quantitative prediction
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Small ◽  
2022 ◽  
pp. 2105673
Author(s):  
Julia Razlivina ◽  
Nikita Serov ◽  
Olga Shapovalova ◽  
Vladimir Vinogradov

2022 ◽  
Vol 2022 ◽  
pp. 1-16
Author(s):  
Yawen Ke ◽  
Xiaofeng Xia

The real-time operating system (RTOS) has a wide range of application domains and provides devices with the ability to schedule resources. Because of the restricted resources of embedded devices and the real-time constraints of RTOS, the application of cryptographic algorithms in these devices will affect the running systems. The existing approaches for RTOS ciphers’ evaluation are mainly provided by experimental data performance analysis, which, however, lack a clear judgment on the affected RTOS performance indicators, such as task schedulability, bandwidth, as well as a quantitative prediction of the remaining resources of RTOS. By focusing on task schedulability in RTOS, this paper provides a timed automaton-based quantitative approach to judge the feasibility of ciphers in embedded RTOS. First, a cryptographic algorithm execution overhead estimation model is established. Then, by combining the overhead model with a sensitivity analysis method, we can analyze the feasibility of the cryptographic algorithm. Finally, a task-oriented and timed automaton-based model is built to verify the analysis results. We take AES as a case study and carry out experiments on embedded devices. The experimental results show the effectiveness of our approach, which will provide specific feasibility indicators for the application of cryptographic algorithms in RTOS.


2022 ◽  
pp. 1-36
Author(s):  
Xiaojie Ma ◽  
Luqi Liu ◽  
Zhong Zhang ◽  
Yueguang Wei

Abstract We study the bending stiffness of symmetrically bent circular multilayer van der Waals (vdW) material sheets, which corresponds to the non-isometric configuration in bulge tests. Frenkel sinusoidal function is employed to describe the periodic interlayer tractions due to the lattice structure nature and the bending stiffness of sheets is theoretically extracted via an energetic consideration. Our quantitative prediction shows good agreement with recent experimental results, where the bending stiffness of different types of sheets with the comparable thickness could follow a trend opposite to their Young's moduli. Based on our model, we propose that this trend may experience a transition as the thickness decreases. Apart from the apparent effects of Young's modulus and interlayer shear strength, the interlayer distance is also found to have an important impact on the bending stiffness. In addition, according to our analysis on the size effect, the bending stiffness of such symmetrically bent circular sheets can steadily own a relatively large value, in contrast to the cases of isometric deformations.


Author(s):  
Muhammad Awais ◽  
Michael Altgen ◽  
Mikko Mäkelä ◽  
Tiina Belt ◽  
Lauri Rautkari

AbstractThe uptake of moisture severely affects the properties of wood in service applications. Even local moisture content variations may be critical, but such variations are typically not detected by traditional methods to quantify the moisture content of the wood. In this study, we used near-infrared hyperspectral imaging to predict the moisture distribution on wood surfaces at the macroscale. A broad range of wood moisture contents were generated by controlling the acetylation degree of wood and the relative humidity during sample conditioning. Near-infrared image spectra were then measured from the surfaces of the conditioned wood samples, and a principal component analysis was applied to separate the useful chemical information from the spectral data. Moreover, a partial least squares regression model was developed to predict moisture content on the wood surfaces. The results show that hyperspectral near-infrared image regression can accurately predict the variations in moisture content across wood surfaces. In addition to sample-to-sample variation in moisture content, our results also revealed differences in the moisture content between earlywood and latewood in acetylated wood. This was in line with our recent studies where we found that thin-walled earlywood cells are acetylated faster than the thicker latewood cells, which decreases the moisture uptake during the conditioning. Dynamic vapor sorption isotherms validated the differences in moisture content within earlywood and latewood cells. Overall, our results demonstrate the capabilities of hyperspectral imaging for process analytics in the modern wood industry. Graphical abstract


MAUSAM ◽  
2021 ◽  
Vol 63 (1) ◽  
pp. 17-28
Author(s):  
S. BALACHANDRAN ◽  
B. GEETHA

