Mean TTC under different weights. by Jianrong Cai (14245136)

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SD and mean TTC under different weights. by Jianrong Cai (14245136)

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Results of mean differences. by Ekkarat Meephu (15307058)

“…A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. …”

SD under different weights. by Jianrong Cai (14245136)

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Statistical results of TTC under different penetration rates of CAVs. by Jianrong Cai (14245136)

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Model framework. by Jianrong Cai (14245136)

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Vehicle travel time. by Jianrong Cai (14245136)

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SD under different penetration rates. by Jianrong Cai (14245136)

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Notation description. by Jianrong Cai (14245136)

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Five-stage speed adjustment. by Jianrong Cai (14245136)

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Vehicle fuel consumption and pollutant emissions at different penetration rate of CAVs. by Jianrong Cai (14245136)

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The mixed traffic flow of CAV and HV in tunnel entrance zone. by Jianrong Cai (14245136)

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Vehicle trajectory under different penetration rates of CAVs. by Jianrong Cai (14245136)

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Parameter values. by Jianrong Cai (14245136)

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Data_Sheet_1_Planning With Patient-Specific Rectal Sub-Region Constraints Decreases Probability of Toxicity in Prostate Cancer Radiotherapy.pdf by Caroline Lafond (6790544)

“…A recent geometry-based model proposed to optimize the planning by determining the achievable mean doses (AMDs) to the organs at risk (OARs), taking into account the overlap between the planning target volume (PTV) and OAR. …”

Snake optimizer algorithm optimization process. by Li Wang (15202)

“…Experimental results demonstrate that the SO-LSTM-based positioning method yields a maximum positioning error of 0.1125 meters and a root mean square error of 0.0589 meters. In comparison to conventional approaches such as the least squares method (LS) and the Kalman filter method (KF), the proposed SO-LSTM-based positioning method significantly reduces the root mean square error (RMSE) by 63.39% and 58.01%, respectively, while also decreasing the maximum positioning error (MPE) by 60.77% and 52.65%.…”

Intra-hospital patient transfer algorithm. by Ekkarat Meephu (15307058)

“…A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. …”

Detail results of simulation. by Ekkarat Meephu (15307058)

“…A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. …”

Data collection for simulation modeling. by Ekkarat Meephu (15307058)

“…A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. …”

Waiting time results. by Ekkarat Meephu (15307058)

“…A simulation was conducted to identify 20 scenarios in order to discover the optimal scenario in which where the number of requests (stretchers or wheelchairs) was increased, while the number of staff was decreased to determine mean waiting times and confidence intervals. …”