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Introduction: The focus of this study was to determine the set-up errors so as to estimate the margin between the Clinical Target Volume (CTV) and the Planning Target Volume (PTV) and to suggest optimum margins for planning target volume (PTV) coverage in thorax cancers. Methods: In the present study data from 51 patients was incorporated. A total of 1308 portal images were examined. Set up errors were estimated by superimposing a digitally reconstructed radiograph (DRR), using an electronic portal image device (EPID) as a reference image. The Medio-Lateral (ML), Cranio-Caudal (CC), and Antero-Posterior (AP) directions were subsequently evaluated. According to the shifts obtained, systematic and random errors were computed. The van Herk formula was employed to determine the values for the clinical-to-planning target volume (CTV-PTV) margins. Results: The systematic error was found to be between 1.0 mm and 1.7 mm, 1.0 mm and 1.8 mm, and 2.1 mm and 3.1 mm along the x, y, and z axis. In the x, y, and z axis, the random error varied from 0.5 mm to 0.7 mm, 0.4 mm to 0.8 mm, and 0.7 mm to 1.7 mm, respectively. Based on the Van Herk equation, the PTV margin following our findings was estimated to be 4.7 mm, 3.3 mm, 8.8 mm for lung, 3.6 mm, 2.7 mm, and 5.7 mm for oesophagus, and 3.0 mm, 4.9 mm, and 8.6 mm for breast in the x, y, and z dimensions respectively. Conclusion: This study demonstrates that an 8.8 mm extension of CTV to PTV margin for the lung, 5.7 mm for the oesophagus, and 8.6 mm for the breast, serving as an upper limit, is sufficient to guarantee that 90% of patients diagnosed with thoracic cancers will receive a cumulative CTV dose that is at least 95% of the prescribed dose.
EN
This research investigates the nexus between socioeconomic indicators and the diffusion of electric vehicles (EVs), with a specific focus on India’s transition to electric mobility amidst its distinct challenges and opportunities as a developing nation. Utilizing the Rogers framework of diffusion of innovation, the study examines fifteen socioeconomic indicators across 36 countries to discern their influence on EV diffusion. The findings underscore the inadequacy of replicating diffusion models from developed economies for India, advocating instead for tailored strategies aligned with its unique socioeconomic context. Drawing insights from China’s successful EV market, characterized by dedicated manufacturing hubs and forward-looking policies, the study recommends a nuanced approach addressing affordability, infrastructure, and incentives to ensure a seamless transition. However, the research acknowledges limitations due to the examination’s narrow scope, prompting future investigations to expand the range of indicators and countries considered for a more comprehensive understanding. Ultimately, this study contributes practical recommendations for policymakers and stakeholders, emphasizing the importance of aligning EV transition strategies with India’s socioeconomic realities to foster a sustainable and economically viable transportation landscape.
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