Application of the x-ray measurement model to image processing of x-ray radiographs

A computational model has been developed at which simulates the film response to the interaction of x-rays with a sample[1,2]. By using a CAD model as a virtual part, film densities of the radiograph are predicted. The number of photons which reach the film is based on the thickness of the part, part geometry, and the material absorption coefficient. Also taken into consideration are the x-ray beam characteristics, film properties, and the experimental configuration. The model generated images can vary in size and resolution, depending on the user chosen parameters. Noise is calculated using a Gaussian noise distribution and adjusted for the film type. The result of this simulation is a two-dimensional numerically generated digital image, which represents a radiograph of the part. This result can be used to analyze the flaws in an actual radiograph with the same set-up and exposure parameters

Estimating Nosocomial Infection and its Outcomes in Hospital Patients in England with a Diagnosis of COVID-19 Using Machine Learning

BACKGROUND: COVID-19 nosocomial infections (NIs) may have played a significant role in the dynamics of the pandemic in England, but analysis of their impact at the national scale has been lacking. Our aim was to provide a comprehensive account of NIs, identify their characteristics and outcomes in patients with a diagnosis of COVID-19 and use machine learning modelling to refine these estimates. METHODS: From the Hospital Episodes Statistics database all adult hospital patients in England with a diagnosis of COVID-19 and discharged between March 1st 2020 and March 31st 2021 were identified. A cohort of suspected COVID-19 NIs was identified using four empirical methods linked to hospital coding. A random forest classifier was designed to model the relationship between acquiring NIs and the covariates: patient characteristics, comorbidities, frailty, trust capacity strain and severity of COVID-19 infections. FINDINGS: In total, 374,244 adult patients with COVID-19 were discharged during the study period. The four empirical methods identified 29,896 (8.0%) patients with NIs. The random forest classifier estimated a mean NI rate of 10.5%, with a peak close to 18% during the first wave, but much lower rates thereafter and around 7% in early spring 2021. NIs were highly correlated with longer lengths of stay, high trust capacity strain, greater age and a higher degree of patient frailty. NIs were also found to be associated with higher mortality rates and more severe COVID-19 sequelae, including pneumonia, kidney disease and sepsis. INTERPRETATION: Identification of the characteristics of patients who acquire NIs should help trusts to identify those most at risk. The evolution of the NI rate over time may reflect the impact of changes in hospital management practices and vaccination efforts. Variations in NI rates across trusts may partly reflect different data recording and coding practice

A genetic programming based fuzzy regression approach to modelling manufacturing processes

Fuzzy regression has demonstrated its ability to model manufacturing processes in which the processes have fuzziness and the number of experimental data sets for modelling them is limited. However, previous studies only yield fuzzy linear regression based process models in which variables or higher order terms are not addressed. In fact, it is widely recognised that behaviours of manufacturing processes do often carry interactions among variables or higher order terms. In this paper, a genetic programming based fuzzy regression GP-FR, is proposed for modelling manufacturing processes. The proposed method uses the general outcome of GP to construct models the structure of which is based on a tree representation, which could carry interaction and higher order terms. Then, a fuzzy linear regression algorithm is used to estimate the contributions and the fuzziness of each branch of the tree, so as to determine the fuzzy parameters of the genetic programming based fuzzy regression model.To evaluate the effectiveness of the proposed method for process modelling, it was applied to the modelling of a solder paste dispensing process. Results were compared with those based on statistical regression and fuzzy linear regression. It was found that the proposed method can achieve better goodness-of-fitness than the other two methods. Also the prediction accuracy of the model developed based on GP-FR is better than those based on the other two methods

Assessing Internet addiction using the parsimonious Internet addiction components model - a preliminary study [forthcoming]

Internet usage has grown exponentially over the last decade. Research indicates that excessive Internet use can lead to symptoms associated with addiction. To date, assessment of potential Internet addiction has varied regarding populations studied and instruments used, making reliable prevalence estimations difficult. To overcome the present problems a preliminary study was conducted testing a parsimonious Internet addiction components model based on Griffiths’ addiction components (2005), including salience, mood modification, tolerance, withdrawal, conflict, and relapse. Two validated measures of Internet addiction were used (Compulsive Internet Use Scale [CIUS], Meerkerk et al., 2009, and Assessment for Internet and Computer Game Addiction Scale [AICA-S], Beutel et al., 2010) in two independent samples (ns = 3,105 and 2,257). The fit of the model was analysed using Confirmatory Factor Analysis. Results indicate that the Internet addiction components model fits the data in both samples well. The two sample/two instrument approach provides converging evidence concerning the degree to which the components model can organize the self-reported behavioural components of Internet addiction. Recommendations for future research include a more detailed assessment of tolerance as addiction component

