Probability Of False Negatives Research Articles

D-ARP: An Efficient Scheme to Detect and Prevent ARP Spoofing

Nowadays, cyber-attack is a severe criminal violation, and it is one of the most active fields of research. Man-in-the-middle attack (MITM) is a type of cyber-attack in which an unauthorized third party secretly accesses the communication between two hosts in the same network to read=modify the transferred data between them. ARP spoofing-based MITM attack exploits ARP protocol weakness where the attacker associates its MAC address with the IP address of an intended legitimate host. Although there are many defense approaches for ARP spoofing based-MITM attacks, these methods are uncompleted or have a performance overhead since they modify the original ARP protocol. Also, some of these approaches depend on the centralized server which leads to a single point of failure. This paper presents a detection scheme for ARP spoofing-based MITM attack called D-ARP which is compatible with the original ARP protocol. The main idea of D-ARP is to send an ARP packet signed with a key in parallel with the original ARP packets to make a correlation between requests and replies. Each host records the signed ARP packets whether it is a request or a reply in a log file. Based on this correlation, D-ARP matches the injected key to detect ARP spoofing if there is a duplicate or conflict in the MAC address. For more reliability, D-ARP uses the DHCP server and the Nmap feature to detect the MAC addresses of MITM attackers. Moreover, this scheme also offers a module for Admin to create a trusted list of hosts. The experimental results show that D-ARP is very effective to detect and prevent ARP spoofing with zero false positives and zero false negative probabilities without any modifications in the original ARP protocol.

Evaluation of organic and inorganic gunshot residues in various populations using LC-MS/MS

This work investigated the prevalence of organic and inorganic gunshot residue within two main subpopulations, 1) non-shooters, including groups with low- and high-risk of potentially containing GSR-like residues, and 2) individuals involved in a firing event (shooters, bystanders, and shooters performing post-shooting activities). The study analyzed over 400 samples via a liquid chromatography-mass spectrometry (LC-MS/MS) methodology with complexing agents. Exploratory statistical tools and machine learning algorithms (neural networks, NN) were used to evaluate the resulting mass spectral and quantitative data. This study observed lower occurrences of OGSR compounds in the non-shooter populations compared to IGSR analytes. The presence of GSR on authentic shooters versus other potential sources of false positives, such as bystanders and professions including police officers, agricultural workers, and mechanics, were further assessed by utilizing machine learning algorithms trained with the observed OGSR/IGSR traces. The probability of false negatives was also estimated with groups who performed regular activities after firing. Additionally, the low-risk background set allowed documentation of GSR occurrence in the general population. The probabilistic outputs of the neural network models were utilized to calculate likelihood ratios (LR) to measure the weight of the evidence. Using both the IGSR and OGSR profiles, the NN model’s accuracy ranged from 90 to 99%, depending on the subpopulation complexity. The log-LR histograms and Tippet plots show the method can discriminate between each sub-population and low rates of misleading evidence, suggesting that the proposed approach can be effectively used for a probabilistic interpretation of GSR evidence.

Factors affecting DNA barcoding classification accuracy for piscine prey: An experimental assessment with invasive lionfish

The use of DNA barcoding in fish diet studies is becoming more widespread, but the effect of prey digestion on barcoding accuracy has been poorly studied. We conducted a series of controlled feeding experiments, with red lionfish (Pterois volitans) as predators and Gulf killifish (Fundulus grandis) as prey, designed to test the effect of digestion time (12–42 h), prey item (sample) preservation (inside versus outside stomach), and sample preparation (washed versus unwashed) on DNA barcoding classification accuracy. Competitive interactions occurred between lionfish and killifish DNA during PCR amplification when universal COI primers were utilized; thus, primers were designed that matched the same priming site in both killifish and lionfish DNA. The proportion of amplified killifish DNA relative to lionfish DNA per sample was estimated using multiplex quantitative PCR (qPCR) and shown to decrease with longer digestion times, especially when samples were retained within lionfish stomachs for preservation. The effects of digestion and preservation method on amplifiable DNA resulted in significant effects on barcoding accuracy, resulting in erroneous identification of the predator (i.e., self-DNA) for nearly 25% of samples. Removing prey from predator stomachs prior to preservation significantly increased the proportion of amplified killifish DNA from samples, and enhanced barcoding accuracy. Overall, study results have important implications for the probability of false negatives for prey items, as well as false positives for self-DNA, when utilizing barcoding to characterize piscine diet. Results also highlight the need to conduct more method validation research given the increasing prevalence of DNA barcoding as a means to identify specific prey items in fish diet studies.

