Prediction of the permeability-reducing effect of cement infiltration into sandy soils

2016 ◽  
Vol 17 (3) ◽  
pp. 851-858
Author(s):  
Jinlan Ji ◽  
Guisheng Fan
Keyword(s):  
Sandy Soil ◽  
Linear Models ◽  
Prediction Models ◽  
Transfer Functions ◽  
Water Pressure ◽  
Univariate Analysis ◽  
Ground Surface ◽  
Sandy Soils ◽  
Practical Significance ◽  
Dry Density

Univariate analysis on the permeability-reducing effects of cement infiltration into sandy soil was carried out using a series of experiments on sandy soil infiltrated by adding fine cement grains. The SPSS statistical analysis software was used on these experimental data to construct multivariate prediction models on the permeability-reducing effects of cement infiltration into sandy soils. The results indicate that it is possible to predict permeability-reducing effects using transfer functions. Relatively satisfactory predictions were achieved by inputting the postponed time of water supply, soil dry density, quantity of added cement, water pressure head of cement infiltration, physical clay-silt particle content of soil, and other factors as independent variables. A comparison between the multivariate linear and non-linear models showed that the two models had similar accuracy. The multivariate linear model is relatively simple, and hence can be used to predict permeability-reducing effects. The development of the models has scientific implications for soil modification by altering soil permeability through cement infiltration. It also has practical significance in predictive research on reducing the migration of ground surface pollutants into groundwater.

scholarly journals Scoring system development and validation for prediction choledocholithiasis before open cholecystectomy

2015 ◽  
Vol 143 (11-12) ◽  
pp. 681-687 ◽  
Author(s):  
Tomislav Pejovic ◽  
Miroslav Stojadinovic
Keyword(s):  
Bile Duct ◽  
Clinical Utility ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Open Cholecystectomy ◽  
Univariate Analysis ◽  
Curve Analysis ◽  
Decision Curve Analysis ◽  
Scoring Model ◽  
Validation Set

Introduction. Accurate precholecystectomy detection of concurrent asymptomatic common bile duct stones (CBDS) is key in the clinical decision-making process. The standard preoperative methods used to diagnose these patients are often not accurate enough. Objective. The aim of the study was to develop a scoring model that would predict CBDS before open cholecystectomy. Methods. We retrospectively collected preoperative (demographic, biochemical, ultrasonographic) and intraoperative (intraoperative cholangiography) data for 313 patients at the department of General Surgery at Gornji Milanovac from 2004 to 2007. The patients were divided into a derivation (213) and a validation set (100). Univariate and multivariate regression analysis was used to determine independent predictors of CBDS. These predictors were used to develop scoring model. Various measures for the assessment of risk prediction models were determined, such as predictive ability, accuracy, the area under the receiver operating characteristic curve (AUC), calibration and clinical utility using decision curve analysis. Results. In a univariate analysis, seven risk factors displayed significant correlation with CBDS. Total bilirubin, alkaline phosphatase and bile duct dilation were identified as independent predictors of choledocholithiasis. The resultant total possible score in the derivation set ranged from 7.6 to 27.9. Scoring model shows good discriminatory ability in the derivation and validation set (AUC 94.3 and 89.9%, respectively), excellent accuracy (95.5%), satisfactory calibration in the derivation set, similar Brier scores and clinical utility in decision curve analysis. Conclusion. Developed scoring model might successfully estimate the presence of choledocholithiasis in patients planned for elective open cholecystectomy.

scholarly journals Application of Machine Learning to Include Honking Effect in Vehicular Traffic Noise Prediction

2021 ◽  
Vol 11 (13) ◽  
pp. 6030
Author(s):  
Daljeet Singh ◽  
Antonella B. Francavilla ◽  
Simona Mancini ◽  
Claudio Guarnaccia
Keyword(s):  
Machine Learning ◽  
Linear Models ◽  
Prediction Models ◽  
Road Traffic ◽  
Traffic Noise ◽  
Machine Learning Techniques ◽  
Coefficient Of Determination ◽  
Road Traffic Noise ◽  
Noise Prediction ◽  
Volume Percentage

