Water Release as a Soil Property Relating to Use of Water by Plants

1968 ◽  
Vol 11 (1) ◽  
pp. 0074-0075 ◽  
Author(s):  
Sterling J. Richards
Keyword(s):  
Soil Property ◽  
Water Release

Applying the Method of Lagrange Multipliers to Derive an Estimator for Unsampled Soil Properties

2014 ◽  
Vol 11 (1) ◽  
pp. 15
Author(s):  
Set Foong Ng ◽  
Pei Eng Ch’ng ◽  
Yee Ming Chew ◽  
Kok Shien Ng
Keyword(s):  
Soil Properties ◽  
Lagrange Multipliers ◽  
Unbiased Estimator ◽  
Soil Property ◽  
Minimum Variance ◽  
Distance Method ◽  
True Value ◽  
A Site ◽  
Method Of Lagrange Multipliers ◽  
Inverse Distance

Soil properties are very crucial for civil engineers to differentiate one type of soil from another and to predict its mechanical behavior. However, it is not practical to measure soil properties at all the locations at a site. In this paper, an estimator is derived to estimate the unknown values for soil properties from locations where soil samples were not collected. The estimator is obtained by combining the concept of the ‘Inverse Distance Method’ into the technique of ‘Kriging’. The method of Lagrange Multipliers is applied in this paper. It is shown that the estimator derived in this paper is an unbiased estimator. The partiality of the estimator with respect to the true value is zero. Hence, the estimated value will be equal to the true value of the soil property. It is also shown that the variance between the estimator and the soil property is minimised. Hence, the distribution of this unbiased estimator with minimum variance spreads the least from the true value. With this characteristic of minimum variance unbiased estimator, a high accuracy estimation of soil property could be obtained.

RELEASE OF NUTRIENTS FROM BOTTOM SEDIMENTS IN OSAKA BAY, JAPAN IN 2015

2017 ◽  
Author(s):  
Yukio Komai ◽  
Yukio Komai ◽  
Mana Sakata ◽  
Mana Sakata ◽  
Masaki Nakajima ◽  
...  
Keyword(s):  
Bottom Sediment ◽  
Bottom Sediments ◽  
Sea Water ◽  
Industrial Effluents ◽  
Ammonium Nitrogen ◽  
Water Release ◽  
Osaka Bay ◽  
The Core ◽  
Similar Range ◽  
Sea Area

Osaka Bay is the most polluted enclosed sea area, in which is located the eastern part of the Seto Inland Sea, Japan. There are four kinds of sources on loadings of nutrients to Osaka Bay, which are land including rivers and industrial effluents beside coast, ocean sea water, release from bottom sediment to sea water, and wet and dry deposition from air. The pollutant loadings inflowing from the land to Osaka Bay have been cut by various policies since 1970’s. The concentrations of nutrients in the inner part of Osaka Bay have showed an obvious decreasing tendency. However, the water quality in offshore sea has not satisfied the environmental standard on nutrients. We investigated the amount of nutrients released from bottom sediments. The core samples were taken at two stations in the inner part of Osaka Bay once a month from February to November, 2015. The core incubation experiment in laboratory was conducted for 24 hours according to Tada et.al. The concentrations of ammonium nitrogen (NH4-N) and phosphate phosphorus (PO4-P) were measured by an automatic analyzer. The flux showed similar range with the values investigated in 1986. The results suggested that the flux of nutrients from bottom sediments in the inner part of Osaka Bay has not decreased during summer season at least since 1985. Therefore, the contribution of release from bottom sediment on the nutrients budget would relatively become larger in inner part of Osaka Bay.

scholarly journals Calculation Model for the Mixing Amount of Internal Curing Materials in High-strength Concrete based on Modified MULTIMOORA

2020 ◽  
Vol 27 (1) ◽  
pp. 455-463
Author(s):  
Lixia Guo ◽  
Minghua Wang ◽  
Ling Zhong ◽  
Yanan Zhang
Keyword(s):  
High Strength Concrete ◽  
High Strength ◽  
Calculation Model ◽  
Internal Curing ◽  
Total Water ◽  
Water Release ◽  
Strength Concrete ◽  
Numerical Example ◽  
Comprehensive Performance ◽  
Mixing Method