Using group sizes to optimise archery equipment

One method used by archers and coaches to optimise archery equipment is to measure the size of arrow groups on the target prior to and following an adjustment. The group sizes are then used to determine if an equipment change assisted or detracted from the archer’s performance. A model based on a Monte Carlo method and group size measurements from seven elite archers were used to test the validity of this process. The results showed that this method was neither an effective method nor useful process because the probability of false positives or false negatives was too great. A better approach to optimise archery equipment is to monitor the archer’s skill level or average score over an extended time following any change.

Performance metrics for alternative management strategies for gray seal-commercial fishery interactions in the Northwest Atlantic

The Potential Biological Removal (PBR) level serves as a threshold for management of marine mammal–fishery interactions under the US Marine Mammal Protection Act. The PBR protocol involves classifying fisheries based on the ratio of recent average bycatch mortality to PBR. A simulation-based framework is developed that quantifies the probability of incorrectly concluding that average bycatch mortality exceeds PBR (i.e., a false positive) or incorrectly concluding that average bycatch mortality is less than PBR (i.e., a false negative), with application to the US stock of gray seals in the northwest Atlantic, a stock for which human-caused mortality levels are approaching PBR. The application to this stock of northwest Atlantic gray seals is complicated by the transboundary nature of the population. Consequently, the analyses are based on a population model that includes the US stock and the component of the Canadian stock off southern Nova Scotia, fitted to available data using Bayesian methods. The total error (i.e., false positive or false negative) probability is found to be largely independent of the coefficient of variation (CV) for estimates of bycatch mortality, whereas this probability is an increasing function of the CV for estimates of abundance. For gray seals, a CV for the estimates of abundance of ∼0.2 appears to balance the reduction in the total error probability versus the cost of surveys with greater sampling effort and more precise estimates of abundance.

A game-theoretic approach to adversarial linear Gaussian classification

We employ a game-theoretic model to analyse the interaction between an adversary and a classifier. There are two (i.e., positive and negative) classes to which data points can belong. The adversary wants to maximize the probability of miss-detection for the positive class (i.e., false negative probability) while it does not want to significantly modify the data point so that it still maintains favourable traits of the original class. The classifier, on the other hand, wants maximize the probability of correct detection for the positive class (i.e., true positive probability) subject to a lower-bound on the probability of correct detection for the negative class (i.e., true negative probability). For conditionally Gaussian data points (conditioned on the class) and linear support vector machine classifiers, we rewrite the optimization problems of the adversary and the classifier as convex problems and use best response dynamics to learn an equilibrium of the game. This results in computing a linear support vector machine classifier that is robust against adversarial input manipulations.

Detecting seed bank influence on plant metapopulation dynamics

Abstract Seed banks are known to play a key role in plant metapopulations. However, detecting seed banks remains challenging and requires intense monitoring efforts. Assessing the genuine effect of seed banks on plant metapopulation dynamics (rather than their presence) may offer a much easier while still biologically relevant way to overcome this issue. In this study, we developed a new metric: the seed bank characteristic event (SBCE) probability. Instead of detecting seed bank directly, the SBCE probability measures seed bank contribution to the observed metapopulation dynamics. Exploring seed bank parameters (colonization, germination and seed bank death probabilities, initial proportion of patches containing a seed bank), a wide range of monitoring durations (from 3 to 10 years) and number of patches in the metapopulation (from 10 to 1,000 patches), we examined the conditions under which the SBCE probability is correctly estimated. To test the robustness of our approach, we further introduced false negatives, false positives or parameter heterogeneity between patches. Finally, we applied the SBCE probability method to the monitoring of tree bases plant species in Paris, France, to assess the applicability of the method to real‐world datasets and increase the understanding of plant metapopulation dynamics within an urban environment. Our results indicate that the SBCE probability is well‐estimated when enough monitoring years or number of patches are considered, and for probabilities of false negatives or false positives of up to 0.1. However, the SBCE probability estimation is not robust to colonization probability heterogeneity between patches. When we applied the SBCE probability method to the real monitoring dataset, we found a contrasted contribution of the seed bank to the observed metapopulation dynamics from one street and one species to another. The study suggests that the measurement of seed bank contribution is less data‐demanding than assessment of seed bank presence. Applying the estimation method to the monitoring of tree bases plant species highlights a significant contribution of the seed bank to plant metapopulation dynamics in an urban environment, and illustrates how the method can be applied on real‐world datasets.

