broad series of states of the sea (significant wave height, periods and so on) although the same does not apply for the geometry of the armour layer’s crown, such that the slopes considered are of only two types: 1V/1?5H or 1V/2H and the range of the number of armour layer units is limited except in Martı´n et al. (Table 8). This leads to the application of a method to slopes that largely diverge from those used in the tests on which those methods are based, possibly giving rise to erroneous results. The same occurs in those cases where the actual geometry cannot be fitted into that of the profile tested and the coefficients obtained therefrom (the case of Bradbury and Allsop). In such a case, reduced scale tests have to be carried out (the case of Pedersen and Burcharth), and this involves a high cost due to the formulations for
The last case to be analyzed in the point to point configurations are the long lines. For long lines, it is curious to observe how the TU Darmstadt’s method worsens its performance. While the steady state current’s estimation has been proved to be better for short and medium lines when the Darmstadt dissertation’s equations were applied, in this case the PhD’s equations lead to better results. The difference between the methods is more noticeable for low values of ground resistance, while that difference shortens as the ground resistance increases. The reduction in that error’s difference is not enough to change which approach is better and the PhD outperforms the TUD’s method in every situation by 3% in the most extreme case and 0.3% for higher values of ground resistance.
Practical measurement has proven the results of the calculation. It has been showed that, with properly selected noise barrier upper edge, a noise reduction increase by 6 dB can be obtained. Reductor edges, however, may presently not be taken into consideration in noise map calculations. The software programs I tested had, for instance, not shown any difference between Types 1 and 2. The other types could not even be programmed into the software, due to their profiles, which the software did not recognise. The simulation of the sound area behind the barrier would prove the great importance of taking upper barrier edges into exact consideration. The fact that programs do not distinguish between different types is primarily not a programming error. All countries have standards, e.g. RLS-90, NMPB 96, etc., that form the basis of these calculation. The main reason is that noise calculation standards presently in force use only the Fresnel distance-difference method for calculating diffraction, i.e. practical calculations do not use the results from the modern measurement technology yet.
The path towards the development of new calculation techniques within the plastic method began in 1950, with the demonstration of the Fundamental Theorems of the theory of plasticity and the consolidation of the theory. Plastic theory was initially developed for steel structures, although the research by the Russian A.A. Gvozdev focused on calculating the limit loads in reinforced concrete structures. Indeed, the first text that includes knowledge about plastic theory for the calculation of steel frames to rigorous critical standards, The Steel skeleton. Vol 2: Plastic behavior and design (J. Baker, M.R. Horne and J. Heyman), was not published until 1956 21 . Plastic calculationmethods were
This laboratory session aims to introduce ﬁve diﬀerent methods to the student: using supplied electricity, through the condenser heat balance, Coriolis ﬂow meter, and calculation based on expected compressor’s data (volumetric eﬃciency regression and performance graphs). The two cool- ing capacity calculationmethods based on compres- sor’s data are those commonly used in the refrigeration industry and hence the most interest- ing for the student. However, both cannot be applied for the lower GWP alternatives because of the lack of the available information.
Noise maps describe spatial distributions of noise levels. They allow an efficient visualization of the noise distributions in areas where the land uses are sensitive to noise. Noise mapping is a very efficient noise assessment method in an urban area. For large cities, challenges have to be met in terms of data management, data reduction, calculationmethods, optimization procedures, validation techniques and presentation of results so that the maps can be powerful tools to be used for urban noise planning and design.
An attractive feature of this method is that the only re- quirement to construct the THO basis is solving the Schro¨- dinger equation for the ground state, either analytically or numerically. The THO basis is then obtained by means of Eqs. 共 1 兲 and 共 2 兲 . As the continuum wave functions are ob- tained through the Hamiltonian diagonalization, their calcu- lation does not require the integration of the Schro¨dinger equation. Furthermore, the scattering calculation is equiva- lent to a standard coupled channels calculation with bound states, whose internal energies and wave functions are given by the diagonalization of the deuteron Hamiltonian in this THO basis. The only discrete parameter to be changed in order to investigate convergence is the number of states to be
Faced with this reality, in this article, it is proposed to use a hybrid model consisting of a novel equation (new mathematical model) for initials values of interest rates in COA and a classical numerical method called linear interpolation (LI) that respond to this uncertainty, through which the calculation of interest rates can be established objectively and at the lowest possible time.
We present thereafter the results obtained by applying the Adams formulas to solve two initial value problems. The exact solution in the first one is known and we can then compare this value with those obtained by the different methods applied. On the contrary, in the second one the solution cannot be obtained explicitly and only approximate values can be obtained.
