scholarly journals Terrain coefficients for predicting energy costs of walking over snow

2021 ◽  
Author(s):  
Paul Richmond ◽  
Adam Potter ◽  
David Looney ◽  
William Santee
Keyword(s):  
Predictive Models ◽  
Metabolic Cost ◽  
Data Driven ◽  
Published Data ◽  
Energy Costs ◽  
Snow Density ◽  
Surface Conditions ◽  
Physical Elements ◽  
The Military

Predicting the energy costs of human travel over snow can be of significant value to the military and other agencies planning work efforts when snow is present. The ability to quantify, and predict, those costs can help planners determine if snow will be a factor in the execution of dismounted tasks and operations. To adjust predictive models for the effect of terrain, and more specifically for surface conditions, on energy costs, terrain coefficients (ƞ) have been developed. By applying knowledge gained from prior studies of the effects of terrain and snow, and by leveraging those existing dismounted locomotion models, we seek to outline the steps in developing an improved terrain coefficient (ƞ) for snow to be used in predictive modeling. Using published data, methods, and a well-informed understanding of the physical elements of terrain, e.g., characterization of snow sinkage (z), this study made adjustments to ƞ-values specific to snow. This review of published metabolic cost methods suggest that an improved ƞ-value could be developed for use with the Pandolf equation, where z=depth (h)*(1 - (snow density (ρ0)/1.186)) and ƞ=0.0005z3 + 0.0001z2 + 0.1072z + 1.2604. This paper provides data-driven improvements to models that are used to predict the energy costs of dismounted movements over snow.

scholarly journals CANTARE: finding and visualizing network-based multi-omic predictive models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Janet C. Siebert ◽  
Martine Saint-Cyr ◽  
Sarah J. Borengasser ◽  
Brandie D. Wagner ◽  
Catherine A. Lozupone ◽  
...  
Keyword(s):  
Random Forests ◽  
Predictive Models ◽  
Regression Models ◽  
Dynamic Range ◽  
P Value ◽  
Published Data ◽  
Logistic Regression Models ◽  
Pairwise Models ◽  
Biological Insight ◽  
Analytical Approaches

Abstract Background One goal of multi-omic studies is to identify interpretable predictive models for outcomes of interest, with analytes drawn from multiple omes. Such findings could support refined biological insight and hypothesis generation. However, standard analytical approaches are not designed to be “ome aware.” Thus, some researchers analyze data from one ome at a time, and then combine predictions across omes. Others resort to correlation studies, cataloging pairwise relationships, but lacking an obvious approach for cohesive and interpretable summaries of these catalogs. Methods We present a novel workflow for building predictive regression models from network neighborhoods in multi-omic networks. First, we generate pairwise regression models across all pairs of analytes from all omes, encoding the resulting “top table” of relationships in a network. Then, we build predictive logistic regression models using the analytes in network neighborhoods of interest. We call this method CANTARE (Consolidated Analysis of Network Topology And Regression Elements). Results We applied CANTARE to previously published data from healthy controls and patients with inflammatory bowel disease (IBD) consisting of three omes: gut microbiome, metabolomics, and microbial-derived enzymes. We identified 8 unique predictive models with AUC > 0.90. The number of predictors in these models ranged from 3 to 13. We compare the results of CANTARE to random forests and elastic-net penalized regressions, analyzing AUC, predictions, and predictors. CANTARE AUC values were competitive with those generated by random forests and  penalized regressions. The top 3 CANTARE models had a greater dynamic range of predicted probabilities than did random forests and penalized regressions (p-value = 1.35 × 10–5). CANTARE models were significantly more likely to prioritize predictors from multiple omes than were the alternatives (p-value = 0.005). We also showed that predictive models from a network based on pairwise models with an interaction term for IBD have higher AUC than predictive models built from a correlation network (p-value = 0.016). R scripts and a CANTARE User’s Guide are available at https://sourceforge.net/projects/cytomelodics/files/CANTARE/. Conclusion CANTARE offers a flexible approach for building parsimonious, interpretable multi-omic models. These models yield quantitative and directional effect sizes for predictors and support the generation of hypotheses for follow-up investigation.

scholarly journals A Data-driven Characterization of Modern Android Spyware

2020 ◽  
Vol 11 (1) ◽  
pp. 1-38
Author(s):  
Fabio Pierazzi ◽  
Ghita Mezzour ◽  
Qian Han ◽  
Michele Colajanni ◽  
V. S. Subrahmanian
Keyword(s):  
Data Driven

Development of a prototype for modelling soil–pipe interaction and its application for predicting uplift resistance to buried pipe movements in sand

2018 ◽  
Vol 55 (10) ◽  
pp. 1451-1474 ◽  
Author(s):  
Yousef Ansari ◽  
George Kouretzis ◽  
Scott W. Sloan
Keyword(s):  
Scale Effects ◽  
Reaction Force ◽  
Failure Surface ◽  
Relative Displacement ◽  
Published Data ◽  
Buried Pipe ◽  
Image Velocimetry ◽  
Close Range ◽  
Uplift Resistance

This paper presents a testing rig for measuring the reactions on rigid pipes buried in sand during episodes of relative displacement. Following a detailed presentation of the 1g prototype, the test preparation procedure, and the characterization of the test sand’s shear strength and dilation potential under the low confining stresses pertinent to the problem, the paper focuses on the workflow devised to obtain accurate measurements of friction and arching effects, and accordingly normalize them to account for scale (stress level) effects. Emphasis is put on demonstrating the effectiveness of the sand deposition method for accurately controlling the density of the sample, and on quantitatively assessing its uniformity. Measurements obtained during a series of uplift tests, including reaction force – pipe displacement curves and images of the developing failure surface, facilitated by particle image velocimetry and close-range photogrammetry techniques, are compared against published data and analytical methods. The results lead to the development of a new simplified formula for calculating the uplift resistance to buried pipe movements in sand: capable of accounting for scale effects, yet simple enough to be used for the analysis of pipes in practice.