Stochastic SIR Household Epidemic Model with Misclassification

In this work, we developed a theoretical framework leading to misclassification of the final size epidemic data for the stochastic SIR (Susceptible-Infective-Removed), household epidemic model, with false negative and false positive misclassification probabilities. Maximum likelihood based algorithm is then employed for its inference. We then analyzed and compared the estimates of the two dimensional model with those of the three and four dimensional models associated with misclassified final size data over arrange of theoretical parameters, local and global infection rates and corresponding proportion infected in the permissible region, away from its boundaries and misclassification probabilities. The adequacies of the three models to the final size data are examined. The four and three-dimensional models are found to outperform the two dimensional model on misclassified final size data.

Loyalty rebates and the more economic approach to EU competition law

ABSTRACT In 2009 the European Commission published the art. 102 guidance regarding abusive exclusionary conduct by dominant undertakings. The guidance is based on the more economic approach to competition policy. This paper investigates the welfare implications of the more economic approach to loyalty rebates. First, the paper presents an economic framework linking weights attached to probabilities of false positives and negatives and the legal norm. After that, the paper discusses cases before the guidance paper (i.a. Hoffmann-La Roche), the guidance paper itself, and cases after the guidance paper (i.a. Intel). In the period after the guidance paper, we observe a non-linear increase in the weight attached to preventing false positives. The paper concludes that to further limit welfare losses due to false positives, the category of rebates that is assumed to be abusive should be defined in a restrictive way. Furthermore, in law enforcement priority should be given to cases that, considering all the circumstances, can be expected to be most harmful to consumers.

Extended Enrichment Procedures Can Be Used To Define False-Negative Probabilities for Cultural Gold Standard Methods for Salmonella Detection, Facilitating Comparisons between Gold Standard and Alternative Methods

Evaluation of alternative detection methods for foodborne pathogens typically involves comparisons against a "gold standard" culture method, which may produce false-negative (FN) results, particularly under worst-case scenarios such as low contamination levels, difficult-to-detect strains, and challenging food matrices (e.g., matrices with a water activity of <0.6). We used extended enrichment times (up to 72 h for both primary and secondary enrichments) to evaluate a gold standard method for Salmonella detection (the U.S. Food and Drug Administration Bacteriological Analytical Manual [BAM] method) in two low-water-activity foods (dry pet food and chocolate) inoculated at low contamination levels (most probable number ca. 1/25 g) with five Salmonella strains. Strains were selected to include those with a poor ability to grow in enrichment media. Among the 100 pet food and 100 chocolate samples tested, 53 and 50, respectively, were positive with the standard BAM method, and 57 and 59, respectively, were positive with the extended BAM method. Thus, the FN probabilities for the standard BAM method were 7% for pet food and 15% for chocolate. An alternative enzyme immunoassay method for detection of Salmonella in chocolate produced FN probabilities of 6 and 20% when compared against the standard and extended BAM methods, respectively. Detection of Salmonella Mississippi was significantly reduced with the alternative method (P = 0.023) compared with the extended BAM method. We calculated a composite reference standard to further define FN probabilities based on variable results from multiple assays (the standard BAM, extended BAM, and alternative methods). Based on this standard, the enzyme immunoassay for Salmonella detection in chocolate had a 28% FN probability and the standard and extended BAM methods had 23 and 9% FN probabilities, respectively. These results provide a framework for how inclusion of extended enrichment times can facilitate evaluation of alternative detection methods.

