Analysis Results. Source: Xxxxxxxx et al. (2013) Table 8 shows the ITHIM predicted health benefits of the two active transportation scenarios. The numbers of change in burden of disease and the % attributable benefits are both compared to the corresponding numbers of the BAU scenario. Table 9 shows the comparison of benefits between the LCD and C/PAG scenarios. Compared with the BAU scenario, the ITHIM model for the C/PAG scenario predicted a reduction in the number of premature deaths by 2,404 and a reduction of 44,866 total DALYs (i.e., the sum of years of life lost and years living with disability) per year (see Table 8). The burden resulting from road traffic injuries increased for the ST scenario by 11% (3,320 DALYs) and 19% (5,907 DALYs) for the C/PAG scenario, compared with BAU, as a result of an increase in collisions involving cars, bicyclists, and pedestrians. Compared with LCD, the C/PAG scenario had the larger net decrease in total disease (see (Table 9). The reduced PM2.5 concentrations associated with LCD compared with BAU resulted in 22 (1%<) premature deaths and 232 (1%<) years of life lost as a result of cardiovascular diseases, lung cancer, and nonmalignant respiratory diseases, suggesting the relatively smaller risk for these diseases from PM2.5 exposure when compared with physical inactivity. The ITHIM implementation in the Bay area demonstrated that active transportation has the potential to substantially lower both the carbon emissions and burden of disease. By committing to a modal shift in favor of active transportation with LCD technologies, a significant level of GHGE reduction goals can be achieved with better public health status for a region. It also demonstrated that ITHIM can be used with a regional travel demand model to estimate and predict the health benefits associated with proposed transportation projects and/or long-range transportation plans for the region. It is important to note that the presentation of estimated health benefits unrealistically assumed that the reductions in premature deaths and DAYLs occur in the planning horizon year (e.g., 2035). However, the achievement of health benefits such as reduction in the prevalence of chronic diseases from increased physical activities requires some time to manifest in the population. In future implementations of ITHIM. According to WHO (2017), based on expert consensus, five years is a reasonable assumption for newly initiated physical activities to reach full benefits, with an increase ...
Analysis Results. With the assembly of data as described above, NAMPO calibrated the ITHIM model for the MPO region. Results of the model runs for the baseline and the four analysis scenarios are shown in Table 13. Table 13 NAMPO ITHIM Analysis Scenarios and Results Baseline Conservative Moderate Aggressive Injury- neutral Per capita miles per week Walk 0.7 1.7 3.7 5.7 1.7 Bike 0.3 1.0 1.5 3.0 1.3 Vehicles 195.9 194.2 191.6 188.1 175.1 Driver 151.8 150.5 148.5 145.8 142.9 Passenger 44.1 43.7 43.1 42.3 32.2 Total1 225.0 225.0 225.0 225.0 206.2 Per capita minutes per week Walk 18.4 37.8 82.2 126.7 37.8 Bike 2.6 8.0 12.0 24.0 10.4 Vehicles 418.1 414.6 409.2 401.7 373.7 Driver 324.0 321.3 317.1 311.3 305.1 Passenger 94.1 93.3 92.1 90.4 68.6 Total2 494.5 515.7 558.8 607.8 477.3 Results-DALYs Chronic Diseases 1,124 2,793 4,375 1212 Injuries / Fatalities −552 −1,240 −1,733 1 Net Change 572 1,552 2,642 1213 Results-Deaths Chronic Diseases 38 109 165 41 Injuries / Fatalities −14 −31 −43 −2 Net Change 24 71 123 39 Economic Cost Savings (Millions) $10 $32 $63 $46 For the conservative scenario, ITHIM predicted 38 deaths avoided due to reduction in chronic disease incidence (1.1% reduction in diabetes and 1.0% in cardiovascular disease) (Xxxxxxxxx et al., 2017). However, 14 additional traffic fatalities incurred as a result of increasing walking and bicycling, resulting in a net 24 deaths averted per year for the conservative scenario. The estimation of XXXXx shows a net improvement of 572 averted DALYs. After multiplying the chronic disease and injuries/fatalities estimates with the economic cost estimates (see Table 12), approximately $10 million was predicted to be saved through decreased direct healthcare expenses and indirect productivity losses. Xxxxxxxxx et al. (2017) noted that ITHIM results in all analysis scenarios revealed that the economic benefits of increasing active transportation attained through increased physical activity were significantly greater than the benefits attained from reduced air pollution. Under the moderate scenario, the predicted benefits also outweighed the predicted xxxxx with net reduction of 71 deaths and 1552 XXXXx avoided per year (see Table 13). The cost estimates for the moderate scenario suggested a saving of $32 million in direct and indirect costs. The aggressive scenario resulted in a net decrease of 123 deaths and 2,642 averted DALYs per year. The economic cost savings of the aggressive scenario was estimated to be $63 million in direct and ...