AbstractThe internal curing technology has been widely applied to high-strength concrete, for it can make the high-strength concrete marked by low shrinkage and durable frost resistance. The key to its extension and application lies in the reasonable mixing amount of internal curing materials. To address this problem, scholars have proposed a method for determining the water demand in internal curing; however, the water release of internal curing materials is difficult to obtain by measurement due to the mixing method. Therefore, this paper proposed a calculation model for the mixing amount of internal curing materials based on the modified MULTIMOORA method (Multi-Objective Optimization on the basis of Ratio Analysis plus full multiplicative form). First, different internal curing materials (super absorbent polymer (SAP), lightweight aggregate (LWA)) and pretreatment methods were selected to calculate their compressive strength, self-shrinkage and frost durability according to a proposed test scheme on the mixing amount of internal curing materials, and in such case, the comprehensive performance evaluation of the above indexes was turned into a multi-attribute decision-making problem. Second, the ordered weighted averaging (OWA) method and the entropy weight method were used to determine the subjective and objective weights of the indexes respectively, to eliminate the impact of outliers in the subjective evaluation values. Finally, the comprehensive performance of each test group was sorted using MULTIMOORA, and based on the sorting results and the calculation model, the mixing amount of internal curing materials was determined. The numerical example application results showed that the mixing amount of SAP curing material calculated based on the model herein was 1.276 kg/m3, and the mixing method adopted the pre-water absorption method with the total water-binder ratio unchanged. The numerical example evaluation results were in good agreement with the test results. The internal curing effect of SAP was better than that of LWA, and reached the best when the mixing amount was calculated at 25 times the water release rate and the requirement for the maximum total water diversion was met. The study may provide new ideas for extension and application of the internal curing technology.

scholarly journals Selenium in wastewater can be adsorbed by modified natural zeolite and reused in vegetable growth

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110198
Author(s):  
Xiao Zhang ◽  
Xinyuan Li ◽  
Zihao Jin ◽  
Sadam Hussain Tumrani ◽  
Xiaodong Ji
Keyword(s):  
Aqueous Solution ◽  
Ph Value ◽  
Soil Improvement ◽  
Pollutant Removal ◽  
Maximum Adsorption Capacity ◽  
Selenium Content ◽  
Water Release ◽  
Soil Release ◽  
Release Ratio ◽  
Water Pollutant

Modified natural zeolites (MNZ) are widely used in pollutant removal, but how to address these MNZ that have adsorbed pollutants must be considered. Selenium is an essential trace element for metabolism and is also a water pollutant. Selenium is adsorbed in the water by MNZ in this study first. Then the Brassica chinensis L. was planted in the soil which contains the MNZ loaded with selenium (MNZ-Se) to explore selenium uptake. MNZ-Se release tests in water and soil were also considered. The results showed the following: (1) The maximum adsorption capacity of MNZ for selenium is 46.90 mg/g. (2) Water release experiments of MNZ-Se showed that regardless of how the pH of the aqueous solution changes, the trend of the release of selenium from MNZ-Se in aqueous solution is not affected and first decreases before stabilizing. (3) Soil release experiments of MNZ-Se showed that the selenium content in the soil increased and reached the concentration in the standard of selenium-rich soil. Addition amount and soil pH value will affect the release ratio. The release ratio of MNZ-Se in the water was higher than that in the soil. (4) With an increase in the soil MNZ-Se content, the selenium content in the soil and B. c increases. Above all, MZN can be a good medium for water pollutant removal and soil improvement.

scholarly journals Acetan and Acetan-Like Polysaccharides: Genetics, Biosynthesis, Structure, and Viscoelasticity

