This study examines three applications of control charts used for monitoring syndromic data with different characteristics. The first part develops a seasonal autoregressive integrated moving average (SARIMA) based surveillance chart, and compares it with the CDC Early Aberration Reporting System (EARS) W2c method using both authentic and simulated data. After successfully removing the long-term trend and the seasonality involved in syndromic data, the performance of the SARIMA approach is shown to be better than the performance of the EARS method in terms of two key surveillance characteristics, the false alarm rate and the average time to detect the outbreaks. In the second part, we propose a generalized likelihood ratio (GLR) control chart to detect a wide range of shifts in the mean of Poisson distributed biosurveillance data. The application of a sign function on the original GLR chart statistics leads to downward-sided, upward-sided, and two-sided GLR chart statistics in an unified framework. To facilitate the use of such charts in practice, we provide detailed guidance on developing and implementing the GLR chart. Under the steady-state framework, this study indicates that the overall GLR chart performance in detecting a range of shifts of interest is superior to the performance of traditional control charts including the EARS method, Shewhart charts, EWMA charts, and CUSUM charts. There is often an excessive number of zeros involved in health care related data. Zero-inflated Poisson (ZIP) models are more appropriate than Poisson models to describe such data. The last part of the dissertation considers the GLR chart for ZIP data under a research framework similar to the second part. Because small sample sizes may influence the estimation of ZIP parameters, the efficiency of MLEs is investigated in depth, followed by suggestions for improvement. Numerical approaches to solving for the MLEs are discussed as well. Statistics for a set of GLR charts are derived, followed by modifications changing them from two-sided statistics to one-sided statistics. Although not a complete study of GLR charts for ZIP processes, due to limited time and resources, suggestions for future work are proposed at the end of this dissertation. / Ph. D.

The risk-adjusted Bernoulli cumulative sum (CUSUM) chart developed by Steiner et al. (2000) is an increasingly popular tool for monitoring clinical and surgical performance. In practice, however, use of a fixed control limit for the chart leads to quite variable in-control average run length (ARL) performance for patient populations with different risk score distributions. To overcome this problem, the simulation-based dynamic probability control limits (DPCLs) patient-by-patient for the risk-adjusted Bernoulli CUSUM charts is determined in this study. By maintaining the probability of a false alarm at a constant level conditional on no false alarm for previous observations, the risk-adjusted CUSUM charts with DPCLs have consistent in-control performance at the desired level with approximately geometrically distributed run lengths. Simulation results demonstrate that the proposed method does not rely on any information or assumptions about the patients' risk distributions. The use of DPCLs for risk-adjusted Bernoulli CUSUM charts allows each chart to be designed for the corresponding particular sequence of patients for a surgeon or hospital. The effect of estimation error on performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is also examined. Our simulation results show that the in-control performance of risk-adjusted Bernoulli CUSUM chart with DPCLs is affected by the estimation error. The most influential factors are the specified desired in-control average run length, the Phase I sample size and the overall adverse event rate. However, the effect of estimation error is uniformly smaller for the risk-adjusted Bernoulli CUSUM chart with DPCLs than for the corresponding chart with a constant control limit under various realistic scenarios. In addition, there is a substantial reduction in the standard deviation of the in-control run length when DPCLs are used. Therefore, use of DPCLs has yet another advantage when designing a risk-adjusted Bernoulli CUSUM chart. These researches are results of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech). Moreover, DPCLs are adapted to design the risk-adjusted CUSUM charts for multiresponses developed by Tang et al. (2015). It is shown that the in-control performance of the charts with DPCLs can be controlled for different patient populations because these limits are determined for each specific sequence of patients. Thus, the risk-adjusted CUSUM chart for multiresponses with DPCLs is more practical and should be applied to effectively monitor surgical performance by hospitals and healthcare practitioners. This research is a result of joint work with Dr. William H. Woodall (Department of Statistics, Virginia Tech) and Mr. Justin Loda (Department of Statistics, Virginia Tech). / Ph. D.

