4 pp. / This fact sheet is to be taken from research conducted by Uhlman and Rahman and published on the WRRC web site as: "Predicting Ground Water Vulnerability to Nitrate in Arizona". Funded by TRIF and peer reviewed by ADEQ. It also follows on "Arizona Well Owner's Guide to Water Supply" and also "Arizona Drinking Water Well Contaminants" (part 1 already submitted, part 2 in process). / Arizona's arid environment and aquifer types allow for the persistence of nitrate contamination in ground water. Agricultural practices and the prevalence of septic systems contributes to this water quality concern, resulting in nitrate exceeding the EPA Maximum Contaminant Level (MCL) in several locations across the state. Working with known nitrate concentrations in 6,800 wells across the state, this fact sheet presents maps showing the probability of nitrate contamination of ground water exceeding the MCL. The importance of monitoring your domestic water supply well for nitrate is emphasized.

Radar target tracking problem has been a popular topic for several decades. Recent works have shown that the performance of tracking algorithms increases as more prior information is used by the system / such as maximum velocity and maximum acceleration of the target, altitude of the target, or the elevation structure of the terrain. In this thesis we will focus on increasing the performance of tracking algorithms making use of benefit from the elevation model of the environment where the target tracker is searching. For a constant target altitude and a certain radar location, we generate a &ldquo / visibility map&rdquo / using the elevation model of the terrain and use this information to estimate the location and the time that the target will reappear. The second aim of this work is to use the visibility map information for improving the performance of track initiation. For that purpose, a special map has been formed, that we call as the &ldquo / track initiation probability map&rdquo / , which shows the target first time appearance density. This information has been used at the initialization part of the track initiation algorithm in order to increase the performance.

Prior to radiation therapy planning, tumours and organs at risk need to be delineated. In recent years, deep learning models have opened the possibility of automating the contouring process, speeding up the procedures and helping clinicians. However, deep learning models, trained using ground truth labels from different clinicians, inevitably incorporate the human-based inter-observer variability as well as other machine-based uncertainties and biases. Consequently, this affects the accuracy of segmentation, representing the primary source of error in contouring tasks. Therefore, clinicians still need to check and manually correct the segmentation and still do not have a measure of reliability. To tackle these issues, researchers have shifted their focus to the topic of probabilistic neural networks and uncertainties in deep learning models. Hence, the main research question of the project is whether a 3D U-Net neural network trained on CT lung cancer images can enhance clinical contouring practice by implementing a probabilistic auto-contouring system. The Monte Carlo dropout technique was employed to generate probabilistic and uncertainty maps. The model calibration was assessed using reliability diagrams, and subsequently, a clinical experiment with a radiation oncologist was conducted. To assess the clinical validity of the uncertainty maps two novel metrics were identified, namely mean uncertainty (MU) and relative uncertainty volume (RUV). The results of this study demonstrated that probability and uncertainty mapping effectively identify cases of under or over-contouring. Although the reliability analysis indicated that the model tends to be overconfident, the outcomes from the clinical experiment showed a strong correlation between the model results and the clinician’s opinion. The two metrics exhibited promising potential as indicators for clinicians to determine whether correction of the predictions is necessary. Hence, probabilistic models revealed to be valuable in clinical practice, supporting clinicians in their contouring and potentially reducing clinical errors.

This thesis summarizes a theory of magnetic resonance and the method of functional magnetic resonance. It is focused on the influence of motion artifacts and image preprocessing methods, especially realign. It deals with the possibility of using movement parameters obtained in the process of alignment of functional scans to create maps that show the expression of motion artifacts. In this thesis, three different methods were designed, implemented a tested. These methods lead to the creation of probability, power and statistical group maps showing areas typically affected by movement artifacts.