Polymers ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 815
Author(s):  
Janja Trček ◽  
Iztok Dogsa ◽  
Tomaž Accetto ◽  
David Stopar
Keyword(s):  
Bacterial Species ◽  
Acetic Acid Bacteria ◽  
Original Description ◽  
Bioinformatic Analysis ◽  
Industrial Sector ◽  
Water Soluble ◽  
Extracellular Polysaccharides ◽  
Genetic Cluster ◽  
Water Release ◽  
Komagataeibacter Xylinus

Bacteria produce a variety of multifunctional polysaccharides, including structural, intracellular, and extracellular polysaccharides. They are attractive for the industrial sector due to their natural origin, sustainability, biodegradability, low toxicity, stability, unique viscoelastic properties, stable cost, and supply. When incorporated into different matrices, they may control emulsification, stabilization, crystallization, water release, and encapsulation. Acetan is an important extracellular water-soluble polysaccharide produced mainly by bacterial species of the genera Komagataeibacter and Acetobacter. Since its original description in Komagataeibacter xylinus, acetan-like polysaccharides have also been described in other species of acetic acid bacteria. Our knowledge on chemical composition of different acetan-like polysaccharides, their viscoelasticity, and the genetic basis for their production has expanded during the last years. Here, we review data on acetan biosynthesis, its molecular structure, genetic organization, and mechanical properties. In addition, we have performed an extended bioinformatic analysis on acetan-like polysaccharide genetic clusters in the genomes of Komagataeibacter and Acetobacter species. The analysis revealed for the first time a second acetan-like polysaccharide genetic cluster, that is widespread in both genera. All species of the Komagataeibacter possess at least one acetan genetic cluster, while it is present in only one third of the Acetobacter species surveyed.

scholarly journals Dealing with multiple experts and non-stationarity in inverse reinforcement learning: an application to real-life problems

2021 ◽  
Author(s):  
Amarildo Likmeta ◽  
Alberto Maria Metelli ◽  
Giorgia Ramponi ◽  
Andrea Tirinzoni ◽  
Matteo Giuliani ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Real World ◽  
Real Life ◽  
User Preferences ◽  
Inverse Reinforcement Learning ◽  
Water Release ◽  
Reward Function ◽  
Model Free ◽  
Conflicting Objectives ◽  
Multiple Experts

AbstractIn real-world applications, inferring the intentions of expert agents (e.g., human operators) can be fundamental to understand how possibly conflicting objectives are managed, helping to interpret the demonstrated behavior. In this paper, we discuss how inverse reinforcement learning (IRL) can be employed to retrieve the reward function implicitly optimized by expert agents acting in real applications. Scaling IRL to real-world cases has proved challenging as typically only a fixed dataset of demonstrations is available and further interactions with the environment are not allowed. For this reason, we resort to a class of truly batch model-free IRL algorithms and we present three application scenarios: (1) the high-level decision-making problem in the highway driving scenario, and (2) inferring the user preferences in a social network (Twitter), and (3) the management of the water release in the Como Lake. For each of these scenarios, we provide formalization, experiments and a discussion to interpret the obtained results.

Biochar-compost addition benefits Phragmites australis growth and soil property in coastal wetlands

2021 ◽  
Vol 769 ◽  
pp. 145166
Author(s):  
Jin-Feng Liang ◽  
Qian-Wei Li ◽  
Jun-Qin Gao ◽  
Jiu-Ge Feng ◽  
Xiao-Ya Zhang ◽  
...  
Keyword(s):  
Phragmites Australis ◽  
Coastal Wetlands ◽  
Soil Property

Framework of Reliability-Based Stochastic Mobility Map for Next Generation NATO Reference Mobility Model

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
K. K. Choi ◽  
Paramsothy Jayakumar ◽  
Matthew Funk ◽  
Nicholas Gaul ◽  
Tamer M. Wasfy
Keyword(s):  
Normal Distribution ◽  
Soil Property ◽  
Region Of Interest ◽  
Mobility Model ◽  
Maximum Speed ◽  
Next Generation ◽  
Input Parameters ◽  
Terrain Elevation ◽  
Dynamic Kriging ◽  
Geotechnical Databases