The data from genome-wide association studies (GWAS) in humans are still predominantly analyzed using single marker association methods. As an alternative to Single Marker Analysis (SMA), all or subsets of markers can be tested simultaneously. This approach requires a form of Penalized Regression (PR) as the number of SNPs is much larger than the sample size. Here we review PR methods in the context of GWAS, extend them to perform penalty parameter and SNP selection by False Discovery Rate (FDR) control, and assess their performance (including penalties incorporating linkage disequilibrium) in comparison with SMA. PR methods were compared with SMA on realistically simulated GWAS data consisting of genotype data from single and multiple chromosomes and a continuous phenotype and on real data. Based on our comparisons our analytic FDR criterion may currently be the best approach to SNP selection using PR for GWAS. We found that PR with FDR control provides substantially more power than SMA with genome-wide type-I error control but somewhat less power than SMA with Benjamini-Hochberg FDR control. PR controlled the FDR conservatively while SMA-BH may not achieve FDR control in all situations. Differences among PR methods seem quite small when the focus is on variable selection with FDR control. Incorporating LD into PR by adapting penalties developed for covariates measured on graphs can improve power but also generate morel false positives or wider regions for follow-up. We recommend using the Elastic Net with a mixing weight for the Lasso penalty near 0.5 as the best method. / Ph. D.

Several warm winter events have occurred across the Southeast in the past decade, including 2 major events in 2017 and 2018 in Georgia and South Carolina. Plants will begin their spring growth sooner than climatology would suggest and then be damaged by early spring frosts in what is commonly known as a "false spring" event. Some species of plants, like peaches and blueberries, which produce buds early in the season, are just an example of some of the agricultural products more at risk than others. As an important measure of dormancy time in plants, chill hours present a measurement capable of tracking phenological shifts in plants. While a lack of required chill hours can delay spring emergence, intense warm periods can override the chilling hour requirement and induce spring emergence. This project involves training self-organizing maps (SOMs) to identify periods of anomalous winter warming based on a reduced number of chill hours within a 5-day temporal period compared to the period's climatological average. A second SOM is nested in the node that produced the most anomalous events to identify the range of warming that occurs in the most anomalous events, the synoptic setups of these events, and when these occurred. Hourly 2-meter temperature from ERA5 is used to conduct this analysis over a domain centered primarily over South Carolina and Georgia with a temporal period of 1980-2020. Climatological examination of chill hour accumulations in the past 4 decades show an overall decrease in chill hour accumulation across the past decade (2011-2020) Results indicated that periods of higher-than-average temperatures are increasing with time while periods of average or lower than average temperatures are decreasing with time. Both results were statistically significant by Mann-Kendall test. The results of the nested SOMs suggest that an increase in patterns of southerly flow (a common pattern for warmer temperatures) is occurring through time. A third SOM investigating early spring hard freezes was inconclusive but illustrated that some years had more early spring frosts than others independent of how many warmer than average periods occurred in the main winter. The use of SOMs for investigating climatological and synoptic changes in winter and early spring proved successful and effective. Future modifications to these SOMs could be used to identify more trends that exist within these seasons. / Master of Science / Several warm winter events have occurred across the Southeast in the past decade, including 2 major events in 2017 and 2018 in Georgia and South Carolina. Plants will begin their spring growth sooner than climatology would suggest and then be damaged by early spring frosts in what is commonly known as a "false spring" event. Some species of plants, like peaches and blueberries, which produce buds early in the season, are just an example of some of the agricultural products more at risk than others. As an important measure of dormancy time in plants, chill hours present a measurement capable of tracking shifts from normal winter to spring transition in plants. While a lack of required chill hours can delay leaf emergence and spring blooms, intense warm periods can override the chilling hour requirement and induce this spring emergence. This project involves training self-organizing maps (SOMs), a machine learning model, to identify periods of anomalous winter warming based on a reduced number of chill hours within a 5-day temporal period compared to the period's climatological average. A second SOM is nested in the node that produced the most anomalously warm events to identify the range of warming that occurs in the most anomalous events, the large-scale meteorological setups of these events, and when these occurred. Hourly 2-meter temperature from ERA5, a climatological dataset, is used to conduct this analysis over a domain centered primarily over South Carolina and Georgia with a temporal period of 1980-2020. Climatological examination of chill hour accumulations in the past 4 decades show an overall decrease in chill hour accumulation across the past decade (2011-2020) Results indicated that periods of higher-than-average temperatures are increasing with time while periods of average or lower than average temperatures are decreasing with time. Both of these trend findings were statistically significant by Mann-Kendall test. The results of the nested SOMs suggest that an increase in patterns of southerly flow (a common pattern for warmer temperatures) is occurring through time. A third SOM investigating early spring hard freezes (temperatures low enough to cause damage to plant cellular structures) was inconclusive but illustrated that some years had more early spring frosts than others independent of how many warmer than average periods occurred in the main winter. The use of SOMs for investigating climatological and synoptic changes in winter and early spring proved successful and effective. Future modifications to these SOMs could be used to identify more trends that exist within these seasons.

