This study explores the prediction of concrete compressive strength using machine learning models, aiming to overcome the time-consuming and complex nature of conventional methods. Four models—an artificial neural network (ANN), a multiple linear regression, a support vector machine, and a regression tree—are employed and compared for performance, using evaluation metrics such as mean absolute deviation, root mean square error, coefficient of correlation, and mean absolute percentage error. After preprocessing 1030 samples, the dataset is split into two subsets: 70% for training and 30% for testing. The ANN model, further divided into training, validation (15%), and testing (15%), outperforms others in accuracy and efficiency. This outcome streamlines compressive strength determination in the construction industry, saving time and simplifying the process.

This study is aimed to establish a wireless sensor network (WSN) gateway model prior to the back-end server for diverse environmental monitoring applications. The design catalogs different sensor data with transmission load balance to incorporate heterogeneity of sensor signals, stability of data transportation, and expenditure of mobile communication. As considering a variety of sensor characteristics for environmental monitoring, the proposed WSN gateway is designed with three bridged functions, including serial listener, transaction logger and Internet listener, to enable analog and digital signal conversion, physical data classification, threshold determination, database redundancy and mobile communication. The criterion-based scheme is created for remotely updating thresholds of monitored data from the back-end server to the gateway. The model is built upon the embedded computer for low power consuming to perform efficient and stable applications. The comprehensive model design can be easily utilized for general WSN surveillance with expansibility and flexibility.

This study rigorously examines the relationship between circular economy service quality and brand value, focusing on the mediating roles of customer satisfaction, brand image, and customer loyalty. Amid the growing adoption of circular economy principles to enhance sustainability and resource efficiency, the impact of such practices on brand value remains underexplored. This research addresses this gap by developing and testing an integrated model of circular economy service quality and its influence on brand value. Using a quantitative methodology, this study collected data from customers engaged with companies employing circular economy practices. A structured survey assessed the perceptions of circular economy service quality, customer satisfaction, brand image, customer loyalty, and brand value. Advanced statistical techniques, including structural equation modeling (SEM), were employed to analyze the data and evaluate the proposed relationships. The results revealed significant positive correlations between circular economy service quality, customer satisfaction, brand image, and customer loyalty. Furthermore, the mediating roles of customer satisfaction, brand image, and customer loyalty in the relationship between circular economy service quality and brand value were validated. High-quality circular economy services fostered customer satisfaction, enhanced brand image, and strengthened customer loyalty, collectively driving increased brand value. This research underscored two critical implications. First, circular economy service quality was identified as a pivotal factor in enhancing brand value. By offering sustainable and efficient services, organizations can deliver superior customer experiences, cultivate a strong brand image, and ultimately boost brand value. Second, the mediating variables—customer satisfaction, brand image, and customer loyalty—were shown to play essential roles in amplifying the impact of circular economy service quality on brand value. Organizations should strategically focus on optimizing these factors to maximize brand performance. These findings provide valuable insights for businesses aiming to integrate sustainability into their operations while enhancing brand equity and market performance.