Effects of bis(2-ethylhexyl) phthalate, γ-hexachlorocyclohexane, and 17β-estradiol on the fry stage of medaka (Oryzias latipes).
The effects of bis(2-ethylhexyl) phthalate (DEHP), γ-hexachlorocyclohexane (γ-HCH), and 17β-estradiol (E2) on the fry stage of medaka were investigated. The medaka fry were exposed to different concentrations (0.01, 0.1, 1, and 10μg/L) of these chemicals for 3 weeks after hatching. Then, mortality, body weight, sex ratio, and gonadosomatic index (GSI) of the matured fish (after 5 months) were measured. Mortality was increased significantly in the 10μg/L E2 group. Distortion of sex ratio was found in 1 and 10μg/L E2 groups. DEHP treated groups showed the GSI reduction only in male fish. All the γ-HCH and parts of the E2 treated groups showed the GSI reduction in both sexes. Exposure of DEHP, γ-HCH, and E2 during the fry stage affected normal maturation of medaka at the concentrations which had no impact on mortality or sex ratio.
Hazard evaluation of diisononyl phthalate and diisodecyl phthalate in a Japanese medaka multigenerational assay.
Reproductive and developmental effects of diisononyl phthalate (DINP) and diisodecyl phthalate (DIDP) were evaluated in a Japanese medaka (Oryzias latipes) multigeneration protocol. Each phthalate was administered via fish flake diets at a concentration of 20 microg/g (1 microg/g fish/day). Two controls were included, untreated and acetone carrier. The F(0) and F(1) generation adults were reared to sexual maturation and the test was ended prior to sexual maturation of the F(2) generation. Biochemical, individual, and population parameters were evaluated: testosterone metabolism, 7-ethoxyresorufin-o-deethylase (EROD) activity, survival, development, growth, gonadal-somatic index, histopathology, sex ratio, and fecundity. Male fish showed a two-fold induction of several testosterone metabolites in the DINP-treated group compared to the untreated control but not the acetone control. In a similar manner, in female fish only the DIDP-treated group expressed greater testosterone hydroxylase activity. There were neither sex- nor treatment-related differences in the results from the EROD assay. A statistically significant transient delay in red blood cell pigmentation was observed. The male-to-female ratio was consistent across treatments and the phenotypic and histological gender classifications were in agreement. Egg production was not significantly different among treatment groups. Neither phthalate elicited an effect on reproduction or development at various levels of biological organization.
Applications of Machine Learning in Process Monitoring and Controls of L-PBF Additive Manufacturing: A Review
One of the main issues hindering the adoption of parts produced using laser powder bed fusion (L-PBF) in safety-critical applications is the inconsistencies in quality levels. Furthermore, the complicated nature of the L-PBF process makes optimizing process parameters to reduce these defects experimentally challenging and computationally expensive. To address this issue, sensor-based monitoring of the L-PBF process has gained increasing attention in recent years. Moreover, integrating machine learning (ML) techniques to analyze the collected sensor data has significantly improved the defect detection process aiming to apply online control. This article provides a comprehensive review of the latest applications of ML for in situ monitoring and control of the L-PBF process. First, the main L-PBF process signatures are described, and the suitable sensor and specifications that can monitor each signature are reviewed. Next, the most common ML learning approaches and algorithms employed in L-PBFs are summarized. Then, an extensive comparison of the different ML algorithms used for defect detection in the L-PBF process is presented. The article then describes the ultimate goal of applying ML algorithms for in situ sensors, which is closing the loop and taking online corrective actions. Finally, some current challenges and ideas for future work are also described to provide a perspective on the future directions for research dealing with using ML applications for defect detection and control for the L-PBF processes.