Tesis por compendio / Goat milk is primarily destined for the production of fermented products, in particular cheese. Therefore, the control of antibiotic residues in milk is of great importance, since these could have negative repercussions on technological properties of the milk as well as on the health of consumers. In milk quality control programs, microbial inhibitor tests are widely applied to detect antibiotics during the screening stage. However, tests are non-specific and may be affected by substances other than antimicrobials which could inhibit the growth of the test micro-organism, causing false positive results. The aim of this thesis was to evaluate the interference, related to the presence of different contaminants in goat milk, on the response of microbial inhibitor tests commonly used in Spain to detect antibiotics (BRT MRL, Delvotest SP-NT MCS and Eclipse 100 tests). The influence of the physicochemical characteristics of goat milk on the false positive outcomes in microbial screening tests was also investigated. The suitability of microbial inhibitor tests for screening antibiotics in colostrum secretions was studied by analysing antibiotic-free colostrum and milk samples from forty-three Murciano-Granadina goats, collected every 12 hours during the first week post-partum. Microbial inhibitor tests were not suitable for the analysis of goat colostrum because they presented a high percentage of doubtful and positive results (up 37.2% in the 36 hours after partum). To evaluate the effect of caprine colostrum on the microbial test response, antimicrobial-free goat milk spiked with different concentrations of colostrum was analysed to calculate the inhibitory concentrations producing 5% of positive results. The highest interferences were obtained for the addition of colostrum from 12 to 24 hours post-partum and the colostrum concentrations producing 5% positive results were between 5.1 and 34.6%. The BRT MRL was the test the most affected. In another study, the interference of detergents and disinfectants used for the cleaning of milking equipment and milk storage tanks of dairy farms was investigated. Antimicrobial-free goat milk was spiked with eight concentrations of different cleaning products (5 acid, 5 alkaline, 5 domestic washing-up liquids, and 1 disinfectant) and analysed using microbial screening tests. The presence of acid detergent and disinfectant based on sodium hypochlorite in goat milk did not affect the microbial test response. However, alkaline detergents at concentrations ≥ 1 ml/l could lead to false positive results in microbial inhibitor tests (up to 16.7%) and from 4 ml/l on 100% positive results were obtained. Regarding the products used for home use, and those used on farms and small size dairies, washing-up liquid containing sodium laureth sulphate and ethanol had the greatest effects on microbial inhibitor tests, even starting from a relatively low concentration (1 ml/l). On the other hand, the presence of a relatively low concentration of detergents in goat milk (0.5 ml/l) slightly modified the detection capability of the microbial inhibitor tests for amoxicillin, ampicillin, benzylpenicillin, and cloxacillin, although the detection of these drugs at MRL (safe level) was not compromised. Antiparasitic agent residues in goat milk could be another possible cause of false positive results in microbial screening tests. An in vitro study to evaluate the effect of seven parasiticides commonly used in dairy goats was carried out. Further two studies, where albendazole and ivermectin were applied to two groups of dairy goats in lactation were performed. It should be noted that the parasiticide ivermectin is banned for the treatment of animals producing milk for human consumption, although its inclusion in this study was considered interesting to understand the potential effect of their residues in milk, in the event the practice was performed illegally. In the in vitro study, raw antibiotic-free milk from goats was spiked individually with eight different concentrations of albendazole, closantel, diclazuril, febendazole, levamisole, diazinon, and ivermectin. The microbial inhibitor test results showed a great variability according to the test and the drug under study. Of the tests considered, the BRT MRL test was the most sensitive to antiparasitic agents, with the lowest concentrations of antiparasitic agent causing 5, 10, and 50% of positive results. Generally, closantel and diazinon were the antiparasitic agents that produced higher interferences in all tests, since low concentrations already resulted in positive results, while only higher concentrations of diclazuril and ivermectin showed an inhibitory effect. To evaluate the