Modulation of emotional appraisal by false physiological feedback during fMRI

BACKGROUND James and Lange proposed that emotions are the perception of physiological reactions. Two-level theories of emotion extend this model to suggest that cognitive interpretations of physiological changes shape self-reported emotions. Correspondingly false physiological feedback of evoked or tonic bodily responses can alter emotional attributions. Moreover, anxiety states are proposed to arise from detection of mismatch between actual and anticipated states of physiological arousal. However, the neural underpinnings of these phenomena previously have not been examined. METHODOLOGY/PRINCIPAL FINDINGS We undertook a functional brain imaging (fMRI) experiment to investigate how both primary and second-order levels of physiological (viscerosensory) representation impact on the processing of external emotional cues. 12 participants were scanned while judging face stimuli during both exercise and non-exercise conditions in the context of true and false auditory feedback of tonic heart rate. We observed that the perceived emotional intensity/salience of neutral faces was enhanced by false feedback of increased heart rate. Regional changes in neural activity corresponding to this behavioural interaction were observed within included right anterior insula, bilateral mid insula, and amygdala. In addition, right anterior insula activity was enhanced during by asynchronous relative to synchronous cardiac feedback even with no change in perceived or actual heart rate suggesting this region serves as a comparator to detect physiological mismatches. Finally, BOLD activity within right anterior insula and amygdala predicted the corresponding changes in perceived intensity ratings at both a group and an individual level. CONCLUSIONS/SIGNIFICANCE Our findings identify the neural substrates supporting behavioural effects of false physiological feedback, and highlight mechanisms that underlie subjective anxiety states, including the importance of the right anterior insula in guiding second-order "cognitive" representations of bodily arousal state

Modulation of emotional appraisal by false physiological feedback during fMRI

BACKGROUND James and Lange proposed that emotions are the perception of physiological reactions. Two-level theories of emotion extend this model to suggest that cognitive interpretations of physiological changes shape self-reported emotions. Correspondingly false physiological feedback of evoked or tonic bodily responses can alter emotional attributions. Moreover, anxiety states are proposed to arise from detection of mismatch between actual and anticipated states of physiological arousal. However, the neural underpinnings of these phenomena previously have not been examined. METHODOLOGY/PRINCIPAL FINDINGS We undertook a functional brain imaging (fMRI) experiment to investigate how both primary and second-order levels of physiological (viscerosensory) representation impact on the processing of external emotional cues. 12 participants were scanned while judging face stimuli during both exercise and non-exercise conditions in the context of true and false auditory feedback of tonic heart rate. We observed that the perceived emotional intensity/salience of neutral faces was enhanced by false feedback of increased heart rate. Regional changes in neural activity corresponding to this behavioural interaction were observed within included right anterior insula, bilateral mid insula, and amygdala. In addition, right anterior insula activity was enhanced during by asynchronous relative to synchronous cardiac feedback even with no change in perceived or actual heart rate suggesting this region serves as a comparator to detect physiological mismatches. Finally, BOLD activity within right anterior insula and amygdala predicted the corresponding changes in perceived intensity ratings at both a group and an individual level. CONCLUSIONS/SIGNIFICANCE Our findings identify the neural substrates supporting behavioural effects of false physiological feedback, and highlight mechanisms that underlie subjective anxiety states, including the importance of the right anterior insula in guiding second-order "cognitive" representations of bodily arousal state

Dalitz Plot Analysis of Ds to K+K-pi+

We perform a Dalitz plot analysis of the decay Ds to K+K-pi+ with the CLEO-c data set of 586/pb of e+e- collisions accumulated at sqrt(s) = 4.17 GeV. This corresponds to about 0.57 million D_s+D_s(*)- pairs from which we select 14400 candidates with a background of roughly 15%. In contrast to previous measurements we find good agreement with our data only by including an additional f_0(1370)pi+ contribution. We measure the magnitude, phase, and fit fraction of K*(892) K+, phi(1020)pi+, K0*(1430)K+, f_0(980)pi+, f_0(1710)pi+, and f_0(1370)pi+ contributions and limit the possible contributions of other KK and Kpi resonances that could appear in this decay.Comment: 21 Pages,available through http://www.lns.cornell.edu/public/CLNS/, submitted to PR

Search for D0 to p e- and D0 to pbar e+

Using data recorded by CLEO-c detector at CESR, we search for simultaneous baryon and lepton number violating decays of the D^0 meson, specifically, D^0 --> p-bar e^+, D^0-bar --> p-bar e^+, D^0 --> p e^- and D^0-bar --> p e^-. We set the following branching fraction upper limits: D^0 --> p-bar e^+ (D^0-bar --> p-bar e^+) p e^- (D^0-bar --> p e^-) < 1.2 * 10^{-5}, both at 90% confidence level.Comment: 10 pages, available through http://www.lns.cornell.edu/public/CLNS/, submitted to PRD. Comments: changed abstract, added reference for section 1, vertical axis in Fig.5 changed (starts from 1.5 rather than 2.0), fixed typo

Charmonium decays to gamma pi0, gamma eta, and gamma eta'

Using data acquired with the CLEO-c detector at the CESR e+e- collider, we measure branching fractions for J/psi, psi(2S), and psi(3770) decays to gamma pi0, gamma eta, and gamma eta'. Defining R_n = B[ psi(nS)-->gamma eta ]/B[ psi(nS)-->gamma eta' ], we obtain R_1 = (21.1 +- 0.9)% and, unexpectedly, an order of magnitude smaller limit, R_2 < 1.8% at 90% C.L. We also use J/psi-->gamma eta' events to determine branching fractions of improved precision for the five most copious eta' decay modes.Comment: 14 pages, available through http://www.lns.cornell.edu/public/CLNS/, published in Physical Review