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.

The present study demonstrates the fabrication of heterogeneous ternary composite photocatalysts consisting of TiO₂, kaolinite, and cement (TKCe),which is essential to overcome the practical barriers that are inherent to currently available photocatalysts. TKCe is prepared via a cost-effective method, which involves mechanical compression and thermal activation as major fabrication steps. The clay-cement ratio primarily determines TKCe mechanical strength and photocatalytic efficiency, where TKCe with the optimum clay-cement ratio, which is 1:1, results in a uniform matrix with fewer surface defects. The composites that have a clay-cement ratio below or above the optimum ratio account for comparatively low mechanical strength and photocatalytic activity due to inhomogeneous surfaces with more defects, including particle agglomeration and cracks. The TKCe mechanical strength comes mainly from clay-TiO₂ interactions and TiO₂-cement interactions. TiO₂-cement interactions result in CaTiO₃ formation, which significantly increases matrix interactions; however, the maximum composite performance is observed at the optimum titanate level; anything above or below this level deteriorates composite performance. Over 90% degradation rates are characteristic of all TKCe, which follow pseudo-first-order kinetics in methylene blue decontamination. The highest rate constant is observed with TKCe 1-1, which is 1.57 h⁻¹ and is the highest among all the binary composite photocatalysts that were fabricated previously. The TKCe 1-1 accounts for the highest mechanical strength, which is 6.97 MPa, while the lowest is observed with TKCe 3-1, indicating that the clay-cement ratio has a direct relation to composite strength. TKCe is a potential photocatalyst that can be obtained in variable sizes and shapes, complying with real industrial wastewater treatment requirements.

Objective: To investigate the value of differential diagnosis of hepatocellular carcinoma (HCC) and cirrhotic nodules via radiomics models based on magnetic resonance images. Background: This study is to distinguish hepatocellular carcinoma and cirrhotic nodules using MR-radiomics features extracted from four different phases of MRI images, concluded T1WI, T2WI, T2 SPIR and delay phase of contrast MRI. Methods: In this study, the four kind of magnetic resonance images of 23 patients with hepatocellular carcinoma (HCC) were collected. Among them, 12 patients with liver cirrhosis were used to obtain cirrhotic nodules (CN). The dataset was used to extract MR-radiomics features from regions of interest (ROI). The statistical methods of MRradiomics features could distinguish HCC and CN. And the ability of radiomics features between HCC and CN was estimated by receiver operating characteristic curve (ROC). Results: A total of 424 radiomics features were extracted from four kind of magnetic resonance images. 86 features in delay phase of contrast MRI，86 features in spir phase of T2WI，86 features in T1WI and 88 features in T2WI showed statistical difference (p < 0.05). Among them, the area under the curves (AUC) of these features larger than 0.85 were 58 features in delay phase of contrast MRI, 54 features in spir phase of T2WI, 62 features in T1WI and 57 features in T2WI. Conclusions: Radiomics features extracted from MRI images have the potential to distinguish HCC and CN.

This study explores the scale efficiency of four star hotels in a small tourist destination in Croatia. The number of overnight stays and the increase in hotel beds are two indicators of the development of a tourist destination. Among the accommodation facilities, hotels play a significant role in the development of a tourist destination, but they are increasingly facing a labor force crisis. Data envelopment analysis is used to rank hotels by efficiency coefficient. The aim of the paper is to investigate the efficiency of the hotel by taking certain inputs and outputs, which are explained in detail in the paper. The paper uses the CCR (Charnes, Cooper, and Rhodes) and BCC (Banker, Charnes, and Cooper) models to calculate hotel scale efficiency and also presents an overview of previous research around the world.

This research presents a comprehensive model for enhancing the road network in Thailand to achieve high efficiency in transportation. The objective is to develop a systematic approach for categorizing roads that aligns with usage demands and responsible agencies. This alignment facilitates the creation of interconnected routes, which ensure clear responsibility demarcation and foster efficient budget allocation for road maintenance. The findings suggest that a well-structured road network, combined with advanced information and communication technology, can significantly enhance the economic competitiveness of Thailand. This model not only proposes a framework for effective road classification but also outlines strategic initiatives for leveraging technology to achieve transportation efficiency and safety.