effect of albendazole residues on the microbial inhibitor test response, eighteen healthy Murciano-Granadina goats in mid-lactation were treated with a single oral administration of the commercially available albendazole registered for dairy sheep (7.5 mg/kg b.w. of active compound) with a withdrawal period of 4 days for milk production in ovine. Albendazole and its metabolite residues in goat milk after under cascade treatment were not detected above MRL from the third day post-administration. However, a high occurrence of non-compliant results was obtained for the BRT MRL test during the first six days after treatment, suggesting that factors related to the albendazole application other than the drug concentration are able to affect the microbial inhibitor test response in some cases. Regarding the ivermectin study, twenty-eight Murciano-Granadina goats infested with Sarcoptes scabiei var. caprae were treated with a subcutaneous injection of ivermectin (200 μg/kg b.w.), with a second dose applied seven days after the first treatment. Drug residues in goat milk were recorded during the first fifteen days of the experiment with concentrations ranging from 8.13 to 24.25 ng/ml. In addition, all the microbial screening tests seem to be affected by the ivermectin treatment, with BRT MRL the most affected (20%) compared with Delvotest SP-NT MCS and Eclipse 100 (6.6 and 5.7%, respectively). These positive results cannot be associated with the ivermectin concentration in goat milk, as the concentrations measured were lower than the inhibitory concentrations as reported in a previous in vitro study for these microbial tests. Thus, as suggested by some authors, interferences could be related to changes or alterations caused by the application of the parasiticide agent or by the parasitic disease itself, which could affect the immune response of the animals favouring the presence of inhibitory substances in milk. The study of the effect of the goat milk composition on the specificity (rate of false positive results) of microbial inhibitor tests for screening antibiotics was also considered. Thus, individual goat milk samples (n=200) were analysed by microbial inhibitor tests using both visual and instrumental classification of the test results. The highest specificity values were obtained for the instrumental interpretation of the test results (94-99% vs 90-96%) due to the occurrence of samples with intermediate colorations (green-yellow, yellow-blue) making the visual classification more difficult and subjective. A relation was found between positive results in BRT MRL and Eclipse 100 tests and an elevated fat content in the goat milk. Positive outcomes in Eclipse 100 were associated with the butyric acid concentration in the milk. Further, the Delvotest SP-NT MCS test response was affected by elevated pH values, high lactoferrrin and myristoleic acid concentrations in the goat milk. This percentage of positive results could be minimized by a pre-treatment prior to microbial inhibitor test analysis, such as fat removal by centrifugation (3,100 g for 10 min at 4 ºC) and/or heating (80 ºC for 10 min). Undoubtedly, improvements on the specificity of the microbial inhibitor tests for screening antibiotics in goat milk are desirable to avoid the destruction of milk compliant for human due to the occurrence of false positive results. The related financial losses affect farmers and dairies. However, it should be noted that the presence of contaminants in goat milk could be avoided by applying good farming practices designed to ensure that milk is obtained from healthy animals under proper hygienic conditions so ensuring the food safety of goat milk and related dairy products. / Romero Rueda, T. (2015). Evaluation of false positive results in microbial inhibitor tests for screening antibiotics in goat milk [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/48552 / Compendio

Fire occurrence was widespread in the central Appalachians pre-European settlement due to Native American ignition and occasional lightning strikes, and continued through European settlement. During this time, low to mixed severity burns supported a suite of ecological communities that were fire adapted. In the mid-20th century, the frequency and intensity of fire decreased regionally, resulting in profound forest composition shifts. Land managers now are prioritizing prescribed fire as a restoration tool in current and transitioning fire dependent communities. However, it is unclear how the re-introduction of fire will affect bat community assemblages, particularly after the severe White-nose Syndrome related population declines of many cave-hibernating bat species. To address this concern we used acoustic