficacy against two common strains of Gram-negative and Gram-positive microorganisms has been developed, produced, and investigated. Sophisticated, modern analytical methods, including ATR-FTIR and 1H NMR spectroscopy, were used

Manganese sulfate and Punica granatum plant extract were used to create MnO₂ nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO₂ NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencie

sensor sampling rate (SSR) may be an effective and crucial field in networked control systems.  Changing sensor sampling period after designing the networked control system is a critical matter for the stability of the system. In this article, a wireless networked control system with multi-rate sensor sampling is proposed to control the temperature of a multi-zone greenhouse. Here, a behavior based Mamdany fuzzy system is used in three approaches, first is to design the fuzzy temperature controller, second is to design a fuzzy gain selector and third is to design a fuzzy error handler. The main approach of the control system design is to control the input gain of the fuzzy temperature controller depending on the cur

Manganese sulfate and Punica granatum plant extract were used to create MnO2 nanoparticles, which were then characterized using techniques like Fourier transform infrared spectroscopy, ultraviolet-visible spectroscopy, atomic force microscopy, X-ray diffraction, transmission electron microscopy, scanning electron microscopy, and energy-dispersive X-ray spectroscopy. The crystal's size was calculated to be 30.94nm by employing the Debye Scherrer equation in X-ray diffraction. MnO2 NPs were shown to be effective in adsorbing M(II) = Co, Ni, and Cu ions, proving that all three metal ions may be removed from water in one go. Ni(II) has a higher adsorption rate throughout the board. Co, Ni, and Cu ion removal efficiencies were 32.79%, 75

The successful implementation of deep learning nets opens up possibilities for various applications in viticulture, including disease detection, plant health monitoring, and grapevine variety identification. With the progressive advancements in the domain of deep learning, further advancements and refinements in the models and datasets can be expected, potentially leading to even more accurate and efficient classification systems for grapevine leaves and beyond. Overall, this research provides valuable insights into the potential of deep learning for agricultural applications and paves the way for future studies in this domain. This work employs a convolutional neural network (CNN)-based architecture to perform grapevine leaf image classifi