Direct 3D Tomographic Reconstruction and Phase-Retrieval of Far-Field Coherent Diffraction Patterns

We present an alternative numerical reconstruction algorithm for direct tomographic reconstruction of a sample refractive indices from the measured intensities of its far-field coherent diffraction patterns. We formulate the well-known phase-retrieval problem in ptychography in a tomographic framework which allows for simultaneous reconstruction of the illumination function and the sample refractive indices in three dimensions. Our iterative reconstruction algorithm is based on the Levenberg-Marquardt algorithm. We demonstrate the performance of our proposed method with simulation studies

Second Overtone Pulsators Among Delta Scuti Stars

We investigate the modal stability of stellar models at masses and luminosity levels corresponding to post main sequence luminous delta scuti pulsators. The envelope models have been computed at fixed mass value, luminosity level and chemical composition (Y=0.28, Z=0.02). According to a nonlinear approach to radial oscillations the present investigation predicts the occurrence of stable second overtone pulsators for the first time. The shape of both light and velocity curves are presented and discussed, providing a useful tool for the identification of second overtone pulsators among the known groups of radially pulsating stars. The period ratios of mixed mode pulsators obtained by perturbing the first and the second overtone radial eigenfunctions are in agreement with observative values. Finally, the physical structure and the dynamical properties of second overtone pulsators are discussed in detail. The role played by the nodal lines in the destabilization of second overtone pulsators is also pointed out.Comment: 20 pages, 11 Postscript figures, uses aaspp4.sty and tighten.st

Design of organic Rankine cycles using a non-conventional optimization approach

The organic Rankine cycle is a suitable technology for utilizing low grade heat for electricity production. Compared to the traditional steam Rankine cycle, the organic Rankine cycle is beneficial, since it enables the choice of a working fluid which performs better than steam at low heat input temperatures and at lowpower outputs. Selecting the process layout of the organic Rankine cycle and the working fluid are two key design decisions which are critical for the thermodynamic and economic performance of the cycle. The prevailing approach used in the design and optimization of organic Rankine cycles is to model the heatexchangers by assuming a fixed minimum temperature difference. The objective of this work is to assess the applicability of this conventional optimization approach and a non-conventional optimization approach. In thenon-conventional optimization approach a total UA-value (the product of the overall heat transfer coefficient and the heat transfer area) is assigned to the cycle, while the distribution of this total UA-value to each of the heat exchangers is optimized. Optimizations are carried out for three different marine engine waste heatsources at temperatures ranging from 90 °C to 285 °C. The results suggest that the conventional optimization approach is not suitable for estimating the performance potential when the temperature profiles in the heat exchangers are closely matched. This is exemplified for the fluid MDM where the temperature profile of preheating aligns with the heat source fluid and for the zeotropic mixture R32/R134a where the temperature profile of condensation aligns with the cooling water. Furthermore, the conventional optimization approach shows weaknesses in evaluating the feasibility of using a recuperator, when the expander outlet temperature is high. In these cases the non-conventional optimization approach is the more suited methodology for designing organic Rankine cycles

Deep Learning-based Anomaly Detection on X-ray Images of Fuel Cell Electrodes

Anomaly detection in X-ray images has been an active and lasting research area in the last decades, especially in the domain of medical X-ray images. For this work, we created a real-world labeled anomaly dataset, consisting of 16-bit X-ray image data of fuel cell electrodes coated with a platinum catalyst solution and perform anomaly detection on the dataset using a deep learning approach. The dataset contains a diverse set of anomalies with 11 identified common anomalies where the electrodes contain e.g. scratches, bubbles, smudges etc. We experiment with 16-bit image to 8-bit image conversion methods to utilize pre-trained Convolutional Neural Networks as feature extractors (transfer learning) and find that we achieve the best performance by maximizing the contrasts globally across the dataset during the 16-bit to 8-bit conversion, through histogram equalization. We group the fuel cell electrodes with anomalies into a single class called abnormal and the normal fuel cell electrodes into a class called normal, thereby abstracting the anomaly detection problem into a binary classification problem. We achieve a balanced accuracy of 85.18\%. The anomaly detection is used by the company, Serenergy, for optimizing the time spend on the quality control of the fuel cell electrodesComment: 10 pages, 9 figures, VISAPP202

Novel mutation in CCBE 1 as a cause of recurrent hydrops fetalis from Hennekam lymphangiectasia-lymphedema syndrome-1.

