This research examines three data mining approaches employing cost management datasets from 391 Thai contractor companies to investigate the predictive modeling of construction project failure with nine parameters. Artificial neural networks, naive bayes, and decision trees with attribute selection are some of the algorithms that were explored. In comparison to artificial neural network’s (91.33%) and naive bays’ (70.01%) accuracy rates, the decision trees with attribute selection demonstrated greater classification efficiency, registering an accuracy of 98.14%. Finally, the nine parameters include: 1) planning according to the current situation; 2) the company’s cost management strategy; 3) control and coordination from employees at different levels of the organization to survive on the basis of various uncertainties; 4) the importance of labor management factors; 5) the general status of the company, which has a significant effect on the project success; 6) the cost of procurement of the field office location; 7) the operational constraints and long-term safe work procedures; 8) the implementation of the construction system system piece by piece, using prefabricated parts; 9) dealing with the COVID-19 crisis, which is crucial for preventing project failure. The results show how advanced data mining approaches can improve cost estimation and prevent project failure, as well as how computational methods can enhance sustainability in the building industry. Although the results are encouraging, they also highlight issues including data asymmetry and the potential for overfitting in the decision tree model, necessitating careful consideration.

This study deals with the impact of Vietnam bank size, loans, credit risk, and liquidity on Vietnam banks’ net interest margin, which are crucial for economic development. High profit margins result in a lower bad debt ratio due to timely loan collection and good liquidity. This study applies a panel data model to evaluate the relationship among bank size, loans, credit risk, liquidity, and marginal profitability, which are increasingly important in commercial bank growth. Data were collected from 2010 to 2022, and test methods were applied to select a good-fit model. Realizing that the factors that have a close correlation and affect the profit margin are 33.6% and 16.07%, 75.2%, 37.51%, 64.30%, and 41.11%, and R2 is 59.04%, respectively, this suggests that financial managers need to develop appropriate strategies and policies to adjust the factors that adversely affect commercial bank profitability.

According to the United Nations, by 2050, about 68% of the world’s population will live in urban areas. This population increase requires environmental resilience and planning ability to reduce the negative environmental impacts associated with growth. In this scenario, life cycle analysis, whose standards were introduced by ISO 14000 series, is an essential tool. From this perspective, smart cities whose concern about environmental sustainability is paramount corroborating SDG 11. This study aims to provide a holistic view of environmental technologies developed by Brazilian inventors, focused on life cycle analysis, which promotes innovation by helping cities build greener, more efficient, resilient, and sustainable environments. The methodology of this article was an exploratory study and investigated the scenario of patents in the life cycle. 209 patent processes with Brazilian inventors were found in the Espacenet database. Analyzing each of the results individually revealed processes related to air quality, solid waste, and environmental sanitation. The review of patent processes allowed mapping of the technological advances linked to life cycle analysis, finding that the system is still little explored and can present competitive advantages for cities.

The Huaiyang Canal, a significant section of the Grand Canal, boasts representative tourist attractions. This study analysis of online reviews from Ctrip and Mahive using R language, Gephi, ROST CM, and SPSS has provided insights into tourists’ perceptions of the Huaiyang Canal’s image. Key findings include: (1) Dominant landscape images encompass gardens, canals, and buildings, emphasizing the historical and cultural assets. Both cultural and natural landscapes equally captivate tourists. (2) The canal’s tourism image perception follows a “garden-history-canal” hierarchy with the canal as the central space and history expanding its tourism features. (3) The perceptions can be categorized into historical and cultural landscapes, man-made projects, and attraction perception. Despite varying tourist numbers in Huaian and Yangzhou, scenic spot experiences are similar. The overall perception of tourists is largely positive, but some express concerns about service attitudes and travel time planning.

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

The Organic Rankine Cycle (ORC) is an electricity generation system that uses organic fluid instead of water in the low temperature range. The Organic Rankine cycle using zeotropic working fluids has wide application potential. In this study, data mining (DM) model is used for performance analysis of organic Rankine cycle (ORC) using zeotropik working fluids R417A and R422D. Various DM models, including Linear Regression (LR), Multi-Layer Perceptron (MLP), M5 Rules, M5 Model Tree, Random Committee (RC), and Decision Tree (DT) models are used. The MLP model emerged as the most effective approach for predicting the thermal efficiency of both R417A and R422D. The MLP’s predicted results closely matched the actual results obtained from the thermodynamic model using Genetron software. The Root Mean Square Error (RMSE) for the thermal efficiency was exceptionally low, at 0.0002 for R417A and 0.0003 for R422D. Additionally, the R-squared (R²) values for thermal efficiency were very high, reaching 0.9999 for R417A and R422D. The findings demonstrate the effectiveness of the DM model for complex tasks like estimating ORC thermal efficiency. This approach empowers engineers with the ability to predict thermal efficiency in organic Rankine systems with high accuracy, speed, and ease.