جستجوی مقالات مرتبط با کلیدواژه "python" در نشریات گروه "پزشکی"

Comparison of normal tissue integral dose and treatment monitor units from 3DCRT, IMRT and Rapid treatment plan for oesophagus, left breast, cervical and oropharynx cancer. The calculated normal tissue integral dose from different treatment plans with static and dynamic leaf positions, such as 3DCRT, IMRT and Rapid arc were compared with the generated MU.

Nine patients from oesophagus, left breast and cervix cancer and twelve patients from oropharynx cancer with a total of one hundred and thirty-five generated plans from 3DCRT, IMRT and Rapid arc were analysed. The normal tissue integral dose (NTID) was calculated from in-house developed Python software using a standard formula from the dose-volume histogram.

The analysis showed that the NTID and MU differed significantly from all three treatment planning methods and cancer sites. The highest integral dose was from IMRT and Rapid Arc in the oropharynx and oesophagus cancer site; cervical cancer had a 50% lower NTID, and left breast cancer had a 25% lower NTID than oesophageal cancer.

The results show that NTID is inversely related to body volume, and that MU depends on the type of treatment planning (greater in IMRT).

This paper contributes to modeling and forecasting gas booking demand in an online retail environment using time series techniques. Our work demonstrates how historical demand data can be utilized to estimate future demand and its impact on the supply chain. The historical demand data were used to create several autoregressive integrated moving average (ARIMA) models using the Box-Jenkins time series procedure. The best model was selected based on four performance criteria: statistical results, maximum likelihood, and standard error. The selected model, ARIMA (1, 1, 1), was validated using additional historical demand data under the same conditions. The results demonstrate that the model can effectively estimate and forecast future demand for gas booking in an online retail environment. These findings will provide trustworthy guidance to the company's management in decision-making.

 The results from the analysis of COVID-19 literature by employing text-mining techniques are of particular importance for researchers, policymakers, and planners of medical sciences at the national and international levels, avoiding parallel research and waste of time and budget. The paper explore emerging topics and the trend of scientific words at the national and international levels in the subject area of COVID-19.

 This applied research was conducted by employing the text-mining and its related algorithms and classifying texts. The population consists of all COVID-19 articles indexed in PubMed Central® (PMC). The number of records retrieved was 160,862 items until June 10, 2021. Among these, 3143 national and 157,719 international COVID-19 articles. Python and its related libraries were applied. The most significant words were also identified and reported based on TF-IDF weighting. Emerging topics were identified according to the weighted average growth.

"COVID", "infect", and "cell" were among the most important words used in international COVID-19 articles. In addition, the most important words in the national COVID-19 articles were "patient", "SARS-Cov", and "COVID".

 Among the most important conclusions that can be inferred from the trend of word change used in the COVID-19 literature is that the most significant words in international literature differ significantly from those in national literature, as international research focuses on COVID-19 and the infections caused by it. In contrast, national research focuses on COVID-19 and patients. Another significant result is the annual word-changing national and international literature.

Dementia is a common medical condition of older people which is marked by the decline of multiple cognitive abilities, such as memory and communication. Currently, there is no effective treatment for curing dementia, making prevention the most priority to this disease. Previous studies showed that cognitive ability training, such as mathematical problem solving, has a potential to slow down cognitive decline. The aim of this project is to create a simple yet interactive mathematical quiz as a way to train one’ cognitive ability and reduce the risk of getting dementia.

The quiz was created by using tkinter module and its built-in functions in Python programming language.

The result showed that the quiz was simple but involved an active role of the user to input the answer. It also did not have certain time limit, preventing the user to feel rushed or burdened in doing the quiz. In addition, three different types of difficulty were provided to give a challenging atmosphere to the game.

As a conclusion, this quiz provides a simple way for people to regularly train their cognitive skill, so the risk of getting dementia, especially in elderly stage, can be reduced.