Hierarchical strategy for rapid finite element analysis

A new methodology is introduced where the natural hierarchical character of model descriptions and simulation results are exploited to expedite analysis of problems. The philosophy and the different concepts involved are illustrated by implementing the strategy to solve some practical problems. The end result was a mix of mechanics, well-designed data structures and software interfaces that forms a rapid analysis environment. This can be very advantageous for cases where a sequence of analyses is required because of safety concerns or cost. When designing a structure, it is common to make frequent modifications to the model during the process. In such cases, the ability to use data from different models within the same analysis environment becomes a major advantage. The proposed system's forte is its hierarchical framework that allows models to communicate with each other and share information with one another. This makes it ideal for global local analyses where solutions from a global model are used to derive the boundary conditions for the local model. The system was also used to conduct a micro mechanical analysis on unidirectional composites that have a non-uniform spatial distribution of the fibers. The hierarchical strategy is not tied to any specific methodology and can be adapted to solve problem using different technologies. This allows the strategy to be used across multiple length scales and governing equations

A Finite Element Framework for Multiscale/Multiphysics Analysis of Structures with Complex Microstructures

This research work has contributed in various ways to help develop a better understanding of textile composites and materials with complex microstructures in general. An instrumental part of this work was the development of an object-oriented framework that made it convenient to perform multiscale/multiphysics analyses of advanced materials with complex microstructures such as textile composites. In addition to the studies conducted in this work, this framework lays the groundwork for continued research of these materials. This framework enabled a detailed multiscale stress analysis of a woven DCB specimen that revealed the effect of the complex microstructure on the stress and strain energy release rate distribution along the crack front. In addition to implementing an oxidation model, the framework was also used to implement strategies that expedited the simulation of oxidation in textile composites so that it would take only a few hours. The simulation showed that the tow architecture played a significant role in the oxidation behavior in textile composites. Finally, a coupled diffusion/oxidation and damage progression analysis was implemented that was used to study the mechanical behavior of textile composites under mechanical loading as well as oxidation. A parametric study was performed to determine the effect of material properties and the number of plies in the laminate on its mechanical behavior. The analyses indicated a significant effect of the tow architecture and other parameters on the damage progression in the laminates

Hierarchical strategy for rapid finite element analysis

A new methodology is introduced where the natural hierarchical character of model descriptions and simulation results are exploited to expedite analysis of problems. The philosophy and the different concepts involved are illustrated by implementing the strategy to solve some practical problems. The end result was a mix of mechanics, well-designed data structures and software interfaces that forms a rapid analysis environment. This can be very advantageous for cases where a sequence of analyses is required because of safety concerns or cost. When designing a structure, it is common to make frequent modifications to the model during the process. In such cases, the ability to use data from different models within the same analysis environment becomes a major advantage. The proposed system's forte is its hierarchical framework that allows models to communicate with each other and share information with one another. This makes it ideal for global local analyses where solutions from a global model are used to derive the boundary conditions for the local model. The system was also used to conduct a micro mechanical analysis on unidirectional composites that have a non-uniform spatial distribution of the fibers. The hierarchical strategy is not tied to any specific methodology and can be adapted to solve problem using different technologies. This allows the strategy to be used across multiple length scales and governing equations

Aerosol Distribution over Europe: a Model Evaluation Study with Detailed Aerosol Microphysics

This paper summarizes an evaluation of model simulations with a regional scale atmospheric climate-chemistry/ aerosol model called REMOTE, which has been extended by a microphysical aerosol module. Model results over Europe are presented and compared with available measurements in surface air focusing on the European distribution and variability of primary and secondary aerosols. Additionally, model results obtained with detailed aerosol microphysics are compared to those based on an aerosol bulk mass approach revealing the impact of dry deposition fluxes on atmospheric burden concentration. An improved determination of elevated ozone and sulfate concentrations could be achieved by considering a diurnal cycle in the anthropogenic emission fluxes. Deviation between modelled and measured organic carbon concentrations can be mainly explained by missing formation of secondary organic aerosols and deficiencies in emission data. Changing residential heating practices in Europe, where the use of wood is no longer restricted to rural areas, need to be considered in emission inventories as well as vegetation fire emissions which present a dominant source of organic carbon.JRC.DDG.H.2-Climate chang

Machine learning based prediction models in male reproductive health: Development of a proof-of-concept model for Klinefelter Syndrome in azoospermic patients

Background Due to the highly variable clinical phenotype, Klinefelter Syndrome is underdiagnosed. Objective Assessment of supervised machine learning based prediction models for identification of Klinefelter Syndrome among azoospermic patients, and comparison to expert clinical evaluation. Materials and methods Retrospective patient data (karyotype, age, height, weight, testis volume, follicle-stimulating hormone, luteinizing hormone, testosterone, estradiol, prolactin, semen pH and semen volume) collected between January 2005 and June 2019 were retrieved from a patient data bank of a University Centre. Models were trained, validated and benchmarked based on different supervised machine learning algorithms. Models were then tested on an independent, prospectively acquired set of patient data (between July 2019 and July 2020). Benchmarking against physicians was performed in addition. Results Based on average performance, support vector machines and CatBoost were particularly well-suited models, with 100% sensitivity and >93% specificity on the test dataset. Compared to a group of 18 expert clinicians, the machine learning models had significantly better median sensitivity (100% vs. 87.5%, p = 0.0455) and fared comparably with regards to specificity (90% vs. 89.9%, p = 0.4795), thereby possibly improving diagnosis rate. A Klinefelter Syndrome Score Calculator based on the prediction models is available on . Discussion Differentiating Klinefelter Syndrome patients from azoospermic patients with normal karyotype (46,XY) is a problem that can be solved with supervised machine learning techniques, improving patient care. Conclusions Machine learning could improve the diagnostic rate of Klinefelter Syndrome among azoospermic patients, even more for less-experienced physicians

