Top PDF Means and method for soil testing

Means and method for soil testing

Means and method for soil testing

Means and method for soil testing Abstract An inexpensive device which is easily operated to accurately measure the Coulomb parameters of the soil. The Coulomb parameters are used in Coulomb's equation to calculate the shearing stresses along a failure surface of the soil. The device includes an instrument to test soil shear strength to which several weights have been added. To obtain the Coulomb parameters, the instrument is placed on the soil to be tested and weights are incrementally added to it. The instrument is rotated at each weight increment and the shearing stresses are read from its calibrated dial. The stresses are plotted on a graph from which the Coulomb parameters are determined. The shearing stress of the soil with any known force applied to it can then be determined.
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Method and means for testing soil samples

Method and means for testing soil samples

Method and means for testing soil samples Abstract A device for testing soil samples is comprised of a closed container having an interior compartment with upper and lower portions. A valve is provided for introducing into the closed container a liquid having the general properties of butane, and a second valve is provided in the container for allowing gas in the closed container to exit therefrom. A soil sample filter container is suspended within the container above the bottom portion thereof. A condensation element is mounted in the closed container and positioned above the filter container to cause gas evaporating from the liquid in the container to liquefy as condensate on the
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Means and method of camera space manipulation

Means and method of camera space manipulation

Means and method of camera space manipulation Abstract A means and method for camera space manipulation includes a manipulator arm extending from a base to an outward end. The arm is movable through a workspace to accomplish various tasks. One or more cameras are movably oriented towards the arm and work space to capture the arm and work space in what will be called camera space or camera vision. A visual cue is associated with the outward end of the manipulator arm.

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II. THE K-MEANS CLUSTERING METHOD

II. THE K-MEANS CLUSTERING METHOD

A Wireless Sensor Network (WSN) is a network of small sensor nodes which are energy constraint devices and have limited data transmission and computational power. Clustering is an important mechanism in large multi-hop wireless sensor networks for obtaining scalability, reducing energy consumption and achieving better network performance. Most of the research in this area has focused on energy-efficient solutions, but has not thoroughly analyzed the network performance, e.g. in terms of data collection rate and time. In this paper we are presenting the clustering of wireless sensor network by using k-means approach, over a large dynamic network. As it is the oldest and simplest method of clustering. This method requires only local communication and synchronization. Due to growing in area of peer to peer and mobile sensor networks, data analysis in large, dynamic network in large garner importance in the near future. Our algorithm shows best result for the large dynamic network. We tested our algorithm in a simulated environment up to 100 nodes in a dynamic environment and analyze its behavior with good accuracy.
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Method and means for gravity table automation

Method and means for gravity table automation

Method and means for gravity table automation Abstract A system for gravity table separation including a gravity table for separating materials and a detector operatively associated with the gravity table for detecting the movement of control particles with respect to the gravity table during its operation. The control particles are of a known characteristic. By calibrating the desired movement of the control particles through the table, any misalignment or deviance of that movement during operation is detected, and adjustments can be made to the operation of the table to bring the control particles back to the desired movement. The separation process can then be controlled to bring about optimum efficiency. Also, the detector can be interfaced with a control component which can automatically adjust the operation of the table in response to whether the control particles are following the desired movement through the table.
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A Smoothed Analysis of the k-means Method

A Smoothed Analysis of the k-means Method

6. CONCLUDING REMARKS In this paper, we settled the smoothed running time of the k-means method for ar- bitrary k and d. The exponents in our smoothed analysis are constant but large. We did not make a huge effort to optimize the exponents as the arguments are intricate enough even without trying to optimize constants. Furthermore, we believe that our approach, which is essentially based on bounding the smallest possible improvement in a single step, is too pessimistic to yield a bound that matches experimental obser- vations. A similar phenomenon occurred already in the smoothed analysis of the 2-opt heuristic for the TSP [Englert et al. 2007]. There it was possible to improve the bound for the number of iterations by analyzing sequences of consecutive steps rather than single steps. It is an interesting question if this approach also leads to an improved smoothed analysis of k-means.
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How Fast is the k-means Method

How Fast is the k-means Method

We present polynomial upper and lower bounds on the number of iterations per- formed by the k-means method (a.k.a. Lloyd’s method) for k-means clustering. Our upper bounds are polynomial in the number of points, number of clusters, and the spread of the point set. We also present a lower bound, showing that in the worst case the k-means heuristic needs to perform Ω(n) iterations, for n points on the real line and two centers. Surprisingly, the spread of the point set in this construction is poly- nomial. This is the first construction showing that the k-means heuristic requires more than a polylogarithmic number of iterations. Furthermore, we present two alternative algorithms, with guaranteed performance, which are simple variants of the k-means method. Results of our experimental studies on these algorithms are also presented.
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EBK Means: A Clustering Technique based on Elbow Method and K Means in WSN

