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Container logistics and empty container repositioning
Published in Dong-Ping Song, Container Logistics and Maritime Transport, 2021
Note that Table 4.2 provides the aggregated trade volume between three regions. The following procedure is adopted to calibrate the aggregated trade volume into 20 individual shipping lines (Song and Carter 2009): Step 1: Splitting. For each shipping route from region j to region k, the aggregated trade volume djk is split into smaller volumes for individual shipping lines proportional to shipping lines’ carrying capacity.Step 2: Perturbation. The trade volume for each individual shipping line is replaced by a perturbed amount. The perturbed amount is generated randomly from a normal distribution with the mean being the volume obtained in Step 1 and the coefficient of variation being 0.3. The coefficient of variation is defined as the ratio of the standard deviation to the mean.Step 3: Scaling. The trade volumes for individual shipping lines in each shipping route are scaled up or down proportionally to preserve the total trade volume in that route.
Case Studies
Published in Nicholas Stergiou, Nonlinear Analysis for Human Movement Variability, 2018
Anastasia Kyvelidou, Leslie M. Decker
In our study, we evaluated the consistency of movement trajectory in robot-assisted surgery using objective measures that examine the amount and the structure of movement variability. Amount of variability is generally measured by the standard deviation, a measure of statistical dispersion, and coefficient of variation, which is a normalized measure of dispersion. The largest LyE, a measure of structure of variability, is used to examine consistency by evaluating the rate of change in movement trajectories; more diverged or less consistent movement is indicated as LyE increases (Stergiou et al. 2004). Thus, reduction in the LyE measure indicated increased consistency of movement trajectories. The structure of variability has been utilized in evaluating consistent patterns in human movement (Kay 1988).
Environmental Reconstruction of Watershed Vegetation Cover to Reflect the Impact of a Hurricane Event
Published in Ni-Bin Chang, Kaixu Bai, Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing, 2018
The standard deviation and coefficient of variation are effective statistical tools to compare between two datasets. For brightness, the standard deviation is 936.51 for the before-landfall and 790.10 for the after-landfall scenario. The standard deviation is lower in the case of the after-landfall than the before-landfall scenario. This indicates that the pixel values of the before-landfall scenario are more clustered around their mean than those of the after-landfall scenario. Yet the coefficient of variation is used to measure the spread or dispersion of data; the higher the value, the greater the dispersion. In this study, the coefficient of variation is 36.22% for the before-landfall scenario and 37.83% for the after-landfall scenario. This indicates that the pixel values for the after-landfall scenario are more dispersed than these of the-before landfall scenario. This conforms to the dispersion phenomenon shown in the tasseled cap plots. The same case is applicable for greenness and wetness as well. The coefficient of variation is significantly higher in case of the after-landfall scenario than these in the before-landfall scenario for greenness. This indicates a greater dispersion of pixel values for the after-landfall scenario than that of the before-landfall scenario, which indicates a significant change in the vegetation cover of the watershed. All of these phenomena prove that there was dispersion of data to some extent in all three transformation cases as evidenced in the TCT plots in Figure 18.10 driven by the hurricane landfall.
On the prediction of intermediate-to-long term bus section travel time with the Burr mixture autoregressive model
Published in Transportmetrica A: Transport Science, 2023
Victor Jian Ming Low, Hooi Ling Khoo, Wooi Chen Khoo
The values of the MPIL, PICP, and APID performance measures obtained at each bus section are reported in a summary of statistics. The summary of statistics includes the smallest value (Min), the median value (Median), the largest value (Max), and the coefficient of variation (CoV) of the performance measures for each bus section group. The statistics provide a more detailed overview of the model performance, i.e. the extraordinary cases (Min and Max) and the average (Median) performance and the performance consistency of the models (CoV). Note that the coefficient of variation, where represents the standard deviation and represents the mean, is a statistical measure of relative variability. The R statistical software version 4.1.1 is used to conduct all model fitting and forecasting procedures.
Effects of partial replacement of fine aggregates with crumb rubber on skid resistance and mechanical properties of cement concrete pavements
Published in International Journal of Pavement Engineering, 2022
Ali Raza Lashari, Yasir Ali, Abdul Salam Buller, Noor Ahmed Memon
Skid resistance is considered a major parameter in cement concrete pavements and has a direct consequence on traffic crash occurrence. As such, this study focusses on determining the effects of crumb rubber on skid resistance performance of rigid pavement when fine aggregate is partially replaced, and the results are presented in Figure 3. This study evaluates skid resistance in wet and dry testing conditions. As such, skid resistance performance is analysed and compared for these two testing regimes by varying rubber content for five replicates and their descriptive statistics (mean, standard deviation and coefficient of variation) are also presented for each mix corresponding to each crumb rubber content. Standard deviation explains the amount of variation or dispersion in the data, whereas the coefficient of variation indicates the relative dispersion of data points around the mean. These statistical measures provide a simple and intuitive way of explaining and understanding laboratory results. A lower skid resistance value reflects less friction between vehicle tyre and pavement surface, thereby leading to the increased propensity in engaging traffic crashes. It is worth mentioning here that skid resistance was measured on the left and right edges of a concrete slab, reflecting real-world traffic movements, where vehicle wheels usually exert pressure, thereby requiring friction. As such, the average values of the British pendulum number are presented in Figure 3.
Experimental investigation of thermal and rheological behavior of silica/ soybean oil nano lubricant in low-temperature performance of internal combustion engine
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2021
Sajjad Kharabati, Seyfolah Saedodin, Seyed Hadi Rostamian
Given different function analyses, the optimal function was a two-degree polynomial function according to Adjusted R2 and p-value to adapt the predicted data to the experimental ones in Table 4. Coefficient of Variation (CV) is a criterion to measure the deviation of the data from the mean value. The lower the CV, the more favorable the model would be (Hemmat Esfe et al. 2015; Sarafraz et al. 2019). The CV was determined to be 1.14 for the fit function, which is an excellent value to confirm the selected model. A signal and noise are defined in each regression for each constant coefficient in the fit function’s predictor. Here, Adeq. Precision, as a ratio of the signals to the noises, determines the relation between the constant predictor coefficients and the signals or noises. The larger the Adeq. Precision, the better the model would be. This value was obtained 296.398 for the selected fit function, which is an excellent value to confirm that the model is not affected by the noises.