A Smart Device System to Identify New Phenotypical Characteristics in Movement Disorders

Parkinson's disease and Essential Tremor are two of the most common movement disorders and are still associated with high rates of misdiagnosis. Collected data by technology-based objective measures (TOMs) has the potential to provide new promising and highly accurate movement data for a better understanding of phenotypical characteristics and diagnostic support. A technology-based system called Smart Device System (SDS) is going to be implemented for multi-modal high-resolution acceleration measurement of patients with PD or ET within a clinical setting. The 2-year prospective observational study is conducted to identify new phenotypical biomarkers and train an Artificial Intelligence System. The SDS is going to be integrated and tested within a 20-min assessment including smartphone-based questionnaires, two smartwatches at both wrists and tablet-based Archimedean spirals drawing for deeper tremor-analyses. The electronic questionnaires will cover data on medication, family history and non-motor symptoms. In this paper, we describe the steps for this novel technology-utilizing examination, the principal steps for data analyses and the targeted performances of the system. Future work considers integration with Deep Brain Stimulation, dissemination into further sites and patient's home setting as well as integration with further data sources as neuroimaging and biobanks. Study Registration ID on ClinicalTrials.gov: NCT03638479

Developing context-specific frameworks for integrated sustainability assessment of agricultural intensity change: An application for Europe

Agriculture plays a central role in achieving most Sustainable Development Goals (SDGs). Sustainable intensi- fication (SI) of agriculture has been proposed as a promising concept for safeguarding global food security, while simultaneously protecting the environment and promoting good quality of life. However, SI often leads to context-specific sustainability trade-offs. Operationalising SI thus needs to be supported by transparent sus- tainability assessments. In this article, we propose a general systematic approach to developing context-specific frameworks for integrated sustainability assessment of agricultural intensity change. Firstly, we specify a comprehensive system representation for analysing how changes in agricultural intensity lead to a multitude of sustainability outcomes affecting different societal groups across geographical scales. We then introduce a procedure for identifying the attributes that are relevant for assessment within particular contexts, and respective indicator metrics. Finally, we illustrate the proposed approach by developing an assessment framework for evaluating a wide range of intensification pathways in Europe. The application of the approach revealed pro- cesses and effects that are relevant for the European context but are rarely considered in SI assessments. These include farmers’ health, workers’ living conditions, cultural heritage and sense of place of rural communities, animal welfare, impacts on sectors not directly related to agriculture (e.g., tourism), shrinking and ageing of rural population and consumers’ health. The proposed approach addresses important gaps in SI assessments, and thus represents an important step forward in defining transparent procedures for sustainability assessments that can stimulate an informed debate about the operationalisation of SI and its contribution towards achieving SDGs

Multicentric validation of proteomic biomarkers in urine specific for diabetic nephropathy

Background: Urine proteome analysis is rapidly emerging as a tool for diagnosis and prognosis in disease states. For diagnosis of diabetic nephropathy (DN), urinary proteome analysis was successfully applied in a pilot study. The validity of the previously established proteomic biomarkers with respect to the diagnostic and prognostic potential was assessed on a separate set of patients recruited at three different European centers. In this case-control study of 148 Caucasian patients with diabetes mellitus type 2 and duration >= 5 years, cases of DN were defined as albuminuria >300 mg/d and diabetic retinopathy (n = 66). Controls were matched for gender and diabetes duration (n = 82). Methodology/Principal Findings: Proteome analysis was performed blinded using high-resolution capillary electrophoresis coupled with mass spectrometry (CE-MS). Data were evaluated employing the previously developed model for DN. Upon unblinding, the model for DN showed 93.8% sensitivity and 91.4% specificity, with an AUC of 0.948 (95% CI 0.898-0.978). Of 65 previously identified peptides, 60 were significantly different between cases and controls of this study. In <10% of cases and controls classification by proteome analysis not entirely resulted in the expected clinical outcome. Analysis of patient's subsequent clinical course revealed later progression to DN in some of the false positive classified DN control patients. Conclusions: These data provide the first independent confirmation that profiling of the urinary proteome by CE-MS can adequately identify subjects with DN, supporting the generalizability of this approach. The data further establish urinary collagen fragments as biomarkers for diabetes-induced renal damage that may serve as earlier and more specific biomarkers than the currently used urinary albumin

Key Data Elements in Myeloid Leukemia

Data standards consisting of key data elements for clinical routine and trial documentation harmonize documentation within and across different health care institutions making documentation more efficient and improving scientific data analysis. This work focusses on the field of myeloid leukemia (ML), where a semantic core of common data elements (CDEs) in routine and trial documentation is established by automatic UMLS-based form analysis of existing documentation models. These CDEs (n=227) were initially reviewed and commented by leukemia experts before they were systematically surveyed by an international voting process through seven hematologists of four countries. The total agreement score was 86%. 116 elements (51%) of these share an agreement score of 100%. This work generated CDEs with language-independent semantic codes and international clinical expert review to build a first approach towards an international data standard for ML. A first version of the CDE list is implemented in the data standard Operational Data Model and additional other data formats for reuse in different medical information systems