Results and Analysis Sample Clauses
The "Results and Analysis" clause defines how the outcomes, data, and findings generated during a project or engagement are to be handled and reported. It typically outlines the responsibilities for collecting, analyzing, and sharing results, and may specify formats, timelines, or standards for reporting. This clause ensures that both parties have a clear understanding of how results will be communicated and used, thereby promoting transparency and accountability in the project.
Results and Analysis. The synthesized traces of the vehicles for a T-junction and highway merging scenarios are given in Fig. 3 and 4, respectively. In the non-cooperative cases in both scenarios, we observed behaviors where vehicles act conservatively and slow down to avoid a low reward due to a T is determined ƒ
Results and Analysis. Describe the results including required details compared · Use diagrams, tables and figures for overview and understanding · Show results vs. requirements vs. state of the art · Describe cooperation of the Participating Partners · Give an interpretation and/or analysis of the results · Highlight major achievements · Highlight major impacts for the European industry (industrial relevance, exploitation plans and business view): o Describe customers commitment reached o Describe access to market and relevant roadmap following Application Experiment o Direct impact on the Selected Third Party o Expected further impact o Further financing plan and achievements
Results and Analysis. The group key agreement protocol TGECDH is implemented using java language. The implementation performs the ECDH group key agreement module. When members join and leave the group dynamically, it constructs a height balanced tree and generates the new group key.
Results and Analysis. When two experiments were compared, the 32To8 ▇▇▇▇▇▇▇-base model achieved the highest F1 Score among all models for unsupervised approaches overall. Also, the 32To8 ▇▇▇▇▇▇▇-base model from Experiment 1 achieved a higher F1 score than the one from Experiment 2, which was therefore used when analyzing results in the following parts. From results produced by single-label unsupervised models as shown in Table 4.10, the 32To8 ▇▇▇▇▇▇▇-base model correctly classified 7 out of 8 sentences in the example post. The sentence corresponded with the only wrong prediction was also classified wrongly by the 32To8 ▇▇▇▇▇▇▇-large model. The model with the most number of wrong predictions in this example was the 32To8 ▇▇▇▇▇▇▇-large model (see Table 4.11), and it classified the first sentence with FEAR while all other models classified it as ANGER. Similarly, for other sentences such as sentences 3, 5, and 7, it predicted completely different results compared with other models. Merged-8 models performed similarily for both ▇▇▇▇▇▇▇-base and ▇▇▇▇▇▇▇-large models in this example, as they had the same number of correct predictions. Results predicted by two-label unsupervised models were shown in Tables 4.12 and
Results and Analysis. This study was conducted to look at the agreement between four commonly used tonometers; with specific advantages for each of these over GAT. We calculated sample size based on published data that showed a good agreement between GAT and NCT using intra class correlation. Our sample size was 60 patients in each group. We recruited 65 patients in the normal IOP category. In the high IOP group on the other hand, we were able to recruit only 42 patients who fulfilled the inclusion and exclusion criteria. Out of the 65 normal patients studied 31 were males and 34 were females. This is depicted in Table 1 and Graph 1. Out of 42 patients with high IOP, there were 29 males and 13 females as shown in Table 2 and Graph 2. GENDER FREQUENCY PERCENTAGE Female 34 52 Total 65 100 GENDER FREQUENCY PERCENTAGE Male 29 69 Female 13 31 Total 42 100
Results and Analysis. The Wall-ACE dynamic wall has been developed, tested and finalised in the form of a wall simulation tool allowing to play with the different material layers. Up to 10 layers can be set in the model. The example below shows an application with 7 layers. The two first internal layers are replaced by air to avoid shifting the cells of the original wall composition. One can then add internal plaster in the layers previously inactivated by using air. As an illustrative example, the typical case of the analysis of thermal energy saving by adding an insulation plaster on the internal side of a wall is presented with the thermal energy savings as well as the condensation risks and occurrence. SUM 0.357 2.558 0.39 Interior 8 - - 0.125 L1 - 0.050 0.034 1.47 L2 - 0.001 0.034 0.029 L3 - 0.120 1.2 0.10 L4 - 0.050 0.08 0.625 L5 - 0.120 1.12 0.11 L6 - 0.015 0.7 0.021 L7 - 0.001 0.034 0.03 Exterior 20 - - 0.050 Wall-ACE_Deliverable_D4.1_vfinal-rev20200122 5 ▇▇ ▇▇▇▇▇▇▇ with 5 ▇▇ ▇▇▇▇▇▇▇ 1 ▇▇ ▇▇▇▇▇▇▇ Suppression of condensation by adding a vapour barrier Æ non insulated 1 cm insulated 5 cm insulated with vapour barrier 5 cm internal plaster insulation Wall-ACE_Deliverable_D4.1_vfinal-rev20200122 The addition of an internal high-performance plaster reduces the thermal losses of the wall, but there is a non-linear increase of condensation between 2 and 3 cm. This can be explained by the reduction of the temperature behind the plaster between 2 and 3 cm thickness. The figure below presents the increase of annual cumulative hours below the dew point temperature behind the plaster in relation to its thickness. Increase from 2 to 3 cm Increase from 3 to 4 cm Increase from 4 to 5 cm The number of hours below the dew point behind the plaster increases from 44 hours per year for 2 cm to 660 hours per year, when the thickness is increased to 3 cm. This example shows how important it is to check the condensation behind the internal insulation and to apply solutions such as applying a vapour barrier (can be a film or special paints). The condensation is reduced by a factor 1000 by applying a vapour barrier adequately placed on the inside face of the plaster.
