Statistical modelling for prediction of axis-switching in rectangular jets

Rectangular nozzles are increasingly used for modern military aircraft propulsion installations, including the roll nozzles on the F-35B vertical/short take-off and landing strike fighter. A peculiar phenomenon known as axis-switching is generally observed in such non-axisymmetric nozzle flows during which the jet spreads faster along the minor axis compared to the major axis. This might affect the under-wing stores and aircraft structure. A computational fluid dynamics study was performed to understand the effects of changing the upstream nozzle geometry on a rectangular free jet. A method is proposed, involving the formulation of an equation based upon a statistical model for a rectangular nozzle with an exit aspect ratio (ARe) of 4; the variables under consideration (for a constant nozzle pressure ratio (NPR)) being inlet aspect ratio (ARi) and length of the contraction section. The jet development was characterised using two parameters: location of the cross-over point (Xc) and the difference in the jet half-velocity widths along the major and minor axes (ΔB30). Based on the observed results, two statistical models were formulated for the prediction of axis-switching; the first model gives the location of the cross-over point, while the second model indicates the occurrence of axis-switching for the given configuration

A sufficient condition for the existence of an anti-directed 2-factor in a directed graph

Let D be a directed graph with vertex set V and order n. An anti-directed hamiltonian cycle H in D is a hamiltonian cycle in the graph underlying D such that no pair of consecutive arcs in H form a directed path in D. An anti-directed 2-factor in D is a vertex-disjoint collection of anti-directed cycles in D that span V. It was proved in [3] that if the indegree and the outdegree of each vertex of D is greater than (9/16)n then D contains an anti-directed hamilton cycle. In this paper we prove that given a directed graph D, the problem of determining whether D has an anti-directed 2-factor is NP-complete, and we use a proof technique similar to the one used in [3] to prove that if the indegree and the outdegree of each vertex of D is greater than (24/46)n then D contains an anti-directed 2-factor

Analysis of Demographic and Clinical Data of Patients to Determine the Effective Markers of Bipolar Disorder

Bipolar disorder is also known as manic depression. Patients with this disease exhibit symptoms of depression and mania or hypomania in a cyclical manner. While depression symptoms are relatively easy to detect, manic and hypomanic symptoms are not. As a result, patients with bipolar disorder are either misdiagnosed as suffering from just depression or are diagnosed late � typically five to ten years from the onset of the disorder. The mood of a bipolar patient misdiagnosed as having depression and treated only for that condition can become elevated to a state of hypermania (Matza et al., 2005 and Charney et al., 2003). A late diagnosis can worsen the bipolar condition as well. Thus, a misdiagnosis or late diagnosis could aggravate the symptoms, and may require the bipolar patient to be hospitalized. This situation is made worse by the presence of psychological and/or physiological comorbidities that commonly coexist in patients with bipolar disorder.In this thesis, we apply logistic regression models, decision trees, and artificial neural networks to detect the existence of bipolar disorder in a patient with that disease at an early stage by analyzing their clinical and sociodemographic data, including comorbidities and prescribed medication. The goal is to apply the aforementioned three techniques to detect the existence of bipolarity in a patient with reasonable accuracy, so that he or she may be presented to psychiatrists for further medical diagnosis and treatment. The techniques will also help in screening out the patients needing treatment for other psychiatric disorders, e.g., major depression, that have symptoms similar to bipolar disorder. We use clinical and demographic data from Cerner Health Facts� database and the techniques identify the variables that can help detect bipolarity. We compare the three techniques relative to their effectiveness in detecting bipolar patients for the dataset used in this thesis. Based on the Cerner database, our study also finds that some of the variables identified in the literature as effective predictors of bipolar disorder are not as effective or do not have the same relationship with bipolar disorder.Industrial Engineering & Managemen

Influence of androgens on urinary excretion of 17-hydroxy steroids and amino acids in male rats

Studies were carried out in male albino rats on the influence of age, castration and substitution by testosterone on urinary amino acid excretion pattern and excretion of 17-hydroxy corticoids in the urine. While age had no effect on amino acid excretion, corticoid activity decreased with age. Castration caused increased excretion of amino acid and slight increase in corticoid excretion, testosterone produced nitrogen retention and decrease in corticosteroid excretion

Effects of upstream nozzle geometry on rectangular free jets

This study is aimed at understanding the effects of changing the upstream nozzle geometry on the development of rectangular free jets. An existing converging rectangular nozzle with an exit aspect ratio of 4 and a circular inlet (AR4 nozzle) has been used as the basic configuration for this work. The study is primarily based on the results of numerical simulations wherein the internal geometry variation is accomplished by changing the inlet aspect ratio (AR,) and the length of the converging section, expressed as a ratio with respect to the length of the nozzle (called 'converging section ratio*, CSR); all the other parameters are kept constant. The results from LDA experiments done on the AR4 nozzle are presented and used as validation data for the CPD simulations. Analyses of the numerical results help in understanding the variation of the jet spreading for different combinations of AR, and CSR. Two parameters are identified for describing the jet development: the cross-over point (XC), defined as the location downstream of the exit where the jet half-velocity-widths (B) along the major and minor axes are equal, and the difference in the half-velocity-widths at 30 nozzle equivalent diameters (Dm) from the exit (AB30), to ascertain the occurrence of axis-switching. For a given AR, XC varies linearly with CSR; the variation of XC is non-linear with AR, for a constant CSR. The A1330 variation is non-linear with both AR, and CSR; the other variable being kept constant. The data obtained from the simulations are further used to propose two parametric models which can be used to predict the occurrence of axis-switching, within the scope of this work. The parametric models are validated and future work is proposed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

GEFF: Graph Embedding for Functional Fingerprinting

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task-dominant, subject dominant or neither, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states.Comment: 30 pages; 6 figures; 5 supplementary figure

GEFF: Graph Embedding for Functional Fingerprinting

It has been well established that Functional Connectomes (FCs), as estimated from functional MRI (fMRI) data, have an individual fingerprint that can be used to identify an individual from a population (subject-identification). Although identification rate is high when using resting-state FCs, other tasks show moderate to low values. Furthermore, identification rate is task-dependent, and is low when distinct cognitive states, as captured by different fMRI tasks, are compared. Here we propose an embedding framework, GEFF (Graph Embedding for Functional Fingerprinting), based on group-level decomposition of FCs into eigenvectors. GEFF creates an eigenspace representation of a group of subjects using one or more task FCs (Learning Stage). In the Identification Stage, we compare new instances of FCs from the Learning subjects within this eigenspace (validation dataset). The validation dataset contains FCs either from the same tasks as the Learning dataset or from the remaining tasks that were not included in Learning. Assessment of validation FCs within the eigenspace results in significantly increased subject-identification rates for all fMRI tasks tested and potentially task-independent fingerprinting process. It is noteworthy that combining resting-state with one fMRI task for GEFF Learning Stage covers most of the cognitive space for subject identification. Thus, while designing an experiment, one could choose a task fMRI to ask a specific question and combine it with resting-state fMRI to extract maximum subject differentiability using GEFF. In addition to subject-identification, GEFF was also used for identification of cognitive states, i.e. to identify the task associated to a given FC, regardless of the subject being already in the Learning dataset or not (subject-independent task-identification). In addition, we also show that eigenvectors from the Learning Stage can be characterized as task- and subject-dominant, subject-dominant or neither, using two-way ANOVA of their corresponding loadings, providing a deeper insight into the extent of variance in functional connectivity across individuals and cognitive states