Thermoelasticity and ArUco marker-based model validation of polymer structure: application to the San Giorgio’s bridge inspection robot

<p>Experimental procedures are often involved in the numerical models validation. To define the behaviour of a structure, its underlying dynamics and stress distributions are generally investigated. In this research, a multi-instrumental and multi-spectral method is proposed in order to validate the numerical model of the Inspection Robot mounted on the new San Giorgio's Bridge on the Polcevera river. An infrared thermoelasticity-based approach is used to measure stress-concentration factors and, additionally, an innovative methodology is implemented to define the natural frequencies of the Robot Inspection structure, based on the detection of ArUco fiducial markers. Established impact hammer procedure is also performed for the validation of the results.</p>

Lake area and volume variation in the endorheic basin of the Tibetan Plateau from 1989 to 2019

The Tibetan Plateau, known as "the third pole of the Earth", is a region susceptible to climate change. With little human disturbance, lake storage changes serve as a unique indicator of climate change, but comprehensive lake storage data are rare in the region, especially for the lakes with an area less than 10 km2 which are the most sensitive to environmental changes. In this paper, we completed a census of annual lake volume change for 976 lakes larger than 1 km2 in the endorheic basin of the Tibetan Plateau (EBTP) during 1989–2019 using Landsat imagery and digital terrain models. Validation and comparison with several existing studies indicate that our data are more reliable. Lake volume in the EBTP exhibited a net increase of 193.45 km3 during the time period with an increasing rate of 6.45 km3 year−1. In general, the larger the lake area, the greater the lake volume change, though there are some exceptions. Lakes with an area less than 10 km2 have more severe volume change whether decreasing or increasing. This research complements existing lake studies by providing a comprehensive and long-term lake volume change data for the region. The dataset is available on Zenodo (https://doi.org/10.5281/zenodo.5543615, Wang et al., 2021).

Dynamics of glass windows in mining vehicles under the impacts of rock pieces: numerical and analytical comparison for computational models validation

This study represents the results of linear dynamics analysis of glass plates subjected to rock pieces impacts occurring in underground machines’ windows. The aim of the work is to provide analytical and numerical solutions, obtain frequencies and plate displacement, and compare results of stress calculation for different models. The work performs finite element method (FEM) computations within a modal analysis in 3D statement including a mesh-size convergence analysis. Given approach is a basement for estimation of safety work conditions for operators in cabins of underground mining vehicles when glass windows are subjected to rock bursts and damages.

Machine learning based decipherment of Cell Population Data: a promising hospital front-door screening tool for COVID-19

Introduction/Objective Key challenges against early diagnosis of COVID-19 are its symptoms sharing nature and prolong SARS-CoV-2 PCR turnaround time. Hither machine learning (ML) tools experienced by routinely generated clinical data; potentially grant early prediction. Methods/Case Report Routine and earlier diagnostic data along demographic information were extracted for total of 21,672 subsequent presentations. Along conventional statistics, multilayer perceptron (MLP) and radial basis function (RBF) were applied to predict COVID-19 from pre-pandemic control. Three feature sets were prepared, and performance evaluated through stratified 10-fold cross validation. With differing predominance of COVID-19, multiple test sets were created and predictive efficiency was evaluated to simulate real-fashion performance against fluctuating course of pandemic. Models validation was also inducted in prospective manner on independent dataset, equating framework forecasting to conclusions from PCR. Results (if a Case Study enter NA) RBF model attained superior cross entropy error 20.761(7.883) and 20.782(3.991) for Q-Flags and Routine Items respectively while MLP outperformed for cell population data (CPD) parameters with value of 6.968(1.259) for ‘training(testing)’. Our CPD driven MLP framework in challenge of lower (&lt;5%) COVID-19 predominance affords greater negative predictive values (NPV &gt;99%). Higher accuracy (%correct 92.5) was offered during prospective validation using independent dataset. Sensitivity analysis advances illusive accuracy (%correct 94.1) and NPV (96.9%). LY-WZ, Blasts/Abn Lympho?, ‘HGB Interf?’, and ‘RBC Agglutination?’ are among novel enlightening study attributes. Conclusion CPD driven ML tools offer efficient screening of COVID-19 patients at presentation to hospital to backing early expulsion and directing patients’ flow-from amid the initial presentation to hospital.

A comparative review of peridynamics and phase-field models for engineering fracture mechanics

Computational modeling of the initiation and propagation of complex fracture is central to the discipline of engineering fracture mechanics. This review focuses on two promising approaches: phase-field (PF) and peridynamic (PD) models applied to this class of problems. The basic concepts consisting of constitutive models, failure criteria, discretization schemes, and numerical analysis are briefly summarized for both models. Validation against experimental data is essential for all computational methods to demonstrate predictive accuracy. To that end, The Sandia Fracture Challenge and similar experimental data sets where both models could be benchmarked against are showcased. Emphasis is made to converge on common metrics for the evaluation of these two fracture modeling approaches. Both PD and PF models are assessed in terms of their computational effort and predictive capabilities with their relative advantages and challenges are summarized.

Simulating Spatio-Temporal Patterns of Bicycle Flows with an Agent-Based Model

Transport planning strategies regard cycling promotion as a suitable means for tackling problems connected with motorized traffic such as limited space, congestion, and pollution. However, the evidence base for optimizing cycling promotion is weak in most cases, and information on bicycle patterns at a sufficient resolution is largely lacking. In this paper, we propose agent-based modeling to simulate bicycle traffic flows at a regional scale level for an entire day. The feasibility of the model is demonstrated in a use case in the Salzburg region, Austria. The simulation results in distinct spatio-temporal bicycle traffic patterns at high spatial (road segments) and temporal (minute) resolution. Scenario analysis positively assesses the model’s level of complexity, where the demographically parametrized behavior of cyclists outperforms stochastic null models. Validation with reference data from three sources shows a high correlation between simulated and observed bicycle traffic, where the predictive power is primarily related to the quality of the input and validation data. In conclusion, the implemented agent-based model successfully simulates bicycle patterns of 186,000 inhabitants within a reasonable time. This spatially explicit approach of modeling individual mobility behavior opens new opportunities for evidence-based planning and decision making in the wide field of cycling promotion

A System-Wide Understanding of the Human Olfactory Percept Chemical Space

The fundamental units of olfactory perception are discrete 3D structures of volatile chemicals that each interact with specific subsets of a very large family of hundreds of odorant receptor proteins, in turn activating complex neural circuitry and posing a challenge to understand. We have applied computational approaches to analyze olfactory perceptual space from the perspective of odorant chemical features. We identify physicochemical features associated with ~150 different perceptual descriptors and develop machine-learning models. Validation of predictions shows a high success rate for test set chemicals within a study, as well as across studies more than 30 years apart in time. Due to the high success rates, we are able to map ~150 percepts onto a chemical space of nearly 0.5 million compounds, predicting numerous percept–structure combinations. The chemical structure-to-percept prediction provides a system-level view of human olfaction and opens the door for comprehensive computational discovery of fragrances and flavors.