EDNA‐based monitoring: Advancement in management and conservation of critically endangered killifish species

AbstractEcosystems are currently changing at unprecedented rates due to anthropogenic influences. Application of appropriate management regimes and mitigation measures requires knowledge of ecological community composition and monitoring of any changes that occur. Environmental DNA‐based monitoring is becoming increasingly common and offers substantial potential as a noninvasive method associated with highly repeatable and reliable results. In this study, we monitored river systems in Western Greece that have been strongly impacted by anthropogenic activities and the spread of an alien invasive fish species, the Eastern mosquitofish (Gambusia holbrooki). This invasive species has been credited as the major cause for the drastic decline of two endemic killifish species (Valencia letourneuxi and Valencia robertae). Here, we investigated the efficacy of an environmental DNA (eDNA‐based) method of detection for all three species, as an alternative to conventional monitoring methods. Initially, a mesocosm experiment provided material for the design and validation of the sampling protocol. This was followed by two sampling periods in the field conducted in autumn 2017 and 2018, comparing the novel eDNA assays with the conventional surveying methods in six and 20 systems, respectively. eDNA detection consistently outperformed the traditional monitoring methods for both V. letourneuxi and V. robertae and was comparable for the invasive G. holbrooki. This supports the now increasing body of literature, highlighting the benefits of species‐specific, targeted eDNA assays for the assessment of threatened and/or invasive species, one which can be utilized by conservation organizations and government bodies alike. However, we note that care should always be taken when designing such tools and strict validation steps should be adhered to, particularly with respect to minimizing the probability of false positives and negatives.

Characterizing the Distribution of Ppm Gluten in Gluten Free Oatmeal Servings Contaminated with a Barley Kernel

Oats are regularly contaminated with gluten-containing grains like wheat, barley and rye. For producers of gluten free oatmeal, contamination potential makes it prudent to understand the consequences in terms of gluten dosing, as labeling requirements specify a gluten maximum. To do this, statistical simulation has been used to produce virtual oat servings (40g) contaminated with either two row or six row spring barley. The results are probability distributions for &amp;lsquo;actual&amp;rsquo; ppm gluten (free of measurement influences) and &amp;lsquo;as measured&amp;rsquo; via R5 ELISA. Findings show &amp;lsquo;actual&amp;rsquo; ppm gluten to be normally distributed with 57 ppm and 41 ppm gluten averages with 14 and 12 ppm standard deviations (stdevs) for two and six row barley, respectively. &amp;lsquo;As measured&amp;rsquo; ppm gluten results are lognormally distributed with 61 and 44 ppm gluten averages with 63 and 47 stdevs for two and six row barley, respectively, employing an 80% analytical recovery rate and a multiplier of 1. These analyses show that &amp;lsquo;as measured&amp;rsquo; results possess false negative probabilities (relative to a &amp;lt; 20 ppm gluten regulatory requirement) from 0.14 to 0.34 depending on recovery rate and barley type. This work highlights the need for non-homogenous grinding issues to be addressed in whole grain gluten assessment, for analytical recovery rates to be defined for gliadin in oats, and for appropriate conversion factors to be determined in order to attain capable measurement of gluten in oats due to barley kernel contamination.

Accuracy of panoramic radiography for degenerative changes of the temporomandibular joint

