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The aim of the current study is to inspect the influence of high temperatures on the compressive and split-tensile-strength (STS) of concrete mixtures produced by replacing natural river sand with waste-foundry sand (WFS) at 25%, 50%, 75% and 100%. When the experimental findings and the projected outcomes were compared by IS:456-2000 code equations, the STS results predicted by the suggested mathematical equations exhibit lower variations. It is proposed to employ the presented mathematical formulas to evaluate the STS of concrete cylindrical specimens at higher temperatures.

After fabricating, concrete mixtures were allowed to cure for 28 days. For the purpose of avoiding explosive spalling during the heating process, concrete samples are taken out from the curing chamber after 28 days and allowed to dry for two days. The manufactured concrete specimen is exposed to 100 °C, 200 °C, 300 °C, 400 °C, 500 °C and 600 °C temperatures for a duration of 2 h. After the specimens have cool down to room temperature (RT), the physical test, ultrasonic-pulse-velocity (UPV) test, compressive strength test and STS test are carried out.

With an increase in WFS content, concrete specimens' residue compressive-strength and STS decreases. The STS of samples declines as the WFS content rises with increase in temperature interval. According to the UPV test, the concrete samples quality is “good” up to 400 °C; after 500 °C, it ranges from “doubtful to poor.” The UPV values of various mixes declined as the temperature increased. Mass losses increase with exposure to greater temperatures and with an increase in the proportions of WFS in concrete specimens. For mixtures MWFS-0, MWFS-1, MWFS-2, MWFS-3 and MWFS-4 (0%, 25%, 50%, 75% and 100% WFS content), no cracks were present on any of the samples below 400 °C. Concrete surfaces start to show cracks whenever the intervals of temperature increase above 400 °C.

In this investigation, WFS elements are totally substituted for natural sand in concrete mixtures. The residue strength properties, including residual compressive strength and residual STS, were found to be lower after exposures to greater temperature when comparisons were made to referral mixtures. When comparing specimens’ compressive strength, higher temperatures have more effects on the STS of samples with higher WFS contents.

This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques.

This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data.

The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation.

This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences.

This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services.

This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

In today’s highly competitive job market, efficient recruitment processes play a crucial role in enabling organizations to attract top talent. This study aims to streamline and reduce a US-based company’s average recruitment cycle time.

The approach integrates Lean Six Sigma tools within the DMAIC framework to reduce recruitment cycle times. It involves evaluating each stage of recruitment, identifying opportunities, and implementing improvements, with average cycle times tracked and compared before and after.

The improvement findings show a remarkable 25.2% decrease in recruitment cycle times. Notably, approvals saw a substantial improvement, with average approval times reduced by 50.0%. Both hourly and salary positions experienced reductions in cycle times, and all job levels exhibited decreases. These findings highlight the effectiveness of the changes in streamlining recruitment processes.

This study distinguishes itself by applying Lean Six Sigma tools within the DMAIC framework to optimize human resources (HR) recruitment. The systematic use of these tools reduced recruitment cycle times, improved decision-making and enhanced efficiency, showcasing their versatility beyond manufacturing. These findings offer practical implications for improving HR and organizational performance, providing valuable insights for the field.