A vehicular road traffic noise prediction methodology based on machine learning techniques has been presented. The road traffic parameters that have been considered are traffic volume, percentage of heavy vehicles, honking occurrences and the equivalent continuous sound pressure level. Leq A method to include the honking effect in the traffic noise prediction has been illustrated. The techniques that have been used for the prediction of traffic noise are decision trees, random forests, generalized linear models and artificial neural networks. The results obtained by using these methods have been compared on the basis of mean square error, correlation coefficient, coefficient of determination and accuracy. It has been observed that honking is an important parameter and contributes to the overall traffic noise, especially in congested Indian road traffic conditions. The effects of honking noise on the human health cannot be ignored and it should be included as a parameter in the future traffic noise prediction models.

scholarly journals Risk prediction in multicentre studies when there is confounding by cluster or informative cluster size

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Menelaos Pavlou ◽  
Gareth Ambler ◽  
Rumana Z. Omar
Keyword(s):  
Risk Prediction ◽  
Cluster Size ◽  
Linear Models ◽  
Prediction Models ◽  
Predictive Accuracy ◽  
Clustered Data ◽  
Predictor Variable ◽  
Simulated Data ◽  
Predictive Ability ◽  
Informative Cluster Size

Abstract Background Clustered data arise in research when patients are clustered within larger units. Generalised Estimating Equations (GEE) and Generalised Linear Models (GLMM) can be used to provide marginal and cluster-specific inference and predictions, respectively. Methods Confounding by Cluster (CBC) and Informative cluster size (ICS) are two complications that may arise when modelling clustered data. CBC can arise when the distribution of a predictor variable (termed ‘exposure’), varies between clusters causing confounding of the exposure-outcome relationship. ICS means that the cluster size conditional on covariates is not independent of the outcome. In both situations, standard GEE and GLMM may provide biased or misleading inference, and modifications have been proposed. However, both CBC and ICS are routinely overlooked in the context of risk prediction, and their impact on the predictive ability of the models has been little explored. We study the effect of CBC and ICS on the predictive ability of risk models for binary outcomes when GEE and GLMM are used. We examine whether two simple approaches to handle CBC and ICS, which involve adjusting for the cluster mean of the exposure and the cluster size, respectively, can improve the accuracy of predictions. Results Both CBC and ICS can be viewed as violations of the assumptions in the standard GLMM; the random effects are correlated with exposure for CBC and cluster size for ICS. Based on these principles, we simulated data subject to CBC/ICS. The simulation studies suggested that the predictive ability of models derived from using standard GLMM and GEE ignoring CBC/ICS was affected. Marginal predictions were found to be mis-calibrated. Adjusting for the cluster-mean of the exposure or the cluster size improved calibration, discrimination and the overall predictive accuracy of marginal predictions, by explaining part of the between cluster variability. The presence of CBC/ICS did not affect the accuracy of conditional predictions. We illustrate these concepts using real data from a multicentre study with potential CBC. Conclusion Ignoring CBC and ICS when developing prediction models for clustered data can affect the accuracy of marginal predictions. Adjusting for the cluster mean of the exposure or the cluster size can improve the predictive accuracy of marginal predictions.

scholarly journals Role of Biochar in Improving Sandy Soil Water Retention and Resilience to Drought

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 407
Author(s):  
Ling Li ◽  
Yong-Jiang Zhang ◽  
Abigayl Novak ◽  
Yingchao Yang ◽  
Jinwu Wang
Keyword(s):  
Climate Variability ◽  
Sandy Soil ◽  
Water Retention ◽  
Sandy Soils ◽  
Crop Productivity ◽  
Future Research ◽  
Soil Water Retention ◽  
The World ◽  
Water Holding

