Stride Lengths during Maximal Linear Sprint Acceleration Obtained with Foot-Mounted Inertial Measurement Units

Inertial measurement units (IMUs) fixed to the lower limbs have been reported to provide accurate estimates of stride lengths (SLs) during walking. Due to technical challenges, validation of such estimates in running is generally limited to speeds (well) below 5 m·s−1. However, athletes sprinting at (sub)maximal effort already surpass 5 m·s−1 after a few strides. The present study aimed to develop and validate IMU-derived SLs during maximal linear overground sprints. Recreational athletes (n = 21) completed two sets of three 35 m sprints executed at 60, 80, and 100% of subjective effort, with an IMU on the instep of each shoe. Reference SLs from start to ~30 m were obtained with a series of video cameras. SLs from IMUs were obtained by double integration of horizontal acceleration with a zero-velocity update, corrected for acceleration artefacts at touch-down of the feet. Peak sprint speeds (mean ± SD) reached at the three levels of effort were 7.02 ± 0.80, 7.65 ± 0.77, and 8.42 ± 0.85 m·s−1, respectively. Biases (±Limits of Agreement) of SLs obtained from all participants during sprints at 60, 80, and 100% effort were 0.01% (±6.33%), −0.75% (±6.39%), and −2.51% (±8.54%), respectively. In conclusion, in recreational athletes wearing IMUs tightly fixed to their shoes, stride length can be estimated with reasonable accuracy during maximal linear sprint acceleration.

Modelling and Simulation of Phase Formations in Martensitic Stainless Steels

Modelling and simulation of solidification processes and solid-state phase transformation have become key instruments in the field of alloy development and heat treatment optimization. Apart from equilibrium-controlled processes, also diffusion-based effects need to be considered. This contribution presents some typical approaches at the example of martensitic stainless steels. Important aspects affecting the production and properties of these steels, such as alloying limits with nitrogen, the formation of ledeburitic structures, or the retained austenite content after heat treatment, can be predicted with reasonable accuracy.

Simple decision rules to predict local surges in COVID-19 hospitalizations during the winter and spring of 2022

Low rates of vaccination, emergence of novel variants of SARS-CoV-2, and increasing transmission relating to seasonal changes leave many U.S. communities at risk for surges of COVID-19 during the winter and spring of 2022 that might strain hospital capacity, as in previous waves. The trajectories of COVID-19 hospitalizations during this period are expected to differ across communities depending on their age distributions, vaccination coverage, cumulative incidence, and adoption of risk mitigating behaviors. Yet, existing predictive models of COVID-19 hospitalizations are almost exclusively focused on national- and state-level predictions. This leaves local policymakers in urgent need of tools that can provide early warnings about the possibility that COVID-19 hospitalizations may rise to levels that exceed local capacity. In this work, we develop simple decision rules to predict whether COVID-19 hospitalization will exceed the local hospitalization capacity within a 4- or 8-week period if no additional mitigating strategies are implemented during this time. These decision rules use real-time data related to hospital occupancy and new hospitalizations associated with COVID-19, and when available, genomic surveillance of SARS-CoV-2. We showed that these decision rules present reasonable accuracy, sensitivity, and specificity (all ≥80%) in predicting local surges in hospitalizations under numerous simulated scenarios, which capture substantial uncertainties over the future trajectories of COVID-19 during the winter and spring of 2022. Our proposed decision rules are simple, visual, and straightforward to use in practice by local decision makers without the need to perform numerical computations.

Optimized jk-nearest neighbor based online signature verification and evaluation of the main parameters

In this paper, we propose an enhanced jk-nearest neighbor (jk-NN) classifier for online signature verification. After studying the algorithm's main parameters, we use four separate databases to present and evaluate each algorithm parameter. The results show that the proposed method can increase the verification accuracy by 0.73-10% compared to a traditional one class k-NN classifier. The algorithm has achieved reasonable accuracy for different databases, a 3.93% error rate when using the SVC2004 database, 2.6% for MCYT-100 database, 1.75% for the SigComp'11 database, and 6% for the SigComp'15 database.The proposed algorithm uses specifically chosen parameters and a procedure to pick the optimal value for K using only the signer's reference signatures, to build a practical verification system for real-life scenarios where only these signatures are available. By applying the proposed algorithm, the average error achieved was 8% for SVC2004, 3.26% for MCYT-100, 13% for SigComp'15, and 2.22% for SigComp'11.

Comparison of PM2.5 in Seoul, Korea Estimated from the Various Ground-Based and Satellite AOD

Based on multiple linear regression (MLR) models, we estimated the PM2.5 at Seoul using a number of aerosol optical depth (AOD) values obtained from ground-based and satellite remote sensing observations. To construct the MLR model, we consider various parameters related to the ambient meteorology and air quality. In general, all AOD values resulted in the high quality of PM2.5 estimation through the MLR method: mostly correlation coefficients >~0.8. Among various polar-orbit satellite AODs, AOD values from the MODIS measurement contribute to better PM2.5 estimation. We also found that the quality of estimated PM2.5 shows some seasonal variation; the estimated PM2.5 values consistently have the highest correlation with in situ PM2.5 in autumn, but are not well established in winter, probably due to the difficulty of AOD retrieval in the winter condition. MLR modeling using spectral AOD values from the ground-based measurements revealed that the accuracy of PM2.5 estimation does not depend on the selected wavelength. Although all AOD values used in this study resulted in a reasonable accuracy range of PM2.5 estimation, our analyses of the difference in estimated PM2.5 reveal the importance of utilizing the proper AOD for the best quality of PM2.5 estimation.