EBK Means: A Clustering Technique based on Elbow Method and K Means in WSN

WSN consist of hundreds of thousands of small and cost effective sensor nodes. Sensor nodes are used to sense the environmental or physiological parameters like temperature, pressure, etc. For the connectivity of the sensor nodes, they use wireless transceiver to send and receive the inter-node signals. Sensor nodes, because connect their selves wirelessly, use routing process to route the packet to make them reach from source to destination. These sensor nodes run on batteries and they carry a limited battery life. Clustering is the process of creating virtual sub-groups of the sensor nodes, which helps the sensor nodes to lower routing computations and to lower the size routing data. There is a wide space available for the research on energy efficient clustering algorithms for the WSNs. LEACH, PEGASIS and HEED are the popular energy efficient clustering protocols for WSNs. In this research, we are working on the development of a hybrid model using LEACH based energy efficient and K-means based quick clustering algorithms to produce a new cluster scheme for WSNs with dynamic selection of the number of the clusters automatically. In the proposed method, finding an optimum „k‟ value is performed by Elbow method and clustering is done by k-means algorithm, hence routing protocol LEACH which is a traditional energy efficient protocol takes the work ahead of sending data from the cluster heads to the base station. The results of simulation show that at the end of some certain part of running the proposed algorithm, at some point the marginal gain will drop dramatically and gives an angle in the graph. The correct „k‟
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The Impact of Violating Factor Scaling Method Assumptions On Latent Mean Difference Testing in Structured Means Models

The Impact of Violating Factor Scaling Method Assumptions On Latent Mean Difference Testing in Structured Means Models

39 assumptions underlying the two factor-scaling methods may affect the power of the LRT κ . Neither model size nor model complexity was varied in this study. For simplicity, a two-group, one-factor CFA model with six indicator variables was the true generating model. Future researchers could consider more complex models (for example, more observed indicators and/or additional latent variables) to investigate whether varying the model size and/or model complexity would affect the testing and description of the latent mean difference across groups. Future research that includes models with more observed indicators could likewise investigate more severe loading non-invariance conditions. Further, mean comparisons between more than two groups are not uncommon and, hence, the impact of including more than two groups on latent mean comparisons could be examined in future investigations. In addition, multivariate normal data were generated. Future studies could also explore the implications of violating the assumption of normality when using the
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On a method of construction of new means with applications

On a method of construction of new means with applications

However, the extension of the logarithm, identric and Seiffert means from two to three or more variables does not appear to be obvious from the above expressions of these means. In this sense, we refer the reader to [, –] for some extensions about the logarithmic and identric means. Here, we will derive other extensions of these latter means from our above study. In fact, the above transformation for means with two variables can be imme- diately stated in a similar manner for means involving several variables. For instance, we can define
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A REVIEW ON TESTING FOR EQUALITY
OF MEANS AGAINST ORDERED MEANS

A REVIEW ON TESTING FOR EQUALITY OF MEANS AGAINST ORDERED MEANS

Hypothesis testing is a method of making statistical decision using experimental data. One use of hypothesis testing is in deciding whether experimental result contains enough information to cast doubt on conventional wisdom. Hypothesis testing are performed by many researcher in various fields of inquiry, usually to discover something about particular process. Literally, hypothesis testing is a method of testing a claim about a parameter in a population, using data measured in a sample[3,4]. In this method, we test some claim by determine a likelihood that a sample statistic could have been selected if the hypothesis regarding the population parameter were true.
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Means Testing Social Care in England

Means Testing Social Care in England

The proposed formula contains upper capital limit which is designed to protect the tax-payer. A problem with capital limits is that they introduce complexity and change behaviour, in this case by encouraging people to spend down or give away their assets in order to stay within them. In the proposed method a cut-off of £118,000 will restrict state funding to those with assets below this figure. In the preferred method, by contrast, the upper limit is different for each care tariff. Because it is not based on a single global value, it should discourage the early disposal assets since the tariff they receive will not be known in advance. For cost control purposes, we therefore believe a tariff system based on the care package and not an arbitrary upper limit is hence a better and more logical approach.
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Shear Failure Criterion and Constant Volume Ring Shear Testing Method for Clayey Soil

Shear Failure Criterion and Constant Volume Ring Shear Testing Method for Clayey Soil