Neuroimaging techniques (e.g., fMRI) have been used to analyze the pattern of brain activity during diverse calculation tasks. It has been demonstrated that different brain areas are active dur- ing arithmetical tasks, but the specific pattern of brain activity depends on the particular type of task that is used. At minimum, the following brain areas become activated during calculation: the upper cortical surface and anterior aspect of the left middle frontal gyrus (Burbaud et al., 1995); the supramarginal and angular gyrus (bilaterally) (Rueckert et al., 1996); the left dorsolateral prefron- tal and premotor cortices, Broca’s area, and the inferior parietal cortex (Burbaud et al., 1999); and the left parietal and inferior occipitotemporal regions (lingual and fusiform gyri) (Rickard et al., 2000). The diversity of brain areas involved in arithmetical processes supports the assumption that calculation ability repre- sents a multifactor skill, including verbal, spatial, memory, body knowledge, and executive function abilities (Ardila and Rosselli, 2002). Dehaene et al. (2004), however, proposed that regardless of the diversity of areas that become active during arithmetical tasks, the human ability for arithmetic is associated with activation of very specific brain areas, in particular, the intraparietal sulcus. Neuroimaging studies with humans have demonstrated that the intraparietal sulcus is systematically activated during a diversity of number tasks and could be regarded as the most crucial brain region in the understanding and use of quantities (Ashkenazi et al., 2008). These observations have been supported using brain electrostimu- lation (Roux et al., 2009). Other brain areas, such as the precentral area and the inferior prefrontal cortex, are also activated when subjects engage in mental calculations. Ro Ş ca (2009) has proposed that there exists a fronto–parieto–subcortical circuit responsible for complex arithmetic calculations and that procedural knowledge relies on a visuo-spatial sketchpad that contains a representation of each sub-step of the procedure.
On this paper, a mathematical model aimed towards the determination of this outlay is developed, where mainly those process performance metrics are taken into account as well as other control variables, to end with a framework that makes the calculation possible. The result of this analysis is a formula whose input elements are precisely those variables; the output represents the actual amount of the productivity bonus in some predeﬁned currency.
previously possible; as a result, we can also begin to define the problematic of this calculation. If this is the case, it is because, as we have noted repeatedly, one sees in practice an intimate intermingling of two types of calculation: a monetary and a nonmonetary calculation. This, as yet, is only in its infancy, but these beginnings are already sufficient for theoretical analysis to take hold of them in order to specify their content and precise forms. In order to undertake this analysis (which has, as yet, only been outlined here) we must examine more closely the two types of calculation that exist in present-day transitional social formations. In this way we will see both what the relations are that they support, and the differences which separate them. The first question we must examine is that of the relations of production whose existence is signaled by the presence of commodity categories.
pressure measurements, but do not exactly corre- spond to these patients or these healthy subjects. At other hand, the values of variables as S, E and c, should also have variations in the day, but there are not measurements of these variables during 24 hours, there are only reported intervals for the values of these variables for healthy people or for certain kind of patients. For this situation, functions that generate one random value in the range of values generally accepted were used, each time the efficiency is calculated. However, if the randomization is uniform it is possible to obtain huge variation with values that do not even have physical sense (e.g. efficiencies close to zero or one). To avoid this problem Gaussian random functions were used in the calculation, which give us distribution values at appropriate intervals, but always more or less close to the average, which from a physiological standpoint seems to make much more sense. However, in all cases the sepa- ration that was shown in Figure 6 is still observed, i.e. the efficiency values of the healthy are sepa- rated from the patients. Cardiac efficiency is an important biomedical research subject with mul- tiple applications, because in pathophysiological disease states mechanical efficiency is reduced and it has been hypothesized that the increased energy expenditure relative to work contributes to progression of the disease 30 . For instance, obesity
The work shows a summary of the most significant shells foundations built in Cuba in the last decades and the developments related to these in term of methods of calculation of plates and shells of complex geometry using reference surfaces and reference bodies, from the generalization of the projected solicitations method (Pücher, 1934) with the use of referential surfaces (Hernández, 1970) and other developments made in the mechanics of deformable solid by the Method of Duality (Rianitsiyn, 1974; Castañeda, 1993) and the Static-Geometric Analogy in the mechanics of the deformable solid (Castañeda, 1985).It also includes a summary of the research developed in recent years on the stress-strain states of soil under and inside shell foundations for chimneys of 74.5 m in sugar industries (Cobelo, 2004), comparative studies made of these with the use of the FEM (González, 2010) and other research projects currently running (Álvarez, 2010 ).
Next, I assessed a Monte Carlo simulation model where demand functions are steeper thus storage plays a greater role and is more frequent. Tables 10 to 18 present the results of the second parameterization (3.1). Results show that small samples bias increases for the Simulation estimators. In particular SMM, IND (M1), EMM (M1), and EMM (M2) tend to underestimate parameters a and b. I observe that IND (M2) underestimate a and over-estimates b while EMM (M3) and EMM (M4), over-estimates a and underestimate b. As the sample size increases, the bias is reduced and for a sample size T = 10000 the average RMSE for all estimates converge to 0.01 for a and 0.03 for b. For small sample, the performance of PML estimator underestimates a and over-estimates b. In general terms, both parameter estimates are more efficient than the Simulation estimators. Yet, as sample size increases parameter a tend to stabilize at 0.94. I observe that although bias might be small compared to CML and UML methods for a sample T = 10000, estimators have substantially better precision where the bias and the RSME converge to almost zero.
42 Other studies on graduate and undergraduate groups comparing control (no technique) against TRIZ reached same conclusions that Chiu & Shu (2008) and Vargas, et. al. (2012) about novelty outcomes for TRIZ but decreased the quantity of ideas generated. These authors also highlighted that the use of TRIZ method enable designers to reach analogous domains or concepts specially if they method is suported with software enabling tools to access patent content (successful novelty and analogous solutions). Finally the authors conclude that “some ideation methods are better for some tasks, depending on the outcome sought”.