Model Reduction Applied to Empirical Models for Biomass Gasification in Downdraft Gasifiers

Various modeling approaches have been suggested for the modeling and simulation of gasification processes. These models allow for the prediction of gasifier performance at different conditions and using different feedstocks from which the system parameters can be optimized to design efficient gasifiers. Complex models require significant time and effort to develop, and they might only be accurate for use with a specific catalyst. Hence, various simpler models have also been developed, including thermodynamic equilibrium models and empirical models, which can be developed and solved more quickly, allowing such models to be used for optimization. In this study, linear and quadratic expressions in terms of the gasifier input value parameters are developed based on linear regression. To identify significant parameters and reduce the complexity of these expressions, a LASSO (least absolute shrinkage and selection operator) shrinkage method is applied together with cross validation. In this way, the significant parameters are revealed and simple models with reasonable accuracy are obtained.

On mesh requirements for Large Eddy Simulation with Wall Functions

The results of using Large Eddy Simulation with Wall Functions (WFLES) in application to basic wall-bounded flows, such as turbulent boundary layer and channel flow cases are presented. In particular, it is shown that WFLES is suitable for predicting wall bounded flows and provides reasonable accuracy when using appropriate grids. The grid for WFLES should have isotropic cells with size smaller than 10% of the boundary layer thickness. Using coarser grids or anisotropic cells leads to significant reduction of accuracy.

Synchronized coupling of thermal mass and buoyancy ventilation: wood versus concrete

This study describes an experiment that validates scaling rules for the design of thermal mass, coupled with buoyancy ventilation, suggesting that wood can perform as well as concrete if these rules are respected. The scaling rules potentially offer a shortcut for early design, showing how to tune the interior temperature and rate of buoyancy ventilation by adjusting the thickness and surface area of an internal thermal mass. A pair of test chambers (H~1m), comparing wood and concrete internal thermal masses, were located in Alabama, USA and Montreal, Canada, and left outside in sun- and-wind-sheltered environments for consecutive months. The thermal mass thicknesses were optimized so the chambers would maintain similar interior temperatures and airflow rates. The scaling rules predicted the behavior of the chambers with reasonable accuracy and both the concrete and wood thermal masses performed equivalently. For instance, the test chambers in Alabama were both designed to damp the maximum exterior temperature by a factor 1-1/Ai ≈ 0.7 and produce a maximum ventilation flow rate of Q ≈ 0.37 l/s. The measured damping was 1-1/Ai = 0.81±0.1 and 1-1/Ai = 0.81±0.13 for the concrete and wood chambers, respectively, while the maximum flow rates were 0.374±0.03 and 0.36±0.04 l/s, respectively.

Energetic and Geometric Characteristics of the Substituents: Part 2: The Case of NO2, Cl, and NH2 Groups in Their Mono-Substituted Derivatives of Simple Nitrogen Heterocycles

Variously substituted N-heterocyclic compounds are widespread across bio- and medicinal chemistry. The work aims to computationally evaluate the influence of the type of N-heterocyclic compound and the substitution position on the properties of three model substituents: NO2, Cl, and NH2. For this reason, the energetic descriptor of global substituent effect (Erel), geometry of substituents, and electronic descriptors (cSAR, pEDA, sEDA) are considered, and interdependences between these characteristics are discussed. Furthermore, the existence of an endocyclic N atom may induce proximity effects specific for a given substituent. Therefore, various quantum chemistry methods are used to assess them: the quantum theory of atoms in molecules (QTAIM), analysis of non-covalent interactions using reduced density gradient (RDG) function, and electrostatic potential maps (ESP). The study shows that the energetic effect associated with the substitution is highly dependent on the number and position of N atoms in the heterocyclic ring. Moreover, this effect due to interaction with more than one endo N atom (e.g., in pyrimidines) can be assessed with reasonable accuracy by adding the effects calculated for interactions with one endo N atom in substituted pyridines. Finally, all possible cases of proximity interactions for the NO2, Cl, and NH2 groups are thoroughly discussed.

On the robustness of generalization of drug–drug interaction models

Background Deep learning methods are a proven commodity in many fields and endeavors. One of these endeavors is predicting the presence of adverse drug–drug interactions (DDIs). The models generated can predict, with reasonable accuracy, the phenotypes arising from the drug interactions using their molecular structures. Nevertheless, this task requires improvement to be truly useful. Given the complexity of the predictive task, an extensive benchmarking on structure-based models for DDIs prediction was performed to evaluate their drawbacks and advantages. Results We rigorously tested various structure-based models that predict drug interactions using different splitting strategies to simulate different real-world scenarios. In addition to the effects of different training and testing setups on the robustness and generalizability of the models, we then explore the contribution of traditional approaches such as multitask learning and data augmentation. Conclusion Structure-based models tend to generalize poorly to unseen drugs despite their ability to identify new DDIs among drugs seen during training accurately. Indeed, they efficiently propagate information between known drugs and could be valuable for discovering new DDIs in a database. However, these models will most probably fail when exposed to unknown drugs. While multitask learning does not help in our case to solve the problem, the use of data augmentation does at least mitigate it. Therefore, researchers must be cautious of the bias of the random evaluation scheme, especially if their goal is to discover new DDIs.