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Determination makers in huge scale interconnected community structures require simulation types for choice help. The behaviour of those platforms depends on many actors, positioned in a dynamic, multi-actor, multi-objective and multi-level setting. How can such structures be modelled and the way can the socio-technical complexity be captured? Agent-based modelling is a confirmed method of deal with this problem. This e-book offers a realistic creation to agent-based modelling of socio-technical platforms, in accordance with a strategy that has been constructed at TU Delft and which has been deployed in a great number of case reports. The e-book contains elements: the 1st provides the history, idea and method in addition to functional directions and tactics for construction versions. within the moment half this thought is utilized to a couple of case experiences, the place for every version the improvement steps are awarded widely, getting ready the reader for growing personal types.

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1 Introduction Radiation therapy is with surgery and chemotherapy one of the three main treatment approaches for cancer, used for around 50% of all patients. With this modality, patients are irradiated with beams of ionizing radiation attempting to sterilize all cancer cells while minimizing the collateral effects on the surrounding healthy organs and tissues. An important type of radiation therapy is intensitymodulated radiation therapy (IMRT), a modern technique where the radiation beam is modulated by a multileaf collimator allowing the irradiation of the patient using non-uniform radiation fields from selected angles.

The BAO problem is characterized by having multiple local minima, so it is expected that in each run of the algorithm a different solution is found. This is illustrated in Fig. 7, where the equidistant solution is shown [black solid line] together with 5 other solutions that were calculated in each of the algorithm’s runs. Fig. 7. Different runs of the algorithm usually end up with different solutions r_init 18 36 72 18 18 36 36 72 72 18 18 36 36 36 Average maximum minimum l_failure 5 20 5 20 5 20 5 20 5 20 - Steps 5, 6 5, 6 5, 6 5, 6 5, 6 5, 6 5a, 6a 5a, 6a 5a, 6a 5a, 6a 5b 3,09% 3,27% 2,71% 1 2,75% 3,14% 3,24% 3,00% 3,24% 3,22% 3,25% 2,71% 2,93% 3,05% 3,19% 3,06% 3,27% 3,22% 6,38% 7,05% 5,58% 2 6,74% 7,05% 6,17% 5,58% 5,76% 5,87% 5,95% 6,59% 7,00% 6,22% 6,19% 6,97% 6,74% 6,40% 7,76% 8,92% 6,90% 3 7,49% 8,03% 8,67% 7,22% 7,71% 8,74% 8,82% 7,32% 7,03% 7,17% 8,92% 7,39% 6,90% 7,23% 4,57% 5,12% 3,78% 4 5,12% 4,38% 3,78% 4,52% 4,88% 4,41% 4,72% 4,56% 4,35% 4,76% 4,56% 4,29% 5,00% 4,70% 7,64% 8,37% 6,94% 5,82% 6,83% 4,75% Patients 5 6 7,28% 5,88% 7,56% 5,59% 7,88% 6,83% 7,77% 4,75% 8,15% 6,41% 7,85% 5,28% 7,64% 6,43% 6,94% 5,70% 7,99% 6,04% 7,32% 6,25% 7,46% 5,04% 7,56% 5,66% 7,20% 5,63% 8,37% 5,94% 14,96% 16,81% 13,29% 7 14,65% 15,48% 14,16% 14,94% 14,40% 14,43% 14,57% 15,31% 13,29% 16,81% 16,44% 15,71% 14,77% 14,52% 7,71% 9,29% 6,62% 8 7,22% 8,33% 8,36% 6,67% 9,29% 7,12% 7,95% 6,62% 7,91% 7,94% 6,98% 9,18% 7,25% 7,08% 5,80% 6,82% 5,10% 9 6,82% 5,58% 5,43% 5,54% 6,63% 5,92% 5,95% 5,10% 6,17% 5,39% 5,20% 5,25% 6,51% 5,74% Table 3.

Elodie Edoh-Alove, Sandro Bimonte, and Yvan B´edard Growing Sustainable Behaviors in Local Communities through Smart Monitoring Systems for Energy Efficiency: RENERGY Outcomes . . . Francesco Scorza, Alessandro Attolico, Vincenzo Moretti, Rosalia Smaldone, Domenico Donofrio, and Giuseppe Laguardia Author Index . . . . . . . . . . . . . . . . . . . . . . . . . 774 787 795 IMRT Beam Angle Optimization Using DDS with a Cross-Validation Approach for Configuration Selection Joana M.

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