Data-Driven Predictive Models of Diffuse Low-Grade Gliomas Under Chemotherapy

2019 ◽  
Vol 23 (1) ◽  
pp. 38-46 ◽  
Author(s):  
Meriem Ben Abdallah ◽  
Marie Blonski ◽  
Sophie Wantz-Mezieres ◽  
Yann Gaudeau ◽  
Luc Taillandier ◽  
...  
Keyword(s):  
Predictive Models ◽  
Data Driven ◽  
Low Grade ◽  
Low Grade Gliomas

On the crack characterization of reinforced concrete structures: Experimental and data-driven numerical study

Structures ◽  
2021 ◽  
Vol 30 ◽  
pp. 134-145
Author(s):  
Sandeep Das ◽  
Subhrajit Dutta ◽  
Dibyendu Adak ◽  
Shubhankar Majumdar
Keyword(s):  
Reinforced Concrete ◽  
Numerical Study ◽  
Concrete Structures ◽  
Data Driven ◽  
Reinforced Concrete Structures

Characterization of groundwater potential zones in water-scarce hardrock regions using data driven model

2021 ◽  
Vol 80 (24) ◽  
Author(s):  
Dipankar Ruidas ◽  
Subodh Chandra Pal ◽  
Abu Reza Md. Towfiqul Islam ◽  
Asish Saha
Keyword(s):  
Groundwater Potential ◽  
Data Driven ◽  
Groundwater Potential Zones ◽  
Using Data ◽  
Water Scarce

Damage Detection in Flexible Propeller Beam Structures by Exploiting Impact-Induced Coupled Acceleration Signals

2011 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Data Driven ◽  
Distributed Information ◽  
Modal Expansion ◽  
Shape Difference ◽  
Spatio Temporal ◽  
Temporal Sample ◽  
High Level ◽  
Proper Orthogonal ◽  
Acceleration Signals

A local damage at the tip of a composite propeller is diagnosed by properly comparing its impact-induced free coupled dynamics to that of a pristine wooden propeller of the same size and shape. This is accomplished by creating indirectly via collocated measurements distributed information for the coupled acceleration field of the propellers. The powerful data-driven modal expansion analysis delivered by the Proper Orthogonal Decomposition (POD) Transform reveals that ensembles of impact-induced collocated coupled experimental acceleration signals are underlined by a high level of spatio-temporal coherence. Thus they furnish a valuable spatio-temporal sample of coupled response induced by a point impulse. In view of this fact, a tri-axial sensor was placed on the propeller hub to collect collocated coupled acceleration signals induced via modal hammer nondestructive impacts and thus obtained a reduced order characterization of the coupled free dynamics. This experimental data-driven analysis reveals that the in-plane unit components of the POD modes for both propellers have similar shapes-nearly identical. For the damaged propeller this POD shape-difference is quite pronounced. The shapes of the POD modes are used to compute indices of difference reflecting directly damage. At the first POD energy level, the shape-difference indices of the damaged composite propeller are quite larger than those of the pristine wooden propeller.

Data-driven modeling and characterization of anti-angiogenic molecule effects on tumoral vascular density

2015 ◽  
Vol 20 ◽  
pp. 52-60 ◽  
Author(s):  
J.-B. Tylcz ◽  
K. El Alaoui-Lasmaili ◽  
E.-H. Djermoune ◽  
N. Thomas ◽  
B. Faivre ◽  
...  
Keyword(s):  
Data Driven ◽  
Vascular Density ◽  
Data Driven Modeling

Application of Atom Probe Microanalysis for Understanding Microstructure Evolution in Nickel Base Superalloy Welds

2000 ◽  
Vol 6 (S2) ◽  
pp. 350-351
Author(s):  
S. S. Babu ◽  
S. A. David ◽  
M. K. Miller
Keyword(s):  
Microstructure Evolution ◽  
Predictive Models ◽  
Strong Relationship ◽  
Atom Probe ◽  
Systematic Investigation ◽  
Nickel Base Superalloy ◽  
Cooling Conditions ◽  
Nickel Base ◽  
Base Superalloy

The characterization of the microstructure evolution during welding of nickel base superalloys is required for efficient reuse and reclamation of used and failed components. Previous atom probe analysis of electron-beam and laser-beam welds revealed complex alloying elemental partitioning between the γ and γ phases. Rapid cooling conditions in the weld leads to non-equilibrium partitioning and large amplitude Cr and Co levels in the γ phase. These results indicated that there is a strong relationship between weld cooling rate and the precipitation of γ′ precipitates from the γ phase. To understand and develop predictive models, a systematic investigation of the microstructure evolution in CM247DS alloy under controlled thermomechanical conditions are being performed. This paper describes some recent results on the elemental partitioning between γ and γ′ phases obtained with atom probe microanalysis.

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