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.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.

Phytomediation is an environmentally friendly green rehabilitation technology that is often incorporated with an application to improve calcium peroxide and phytohormones required for the growth of agricultural plants with the expectation to improve the effectiveness of plant rehabilitation. This study mainly consists of two parts: (1) water culture experiment and (2) pot culture experiment. In the water culture experiment, we attempt to understand the influence of the addition of calcium peroxide, phytohormones (IAA and GA₃) and a chelating agent on the growth of sunflower plants. However, in the pot culture experiment, when hormones and the chelating agent EDTA are introduced to different plant groups at the same time, if the nutrition in the water required by plants is not available, the addition of the hormone cannot negate the toxicity caused by EDTA. In terms of calcium peroxide, due to quick release of oxygen in water, this study fails to apply calcium peroxide to the water culture experiment.

When the pot culture experiment is used to examine the influence of hormones at different concentration levels on the growth of sunflowers, GA₃ 10^-8 M is reported to have the optimal effectiveness, followed by IAA 10^-8 M; IAA 10^-12 M has the lowest effectiveness. According to an accumulation analysis of heavy metals at different levels, GA₃ concentrates in leaves to transport nutrition in soil to leaves. This results in an excellent TF value of 2.329G of GA₃ than 1.845 of the control group indicating that the addition of the hormone and chelating agent to GA₃ increases the TF value and the chelating agent is beneficial to the sunflower plant. If we examine phytoattenuation ability, the one-month experiment was divided into three stages for ten days each. The concentration level of heavy metals in the soil at each stage dropped continuously while that of the control group decreased from 31.63 mg/kg to 23.96 mg/kg, GA₃ from 32.09 mg/kg to 23.04 mg/kg and EDTA from 30.65 mg/kg to 25.93 mg/kg indicating the quickest growth period of the sunflowers from the formation of the bud to blossom. During the stage, the quick upward transportation of nutrition results in quick accumulation of heavy metals; the accumulated speed of heavy metals is found higher than that of directly planted plants. This study shows an improvement in the effectiveness of the addition of hormones on plant extraction and when rehabilitation is incorporated with sunflowers with the beginning bud formation, better treatment effectiveness can be reached.

The exploitation of timber has had a profound impact on tropical forest areas and their structures. This study assessed the effect of selective logging on natural regeneration and soil characteristics in post-loading bay sites at the Pra-Anum forest reserve in Ghana, West Africa. The results showed no difference in the number of species enumerated in the loading bays and the undisturbed area. More trees were observed in the RAT and RNT plots than in the undisturbed area. Relative to the RAT plot, species on the RNT and the undisturbed area were less diverse and less evenly distributed. Mean tree height, diameter, and basal area were higher in the RAT and RNT plots than in the undisturbed plots. Soil bulk density was lower in the RAT and undisturbed plot than in the RAT plot and increased with increased depth. Soil organic matter was 44% and 27% more in the undisturbed and RAT plots, respectively, than in the RNT plot and accounted for 84.75%, 83.97% and 45.33% of variations in soil bulk density, pH, and CEC. The study provides insight into the need to rehabilitate highly disturbed areas in forests, particularly the addition of topsoil on loading bays, skid trails, roads, and gaps after logging to improve the productivity of the forest soils.

The ultimate objective of the study was to investigate the effects of being landlocked on the living standards in Sub-Saharan African (SSA) countries from 1991 to 2019. Adopting the two-step estimation technique of System GMM (generalized method of moments), the study found that being landlocked has a negative and significant effect on the living standards in SSA countries when using GDP per capita as the living standard measure. Moreover, the historical living standard experiences of SSA countries have a positive and significant influence on the current living standard level. In addition, the population growth rate has a positive and significant effect on the living standards in SSA countries. On the other hand, the official exchange rate, broad money as a percentage of GDP, and inflation have a negative and significant effect on the living standards in SSA countries. Generally, the estimated result reveals the existence of a significant variation in the living standards in landlocked and coastal SSA countries. This study suggests that regional integration between landlocked and transit countries should be improved to minimize entry costs and increase access to global markets for landlocked countries. We argue that this study is of interest to landlocked and coastal countries to increase trade integration and promote the development of both groups, and it will contribute to the scarce empirical evidence.