Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-based inversion algorithms were used to reconstruct ground electrical properties (GEP) such as relative dielectric permittivity (RDP) from ground penetrating radar (GPR) data for contamination assessment, their inherent non-linear nature can introduce significant uncertainty and non-uniqueness into the reconstructed models. This is a challenge that traditional ML methods are not explicitly designed to address. In this study, a probabilistic hybrid technique has been introduced that combines the DeepLabv3+ architecture-based deep convolutional neural network (DCNN) with an ensemble prediction-based Monte Carlo (MC) dropout method. Different MC dropout rates (1%, 5%, and 10%) were initially evaluated using 1D and 2D synthetic GPR data for accurate and reliable RDP model prediction. The optimal rate was chosen based on minimal prediction uncertainty and the closest alignment of the mean or median model with the true RDP model. Notably, with the optimal MC dropout rate, prediction accuracy of over 95% for the 1D and 2D cases was achieved. Motivated by these results, the hybrid technique was applied to field GPR data collected over an AMD-impacted wetland near Silverton, Colorado. The field results underscored the hybrid technique's ability to predict an accurate subsurface RDP distribution for estimating the spatial extent of AMD-induced contamination. Notably, this technique not only provides a precise assessment of subsurface contamination but also ensures consistent interpretations of subsurface condition by different environmentalists examining the same GPR data. In conclusion, the hybrid technique presents a promising avenue for future environmental studies in regions affected by AMD or other contaminants that alter the natural distribution of GEP.
126 Malay children from higher income families were followed-up regularly from birth to six years of age in the University Hospital, Kuala Lumpur. Their developmental performance was compared to that of Denver children. Generally, Malaysian and Denver children appear to be similar in their development during the first six years of life except for some minor differences in the personal-social, language and gross motor sectors. Malaysians appear to be slower in self-care but more advanced in "helping around the house", "playing interactive games" and in "separating from mother". They were slightly slower in gross motor function during the first year of life but more advanced during the second year of life. However, they were slightly more advanced in language development. The differences in development between the two groups of children are discussed and it is concluded that the differences can partly be explained by differences in socio-economic or cultural differences between the two groups of children. However, the influence of genetic factors cannot be dismissed.
A diesel engine has been a desirable machine due to its better fuel efficiency, reliability, and higher power output. It is widely used in transportations, locomotives, power generation, and industrial applications. The combustion of diesel fuel emits harmful emissions such as unburned hydrocarbons (HC), particulate matter (PM), nitrogen oxides (NOx), and carbon monoxides (CO). This article presents data on the efficiency, combustion, and emission of a 4-stroke diesel engine. The engine is a 6.8 L turbocharged 6-cylinder Tier II diesel engine fitted with a common rail injection system. The test was carried out at the Powerhouse Energy Campus, Colorado State University Engines and Energy Conversion facility. The ISO Standard 8178:4 Cycle D2 cycle was adopted for this study consists of five test runs at 1800 rpm. During the testing, CO, carbon dioxide (CO2), oxygen (O2), NOx, PM, unburned HC as a total HC (THC), methane (CH4), formaldehyde (CH2O), and volatile organic compound (VOC) emissions were measured. At the same time, the data acquisition system recorded the combustion data. The engine's performance is characterized by the brake specific fuel combustion (BSFC) and thermal efficiency. A dataset of correlations among the parameters was also presented in this article.
Diagnosis of active mycobacterial disease in orangutans (Pongo pygmaeus) has been impeded by high levels of non-specific intradermal skin test reactivity to mycobacterial antigens. This may be due in part to cross reactivity between antigens, tuberculin concentrations used or other species-specific factors. Antigen 85 (Ag85) complex proteins are major secretory products of actively growing mycobacteria, and measurement of serum Ag85 could provide a method for determining active mycobacterial infections that was not dependent on host immunity. Serum Ag85 was measured by dot-immunobinding assay using monoclonal anti-Ag85, purified Ag85 standard and enhanced chemiluminescence technology in coded serum samples from 14 captive orangutans from a zoo in Colorado, 15 semi-captive orangutans in Malaysia, and 19 free-ranging wild orangutans in Malaysia. Orangutans from Colorado (USA) were culture negative for Mycobacterium tuberculosis and M. avium, although all had laboratory suspicion or evidence of mycobacterial infection; median serum Ag85 was 10 microU/ml (range, <0.25-630 microU/ml). Of the semi-captive orangutans, six were skin test reactive and two were culture positive for M. avium on necropsy. Median serum Ag85 for this group was 1,880 microU/ml (0.75-7,000 microU/ml), significantly higher than that of Colorado zoo or free-ranging Malaysian orangutans. Median serum Ag85 in the latter group was 125 microU/ml (range, 0.75-2,500 microU/ml). These data suggest that suggest that additional studies using more specific reagents and more samples from animals of known status are appropriate.