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AbstractA non-traditional friction stir welding (FSW) is used to joint the materials that are challenging to join together by utilizing the fusion welding processes. This study investigated the influence of the welding speed ratio (υ) to rotating speed (ꞷ) on the weld quality and found the relationship between mechanical qualities and welding peak temperature. With increase in (ꞷ/υ), there was a corresponding rise in peak temperature, leading to the expansion of both the nugget zone (NZ) and the softened region within the joint. As (ꞷ/υ) increased because of the increased heat, as a result in the joint hardness decreased. The joint ultimate tensile strength (UTS) increased with increasing (ꞷ/υ), then decreased, with the maximum value reaching 84.75% of the base material at a (ꞷ/υ) of 9.6. As a result of the low (ꞷ/υ), a flexural crack formed. In comparison to the base material, the thermos mechanically affected zone (TMAZ) and the nugget zone (NZ) showed greater impact strength. The impact strengths first increased and subsequently dropped with an increase in (ꞷ/υ). The tensile specimens’ fracture surfaces showed dimples, signifying a ductile mode of fracture. [ABSTRACT FROM AUTHOR]

This paper mainly dealt with the evaluation of the structural stability of four storied building using nondestructive on-destructive testing methods. During the construction stage, there are many tests available to assess the excellence of concrete. The quality of concrete mainly depends on the quality of materials, concrete grade, and water-cement ratio. In the case of existing structures, to check the quality of concrete destructive tests are not possible, meanwhile, concrete quality will be assessed by using non-destructive testing (NDT) techniques such as rebound hammer, ultrasonic pulse velocity (UPV) etc. In this present study, an attempt has been made to check the quality of concrete in an existing four-storied building using non-destructive testing methods such as rebound hammer test and ultrasonic pulse velocity test. Moreover, the stability of the structure was also assessed. Non-destructive testing method was chosen since existing information of the structure was unavailable. Test results showed that the basement (B1) was susceptible to corrosion, and the compressive strength was not in the recommended range. Ultrasonic pulse velocity (UPV) results also proved that the average quality of the concrete was poor. Hence, significant suggestions were given for necessary retrofitting measures to improve the stability of the structure. [ABSTRACT FROM AUTHOR]

The increasing need for effective energy storage solutions has led to the prominence of lithium-ion batteries as a crucial technology across multiple industries. The proficient administration of these batteries is imperative in order to guarantee maximum efficiency, prolong their longevity, and uphold safety measures. This study presents a novel methodology for enhancing battery management systems (BMS) through the integration of cloud-based solutions, artificial intelligence (AI), and machine learning approaches. In this study, we present a conceptual framework that utilises cloud computing to augment the practical functionalities of battery management systems (BMS) specifically in the context of lithium-ion batteries. The incorporation of cloud computing facilitates the implementation of scalable data storage, remote monitoring, and processing resources, hence enabling the execution of real-time analysis and decision-making processes. By leveraging the capabilities of machine learning and artificial intelligence, our methodology focuses on addressing crucial battery metrics, including the state of charge (SoC) and state of health (SoH). Through the ongoing collection and analysis of data obtained from battery systems that are deployed in real-world settings, the framework iteratively improves its predictive models, hence facilitating precise assessment of battery states. Ensuring safety is a crucial element in the management of batteries. The solution we propose utilises anomaly detection algorithms driven by artificial intelligence to detect potential safety issues, facilitating prompt responses and mitigating dangerous circumstances. In order to showcase the efficacy of our methodology, we offer practical implementations in several industries, encompassing the integration of renewable energy, use of electric vehicles, and optimisation of industrial processes. Through the utilisation of cloud-based machine learning techniques, we are able to enhance the efficiency of energy storage and consumption, while simultaneously enhancing the dependability and security of battery systems. This study highlights the potential of the proposed framework to revolutionise battery management paradigms, thereby guaranteeing secure and efficient energy prospects for a sustainable future. [ABSTRACT FROM AUTHOR]

The utilization of banana fiber derived from micro-cellulose (MC) was exploited as a supporting material for advanced oxidation process (AOP) on the degradation of methylene blue and methyl violet dyes in the presence of H 2 O 2 -UV in aqueous medium for the first time using green chemistry protocols. Additionally, it was also effectively utilized for the adsorption of methylene blue dye using addition of H 2 O 2 in the presence of sunlight. The MC powder was fabricated using an acid alkali process from the pseudo-stem of a banana tree. The as-fabricated MC powder was systematically characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field-emission scanning electron microscopy (FE-SEM), energy-dispersive X-ray spectrometer (EDX), and zero point charge (pH zpc ). The AOP assisted degradation of dye molecules was monitored by using calorimetric techniques as a function of dye concentration and pH in a batch reactor. In a short period of time, the maximum degradation efficiency of 98 % of methylene blue was achieved using MC powder assisted H 2 O 2 under UV irradiation at a minimum irradiation time of 120 min at pH 7.0 using dosage of 0.2 g/L. However, in the absence of UV light, the degradation efficiency of MC powder assisted H 2 O 2 was only about 5-10 % without UV light irradiation. The dye removal was studied as a function of various operational parameters such as pH (3-11), catalyst dose (0.2-0.6 g/L), and initial dye concentration (100-400 mg/L). In the presence of H 2 O 2 -sunlight and 0.2 g/L of dosage at pH 7.0 at a minimum contact time of 120 min, MC fiber showed maximum adsorption capacities of 98% and 85% for 100 mg/L and 400 mg/L of methylene blue concentrations. According to the obtained data, the adsorption of methylene blue dye on MC follows the Freundlich isotherm model (R 2  = 0.9886) and pseudo-first-order kinetic model (R 2  = 0.9596) due to the higher regression coefficients. This process of dye degradation and adsorption process is a novel one and environmentally benign for an effective removal of hazardous dyes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)