Background:Temporomandibular joint (TMJ) morphologies are complex, and changes in joint components have been studied extensively. TMJ conditions have many different etiologies, appearances, and relevant clinical significance.Objective:The objective of this study was to evaluate the concordance of the diagnosis of degenerative changes in the TMJ in panoramic radiographs.Materials and Methods:Cone beam computed tomography (CBCT) was used as a reference standard. Images of 84 patients (168 TMJs), 61 females (72.6%) and 23 males (27.4%) with an average age of 47.4 years, were evaluated by three radiologists who established the presence or absence of degenerative changes in the jaw heads. The data were collected and organized in a spreadsheet. Concordance between evaluators was analyzed using agreement percentages, statistical κ, and confidence intervals. In the analysis of compliance with CBCT, sensitivity, specificity, predictive positive and negative values, and probabilities of false positives and negatives were also calculated. All inferential tests were performed with a 5% significance level.Results:The percentage of agreement among raters in panoramic radiographs ranged from 66.7% to 82.9%, considered mild to moderate. Correlation between radiographic and tomographic images ranged from 45.5% to 64.9% in the inter-evaluator reviews, representing a very mild agreement. Sensitivity ranged from 28.6% to 58.7% and specificity from 66.7% to 100.0%. Positive predictive value ranged from 77.1% to 100.0%; the negative predictive value was lower, ranging from 32.2% to 54.8%. The probability of false negatives was higher than that of false positives, ranging from 45.2% to 67.8%.Conclusion:The rater did not reach acceptable diagnosis levels.

The Diagnostic Accuracy and Postoperative Outcomes of Cervical Cancer Patients for MR-invisible or MR-visible Diagnosis of Combined T2- and Diffusion-weighted 3T MRI Using the External Phased-array Receiver.

This study aimed to determine the diagnostic accuracy and postoperative outcomes of early-stage cervical cancer patients [2009 FIGO stages IA2-IB1 (<2 cm)] diagnosed with magnetic resonance (MR)-invisible disease or MR-visible disease using the external phased-array receiver. Between 2007 and 2014, 110 patients with a FIGO clinical stage IA2-IB1 (<2 cm) cervical cancer underwent primary surgical treatment after external array coil T2W and DW MR imaging following the diagnostic biopsy procedure. The median histological size of MR-invisible vs. MR-visible diagnosis was 3±6.4 mm and 16±5.2 mm. Eighty-five of the 110 patients had histologically residual tumor. The sensitivity, specificity, PPV, and NPV of tumor diagnosis were 63.5%, 92.0%, 96.4%, and 42.6%, respectively. Histological estimates of 54 (49.1%) MR-invisible vs. 56 (50.9%) MR-visible diagnoses were identified as 23 true-negative (TN) and 31 false-negative (FN) vs. 54 true-positive (TP) and 2 false-positive (FP). The recurrence-free rate was 98.1% in the MR-invisible group and 91.1% in the MR-visible group. The overall survival rates were 100% and 92.9%, respectively. A preoperative MR-invisible diagnosis in early-stage cervical cancer patients led to a high probability of FN and was associated with underdiagnosis.

A Secure and Robust Image Hashing Scheme Using Gaussian Pyramids

Image hash is an alternative to cryptographic hash functions for checking integrity of digital images. Compared to cryptographic hash functions, an image hash or a Perceptual Hash Function (PHF) is resilient to content preserving distortions and sensitive to malicious tampering. In this paper, a robust and secure image hashing technique using a Gaussian pyramid is proposed. A Gaussian pyramid decomposes an image into different resolution levels which can be utilized to obtain robust and compact hash features. These stable features have been utilized in the proposed work to construct a secure and robust image hash. The proposed scheme uses Laplacian of Gaussian (LOG) and disk filters to filter the low-resolution Gaussian decomposed image. The filtered images are then subtracted and their difference is used as a hash. To make the hash secure, a key is introduced before feature extraction, thus making the entire feature space random. The proposed hashing scheme has been evaluated through a number of experiments involving cases of non-malicious distortions and malicious tampering. Experimental results reveal that the proposed hashing scheme is robust against non-malicious distortions and is sensitive to detect minute malicious tampering. Moreover, False Positive Probability (FPP) and False Negative Probability (FNP) results demonstrate the effectiveness of the proposed scheme when compared to state-of-the-art image hashing algorithms proposed in the literature.