Figure 3. Water content versus effective normal stresses before and after shearing. Figure 4. Definition of the Hvorslev criterion. behaves elasto-plastically under effective stresses. The cohesion of saturated soil is a function of its water con- tent. But since the water content is proportional to the void ratio at saturation, the cohesion is also a function of the void ratio. That means that two specimens with different degrees of over-consolidation have different void ratios and thus their respective cohesions are varied. However, shear box tests have traditionally been carried out without considering the state of over-consolidation of the clay. This introduces inconsistencies, which may be illustrated by the following hypothetical example. Consider two soil specimens, which have been taken from the same depth and are being tested in the shear box under different normal stress levels. Since both specimens were surcharged equally, they have different states of over-consolidation during the testing. The results of such tests are frequently used to define a single failure envelope. Since the specimens behave differently under these test- ing conditions, and thus have different values of true cohesion, fitting them on one line is contradictory. This line will vary in relation to the testing conditions. Shear parameters, which are obtained from randomly distri- buted results of shear tests that depend upon contingencies are fictitious.
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Soil Method of Analysis

Soil Method of Analysis

NCR-13 wants it clearly understood that the publication of these tests and procedures in no way implies that the ultimate has been reached. Research and innovation on methods of soil testing should con- tinue. The committee strongly encourages increased research efforts to devise better, faster, less expensive and more accurate soil tests. With the high cost of fer- tilizer, and with the many soil related environmental concerns, it is more important than ever that fertilizer be applied only where needed and in the amount of each element needed for the response goal. The best hope of attaining this goal is better soil tests and bet- ter correlations with plant response. NCR-13 stands ready to evaluate promising new soil tests, and with clear justification will move quickly to revise their rec- ommendations.
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Means Testing Adult Social Care in England

Means Testing Adult Social Care in England

The proposed formula contains an upper capital limit which is designed to protect the tax payer. A problem with capital limits is that they introduce complexity and change behaviour, in this case by encouraging people to spend down or give away their assets in order to stay within them. In the proposed method, a cut-off of £118,000 will restrict state funding to those with assets below this figure. In the preferred method, by contrast, the upper limit is different for each care tariff. Because it is not based on a single global value, it should discourage the early disposal of assets, since the tariff they receive will not be known in advance. For cost control purposes, we therefore believe a tariff system based on the care package and not an arbitrary upper limit is a better and more logical approach.
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Does Means Testing Exacerbate Early Retirement?

Does Means Testing Exacerbate Early Retirement?

Means testing does create disincentives to work and to save. However, these costs are presumably offset by the redistributive advantages of focussing state benefits on poorer households. Means testing provides a method for ensuring that everyone has an adequate standard of living in retirement, while limiting the associated costs to the taxpayer. This consideration is critical, given the projected ageing of the population. Furthermore, it is certainly not clear that means testing reduces the level of aggregate savings. This is because the more affluent parts of society, who generate most of the country’s savings, are not subject to means testing, but are affected by the consequent tax rises that would be required to fund any move to a universal pension.
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Method Statement FOR. Soil Investigation

Method Statement FOR. Soil Investigation

Infratech ASTM CO., LTD. 6 4.3 Particle Size Analysis Particle size analysis will be performed by means of sieving (ASTM D 422). For oven-dry materials, sieving is carried out for particles that are being retained on a 0.063 mm sieve. In sieve analysis, the mass of soil retained on each sieve is determined and expressed as a percentage of the total mass of the sample. The particle size is plotted on a logarithmic scale so that two soils having the same degree of uniformity are represented by curves of the distribution plot. In Hydrometer analysis is based on the principle of sedimentation of soil grains in water. When a soil specimen is dispersed in water, the particles settle at different velocities, depending on their shape, size, and weight. For simplicity, it is assumed that soil particles are spheres and the velocity of soil particles can be express by Stokes’ law.
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Lifeline or Means Testing? Electric Utility Subsidies in Honduras

Lifeline or Means Testing? Electric Utility Subsidies in Honduras

In this chapter we provided a partial evaluation of the lifeline or increas- ing block tariff electricity subsidy in Honduras. With funding from the gov- ernment, the public utility is offering electricity at greatly subsidized rates for those households with monthly consumption below 300 kWh. Because the lifeline threshold is set so high, 83.5 percent of the utility’s residential clients benefit from the subsidy. At the same time, 81.8 percent of the sub- sidy may well be spent on nonpoor households. While this last statistic could be lower if we were using a different method for measuring poverty, it remains true that the impact on poverty of the subsidy is rather small in comparison to its cost. The fact that the current subsidy is badly targeted does not mean that it could not be improved by reducing the lifeline thresh- old. A lower lifeline subsidy as currently being considered by the govern- ment would have the potential of being more effective. Alternative proxy
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Testing Soil for Micronutrients

Testing Soil for Micronutrients

Blevins and Massey (4) indicate, however, that millet, did not show typical copper deficiency symptoms when grown in the greenhouse on soil containing 0.2 ppm EDTA-extractable copper, al[r]

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Soil testing is an excellent measure of soil fertility.

Soil testing is an excellent measure of soil fertility.

Soil Testing Is an Excellent Investment for Garden, Lawn, and Landscape Plants, and Commercial Crops-page 2?. Figure 2.[r]

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