Many studies opt to use default car-following and lane-changing parameters. However, the traffic com- position, network geometry, vehicle ages, engine size, and (most of all) driver behaviour vary significantly in different parts of the world. Thus, the default param- eters of the simulation software should be carefully examined in order to obtain reliable results. As an ex- ample, it has been noted that lane-changing is a high- ly strong characteristic of Istanbul traffic and drivers are frequent and aggressive in cutting and overtaking, taking every opportunity to change lanes at the slight- est opening . As explained in , the two mod- els of driving behaviour parameters are Wiedemann 74 (W74) and Wiedemann 99 (W99). The W74 model, generally, has been used for urban arterials and merg- ing areas. The W99 has been utilized in modelling freeways and diverging areas. Tables 1-4 outline the general parameters, lane-changing, W74, and W99 models parameters respectively. The first column con- tains the ID of each parameter used by VISSIM during COM interface, along with the parameter description, their range, and defaultvalues in other columns.
their hyper-parameter values. Several approaches have been proposed to choose these values. Some machine learning tools suggest hyper-parameter values for SVMs regardless of the dataset analyzed, or employ simple heuristics . Al- though these values can induce models with good predictive performance  this does not occur in many situations, requiring a fine tuning process [4, 13, 25]. However, the optimization of these hyper-parameters usually has a high com- putational cost, since a large number of candidate solutions needs to be evalu- ated. An alternative is to generate a new set of defaultvalues by optimizing these hyper-parameter values over several datasets rather than for each one. The op- timized common values may improve the model accuracy, when compared with the use of the defaultvalues, and reduce the computation cost to induce models, when compared with a optimization for each dataset.
As seen from the tables, often several of the 12 1-D K-S probabilities for some cases give acceptable fits, but it is difficult to find a single model (or a parameter variation) where all are really good fits. In other words, none of the models discussed here simultaneously give good fits to the data from all of the three radio surveys considered, 3C, 6C and 7C. As noted above, P and z seem to correlate together in most cases because they are related when we pick up radio sources by imposing a flux limit on them. Once again, in some cases the P and/or z fits are good and those to D are bad; and vice versa in other cases. The 1-D K-S statistic for the model runs which gave any improvement over the “improved” default case (x = 3, otherwise Default model parameters) are examined from Tables 4.4, 4.5 and 4.6. Listed according to the performance rank (the best one first) the modified parameters (with all others set to their defaultvalues listed in Table 2.1), having combined 1-D K-S statistics better or as good as the default are as follows.
This work defines how a trajectory mining method could be used to adapt the path to the user by producing a set of defaultvalues. These values are considered to be the predictions for the directions that the user should follow in order to travel between his current location to the intended destination. The trajectory mining module process uses data from previous executions of the system, which are used to obtain the best travelling path (according to user preferences) and critical points, such as intersections in which the user may take the wrong turn. Through this set of defaultvalues the Speculative Computation module is used as a mechanism that determines if it is necessary to issue an alert or not. The integration of these two modules are the main contribution of this work. The speculative framework is independent of how the trajectory mining is achieved using the calculated values to ensure the correct travel. A structured reasoning method is provided through the combination of these modules. After preprocessing the data, it is possible to apply different trajectory mining techniques. Our future goal is to determine the most appropriate one for the problem.
To validate calibration, a comparison between real, default and calibrated val- ues of travel times and follow distances was performed (Figure 3 and Figure 4). Data analysis of travel times and follow distances’ peaks show a similar behavior, thus validating the simulation. Histograms also reflect how simulated results us- ing adjusted VISSIM parameter values (presented in Table 2) match field data. Iterative (manual method) results do not precisely overlap with real and cali- brated travel time values (Figure 3(a)), however major peaks coincide, again va- lidating calibration; defaultvalues cannot be used in PDJ because they do not overlap and are offset (Figure 3(b), Figure 3(c)). Manual calibration offers then better results in follow distances (Figure 3(d)), with a major coincidence be- tween the major peaks.
The next function (“Search for non existing HTML templates”) works a bit different from the others because it does not replace a value by a new value – it clears the references to HTML-templates which do not exist in the filesystem from Flexforms in tt_news plugin elements (recommanded way is to set the html template in TS). The main reason for writing this function was to get rid of the old “tt_news_v2_template.html” which was inserted as default in tt_news 2.5.2. This template still existst in tt_news but I moved it to the res/ folder. If the updater finds content-elements with configured HTML templates that actually exist in the filesystem it will not touch this records.
read counts. Previous approaches include (i) global scal- ing so that a summary statistic such as the mean or upper-quartile value of read counts for each sample (or library) becomes a common value and (ii) standardiza- tion of distribution so that the read count distributions become the same across samples [12-15]. Some groups recently reported that over-representation of genes with higher expression in one of the samples, i.e., biased dif- ferential expression, has a negative impact on data nor- malization and consequently can lead to biased estimates of true differentially expressed genes (DEGs) [15,16]. To reduce the effect of such genes on the data normalization step, Robinson and Oshlack reported a simple but effective global scaling method, the trimmed mean of M values (TMM) method, where a scaling fac- tor for the normalization is calculated as a weighted trimmed mean of the log ratios between two classes of samples (i.e., Samples A vs. B) . The concept of the TMM method is the basis for developing our normaliza- tion strategy.