A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the next generation NATO reference mobility model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modeling and simulation capabilities. The framework is for carrying out uncertainty quantification (UQ) and reliability assessment for Speed Made Good and GO/NOGO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction, and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters that will cover the regions of interest are first identified. Within these ranges of input parameters, a dynamic kriging (DKG) surrogate model is obtained for the maximum speed of the nevada automotive test center (NATC) wheeled vehicle platform complex terramechanics model. Finally, inverse reliability analysis using Monte Carlo simulation is carried out to generate the reliability-based stochastic mobility maps for Speed Made Good and GO/NOGO decisions. It is found that the deterministic map of the region of interest has probability of only 25% to achieve the indicated speed.

scholarly journals Research trends of microplastics in the soil environment: Comprehensive screening of effects

2021 ◽  
Author(s):  
Shin Woong Kim ◽  
Matthias C. Rillig
Keyword(s):  
Soil Properties ◽  
Additional Data ◽  
Soil Property ◽  
Dry Weight ◽  
Current Data ◽  
Soil Biota ◽  
Soil Parameters ◽  
Soil Environment ◽  
Soil System ◽  
Sensitivity Distribution

AbstractWe collated and synthesized previous studies that reported the impacts of microplastics on soil parameters. The data were classified and integrated to screen for the proportion of significant effects, then we suggest several directions to alleviate the current data limitation in future experiments. We compiled 106 datasets capturing significant effects, which were analyzed in detail. We found that polyethylene and pellets (or powders) were the most frequently used microplastic composition and shape for soil experiments. The significant effects mainly occurred in broad size ranges (0.1–1 mm) at test concentrations of 0.1%–10% based on soil dry weight. Polyvinyl chloride and film induced significant effects at lower concentrations compared to other compositions and shapes, respectively. We adopted a species sensitivity distribution (SSD) and soil property effect distribution (SPED) method using available data from soil biota, and for soil properties and enzymes deemed relevant for microplastic management. The predicted-no-effect-concentration (PNEC)-like values needed to protect 95% of soil biota and soil properties was estimated to be between 520 and 655 mg kg−1. This study was the first to screen microplastic levels with a view toward protecting the soil system. Our results should be regularly updated (e.g., quarterly) with additional data as they become available.

scholarly journals A Comparative Study of Various Methods of Handling Missing Data in UNSODA

Agriculture ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 727
Author(s):  
Yingpeng Fu ◽  
Hongjian Liao ◽  
Longlong Lv
Keyword(s):  
Missing Data ◽  
Missing Values ◽  
Soil Property ◽  
Particle Density ◽  
Organic Matter Content ◽  
Nonparametric Tests ◽  
Matter Content ◽  
Support Vector ◽  
Soil Database ◽  
Property Data

UNSODA, a free international soil database, is very popular and has been used in many fields. However, missing soil property data have limited the utility of this dataset, especially for data-driven models. Here, three machine learning-based methods, i.e., random forest (RF) regression, support vector (SVR) regression, and artificial neural network (ANN) regression, and two statistics-based methods, i.e., mean and multiple imputation (MI), were used to impute the missing soil property data, including pH, saturated hydraulic conductivity (SHC), organic matter content (OMC), porosity (PO), and particle density (PD). The missing upper depths (DU) and lower depths (DL) for the sampling locations were also imputed. Before imputing the missing values in UNSODA, a missing value simulation was performed and evaluated quantitatively. Next, nonparametric tests and multiple linear regression were performed to qualitatively evaluate the reliability of these five imputation methods. Results showed that RMSEs and MAEs of all features fluctuated within acceptable ranges. RF imputation and MI presented the lowest RMSEs and MAEs; both methods are good at explaining the variability of data. The standard error, coefficient of variance, and standard deviation decreased significantly after imputation, and there were no significant differences before and after imputation. Together, DU, pH, SHC, OMC, PO, and PD explained 91.0%, 63.9%, 88.5%, 59.4%, and 90.2% of the variation in BD using RF, SVR, ANN, mean, and MI, respectively; and this value was 99.8% when missing values were discarded. This study suggests that the RF and MI methods may be better for imputing the missing data in UNSODA.

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