detectors to sample bat activity levels in burned and unburned environments to examine habitat and temporal effects of fire on bat species in a repeatedly burned landscape. We found evidence for weak positive fire effects on the northern long-eared bat, Indiana bat, little brown bat, big brown bat/silver-haired bat group, high frequency phonic group, and total bat activity. Temporal effects of fire were only apparent for the big brown bat, where we observed a negative relationship between activity and time since fire. Additionally, historic wildfires may offer a suitable surrogate to assess long-term burn impacts on bats, which in turn can be used to better inform bat and prescribed fire relationships. To examine effects of historic fire on bats, we assessed bat presence using acoustic detections at 16 paired burned and unburned forest stands in Shenandoah National Park. Overall, we found few or mostly equivocal relationships of bat occupancy across species relative to burn condition or time since fire at SNP, indicating there is little evidence to support the concept that fire has a significant ecological effect on bats in this portion of the central Appalachians. Riparian areas are particularly important for bats, and serve as foraging and drinking areas, roost sites, and travel corridors. Because fire impacts dry upland and mesic riparian areas differently, is possible that fire will impact bats differently in burned and riparian habitats. To examine fire effects on bats in riparian and upland habitats, we used paired sampling to monitor bat activity in burned, unburned, riparian, and non-riparian areas. Burn and riparian variables had empirical support to explain activity of all bat species. However, coefficients for these species were small and confidence intervals overlapped zero indicating that differences between habitat configurations were marginal. Our results suggest bats have somewhat species-specific responses to fire that differ between upland and riparian habitats, but that large landscape level prescribed fire has a slightly positive to neutral impact on all bats species identified in at our study site post-fire suppression. / Master of Science / Fire occurrence was widespread in the central Appalachians pre-European settlement from to Native American ignition and occasional lightning strikes, and anthropogenic burning continued through European settlement. During this time, burns supported many ecological communities that were fire adapted, i.e., oak (Quercus spp) and pine (Pinus spp)-dominated types. In the mid-20th century, fire decreased regionally, resulting in changes to forest composition. Land managers now are prioritizing prescribed fire as a tool to restore or re-establish fire dependent communities. However, it is unclear how the re-introduction of fire will affect bats, particularly after the severe White-nose Syndrome related population declines of many bat species. To address this concern, I used acoustic detectors to measure bat activity levels in burned and unburned landscapes to examine habitat and temporal effects of fire on bat species in a repeatedly burned landscape on the northwestern portion of the George Washington National Forest. I found evidence for weak positive fire effects on the northern long-eared bat, Indiana bat, little brown bat, big brown bat/silver-haired bat group, high frequency phonic group, and total bat activity. Temporal effects of fire were only apparent for the big brown bat, where we observed decreasing activity as time since fire increased. Because riparian areas are particularly important for bats in the region as foraging and drinking areas, roost sites, and travel corridors, I also focally compared burned and unburned riparian areas. Burn and riparian variables had support to explain activity of all bat species, however differences between habitat types were marginal. My results suggest bats have somewhat species-specific responses to fire that differ between upland and riparian habitats, but that large landscape level prescribed fire has a slightly positive to neutral impact on all bats species identified at our study site post-fire suppression. Lastly, examining effects of historic wildfires may allow managers to infer long-term burn impacts not yet observable with current prescribed burning. To examine effects of historic fire on bats, I assessed bat presence using acoustic detections at paired burned and unburned forest stands in Shenandoah National Park. Overall, I found few relationships of bat occupancy across species relative to burn condition or time since fire, indicating that fire likely does not have a significant ecological effect on bats in this portion of the central Appalachians.