The value i, which controls the rate of expansion of the cloud, can be adjusted to simulate dispersion in unstable, neutral and stable conditions (Pasquill and Smith, 1983). Crabbe et al (1994), Bird et al (1996), Miller et al (1999) and Thistle (2000) confirm that the effect of stability is to significantly increase spray drift deposition values recorded close to ground. Further work is required to adapt the GDS model for super stable or temperature inversion conditions. Algorithms do exist for Gaussian air pollution models (without sedimentation) to account for inversion capping which may be of assistance in this task.
The results are exact fits obtained with 25,000 scenarios using Sobol sequences and assuming no events before time zero. The procedure is very efficient taking just a couple of seconds for one simulation. Figure 1 displays the probability distributions computed with 25,000 and 100,000 scenarios. Even with as few as 25,000 scenarios, the procedure is able to capture the main features of the distribution. Figure 2 shows the effect of varying µ on the 5-year joint probability distribution for fixed values of the other model parameters (the effect is significant just on the tail of the distribution since λ is not varied). It appears that µ has minimal effect on the distribution and the calibration results as long as it is not too small remaining of the order of 10 or more, or response times are not more than about a month or two.
This section examines the details of three end-use sectors and compares their contri- bution to the mitigation of the electricity sector. As shown in Fig. 8, the electricity sector accounts for more abatement than any other sector (industry, transport, resi- dential and commercial) and is fully decarbonized by 2050 in the default mitigation scenario (80%DEF) in all the models. Although the other sectors also contribute to the mitigation effort to achieve the 80% GHG emissions reduction target, the electricity sector is by far the most important. The results suggest that the mitigation options in the power sector appear rather “early on the marginal abatement cost (MAC) curve.” Emission reductions in the electricity sector are achieved through CI reduction rather than through EI improvements. Absolute electricity consumption remains more or less constant throughout the period, even decreasing in the 80%DEF in 2050. However, between 2010 and 2050, the share of electricity in final energy consumption increases considerably, by 15% (BASE), 30% (40%DEF), and 80% (80%DEF), pointing to the importance of electrification in the future energy system.
This dimension hierarchy is a classification scheme defined for use in accounting, whose purpose is to provide a framework for the recording of values or value flows, to guarantee an orderly ren- dering of accounts. It is a coding structure, representing all of the detailed level categories that record why a financial event has occurred.
Robert Merton (1974) developed a model by using the European call option of BSM. It is a structural model because it provides the relationship between the debt and the value of the firm. It assumes that the firm provides both equity ( E ) as well as debt ( X ), such that the value of the firm ( V ) is E + X . An important assumption of this model is that a firm does not pay dividends until time T (maturity). The firm will default only when the value of the firm is less than the face value of debt ( X ) and it will happen only at maturity period. In Merton’s model, we have two possible scenarios:
From a partnership-development perspective, there appears to be several opportunities for working with for-profits on financial education and default reduction efforts. The advantages of working with the largest for-profits include access to students at the point of making a loan and well-funded administrative personnel support and marketing. While research only loosely supports the effectiveness of counseling in reducing student loan default rates, research from the nonprofits working with high risk populations in homeownership indicates that counseling helps in guiding borrowers to smarter repayment options, but not in reducing default propensities. 43 Jacki White, Loan Fund Manager at NeighborWorks of Western Vermont, echoed this sentiment, “When we are intense in emphasizing what will happen [if they do not repay], the better the behavior… when people do not know what to do, they try to ignore the problem hoping it will go away, but it doesn’t.” 44
Consider a very simple workflow W capturing an emergency plan designed to clean coastal areas affected by an oil spill. Suppose that W is composed of 4 subworkflows: W1 determines the type of oil spilled, setting the value of a parameter OilType; W2 determines the type of coastal area affected, setting the value of another parameter CoastalAreaType; and W3 and W4 define cleaning procedures for two different combinations of oil type and coastal area type. Assume that they may all run in parallel, but W3 and W4 have pre-conditions that depend on the values of OilType and CoastalAreaType. Furthermore, assume that W3 contains an abstract workflow, corresponding, say, to node CB of the ontology shown in Figure 1.