Analysis Results. Source: Xx et al. (2019) Figure 7 shows the results of region-wide predicted health benefits from both physical activity and traffic injury under the adopted 2016 plan for non-Hispanic white and people of color. Figure 8 shows the comparison of region-wide predicted health benefits of the three future alternatives compared with base year 2012. Source: Xx et al. (2019) Figure 7 Predicted health benefits of the adopted 2016 MTP/SCS from physical activity and traffic injury compared with baseline year 2012. It can be seen in Figure 7 that both race groups exhibit a negative value in premature death reduction in 2020, which is caused by predicted increase in traffic injuries from increased non- motorized travel distances from 2012 to 2020. For 2027 and 2036, reductions in premature deaths are predicted for both physical activity and traffic injury, because that active travel time increases and motorized travel distance decreases. The reduction in premature deaths from increased physical activities (i.e., reduction in chronic disease incidence) exceeds deaths due to traffic injuries for the years 2027 and 2036. Thus, the net effect is an overall reduction in premature death for 2027 and 2036. For three alternative scenarios in Error! Reference source not found., non-Hispanic white population shows increasing health benefits for all categories (physical activity, traffic injury, and total) from S1 to S2 and S3, resulting from increased active transportation time and distance. However, for people of color, the reduction in premature deaths from physical activity in S1 is larger than S2, although the overall active travel time in S1 is actually smaller than S2. This is caused by the larger portion of older adults in the people of color category than in the non-Hispanic white category. If a subcategory that exhibits an increase in active travel has a high baseline health burden (e.g., older adults) it will affect the estimated health outcome more than age/gender subgroups with a lower baseline health burden (e.g., younger generations). If the health benefits of the entire region were aggregated, the difference in health benefits or costs for subgroups of the population cannot be discovered. Such results demonstrate the value of performing health impacts assessment for different subgroups of the population. Source: Xx et al. (2019)
Analysis Results. The ITHIM results indicated that increased participation in active transportation can result in reduced premature death due to reduced incidence in chronic disease and respiratory conditions prevented by increased physical activity and reduced air pollution (Figure 12). For example, the conservative scenario showed that 38 premature deaths from the diseased can be averted by increasing the average per capita walking and cycling distance for transportation. However, the increased active transportation inevitably incurs an estimated 14 roadway fatalities. This resulted in a net prediction of 24 deaths averted. The aggressive scenario shows that engagement in active transportation for 150 minutes per capita per week per, as recommended in the 2018 Physical Activity Guidelines, the region can see a net reduction of 123 premature death averted (Xxxxxx and Xxxxxxxxx, 2017). ITHIM also provided disease-specific benefits by active transportation (see Table 22). For example, under the moderate scenario, the model predicts a 4% reduction in disease burden (i.e., measured by premature deaths) for cardiovascular disease and 3.9% in diabetes, as well as between 1% and 2% of reductions in depression, dementia, breast cancer, and colon cancer (Transportation for America, 2016). Source: Xxxxxx and Xxxxxxxxx (2017) Figure 12 Changes in premature death by scenarios. Table 22 Predicted Decrease in Chronic Diseases Due to Increased Active Transportation Moderate Scenario Changes in Premature Death/Year % Change Changes in Direct and Indirect Costs (Millions) Cardiovascular Diseases -84.0 -3.0% -$44.4 Diabetes -9.3 -3.0% -$35.8 Depression 0.0 -1.1% -$5.7 Dementia -11.6 -1.3% -$15.0 Breast Cancer -2.2 -1.2% -$1.9 Colon Cancer -2.0 -1.1% -$1.7 Road Traffic Crashes 30.6 15.4% -$72.7 Total -78.7 -0.7% -$31.8 Source: Xxxxxxxxx, Xxxxxx, Xxxxxxxx, and Xxxxxx (2017) Table 22 also shows the estimated direct and indirect costs saved from reduced disease burden of the moderate scenario. The estimated financial cost savings of improved health through active transportation in the region were intended to be presented to policy makers, stakeholders, and citizens. The direct costs for the diseases modeled in ITHIM and the associated indirect costs of lost productivity were estimated by scaling the national estimates to the population of GNRC (Xxxxxx and Xxxxxxxxx, 2017). The direct and indirect costs of each of the disease were then multiplied by the change in incidence of the disease predict...
Analysis Results. Figure 15 through Figure 20 show the respective reductions in DALYs. These maps demonstrate the variation in total death and DALYs reductions driven by variations in differences in demographics and /travel health behavior in the ZIP code areas. Adapted from: Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) Figure 15 Total expected reduction in deaths from changes in physical activity. Adapted from: Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) Figure 16 Total expected reduction in deaths from traffic injury. Adapted from: Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) Figure 17 Total expected reduction in death from physical activity and traffic injury combined. Adapted from: Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) Figure 18 Total expected reduction in DALYs from changes in physical activity Adapted from: Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) Figure 19 Total expected reduction in DALYs from changes in physical activity. Adapted from: Xxxxxxxxxx, Xxxxxx, Xx, Igbinedion, and London (2017) Figure 20 Total expected reduction in DALYs from traffic injury.