In recent years, plants in sandy soils have been impacted by increased climate variability due to weak water holding and temperature buffering capacities of the parent material. The projected impact spreads all over the world, including New England, USA. Many regions of the world may experience an increase in frequency and severity of drought, which can be attributed to an increased variability in precipitation and enhanced water loss due to warming. The overall benefits of biochar in environmental management have been extensively investigated. This review aims to discuss the water holding capacity of biochar from the points of view of fluid mechanics and propose several prioritized future research topics. To understand the impacts of biochar on sandy soils in-depth, sandy soil properties (surface area, pore size, water properties, and characteristics) and how biochar could improve the soil quality as well as plant growth, development, and yield are reviewed. Incorporating biochar into sandy soils could result in a net increase in the surface area, a stronger hydrophobicity at a lower temperature, and an increase in the micropores to maximize gap spaces. The capability of biochar in reducing fertilizer drainage through increasing water retention can improve crop productivity and reduce the nutrient leaching rate in agricultural practices. To advance research in biochar products and address the impacts of increasing climate variability, future research may focus on the role of biochar in enhancing soil water retention, plant water use efficiency, crop resistance to drought, and crop productivity.

Performance of the Surprise Question Compared to Prediction Models in Hemodialysis Patients: A Prospective Study

2017 ◽  
Vol 46 (5) ◽  
pp. 390-396 ◽  
Author(s):  
Rakesh Malhotra ◽  
Xia Tao ◽  
Yuedong Wang ◽  
Yuqi Chen ◽  
Rebecca H. Apruzzese ◽  
...  
Keyword(s):  
Linear Models ◽  
Prediction Models ◽  
Clinical Laboratory ◽  
Mortality Prediction ◽  
Operating Characteristics ◽  
Dialysis Treatment ◽  
Mortality Probability ◽  
A Prospective Study ◽  
Surprise Question

Background: The surprise question (SQ) (“Would you be surprised if this patient were still alive in 6 or 12 months?”) is used as a mortality prognostication tool in hemodialysis (HD) patients. We compared the performance of the SQ with that of prediction models (PMs) for 6- and 12-month mortality prediction. Methods: Demographic, clinical, laboratory, and dialysis treatment indicators were used to model 6- and 12-month mortality probability in a HD patients training cohort (n = 6,633) using generalized linear models (GLMs). A total of 10 nephrologists from 5 HD clinics responded to the SQ in 215 patients followed prospectively for 12 months. The performance of PM was evaluated in the validation (n = 6,634) and SQ cohorts (n = 215) using the areas under receiver operating characteristics curves. We compared sensitivities and specificities of PM and SQ. Results: The PM and SQ cohorts comprised 13,267 (mean age 61 years, 55% men, 54% whites) and 215 (mean age 62 years, 59% men, 50% whites) patients, respectively. During the 12-month follow-up, 1,313 patients died in the prediction model cohort and 22 in the SQ cohort. For 6-month mortality prediction, the GLM had areas under the curve of 0.77 in the validation cohort and 0.77 in the SQ cohort. As for 12-month mortality, areas under the curve were 0.77 and 0.80 in the validation and SQ cohorts, respectively. The 6- and 12-month PMs had sensitivities of 0.62 (95% CI 0.35–0.88) and 0.75 (95% CI 0.56–0.94), respectively. The 6- and 12-month SQ sensitivities were 0.23 (95% CI 0.002–0.46) and 0.35 (95% CI 0.14–0.56), respectively. Conclusion: PMs exhibit superior sensitivity compared to the SQ for mortality prognostication in HD patients.

The Analysis of Stress Distribution of Hammer Bottom during Dynamic Compaction on Sandy Soil Foundation

2011 ◽  
Vol 250-253 ◽  
pp. 1460-1463
Author(s):  
Jian Qi Wu ◽  
Jian Hong Deng ◽  
Xiao Ping Wang
Keyword(s):  
Comparative Analysis ◽  
Stress Distribution ◽  
Sandy Soil ◽  
Field Monitoring ◽  
Dynamic Compaction ◽  
Dry Density ◽  
Test Results ◽  
Red Sandstone ◽  
Soil Foundation

Obtained stress distribution of hammer bottom according to the analysis of horizontal and vertical red sandstone fill dry density of the hammer bottom after dynamic compaction; affirmed the stress distribution situation of the hammer bottom through comparative analysis of the test results by laboratory and field monitoring.

