Improving fecal bacteria estimation using machine learning and explainable AI in four major rivers, South Korea.

This study addresses the critical public health issue of fecal coliform contamination in the four major rivers in South Korea (Han, Nakdong, Geum, and Yeongsan rivers) by applying advanced machine learning (ML) algorithms combined with Explainable Artificial Intelligence to enhance both prediction accuracy and interpretability. Both traditional and machine learning models often face challenges in accurately estimating fecal coliform levels due to the complexity of environmental variables and data limitations. To address this limitation, we employed two tree-based models (i.

Classifying eutrophication spatio-temporal dynamics in river systems using deep learning technique.

Eutrophication is a major cause of water quality degradation in South Korea, owing to severe algal blooms. To manage eutrophication, the South Korean government provided the Trophic State Index (TSIko), which was revised according to Carlson's TSI. The TSIko levels were simulated using mechanistic water quality modeling.

Is Additive Manufacturing an Environmentally and Economically Preferred Alternative for Mass Production?

The manufacturing sector accounts for a large percentage of global energy use and greenhouse gas emissions, and there is growing interest in the potential of additive manufacturing (AM) to reduce the sector's environmental impacts. Across multiple industries, AM has been used to reduce material use in final parts by 35-80%, and recent publications have predicted that AM will enable the fabrication of customized products locally and on-demand, reducing shipping and material waste. In many contexts, however, AM is not a viable alternative to traditional manufacturing methods due to its high production costs.

Picropodophyllotoxin Inhibits Cell Growth and Induces Apoptosis in Gefitinib-Resistant Non-Small Lung Cancer Cells by Dual-Targeting EGFR and MET.

Patients with non-small-cell lung cancer (NSCLC) with epidermal growth factor receptor (EGFR) amplification or sensitive mutations initially respond to the tyrosine kinase inhibitor gefitinib, however, the treatment becomes less effective over time by resistance mechanism including mesenchymal-epithelial transition (MET) overexpression. A therapeutic strategy targeting MET and EGFR may be a means to overcoming resistance to gefitinib. In the present study, we found that picropodophyllotoxin (PPT), derived from the roots of , inhibited both EGFR and MET in NSCLC cells.

Experimental evaluation of sodium silicate-based nanosilica against chloride effects in offshore concrete.

This study investigates the effect of a new pore filling material, named sodium silicate-based nanosilica (SS), on resisting the diffusion of the chloride ions. The proposed SS is chosen, mainly due to its smaller particle size, compared to the conventional ethyl silicate-based nanosilica. Each particle of SS is chemically treated to have the negative (-) charge on its surface.

Tolerance optimization of a mobile phone camera lens system.

In the manufacturing process for the lens system of a mobile phone camera, various types of assembly and manufacturing tolerances, such as tilt and decenter, should be appropriately allocated. Because these tolerances affect manufacturing cost and the expected optical performance, it is necessary to choose a systematic design methodology for determining optimal tolerances. In order to determine the tolerances that minimize production cost while satisfying the reliability constraints on important optical performance indices, we propose a tolerance design procedure for a lens system.