This thesis investigates the integration of State-of-the-Art (SOTA) Large Language Models (LLMs) into the process of reassessing security findings generated by Static Application Security Testing (SAST) tools. The primary objective is to determine whether LLMs are able to detect false positives (FPs) while maintaining a high true positive (TP) rate, thereby enhancing the efficiency and effectiveness of security assessments. Four consecutive experiments were conducted, each addressing specific research questions. The initial experiment, using a dataset of security findings extracted from the OWASP Bench- mark, identified the optimal combination of context items provided by the SAST tool Spot- Bugs, which, when used with GPT-3.5 Turbo, reduced FPs while minimizing the loss of TPs. The second experiment, conducted on the same dataset, demonstrated that advanced prompting techniques, particularly few-shot Chain-of-Thought (CoT) prompting combined with Self-Consistency (SC), further improved the reassessment process. The third experiment compared both proprietary and open-source LLMs on an OWASP Benchmark dataset about one-fourth the size of the previously used dataset. GPT-4o achieved the highest performance, detecting 80 out of 128 FPs without missing any TPs, resulting in a perfect TPR of 100% and a decrease in FPR by 41.27 percentage points. Meanwhile, Llama 3.1 70B detected 112 out of the 128 FPs but missed 10 TPs, resulting in a TPR of 94.94% and a reduction in FPR by 56.62 percentage points. To validate these findings in a real-world context, the approach was applied to a dataset generated from the open-source project Mnestix using multiple SAST tools. GPT-4o again emerged as the top performer, detecting 26 out of 68 FPs while only missing one TP, resulting in a TPR decreased by 2.22 percentage points but simultaneously an FPR decreased 37.57 percentage points.:Table of Contents IV List of Figures VI List of Tables VIII List of Source Codes IX List of Abbreviations XI 1. Motivation 1 2. Background 3 3. Related Work 17 4. Concept 31 5. Preparing a Security Findings Dataset 39 6. Implementing a Workflow 51 7. Identifying Context Items 67 8. Comparing Prompting Techniques 85 9. Comparing Large Language Models 101 10.Evaluating Developed Approach 127 11.Discussion 141 12.Conclusion 145 A. Appendix: Figures 147 A.1. Repository Directory Tree 148 A.2. Precision-Recall Curve of Compared Large Language Models 149 A.3. Performance Metrics Self-Consistency on Mnestix Dataset 150 B. Appendix: Tables 151 B.1. Design Science Research Concept 151 C. Appendix: Code 153 C.1. Pydantic Base Config Documentation 153 C.2. Pydantic LLM Client Config Documentation 155 C.3. LLM BaseClient Class 157 C.4. Test Cases Removed From Dataset 158

As falsas memórias são um tipo falha de memória, em que o indivíduo pode (a) reconhecer como tendo visto antes um objeto ou evento que não tenha ocorrido ou (b) não reconhecer algo previamente presenciado. Estes são o falso reconhecimento e a rejeição errada. Segundo a teoria do rastro difuso, dois processos distintos agem em paralelo durante a memorização e reconhecimento, um sobre as informações literais (verbatim) e o outro sobre a essência do significado da palavra (gist). Neste trabalho é proposto um sistema modular de redes neurais artificiais que considera estes dois processos, características funcionais das estruturas cerebrais envolvidas na memorização e fluxo de informação análogo ao ocorrido no cérebro. O modelo neural é validado através de treinamento para armazenar e recuperar listas de palavras semanticamente relacionadas. Na formulação do modelo e da representação foram considerados a representação fonológica e significado das palavras, de forma a simular as computações ocorridas e os resultados obtidos em experimentos efetuados com sujeitos humanos. Nestes experimentos, 12 listas de aproximadamente 15 palavras, cada lista semanticamente relacionadas a um tema são ouvidas e, em seguida, algumas destas palavras, a palavra tema e outras palavras não relacionadas são também ouvidas e os indivíduos respondem se cada palavra fora ouvida previamente. Os resultados obtidos computacionalmente aproximam-se bastante dos resultados obtidos com sujeitos humanos, e o modelo produzido serve como base para estudo das influências dos diversos processos atuantes durante a memorização e reconhecimento. / False memories are a kind of memory failure, in which the subject may (a) recognize as known an never seen object or never happened fact or (b) don\'t recognize something that was