A Bayesian approach to predict the structural responses of FGM plates with uncertain parameters

The authors wish to acknowledge the financial support given by FCT/MEC through Projects: UNIDEMI UID/EMS/00667/2019 and CEMAPRE UID/MULTI/00491/2019. The first author also wishes to thank the support of Project IPL/2019/MOCHVar/ISEL.The increasing complexity associated to both structures and materials have been posing a continuous demand for numerical tools that allow predicting their behavior and simultaneously consider the influence of the uncertainty associated to multiple parameters. This work presents a Bayesian approach to characterize the influence of each material phase's properties and the volume fraction parameters in the behavior of biphasic functionally graded plates.publishersversionpublishe

Approximate Voronoi cells for lattices, revisited

We revisit the approximate Voronoi cells approach for solving the closest vector problem with preprocessing (CVPP) on high-dimensional lattices, and settle the open problem of Doulgerakis-Laarhoven-De Weger [PQCrypto, 2019] of determining exact asymptotics on the volume of these Voronoi cells under the Gaussian heuristic. As a result, we obtain improved upper bounds on the time complexity of the randomized iterative slicer when using less than $2^{0.076d + o(d)}$ memory, and we show how to obtain time-memory trade-offs even when using less than $2^{0.048d + o(d)}$ memory. We also settle the open problem of obtaining a continuous trade-off between the size of the advice and the query time complexity, as the time complexity with subexponential advice in our approach scales as $d^{d/2 + o(d)}$, matching worst-case enumeration bounds, and achieving the same asymptotic scaling as average-case enumeration algorithms for the closest vector problem.Comment: 18 pages, 1 figur

Regularity of Respiratory Waveform Depends on Ventilation Parameters

Entropy is a nonlinear method for quantifying the regularity and order of a system. Entropy was originally born from thermodynamics and is now used in various fields, such as statistical mechanics and information ethics. Approximate Entropy (ApEn) is an index that has been developed to quantify the complexity of data over time. This study aimed to use ApEn measurement to clarify the relationship between the regularity of the respiratory waveform and ventilation parameters for humans in a resting state. The 5 minutes resting respiratory metabolism of thirteen healthy participants was measured, including respiratory rate (RR), tidal volume (VT), minute ventilation (V ̇ E), end-tidal oxygen concentration (ETO2), end-tidal carbon dioxide concentration (ETCO2), end-tidal carbon dioxide tension (PETCO2), inspiration time (TI), expiration time (TE), and respiration time (TTOT), and the ventilatory response to end-tidal carbon dioxide tension (V ̇ E /PETCO2) was calculated. ApEn values and ventilation parameters were examined using Pearson\u27s product-moment correlation coefficient. The ApEn value of the respiratory waveforms of participants was 0.291 ±0.050 (mean±SD); these values were positively correlated with TI, TE, TTOT, ETO2, and PETCO2, and negatively correlated with RR, ETCO2, and V ̇ E/PETCO2. There were no correlations with VT or V ̇ E. The results revealed a correlation between ApEn values and RR, TI, TE, and TTOT. The respiratory waveform of a person with fast respiration and a high respiration rate was regular. The correlation between the regularity of the respiratory waveform and PETCO2 and V ̇ E/PETCO2 showed that those with regular respiratory waveforms had increased sensitivity to CO2 and were in a respiratory state close to hyperventilation. Those with regular respiratory waveforms at rest may have unconsciously felt breathless due to anxiety. The fact that no correlation was observed between VT and V ̇ E supports the notion that the regularity of the respiratory waveform is not determined by ventilation volume but by respiration rate.東京有明医療大学2019年

Export Complexity and Economic Growth: Empirical Analysis for Selected CEE Countries

The principal objective of the paper is to test the hypothesis that export sophistication (complexity) rather than the volume of exports has a more robust impact on economic growth. We applied a dynamic panel specification model (system GMM) to a sample of 22 selected CEE countries in the 2009–2019 period. Estimated research results suggest that export still plays a significant role in determining economic growth in these countries. However, the analysis shows that export sophistication (complexity) has a predominant role in stimulating economic growth for the observed sample of countries. In addition to the main focus of the paper, estimated results show that FDI inflows have a positive and statistically significant direct and indirect impact on economic growth. Moreover, the results indicate that, apart from the direct effects of export sophistication on economic growth, it has an additional positive effect on export performance through structural transformation. When comparing the impact of export sophistication on economic growth, the difference between EU and non-EU economies seems to be mostly insignificant

Med-DANet V2: A Flexible Dynamic Architecture for Efficient Medical Volumetric Segmentation