Results and Analysis. First, we measure the communication overhead in terms of the number of exchanged messages and energy consumption of communication in Table II. We directly count the number of exchanged messages for liteAuth, PSLAP, and HARCI. The energy consumption of communication is calculated based on the number of sent and received messages [37]. To mutually authenticate sensor node and hub server, PSLAP requires four messages to be exchanged among sensor node, access point, and hub server. To be specific, the sensor node first sends an authentication request message to the access point. Then, the access point adds its identity in the message and forwards the message to the hub server. After the hub server verifies the identity of sensor node, it replies an authentication confirmation message through the access point to the sensor node. Finally, the sensor node and hub server mutually au- thenticate each other and establish a session key for further 80 477 1760.0 5468.1134 104 Executions Computation Time (Second) 5000 4000 3000 2000 1400 Energy Consumption (Joule) 1200 1000 800 600 Metrics liteAuth PSLAP HARCI CPU Time 62015.63 ms 1740296.88 ms 5231453.13 ms CPU Cycles 8.93×1010 2.51×1012 7.53×1012 liteAuth PSLAP HARCI 557.12655 835.6898 1000 400 278.5633 185.3339 200 92.6669 278.0008 370.6678 ∗The results are measured based on 104 algorithm executions.
Results and Analysis. In order to increase the probability of success at the end of the project, two parallel research lines based on large-scale GFET array TDM and FDM have been investigated. Each research line has started from the same sensing and digital signal-processing requirements, so their ASIC figures of merit can be compared.
Results and Analysis. There is increase in imports from Japan to India from base year (last year before CEPA came into force) to first year (1st annual year after CEPA came into force), by 9,54, 960 lacs rupees (2095.59 million USDxi), where majorly all commodities imports rose. However, from 1st to 2nd year, imports from Japan decreased by 4,06,000 lacs rupees (890.94 million USD). The reason for this decline could be GDP related, where last two years faced very slow growth as compared to the base year & imports shrank due to prolonged low growth. But the overall growth in imports from base year to second year was positive with increment in imports of approx. 5,50,000 lacs rupees (1206.94 million USD) (Figure 5). Total imports from the rest of the world/ ROW (Total imports to India from the world minus total imports to India from Japan) for base year & second year are calculated for commodities in the analysis and cumulative deterioration in imports from ROW from base year to 2nd year is calculated. Instead of the decline, there was the rise in overall imports from the rest of the world by 1,18,22,765 lacs rupees (25944.18 million USD). Thus the total trade creation from base year to 2nd year for India was amazingly positive with 1,23,71,815 lacs rupees (27149.03 million USD). This completes more than half of the analysis with results favoring enforcement of CEPA. Our next step is to see the welfare implications of the reductions in tariffs. For this purpose, we have to weigh consumer surplus generated in India due to increased imports from Japan, with tariffs revenue loss for ROW due to decreased imports to India from ROW. Consumer surplus is shown in the figure 6 indicates that with the fall in tariffs causing decline in prices from p to p’, imports from Japan increases from q to q’, is raising consumer surplus by the amount shown in triangle ABC. Also, since imports from ROW have actually increased, this proved the welfare to be positive. Mathematically, Consumer surplus (figure 6) for 2010-2014 (0.5*tariff reductions*cumulative increase in imports) & tariffs revenue loss for ROW for 2010- 2014 (cumulative decrease in imports*base year tariffs), are 5,76,852 lacs rupees (1265.86 million USD) and 6,12,88,000 lacs rupees (134,491.98 million USD) respectively. Hence, Welfare is found out to be approx. 6,18,00,000 lacs rupees (135,615.53 million).