already presented him. These are false memories and wrong rejections. According to false memory theory, two parallel processes act during memorization and recognition, one on verbatim information and other on gist information. In this work is proposed a artificial neural network model system that takes in account these two processes, functional issues about brain structures involved on memorization and the an information flow analog to the occurred in the brain. The neural model is validated by training to store in recover lists of semantically related words. In the model and representation scheme formulation, phonological and semantic informations were used intending to simulate brain computations and results of human subjects experiments. In such experiment, 12 lists of something about 15 semantically related words, are heard and, in the second step, in the sequence, many of these words, other related words and not related words are heard in a recognition test, when subjets say if that word was or was not heard during memorization steps. Results obtained from computer tests are very close of human results, and the produced model may be used as a tool for analysis of the influences of the many processes that take place during memorization and recognition.

Advances in digital signal processing (DSP) and computing technology have resulted in the emergence of increasingly adaptive radar systems. It is clear that the Electronic Attack (EA), or jamming, of such radar systems is expected to become a more difficult task. The reason for this research was to address the issue of jamming adaptive radar systems. This required consideration of adaptive jamming systems and the development of a methodology for outlining the features of such a system is proposed as the key contribution of this thesis. For the first time, game-based optimization methods have been applied to a maritime counter-surveillance/counter-targeting scenario involving conventional, as well as so-called ‘smart’ noise jamming.Conventional noise jamming methods feature prominently in the origins of radar electronic warfare, and are still widely implemented. They have been well studied, and are important for comparisons with coherent jamming techniques.Moreover, noise jamming is more readily applied with limited information support and is therefore germane to the problem of jamming adaptive radars; during theearly stages when the jammer tries to learn about the radar’s parameters and its own optimal actions.A radar and a jammer were considered as informed opponents ‘playing’ in a non-cooperative two-player, zero-sum game. The effects of jamming on the target detection performance of a radar using Constant False Alarm Rate (CFAR)processing were analyzed using a game theoretic approach for three cases: (1) Ungated Range Noise (URN), (2) Range-Gated Noise (RGN) and (3) False-Target (FT) jamming.Assuming a Swerling type II target in the presence of Rayleigh-distributed clutter, utility functions were described for Cell-Averaging (CA) and Order Statistic (OS) CFAR processors and the three cases of jamming. The analyses included optimizations of these utility functions, subject to certain constraints, with respectto control variables (strategies) in the jammer, such as jammer power and spatial extent of jamming, and control variables in the radar, such as threshold parameter and reference window size. The utility functions were evaluated over the players’ strategy sets and the resulting matrix-form games were solved for the optimal or ‘best response’ strategies of both the jammer and the radar.

Mindreading is the capacity to attribute psychological states to others and to use those attributions to explain, predict, and understand others’ behaviors. In the past thirty years, mindreading has become the topic of substantial interdisciplinary research and theorizing, with philosophers, psychologists and, more recently, neuroscientists, all contributing to the debate about the nature of the neuropsychological mechanisms that constitute the capacity for mindreading. In this thesis I push this debate forward by using recent results from developmental psychology as the basis for critiques of two prominent views of mindreading. First, I argue that the developmental studies provide evidence of infant mindreading and therefore expose a flaw in José Bermúdez’s view that certain forms of mindreading require language possession. Second, I argue that the evidence of infant mindreading can also be used to undermine Alvin Goldman’s version of Simulation Theory.