Prediction of PFS using a dynamic assessment of ctDNA in patients with EGFR mutated NSCLC with osimertinib therapy

Abstract Background Resistance on the 1st or 2nd generation TKIs in patients with EGFR mutated NSCLC to the therapy occurs on average after 8-12 months. The most common (49-60%) resistance mechanism is the appearance of the T790M. Its determination is possible by different methods of examination: ctDNA (false-negative probability 23%) and tumor tissue (sensitivity of 88%). The appointment of osimertinib in the second line allows to increase ORR, but also PFS relative to standard chemotherapy. Methods In this study we investigated the effect on PFS by the dynamic determination of ctDNA. Included patients with metastatic EGFR-associated NSCLC, in whom, a T790M mutation was detected during treatment TKIs of 1-2 generations.Patients received osimertinib therapy 80 mg / day, daily, until progression of the disease appears. Blood test was taken to conduct a qualitative assessment of ctDNA in dynamics by the RT-PCR method, fist test was done before starting treatment, and then every 2 months. Results From August 2016 to December 2018, 22 patients were identified T790M + associated progression of EGFR + NSCLC. 81.9% (18/22) are women, 18.1% (4/22) are men. The average age is 61.2 years (50-75). 1/22 had smoking experience for more than 30 years. The molecular genetic profile in 16 is represented by ex19del, 5 L858R, 1 - a combination of rare mutations G719S + S768I. The effect of therapy was evaluated in 20/22 patients. PR and SD were registered in 9/20 (45%) and 10/20 (50%) patients, respectively. Median PFS – 18.9 months (CI 95%, 10.6 - 27.1). In 12/22 patients was observed the disappearance of ctDNA T790M + after 2 months of osimertinib therapy. PFS is 24.4 months (CI calculation is NA), in patients with no mutation detected in the second month of treatment compared with the group of patients in which the ctDNA was determined (PFS 7.6 months) (CI 95%, 3.0 –12.9) (p = 0.003). The confidence interval for one of the medians cannot be calculated the 75% percentile was 3.4 +/- 3.3 and 18.8 +/- 6.3. Conclusions The disappearance of ctDNA in plasma after 2 months of treatment with osimertinib is associated with an increase in PFS and can be considered as a predictive marker in patients with metastatic NSCLC EGFR T790M + Grant supported RUSSCO / RakFond 2018-01-YS-ECI. Legal entity responsible for the study St. Petersburg Clinical Research Center of Specialized Types of Care (Oncology). Funding Has not received any funding. Disclosure All authors have declared no conflicts of interest.

Development of a new mathematical tool for early colorectal cancer diagnosis and its possible use in mass screening

The aim of the study is to model a new mathematical tool effective in the diagnosis of colorectal cancer and useful as a mass screening test. This work enrolled 345 subjects from January 2011 to January 2013, in 4 Italian Surgical Departments: 97 (27,4%) out of the total were healthy controls (class 0) whereas 248 (72,6%) patients were affected by colorectal cancer. The cancer patients were divided into four classes, according to TNM staging classification: 74 (20,9%) patients stage I (class 1); 61 (17,2) patients stage II (class 2); 76 (21,5%) patients vstage III (class 3); 37 (10,5%) stage IV (class 4).Blood samples were collected after 24h from hospital admission and 17 biochemical parameters (CEA, ceruloplasmin, haptoglobin, transferrin, TPA, CA 19.9, CA 72.4, PCR, Ca 50, C4 Complement, CA 125, Alfa-1-antitrypsin, alpha-2-Macroglobulin, Ferritin, RBP, Alpha-1-Acid Glycoprotein, Complement C3) were statistically analysed together with the clinical and pathological disease staging (pre and postoperative evaluation, respectively). Evaluation and comparison were made between two groups: healthy controls and the total of affected patients.Using the collected data, it was developed a mathematical model (artificial neural network ANN) allowing the distribution of colorectal cancer and controls (apparently in good health) patients in the two groups. This led to the definition of an index (B-index) that, simply analysing the combined values of the 17 biochemical parameters, decides on the either healthy or disease status of the patient. The use of the B-index, with a neural network based on real values, allows colorectal cancer diagnosis with 80.078% accuracy, probability of false positive (FP) = 0.333, probability of false negatives (FN) = 0.102, a 0.898 sensitivity and 0.667 specificity. However, by using B-index with a neural network based on the implementation of extended reality values our analysis reaches an accuracy of 91.11% in colorectal cancer diagnosis, with probability of false positive (FP) = 0.472, probability of false negatives (FN) = 0.03%, a 0.9997 sensitivity and 0.7642 specificity. The results suggest a promising role for B-index in colorectal cancer mass screening with an easily available, low cost and non-invasive test.

