Search

• Low-energy photon IORT treatments of the breast are increasing in recent years. • The best verification of these treatments is to establish an in vivo dosimetry procedure. • XR-RV3 radiochromic films are a suitable dosimeter for measuring low energy photons at high doses. • An adequate calibration that considers the beam hardening with the distance significantly improves the measurement accuracy. The objectives of the study were to establish a procedure for in vivo film-based dosimetry for intraoperative radiotherapy (IORT), evaluate the typical doses delivered to organs at risk, and verify the dose prescription. In vivo dose measurements were studied using XR-RV3 radiochromic films in 30 patients with breast cancer undergoing IORT using the Axxent® device (Xoft Inc.). The stability of the radiochromic films in the energy ranges used was verified by taking measurements at different depths. The stability of the scanner response was tested, and 5 different calibration curves were constructed for different beam qualities. Six pieces of film were placed in each of the 30 patients. All the pieces were correctly sterilized and checked to ensure that the process did not affect the outcome. All calibration and dose measurements were analyzed using the Radiochromic.com software application. The doses were measured for 30 patients. The doses in contact with the applicator (prescription zone) were 19.8 ± 0.9 Gy. In the skin areas, the doses were as follows: 1–2 cm from the applicator, 1.86 ± 0.77 Gy; 2–5 cm, 0.73 ± 0.14 Gy; and greater than 5 cm, 0.28 ± 0.17 Gy. The dose delivered to the pectoral muscle (tungsten shielding disc) was 0.51 ± 0.27 Gy. The study demonstrated the viability of XR-RV3 films for in vivo dose measurement in the dose and energy ranges applied in a complex procedure, such as breast IORT. The doses in organs at risk were far below the tolerances for cases such as those studied. [ABSTRACT FROM AUTHOR]

Abstract Rankings are a valuable element for city-comparison purposes since results withdrawn from these comparisons can, eventually, support the evaluation of strategic decisions taken by cities. Smart city rankings are not an exception and, as they draw more attention, the number of them exponentially increases. This paper evaluates the appropriateness of existing smart city rankings for quantifying the materialization degree of the smart city concept. The analysis reveals that current rankings generally overlook indicators of the Information and Communication Technologies dimension. To bridge this gap, this work proposes a methodology based on Multiple-Attribute Decision Making that uses technological criteria for designing smart city rankings. The proposed methodology is evaluated against the cities of New York, Seoul, and Santander. Imbalances between results provided by the studied rankings and our evaluation are detected, which suggests the need for a new insight into more suitable and precise evaluation of the smartness degree of cities. Highlights • Evaluation of smart city rankings suitability as a mean of quantifying the smart city concept materialization degree • Current rankings generally overlook indicators related to ICT, as it is the key pillar for the realization of the concept. • An MADM-based methodology to rank smart cities through a transversal smartness dimension • Case study that considers Santander, Seoul, and New York smart cities • The results suggest the need for a new insight into more suitable and precise evaluation of the smartness degree of cities. [ABSTRACT FROM AUTHOR]

Safety on public transport is a major concern for the relevant authorities. We address this issue by proposing an automated surveillance platform which combines data from video, infrared and pressure sensors. Data homogenisation and integration is achieved by a distributed architecture based on communication middleware that resolves interconnection issues, thereby enabling data modelling. A common-sense knowledge base models and encodes knowledge about public-transport platforms and the actions and activities of passengers. Trajectory data from passengers is modelled as a time-series of human activities. Common-sense knowledge and rules are then applied to detect inconsistencies or errors in the data interpretation. Lastly, the rationality that characterises human behaviour is also captured here through a bottom-up Hierarchical Task Network planner that, along with common-sense, corrects misinterpretations to explain passenger behaviour. The system is validated using a simulated bus saloon scenario as a case-study. Eighteen video sequences were recorded with up to six passengers. Four metrics were used to evaluate performance. The system, with an accuracy greater than 90% for each of the four metrics, was found to outperform a rule-base system and a system containing planning alone. [ABSTRACT FROM AUTHOR]