Whole exome sequencing (WES) was used to determine the etiology of recurrent hydrops fetalis in this case of Hennekam lymphangiectasia-lymphedema syndrome-1. WES is a useful approach for diagnosing rare single-gene conditions with nonspecific phenotypes and should be considered early in the diagnostic process of investigating fetal abnormalities

NA0D – The new traumatic dental injury classification of the world health organization

An accurate, clear, and easy-to- use traumatic dental injury (TDI) classification and definition system is a prerequisite for proper diagnosis, study, and treatment. However, more than 50 classifications have been used in the past. The ideal solution would be that TDIs are adequately classified within the International Classification of Diseases (ICD), endorsed by the World Health Organization (WHO). TDI classification provided by the 11th Revision of the ICD (ICD-11), released in 2018, and previous Revisions, failed to classify TDIs satisfactorily. Therefore, in December 2018, a proposal was submitted by Dr's Stefano Petti, Jens Ove Andreasen, Ulf Glendor, and Lars Andersson, to the ICD-11, asking for a change of the existing TDI classification. Proposal #2130 highlighted the TDI paradox, the fifth most frequent disease/condition neglected by most public health agencies in the world, and the limits of ICD-11 classification. Namely, injuries of teeth and periodontal tissues were located in two separate blocks that did not mention dental/periodontal tissues; infraction, concussion, and subluxation were not coded; most TDIs lacked description; and tooth fractures were described through bone fracture descriptions (e.g., comminuted, compression, and fissured fractures). These limitations led to TDI mis-reporting, under-reporting, and non-specific reporting by untrained non-dental healthcare providers. In addition, no scientific articles on TDIs, present in PubMed, Scopus, and Web-of- Science, used the ICD classification. Proposal #2130 suggested to adopt the Andreasen classification, the most widely acknowledged classification used in dental traumatology. The Proposal was reviewed by two WHO teams, two scientific Committees, one WHO Collaborating Center, and the Department of Non-Communicable Disease Prevention at WHO headquarters, and it underwent two voting sessions. In March 2022, the Andreasen classification was accepted integrally. A new entity was generated, called NA0D, “Injury of teeth or supporting structures” (https://icd.who.int/brows e11/l-m/ en#/http://id. who.int/icd /ent ity/141 3338122). Hopefully, this will contribute to increasing the public awareness, and the dental profession's management, of TDIs

HIV Infection among Young People in Northwest Tanzania: The Role of Biological, Behavioural and Socio-Demographic Risk Factors.

BACKGROUND: Young people are at high risk of HIV and developing appropriate prevention programmes requires an understanding of the risk factors for HIV in this age group. We investigated factors associated with HIV among participants aged 15-30 years in a 2007-8 cross-sectional survey nested within a community-randomised trial of the MEMA kwa Vijana intervention in 20 rural communities in northwest Tanzania. METHODS: We analysed data for 7259(53%) males and 6476(47%) females. Using a proximate-determinant conceptual framework and conditional logistic regression, we obtained sex-specific Odds Ratios (ORs) for the association of HIV infection with socio-demographic, knowledge, behavioural and biological factors. RESULTS: HSV-2 infection was strongly associated with HIV infection (females: adjOR 4.4, 95%CI 3.2-6.1; males: adjOR 4.2, 95%CI 2.8-6.2). Several socio-demographic factors (such as age, marital status and mobility), behavioural factors (condom use, number and type of sexual partnerships) and biological factors (blood transfusion, lifetime pregnancies, genital ulcers, Neisseria gonorrhoeae) were also associated with HIV infection. Among females, lifetime sexual partners (linear trend, p<0.001), ≥2 partners in the past year (adjOR 2.0, 95%CI 1.4-2.8), ≥2 new partners in the past year (adjOR 1.9 95%CI 1.2, 3.3) and concurrent partners in the past year (adjOR 1.6 95%CI 1.1, 2.4) were all associated with HIV infection. CONCLUSIONS: Efforts must be intensified to find effective interventions to reduce HSV-2. Effective behavioural interventions focusing on reducing the number of sexual partnerships and risk behaviour within partnerships are also needed. An increase in risky sexual behaviour may occur following marriage dissolution or when a young woman travels outside of her community and interventions addressing the needs of these subgroups of vulnerable women may be important. TRIAL REGISTRATION: ClinicalTrial.gov NCT00248469