Recent works have shown that the computational efficiency of 3D medical image (e.g. CT and MRI) segmentation can be impressively improved by dynamic inference based on slice-wise complexity. As a pioneering work, a dynamic architecture network for medical volumetric segmentation (i.e. Med-DANet) has achieved a favorable accuracy and efficiency trade-off by dynamically selecting a suitable 2D candidate model from the pre-defined model bank for different slices. However, the issues of incomplete data analysis, high training costs, and the two-stage pipeline in Med-DANet require further improvement. To this end, this paper further explores a unified formulation of the dynamic inference framework from the perspective of both the data itself and the model structure. For each slice of the input volume, our proposed method dynamically selects an important foreground region for segmentation based on the policy generated by our Decision Network and Crop Position Network. Besides, we propose to insert a stage-wise quantization selector to the employed segmentation model (e.g. U-Net) for dynamic architecture adapting. Extensive experiments on BraTS 2019 and 2020 show that our method achieves comparable or better performance than previous state-of-the-art methods with much less model complexity. Compared with previous methods Med-DANet and TransBTS with dynamic and static architecture respectively, our framework improves the model efficiency by up to nearly 4.1 and 17.3 times with comparable segmentation results on BraTS 2019.Comment: Accepted by WACV 202

Soil Microbial Networks Shift Across a High-Elevation Successional Gradient.

While it is well established that microbial composition and diversity shift along environmental gradients, how interactions among microbes change is poorly understood. Here, we tested how community structure and species interactions among diverse groups of soil microbes (bacteria, fungi, non-fungal eukaryotes) change across a fundamental ecological gradient, succession. Our study system is a high-elevation alpine ecosystem that exhibits variability in successional stage due to topography and harsh environmental conditions. We used hierarchical Bayesian joint distribution modeling to remove the influence of environmental covariates on species distributions and generated interaction networks using the residual species-to-species variance-covariance matrix. We hypothesized that as ecological succession proceeds, diversity will increase, species composition will change, and soil microbial networks will become more complex. As expected, we found that diversity of most taxonomic groups increased over succession, and species composition changed considerably. Interestingly, and contrary to our hypothesis, interaction networks became less complex over succession (fewer interactions per taxon). Interactions between photosynthetic microbes and any other organism became less frequent over the gradient, whereas interactions between plants or soil microfauna and any other organism were more abundant in late succession. Results demonstrate that patterns in diversity and composition do not necessarily relate to patterns in network complexity and suggest that network analyses provide new insight into the ecology of highly diverse, microscopic communities

Optimizing genetics online resources for diverse readers.

PurposeClear and accurate genetic information should be available to health-care consumers at an individualized level of comprehension. The objective of this study is to evaluate the complexity of common online resources and to simplify text content using automated text processing tools.MethodsWe extracted all text from Genetics Home Reference and MedlinePlus in bulk and analyzed content using natural language processing. We applied custom tools to improve the readability and compared readability before and after text optimization.ResultsCommonly used educational materials were more complex than the recommended reading level for the general public. Genetic health information entries from Genetics Home Reference (n = 1279) were written at a median 13.0 grade level. MedlinePlus entries, which are not exclusively genetic (n = 1030), had a median grade level of 7.7. When we optimized text for the 59 actionable conditions by prioritizing medical details using a standard structure, the average reading grade level improved.ConclusionFactors that increase complexity are long sentences and difficult words. Future strategies to reduce complexity include prioritizing relevant details and using more illustrations. Simplifying and providing standardized online health resources would benefit diverse consumers and promote inclusivity

The SLS-Berlin: Validation of a German Computer-Based Screening Test to Measure Reading Proficiency in Early and Late Adulthood

Reading proficiency, i.e., successfully integrating early word-based information and utilizing this information in later processes of sentence and text comprehension, and its assessment is subject to extensive research. However, screening tests for German adults across the life span are basically non-existent. Therefore, the present article introduces a standardized computerized sentence-based screening measure for German adult readers to assess reading proficiency including norm data from 2,148 participants covering an age range from 16 to 88 years. The test was developed in accordance with the children’s version of the Salzburger LeseScreening (SLS, Wimmer and Mayringer, 2014). The SLS-Berlin has a high reliability and can easily be implemented in any research setting using German language. We present a detailed description of the test and report the distribution of SLS-Berlin scores for the norm sample as well as for two subsamples of younger (below 60 years) and older adults (60 and older). For all three samples, we conducted regression analyses to investigate the relationship between sentence characteristics and SLS-Berlin scores. In a second validation study, SLS-Berlin scores were compared with two (pseudo)word reading tests, a test measuring attention and processing speed and eye-movements recorded during expository text reading. Our results confirm the SLS-Berlin’s sensitivity to capture early word decoding and later text related comprehension processes. The test distinguished very well between skilled and less skilled readers and also within less skilled readers and is therefore a powerful and efficient screening test for German adults to assess interindividual levels of reading proficiency