The Northeast monsoon season of October to December (OND) is the primary season of cyclonic activity over the North Indian Ocean (NIO). The mean number of days of cyclonic activity over NIO during this season is about 20 days. In the present study, statistical prediction for seasonal cyclonic activity over the North Indian Ocean during the cyclone season of October to December is attempted using well known climate indices and regional circulation features during the recent 30 years of 1971-2000.Potential predictors are identified using correlation analysis and optimum numbers of predictors are chosen using screening regression technique. A qualitative prediction for number of Cyclonic Disturbance (CD) days is attempted by analysing the conditional means of the number of CD days during OND over NIO for different intervals of each predictor based on the 30 year data of 1971-2000. Predictions and their validations for the subsequent test period of 2001 to 2009, based on this scheme, are discussed. An attempt for quantitative prediction is also made by developing a multiple regression model for prediction of number of CD days over the NIO during OND using the same predictors. The regression model accounts for 70% of the inter annual variance. The root mean square error of estimate is 5 days and the bias error is 0.36 days. The regression model is cross validated by Jackknife method for each individual year using the data of 29 years from the sample excluding the year under consideration. The model is also tested for independent dataset for the years 2001 to 2009. Salient features of the model performance are discussed.


2021 ◽  
Author(s):  
Hanna Clements ◽  
Autumn Flynn ◽  
Bryce Nicholls ◽  
Daria Grosheva ◽  
Todd Hyster ◽  
...  

The development of predictive tools to assess enzyme mutant performance and physical organic approaches to enzyme mechanistic interrogation are crucial to the field of biocatalysis. While many indispensable tools exist to address qualitative aspects of biocatalytic reaction design, they often require extensive experimental data sets or a priori knowledge of reaction mechanism. However, quantitative prediction of enzyme performance is lacking. Herein, we present a workflow that merges both computational and experimental data to produce statistical models that predict the performance of new substrates and enzyme mutants while also providing insight into reaction mechanism. As a validating case study, this platform was applied to investigate a non-native enantioselective photoenzymatic radical cyclization. Statistical models enabled interrogation of the reaction mechanism, and the predictive capabilities of these same models led to the quantitative prediction of the enantioselectivities of new substrates with several enzyme mutants. This platform was constructed for application to any biocatalytic system wherein mechanistic interrogation, prediction of reaction performance with new substrates, or quantitative performance of enzyme mutants would be desirable. Overall, this proof of concept study provides a new tool to complement existing protein engineering and reaction design strategies.


2021 ◽  
Author(s):  
Vladimir Uversky ◽  
Aleksandra Badaczewska-Dawid ◽  
Davit Potoyan

Abstract The liquid-liquid phase separation (LLPS) of biological macromolecules has emerged as a foundational mechanism underlying the formation of a myriad of membraneless organelles (MLOs), such as stress granules, transcription factor condensates, and chromatin compartments. A molecular grammar of sequences, which would enable a quantitative prediction and understanding of protein phase separation from first principles is currently missing. A major challenge in the field is the sparsity of bioinformatics data and the lack of computational, data-driven tools for biophysical and statistical analysis of proteins capable of phase separation. Here we present the utility of web applications framed within a novel open-source platform for BioInformatic Analysis of liquid-liquid Phase-Separating protein Sequences, https://biapss.chem.iastate.edu/. BIAPSS combines high-throughput interactive data analytics of physicochemical and evolutionary features with a comprehensive repository of bioinformatic data for on-the-fly research of the sequence-dependent properties of proteins with known LLPS behavior. To facilitate exploration of the services and provide the interpretation guideline, we present two attention-getting case studies of FUS and hnRNPDL. This should help the LLPS community uncover the nature of interactions driving the formation of membraneless organelles.


2021 ◽  
Author(s):  
Shakhawat H. Tanim ◽  
Brenton M. Wiernik ◽  
Steven Reader ◽  
Yujie Hu

We systematically review and meta-analyze quantitative prediction models for hurricane evacuation decisions. Drawing on data from 33 prediction models and 29,873 households, we estimate distributions of effects on evacuation decisions for 25 predictors. Mobile home occupancy, evacuation orders, and having an evacuation plan showed the largest positive effects on evacuation, whereas increased age and Black race showed the largest negative effects. These results highlight the importance of both social-economic-structural factors and government action, such as evacuation orders, for enabling evacuation behaviors. Moderator analyses showed that models built using real-hurricane decisions showed larger effects than models of hypothetical decisions, especially for the strongest predictors. Additionally, models in Florida had more consistent results than for other U.S. states, and models with a larger number of covariates showed smaller effect sizes than models with fewer covariates. Importantly, our study improves methodologically and inferentially over previous reviews of this literature.


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