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Theoretical Investigation of Two-phase Cooling Cycles for Electronic Components in More-Electric Aircraft
Investigation On The Effective Use Of Glide And A Suction-line Liquid-line Heat Exchanger To Improve Performance In Air Conditioning And Heat Pump
Experimental Study On Battery Cooling/Preheating Using Heat Pipe-assisted Hybrid Fin Under Extreme Conditions
Sizing and Control Design of Solar Thermal Absorption Refrigeration for Horticultural Cold Storage in Hot-Humid Climates
Optimization and Experimental Validation of Annular Finned PCM-HX for a Domestic Hot Water Heater Application
Parentally Exposed Zebrafish Larvae Have Altered Craniofacial Measurements: Multigeneration Developmental Atrazine Toxicity
Atrazine is a herbicide used throughout the midwestern United States to prevent broadleaf weeds in crops. The U.S. Environmental Protection Agency (EPA) has set the maximum contaminant level at 3 ppb (μg/L) in drinking water. Atrazine is an endocrine disrupter interfering with the function of hormones and disrupting normal physiology and homeostasis throughout development and the life course of an organism. The zebrafish model was used to test the hypothesis that an embryonic parental atrazine exposure will cause modifications in morphology in developing offspring. AB adult zebrafish were bred. Embryos were collected and exposed to atrazine concentrations of 0, 0.3, 3, or 30 ppb from 1 to 72 hours post fertilization (hpf; the end of embryogenesis). Atrazine exposure ceased at 72 hpf and larvae were grown into adulthood in aquaria water (F0). Atrazine F0 adult zebrafish were then bred within their treatment group. Their embryos were collected and placed in petri dishes in aquaria water until 120 hpf. At 120 hpf, larvae were collected for morphological analysis including general morphology measurements and co-staining with alcian blue and alizarin red for cartilage and skeletal assessments. Head length and ratio of head length to total length was significantly increased in the F1 0.3 and 30 ppb atrazine groups (p \u3c 0.05). The posteriorly positioned notochord indicated delayed ossification and skeletal growth. These findings signify that a single embryonic parental exposure leads to changes in craniofacial development in their offspring
Machine Learning of Big Data: A Gaussian Regression Model to Predict the Spatiotemporal Distribution of Ground Ozone
Tracking pollution levels on the ground is important to the environment and public health. One of the pollutants of concern is ozone, which, at high concentrations, can cause respiratory and cardiovascular problems. The National Center for Atmospheric Research (NCAR) has published valuable ozone data obtained from ground-based sensors installed at selected locations. Because it is unfeasible to measure the exact ozone levels everywhere at any time, it would be valuable to predict the temporal-spatial distributions of ozone concentration based on existing data. This would help us better understand the patterns and trends in the data and make better decisions to reduce pollution. Motivated by this, the objective of this paper was to build predictive models to illustrate the temporal-spatial structure of the large amount of ozone data. The training data included measurements of ozone in 513 locations in the eastern states of the United States spanning five years. We used a machine-learning method called Gaussian process regression (GPR) with a covariance function that describes the temporal-spatial relationship between data points. With this method, we were able to observe the trends and dynamics of ozone formation. Additionally, maps were created to visualize the spatial and temporal distribution of ozone concentrations. The results demonstrate that the GPR method with the Matérn covariance function was able to give a reliable estimate of the uncertainty as well as the mean ozone concentration at various locations and times, which helps us better understand the dynamics of ozone formation