LABORATORY FOOTING TEST ON PARTIALLY SATURATED SANDY SOIL

2016 ◽  
Vol 78 (5-5) ◽  
Author(s):  
J. D. Nyuin ◽  
M. J. Md Noor ◽  
Y. Ashaari ◽  
C. Petrus ◽  
A. Albar
Keyword(s):  
Sandy Soil ◽  
Ground Surface ◽  
Matric Suction ◽  
Shallow Foundation ◽  
Analysis And Design ◽  
Test Tank ◽  
Partially Saturated ◽  
Saturated Condition ◽  
Conventional Analysis ◽  
The Relationship

Conventional analysis and design of shallow foundation are based on the assumption that the soil is under fully saturated condition. However, shallow foundations are typically constructed near the ground surface where the soil is under partially saturated condition. Therefore, more research to investigate the behaviour of shallow foundation in unsaturated soil is very essential in order to aid engineers in making good analysis and design. This paper presents a series of laboratory footing tests conducted on unsaturated sandy soil. A specially designed test tank was fabricated for the test. Square footings of two different sizes (100 mm x 100 mm and 150 mm x150 mm) were used and loaded on Rawang sand which has residual suction value of 10 kPa. The measured values of matric suction of the soil in the test tank were in the range of 0 to 30 kPa. Based on the results, it was observed that bearing capacities of shallow foundation under fully saturated condition were the lowest compared to soil under unsaturated conditions. The highest values were measured at matric suction equals to residual suction (i.e 10 kPa). Furthermore, the relationship between the bearing capacities of shallow foundation with the matric suction was observed to be non-linear.    

scholarly journals Short-term velocity variations at three rock glaciers and their relationship with meteorological conditions

2016 ◽  
Vol 4 (1) ◽  
pp. 103-123 ◽  
Author(s):  
V. Wirz ◽  
S. Gruber ◽  
R. S. Purves ◽  
J. Beutel ◽  
I. Gärtner-Roer ◽  
...  
Keyword(s):  
Pore Water Pressure ◽  
Water Pressure ◽  
Ground Surface ◽  
Climatic Conditions ◽  
Water Infiltration ◽  
High Temporal Resolution ◽  
Rock Glacier ◽  
Short Term ◽  
Rock Glaciers ◽  
Velocity Variations

Abstract. In recent years, strong variations in the speed of rock glaciers have been detected, raising questions about their stability under changing climatic conditions. In this study, we present continuous time series of surface velocities over 3 years of six GPS stations located on three rock glaciers in Switzerland. Intra-annual velocity variations are analysed in relation to local meteorological factors, such as precipitation, snow(melt), and air and ground surface temperatures. The main focus of this study lies on the abrupt velocity peaks, which have been detected at two steep and fast-moving rock glacier tongues ( ≥  5 m a−1), and relationships to external meteorological forcing are statistically tested.The continuous measurements with high temporal resolution allowed us to detect short-term velocity peaks, which occur outside cold winter conditions, at these two rock glacier tongues. Our measurements further revealed that all rock glaciers experience clear intra-annual variations in movement in which the timing and the amplitude is reasonably similar in individual years. The seasonal decrease in velocity was typically smooth, starting 1–3 months after the seasonal decrease in temperatures, and was stronger in years with colder temperatures in mid winter. Seasonal acceleration was mostly abrupt and rapid compared to the winter deceleration, always starting during the zero curtain period. We found a statistically significant relationship between the occurrence of short-term velocity peaks and water input from heavy precipitation or snowmelt, while no velocity peak could be attributed solely to high temperatures. The findings of this study further suggest that, in addition to the short-term velocity peaks, the seasonal acceleration is also influenced by water infiltration, causing thermal advection and an increase in pore water pressure. In contrast, the amount of deceleration in winter seems to be mainly controlled by winter temperatures.

scholarly journals THE EFFECT GRAIN-SIZE COMPOSITION ON THERMAL CONDUCTIVITY OF SANDY SOILS

