Yong In University

This study aimed to assess consumer spray products containing target metals, particularly focusing on solution levels and airborne levels after spraying from 124 products in spray products using an Inductively coupled plasma-mass spectrometry and an Inductively coupled plasma-optical emission spectrometer. The spray release method classified consumer products into propellant and trigger types, and air levels were measured at distances of 1 m, 3 m, and 5 m from the clean room. Of the 19 measured target metals, 18 were detected in consumer product solutions, while Cd was not detected. Propellant-type coating products showed higher Si levels than trigger-type coating products (p > 0.05), and Si showed the highest airborne concentrations at 1, 3, and 5 m. Nanomaterials such as Si and Ti were detected in higher levels than other target metals (Pb, Ni, and Cr) at the furthest distance of 5 m. In conclusion, target metals were detected in the product solution and the air. In conclusion, Ti and Si were frequently identified in both product solutions and air samples, indicating their potential relevance for inhalation exposure following spray use. Therefore, consumer spray products should be used with caution and in well-ventilated environments, as airborne metal exposure may occur during spray use.

Per- and polyfluoroalkyl substances (PFAS) are increasingly recognized as airborne contaminants of concern, yet their distribution across environments remains under-characterized. In particular, concentrations in industrial settings have hardly been studied compared to general environments. This review synthesizes findings from 25 studies reporting airborne PFAS across four categories: outdoor general environments, indoor general environments, outdoor occupational environments, and indoor occupational environments. Reported levels were generally lowest outdoors (0.5-3.0 ng/m3), higher in general indoor settings (3.0-15.0 ng/m3), and highest in occupational environments, sometimes exceeding 100 ng/m3. Volatile precursors such as fluorotelomer alcohols dominated most environments, while particle-bound ionic PFAS were more common in occupational settings, suggesting distinct exposure pathways. Across studies, airborne PFAS levels were lowest outdoors, higher indoors, and highest in occupational settings. The complexity of PFAS behavior, influenced by chemical species and particle/gas-phase partitioning, underscores challenges in exposure assessment. Despite the relatively low airborne PFAS concentrations, this review confirms that inhalation remains a critical exposure pathway due to the persistence, bioaccumulation, and toxicity of PFAS. Standardized measurement methods, geographically diverse monitoring, and integration with exposure modeling are urgently needed. Strengthening regulatory frameworks and establishing protective exposure limits will be essential to mitigate long-term health impacts.

This study develops a nonlinear dynamic modeling framework to analyze and predict performance behavior in industrial environments using competitive-intelligence-related variables. Four organizational resource components are formulated as elements of a discrete-time state vector, and their influence on system output is modeled through a nonlinear state-transition function. Empirical observations collected from a steel manufacturing company were used to identify the unknown dynamics through a feed-forward artificial neural network trained via a gradient-based optimization procedure. Reliability of the measurement instrument was verified using Cronbach’s alpha coefficients of 0.92 and 0.86 for the independent and dependent constructs, respectively. The identified model demonstrates stable convergence, with the minimum prediction error achieved near iteration 1500, and outperforms a linear baseline in mean-squared error and correlation accuracy. The proposed formulation provides a mathematically oriented approach for reconstructing performance-driven system behavior and establishes a foundation for future extensions involving adaptive estimation, robust analysis, and optimal control strategies in industrial systems.

This study presents a process-resolved airborne characterization of a plastic button manufacturing facility using an unsaturated polyester resin, integrating particle size distributions, volatile organic compounds (VOCs), metals, inorganic anions, phthalates, and per- and polyfluoroalkyl substances (PFAS). Particle number concentrations (10 nm–10 μm) exhibited unit-dependent submicron maxima, reaching 1.10 × 105 particles·cm−3 in electroplating operations, indicating localized aerosol generation. Emissions displayed clear compositional differentiation across operational domains: styrene was a dominant VOC, with the highest concentration in Plating (Glossy)_1 (1,506 µg·m−3), while wet-chemical lines showed elevated inorganic burdens, including total Cr (526 µg·m−3) and SO42− (4,932 µg·m⁻3). Among semi-volatile organics, di(2-ethylhexyl) phthalate (DEHP) reached a maximum concentration of 2.67 µg·m−3, while PFAS detections were confined to wet-chemical operations, with PFOA and PFOS reaching approximately 0.18 µg·m−3. Principal component analysis resolved solvent-driven, mechanically associated, and wet-chemical emission domains. Risk assessment identified Mn as the principal contributor to the nervous-system HI (Mn, styrene, toluene, xylenes, and n-hexane), whereas F− determined the skeletal HI. The nervous-system and skeletal HIs reached maxima of 1.17 × 103 and 2.26 × 102, respectively, while benzene-driven carcinogenic risk peaked at 6.87 × 10−5, below the 10⁻4 benchmark. Plastic button manufacturing is thus characterized as a multi-source emission environment structured by discrete unit operations along the production line, with styrene, transition metals, inorganic anions, and semi-volatile additives serving as principal exposure determinants. These findings establish a quantitative framework for occupational exposure prioritization in plastic button manufacturing systems.

Purpose: Minichromosome maintenance protein 2 (MCM2) is a key regulator of DNA replication and has been implicated in tumor progression. While previous studies have demonstrated its role in lung cancer stem cells (CSCs), its role in breast cancer remains unclear. This study aimed to investigate whether MCM2 regulates CSCs and epithelial-mesenchymal transition (EMT) in breast cancer.Methods: MCM2 expression and its prognostic significance were analyzed using public breast cancer datasets. MDA-MB-231 breast cancer cells were transfected with MCM2-specific small interfering RNA. CSC-associated markers and EMT-related proteins were evaluated by Western blotting. Sphere formation, migration, and invasion assays were performed to assess CSC properties and metastatic potential.Results: MCM2 expression was significantly elevated in breast cancer tissues and correlated with poor prognosis. Knockdown of MCM2 reduced the expression of CSC-associated markers, including ALDH1A1, Sox2, Oct4, and CD44. Functional assays showed that MCM2 suppression significantly decreased sphere-forming capacity, indicating impaired CSC properties. In addition, MCM2 knockdown increased E-cadherin expression while decreasing N-cadherin, Slug, Twist, and ZEB1 expression, suggesting inhibition of EMT. Migration and invasion abilities were also markedly reduced.Conclusion: Suppression of MCM2 was associated with reduced CSC and EMT-related phenotypes in MDA-MB-231 breast cancer cells.