A Feasibility Study on the Implementation of Visibility Algorithms for Fault Diagnosis in Aircraft Fuel Systems

This paper discusses the applicability of Visibility Algorithms to detect faults in condition monitoring applications. The general purpose of Visibility Algorithms is to transform time series into graphs and study them through the characterization of their associated network. Degradation of a component results in changes to the network. This technique has been applied using a test rig of an aircraft fuel system to show that there is a correlation between the values of key metrics of visibility graphs and the severity of four failure modes. We compare the results of using Horizontal Visibility algorithms against Natural Visibility algorithms. The results also show how the Kullback-Leibler divergence and statistical entropy can be used to produce condition indicators. Experimental results show that there is little dispersion in the values of condition indicators, leading to a low probability of false positives and false negatives.

Application of an Artificial Neural Network for The Early and Accurate Diagnosis of Parkinson's Disease

Parkinson’s Disease (PD) is one of the most common neurological disorders, affecting more than ten million people globally. The hallmark symptoms of PD are tremors, limb rigidity, and imbalance. PD shares many of these symptoms with other disorders, making it difficult to diagnose. Furthermore, due to the lack of definitive laboratory tests, PD is poorly diagnosed with subjective examinations such as family history evaluations, resulting in high misdiagnosis rates. Recent research shows that an additional symptom, dysphonia, is uniquely present in over 80% of PD patients. Dysphonia is a speaking disorder caused by involuntary muscle movement and other neurological factors in PD. In this project, that unique symptom. A cross-validated neural network was programmed to deliver rapid and accurate diagnoses using biomedical voice data from 195 patients of varying statuses. This automated, machine-learning based PD diagnostic tool was successfully created and functions with over 95% accuracy. This rate includes nearly zero false negatives and few false positives, showing significant improvement over previous attempts which had misdiagnosis rates of nearly 20%. A low probability of false negatives is favorable. The neural network was designed such that overfitting is avoided, and more features/data would further improve the algorithm’s accuracy. An early and accurate diagnosis is critical for treating PD patients, and this project proposes a way to achieve that.

Optimally configuring a measurement system to detect diversions from a nuclear fuel cycle

The civilian nuclear fuel cycle is an industrial process that produces electrical power from the nuclear fission of uranium. Using a measurement system to accurately account for possibly dangerous nuclear material, such as uranium, in a fuel cycle is important because of its possible loss or diversion. A measurement system is defined by a set of measurement methods, or “devices,” used to account for material flows and inventory values at specific locations at facilities in the fuel cycle. We develop a simulation-optimization algorithm and an integer-programming model to find the best, or near-best, resource-limited measurement system with a high degree of confidence. The simulation-optimization algorithm minimizes a weighted sum of false positive and false negative diversion-detection probabilities while accounting for material quantities and measurement errors across a finite, discrete time horizon in hypothetical non-diversion and diversion contexts. In each time period, the estimated cumulative material unaccounted for is compared to a fixed or an optimized threshold value to assess if a “significant amount of material” is lost from a measurement system. The integer-programming model minimizes the population variance of the estimated material loss, i.e., material unaccounted for, in a measurement system. We analyze three potential problems in nuclear fuel cycle measurement systems: (i) given location-dependent device precisions, find the configuration of n devices at n locations ( $$n=3$$ ) that provides the lowest corresponding objective values using the simulation-optimization algorithm and integer-programming model, (ii) find the location at which improving device precision reduces objective values the most using the simulation-optimization algorithm (given the device accuracy is 100%), and (iii) determine the effect of measurement frequency on measurement system configurations and objective values using the simulation-optimization algorithm. We obtain comparable results for each problem at least an order of magnitude faster than existing methods do. Using an optimized, rather than a fixed, detection threshold in the simulation-optimization algorithm reduces the weighted sum of false positive and false negative probabilities.