This study presents a methodology that combines artificial multiple intelligence systems (AMISs) and machine learning to forecast the ultimate tensile strength (UTS), maximum hardness (MH), and heat input (HI) of AA-5083 and AA-6061 friction stir welding. The machine learning model integrates two machine learning methods, Gaussian process regression (GPR) and a support vector machine (SVM), into a single model, and then uses the AMIS as the decision fusion strategy to merge SVM and GPR. The generated model was utilized to anticipate three objectives based on seven controlled/input parameters. These parameters were: tool tilt angle, rotating speed, travel speed, shoulder diameter, pin geometry, type of reinforcing particles, and tool pin movement mechanism. The effectiveness of the model was evaluated using a two-experiment framework. In the first experiment, we used two newly produced datasets, (1) the 7PI-V1 dataset and (2) the 7PI-V2 dataset, and compared the results with state-of-the-art approaches. The second experiment used existing datasets from the literature with varying base materials and parameters. The computational results revealed that the proposed method produced more accurate prediction results than the previous methods. For all datasets, the proposed strategy outperformed existing methods and state-of-the-art processes by an average of 1.35% to 6.78%.

Objective: This study aims to examine the impact of digital devices on public health surveillance, the impact of public health surveillance on resilient cities, and the impact of digital devices on resilient cities. Methods: Questionnaires were issued to residents of Thailand during the severe acute respiratory syndrome coronavirus 2 response (2020–2023). In total, 1025 valid responses were recorded from Thai nationals and expatriates. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were used to assess the model through IBM SPSS 23 and AMOS 23. Results: Digital devices have a strong positive direct effect on public health surveillance ( β = 0.73, p ≤ .001), public health surveillance has a strong positive direct effect on resilient cities ( β = 0.79, p ≤ .001), and digital devices have a low positive direct and a moderate indirect effect on resilient cities ( β = 0.13, p ≤ .001, and β = 0.58, p ≤ .001, respectively). The use of digital devices in data collection, analysis, and dissemination, positively impacted public health surveillance, considering five dimensions: medical and vaccine, individual, health care, epidemiological, and disease. Meanwhile, using digital devices in public health surveillance positively impacted the resilience of cities, considering three dimensions: socioeconomic, institutional, and living. The causal relationship model of the digital device utilization on public health surveillance enhancing the resilience of the cities met all the necessary criteria: X²/ df = 2.802, comparative fit index = 0.953, goodness of fit index = 0.901, normed fit index = 0.935, Tucker–Lewis index = 0.935, root mean square of approximation = 0.048, and root of mean square residual = 0.043. This indicates the model fits the empirical data. Conclusion: Digital devices are vital tools in collecting, analyzing, and disseminating public health surveillance-related data during the public health emergency. This, in turn, can improve medical and vaccine, individual, health care, epidemiological, and disease surveillance, and also enhance cities’ socioeconomic, institutional, and living resilience.