2020 ◽  
Vol 11 (2) ◽  
pp. 19-27
Author(s):  
A. V Zakharov ◽  
S. E Makhover
Keyword(s):  
Thermal Conductivity ◽  
Grain Size ◽  
Sandy Soil ◽  
Alternative Energy ◽  
Radiant Energy ◽  
Sandy Soils ◽  
Size Composition ◽  
Experiment Planning ◽  
Pore Filling ◽  
Grain Size Composition

Today the issue of energy saving is acute. The main sources of energy are radiant energy of the Sun, wind energy, energy of moving water. Therefore, the issue of solving alternative energy sources is relevant. The article aims to solve the problem by using low-potential heat of the soil mass by means of energy-efficient building constructions - foundations. It is necessary to know the thermal characteristics of soils for this. At the moment, methods for determining the thermophysical properties of inert materials with subsequent practical application in the field of construction have been widely studied, but no one of these methods takes into account the grain-size composition. Thus, the study of the connection between the thermal conductivity and the grain-size composition of the soil is important. The aim of the work is to Estimation of thermal conductivity of sandy soils based on grain-size composition. This article presents an analysis of the dependence of the thermal conductivity of the sandy soil of its grain-size composition. The matrix of experiment planning is made; the methodology and technological sequence of the experiment were tested. Statistical processing of the obtained experimental data was carried out. Based on a series of test experiments, it was concluded that there are two factors competing in its thermal conductivity: an increase in λ due to an increase in the degree of pore filling and a decrease in total heat conductivity due to a decrease in the degree of pore filling. These results suggest that grain-size composition has an impact on the thermal conductivity of the sandy soil. During the experiment, the dependence of the thermal conductivity of sandy soils on their grain-size composition was experimentally established.

scholarly journals Applicability of an Automated Model and Parameter Selection in the Prediction of Screening-Level PTSD in Danish Soldiers Following Deployment: Development Study of Transferable Predictive Models Using Automated Machine Learning (Preprint)

2019 ◽  
Author(s):  
Karen-Inge Karstoft ◽  
Ioannis Tsamardinos ◽  
Kasper Eskelund ◽  
Søren Bo Andersen ◽  
Lars Ravnborg Nissen
Keyword(s):  
Machine Learning ◽  
Operating Characteristic ◽  
Linear Models ◽  
Prediction Models ◽  
Characteristic Curve ◽  
Ptsd Symptoms ◽  
Forest Models ◽  
Random Forest Models ◽  
Automated Machine Learning ◽  
Military Rank

BACKGROUND Posttraumatic stress disorder (PTSD) is a relatively common consequence of deployment to war zones. Early postdeployment screening with the aim of identifying those at risk for PTSD in the years following deployment will help deliver interventions to those in need but have so far proved unsuccessful. OBJECTIVE This study aimed to test the applicability of automated model selection and the ability of automated machine learning prediction models to transfer across cohorts and predict screening-level PTSD 2.5 years and 6.5 years after deployment. METHODS Automated machine learning was applied to data routinely collected 6-8 months after return from deployment from 3 different cohorts of Danish soldiers deployed to Afghanistan in 2009 (cohort 1, N=287 or N=261 depending on the timing of the outcome assessment), 2010 (cohort 2, N=352), and 2013 (cohort 3, N=232). RESULTS Models transferred well between cohorts. For screening-level PTSD 2.5 and 6.5 years after deployment, random forest models provided the highest accuracy as measured by area under the receiver operating characteristic curve (AUC): 2.5 years, AUC=0.77, 95% CI 0.71-0.83; 6.5 years, AUC=0.78, 95% CI 0.73-0.83. Linear models performed equally well. Military rank, hyperarousal symptoms, and total level of PTSD symptoms were highly predictive. CONCLUSIONS Automated machine learning provided validated models that can be readily implemented in future deployment cohorts in the Danish Defense with the aim of targeting postdeployment support interventions to those at highest risk for developing PTSD, provided the cohorts are deployed on similar missions.

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