Predicting blast-induced pull using regression tree

2022 ◽  
Vol 15 (2) ◽  
Author(s):  
Aditya Rana ◽  
Narayan Kumar Bhagat ◽  
Atul Singh ◽  
Pradeep Kumar Singh
Keyword(s):  
Regression Tree

Drivers of diversity gradients of a highly mobile marine assemblage in a mesoscale seascape

2020 ◽  
Vol 638 ◽  
pp. 149-164
Author(s):  
GM Svendsen ◽  
M Ocampo Reinaldo ◽  
MA Romero ◽  
G Williams ◽  
A Magurran ◽  
...  
Keyword(s):  
Environmental Gradients ◽  
Regression Tree ◽  
Demersal Fish ◽  
Open Ocean ◽  
Shared Resources ◽  
Thermal Front ◽  
Boosted Regression Tree ◽  
Diversity Gradients ◽  
Α Diversity ◽  
Bottom Depth

With the unprecedented rate of biodiversity change in the world today, understanding how diversity gradients are maintained at mesoscales is a key challenge. Drawing on information provided by 3 comprehensive fishery surveys (conducted in different years but in the same season and with the same sampling design), we used boosted regression tree (BRT) models in order to relate spatial patterns of α-diversity in a demersal fish assemblage to environmental variables in the San Matias Gulf (Patagonia, Argentina). We found that, over a 4 yr period, persistent diversity gradients of species richness and probability of an interspecific encounter (PIE) were shaped by 3 main environmental gradients: bottom depth, connectivity with the open ocean, and proximity to a thermal front. The 2 main patterns we observed were: a monotonic increase in PIE with proximity to fronts, which had a stronger effect at greater depths; and an increase in PIE when closer to the open ocean (a ‘bay effect’ pattern). The originality of this work resides on the identification of high-resolution gradients in local, demersal assemblages driven by static and dynamic environmental gradients in a mesoscale seascape. The maintenance of environmental gradients, specifically those associated with shared resources and connectivity with an open system, may be key to understanding community stability.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

GCS-Pupil Score Has a Stronger Association with Mortality and Poor Functional Outcome than GCS Alone in Pediatric Severe Traumatic Brain Injury

2021 ◽  
pp. 1-8
Author(s):  
Binod Balakrishnan ◽  
Heather VanDongen-Trimmer ◽  
Irene Kim ◽  
Sheila J. Hanson ◽  
Liyun Zhang ◽  
...  
Keyword(s):  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Functional Outcome ◽  
Cerebral Performance Category ◽  
Regression Tree ◽  
Classification And Regression Tree ◽  
Continuous Variables ◽  
Poor Functional Outcome ◽  
Severity Scores ◽  
Pupil Reactivity

<b><i>Background:</i></b> The Glasgow Coma Scale (GCS), used to classify the severity of traumatic brain injury (TBI), is associated with mortality and functional outcomes. However, GCS can be affected by sedation and neuromuscular blockade. GCS-Pupil (GCS-P) score, calculated as GCS minus Pupil Reactivity Score (PRS), was shown to better predict outcomes in a retrospective cohort of adult TBI patients. We evaluated the applicability of GCS-P to a large retrospective pediatric severe TBI (sTBI) cohort. <b><i>Methods:</i></b> Admissions to pediatric intensive care units in the Virtual Pediatric Systems (VPS, LLC) database from 2010 to 2015 with sTBI were included. We collected GCS, PRS (number of nonreactive pupils), cardiac arrest, abusive head trauma status, illness severity scores, pediatric cerebral performance category (PCPC) score, and mortality. GCS-P was calculated as GCS minus PRS. χ<sup>2</sup> or Fisher’s exact test and Mann-Whitney U test compared categorical and continuous variables, respectively. Classification and regression tree analysis identified thresholds of GCS-P and GCS along with other independent factors which were further examined using multivariable regression analysis to identify factors independently associated with mortality and unfavorable PCPC at PICU discharge. <b><i>Results:</i></b> Among the 2,682 patients included in the study, mortality was 23%, increasing from 4.7% for PRS = 0 to 80% for PRS = 2. GCS-P identified more severely injured patients with GCS-P scores 1 and 2 who had worse outcomes. GCS-P ≤ 2 had higher odds for mortality, OR = 68.4 (95% CI = 50.6–92.4) and unfavorable PCPC, OR = 17.3 (8.1, 37.0) compared to GCS ≤ 5. GCS-P ≤ 2 also had higher specificity and positive predictive value for both mortality and unfavorable PCPC compared to GCS ≤ 5. <b><i>Conclusions:</i></b> GCS-P, by incorporating pupil reactivity to GCS scoring, is more strongly associated with mortality and poor functional outcome at PICU discharge in children with sTBI.

scholarly journals Teasing Apart Silvopasture System Components Using Machine Learning for Optimization

Soil Systems ◽  
2021 ◽  
Vol 5 (3) ◽  
pp. 41
Author(s):  
Tulsi P. Kharel ◽  
Amanda J. Ashworth ◽  
Phillip R. Owens ◽  
Dirk Philipp ◽  
Andrew L. Thomas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Linear Regression ◽  
Regression Tree ◽  
Plant Production ◽  
System Level ◽  
Learning Approaches ◽  
Nutrient Distribution ◽  
Wetness Index ◽  
Variable Approach ◽  
Animal Grazing

Silvopasture systems combine tree and livestock production to minimize market risk and enhance ecological services. Our objective was to explore and develop a method for identifying driving factors linked to productivity in a silvopastoral system using machine learning. A multi-variable approach was used to detect factors that affect system-level output (i.e., plant production (tree and forage), soil factors, and animal response based on grazing preference). Variables from a three-year (2017–2019) grazing study, including forage, tree, soil, and terrain attribute parameters, were analyzed. Hierarchical variable clustering and random forest model selected 10 important variables for each of four major clusters. A stepwise multiple linear regression and regression tree approach was used to predict cattle grazing hours per animal unit (h ha−1 AU−1) using 40 variables (10 per cluster) selected from 130 total variables. Overall, the variable ranking method selected more weighted variables for systems-level analysis. The regression tree performed better than stepwise linear regression for interpreting factor-level effects on animal grazing preference. Cattle were more likely to graze forage on soils with Cd levels <0.04 mg kg−1 (126% greater grazing hours per AU), soil Cr <0.098 mg kg−1 (108%), and a SAGA wetness index of <2.7 (57%). Cattle also preferred grazing (88%) native grasses compared to orchardgrass (Dactylis glomerata L.). The result shows water flow within the landscape position (wetness index), and associated metals distribution may be used as an indicator of animal grazing preference. Overall, soil nutrient distribution patterns drove grazing response, although animal grazing preference was also influenced by aboveground (forage and tree), soil, and landscape attributes. Machine learning approaches helped explain pasture use and overall drivers of grazing preference in a multifunctional system.

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