Analysis Results. Table 23 shows travel distances by travel modes for the baseline and ambitious scenarios. At baseline, the population mean of active transport duration in California was 40.5 minutes per person per week. Active transport and transit comprised approximately 4.9% of all distance traveled in the state in 2010. The distances traveled by different modes varied across MPOs in California due to the patterns of land development. The San Francisco Bay Area has the highest walking and cycling distances owing to its moderate to high development densities. The San Xxxxxxx Valley is partially rural while Southern California and San Diego are known for sprawling land developments. Table 24 shows the percent increase of active transportation and transit usage according to the MPO’s preferred SCSs, as compared to the 2010 baseline condition. Table 25 shows the reduction (i.e., compared to the 2010 baseline scenario) in death and XXXXx, predicted by XXXXX, of the preferred SCSs of the MPOs. Table 27 shows the reduction in death and XXXXx by specific health conditions of the preferred SCSs, while Table 27 for the four ambitious scenarios. Table 23 Per Capita Travel Distance and Active Travel Times by Modes and Scenarios Source: Maizlish, Linesch, and Xxxxxxxx (2017) Table 24 Changes in Trips by Modes According to California MPOs’ Preferred SCSs as Compared to 2010 Baseline Source: Maizlish, Linesch, and Xxxxxxxx (2017) Table 25 Reduction in Number and Rate of Deaths and DALYs of the Preferred SCSs Source: Maizlish, Linesch, and Xxxxxxxx (2017); Note: Rate is for every 100,000 people. Table 26 Specific Health Benefits with the MPOs' Preferred SCSs Source: Maizlish, Linesch, and Xxxxxxxx (2017) Table 27 Change in the Burden of Disease and Injury by Scenarios of Walking, Cycling, and Transit Source: Maizlish, Linesch, and Xxxxxxxx (2017) Table 25 shows that health benefits of California MPO’s SCSs follow respective increases in active transportation and transit. The SCSs of San Diego and Southern California had the largest increases in active travel (see Table 24) and the largest decrease in XXXX rates. These two regions had net positive XXXX rates for road traffic injuries. The preferred SCS of Sacramento consists of significantly ambitious transit expansion but modest increase in active transport (see Table 24), resulting in large decreases in XXXX rates from fatalities and injuries from traffic crashes. Table 26 shows that all six MPO SCSs combined can decrease the annual n...
Analysis Results. 6.1 With regard to importation and exportation procedures, the analysis results for compliance with this Normative Instruction shall be supplied by this Ministry or other official Brazilian agencies, or shall be accredited according to Decree No. 3664, of November 17, 2000, to be duly recognized by the Federal Agricultural Inspection Service.
Analysis Results. All results shown are for the parallel phases of applications. The results presented are focused on the data blocks or pages. As can be seen on the Fig. 14 and Fig. 15 the proportion of data blocks and accesses is more relevant. Also, due to the fact that most of the instruction blocks are shared through all four cores, the classification between Private and Shared instruction blocks is less interesting in this case. Finally, we detected no interleaving between data and instruction blocks at different page granularities. Fig. 14: Proportion of blocks distinguishing whether they are instruction or data blocks Fig. 15: Proportion of accesses to data / instruction blocks As commented before, the analysis will basically consist in counting both the number of blocks belonging to each of the classes defined above (static analysis) and the number of accesses realized on each of the block classes (dynamic analysis). This analysis is carried out considering different granularities for data block classification, from block size until pages of different sizes. In all figures, the results obtained for each of the analyzed applications, together with the resulting average values, are displayed.
Analysis Results. First, we analyze in detail, in the three following subsections, the application OpenSSL with the algorithm sha1; and in the forth subsection we compare the obtained results against the other 4 algorithms. Finally, in the fifth subsection, some conclusions are drawn. In all subsequent analysis, pattern size is one byte, block size is 512 bits (64 bytes) and, when applicable, byte alignment is used.
Analysis Results. One of the main problems in information extraction is the plethora of input and result formats and models used by the different analysis components. For example, a POS tagger might take plain text and return plain text where each word is augmented with its POS tag. This makes it very hard to integrate different analysis components for computing combined formats. In MICO, one of the main goals is to make analysis components interoperable. For this reason, we are delevoping (in WP2, WP3 and WP6) a shared metadata model for representing analysis results and inputs. The format for analysis results will use the RDF model (and syntax) as data structure and will be Figure 4 Apache Stanbol Enhancement Structure based on the “enhancement structure” originally defined for text analysis in Apache Stanbol.14 MICO will revise this structure and extend it for the purposes of multimedia analysis. An example of the Apache Stanbol Enhancement Structure is shown in Figure 4. It illustrates the following elements: •
