• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.20
• /
• pp.8779-8782
• /
• 2014

Background: Malignancies of the female reproductive tract are estimated to be the third most common group of cancers in women. Objectives: We here aimed to present their epidemiological features in Golestan province located in Northeast of Iran. Materials and Methods: Data on primary female reproductive cancers diagnosed between 2004-2010 were obtained from Golestan Population-based Cancer Registry (GPCR). CanReg-4 and SPSS software were used for data entry and analysis. Age standardized incidence rates (ASR) (per 100,000 person-years) were calculated using the world standard population. Poisson regression analysis was used to compare incidence rates. P-values of less than 0.05 were considered as significant. Results: A total of 6,064 cancer cases were registered in Golestan females in the GPCR during 2004-2010, of which 652 cases (11%) were female reproductive cancers. Cancers of the ovary (ASR=6.03) and cervix (ASR=4.97) were the most common. We found significant higher rates in females living in cities than in villages. Our results showed a rapid increase in age specific incidence rates of female reproductive cancers at the age of 30 years. Conclusions: We found significant higher rates of female reproductive cancers among residents of cities than villages. Differences in the prevalence of risk factors including reproductive behavior between the two populations may partly explain such diversity. Our results also showed a rapid increase in incidence rates of these cancers in young age females. Further studies are warranted to determine risk factors of female reproductive cancers in our population.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.18 no.10
• /
• pp.274-280
• /
• 2017

The smart-car industry has emerged as the important variable that will decide the future industrial contour of the automotive industry, together with commercialization of electronic vehicles, connected cars, infotainment, telematics, and the autonomous/self-driving car. This study analyzes the strategic position of platform companies and car manufacturers that would determine the future of the smart-car market. The findings of this study show that despite the entry barriers in industrial factors, such as economies of scale, the industrial infrastructure, and global production networks, and technical factors like exclusive head-sector information, car manufacturers may be deprived of their industrial leadership by platform companies with map and user data, big data capabilities, and user interface experience if they lag behind ICT innovation. This insight is based on the emerging importance of software and platforms, and the simplification of car structures, proven by the successful commercialization of electronic vehicles. This study complements existing studies mainly focused on technical aspects of the smart-car industry by examining the strategic dimensions of platform companies and their approach to the future smart-car market by comparing them with existing car manufacturing multinationals.

• Journal of KIISE:Software and Applications
• /
• v.34 no.5
• /
• pp.397-406
• /
• 2007

The problem of finding an optimal alignment between sequence A and B can be solved by dynamic programming algorithm(DPA) efficiently. But, if the length of string was longer, the problem might not be solvable because it requires O(m*n) time and space complexity.(where, $m={\mid}A{\mid},\;n={\mid}B{\mid}$) For space, Hirschberg developed a linear space and quadratic time algorithm, so computer memory was no longer a limiting factor for long sequences. As computers's processor and memory become faster and larger, a method is needed to speed processing up, although which uses more space. For this purpose, we present an algorithm which will solve the problem in quadratic time and linear space. By using division method, It computes optimal alignment faster than LSA, although requires more memory. We generalized the algorithm about division problem for not being divided into integer and pruned additional space by entry/exit node concept. Through the proofness and experiment, we identified that our algorithm uses d*(m+n) space and a little more (m*n) time faster than LSA.

• Therapeutic Science for Rehabilitation
• /
• v.9 no.4
• /
• pp.55-67
• /
• 2020

Objective : This study aimed to understand the burnout experience of hospital occupational therapists through phenomenological research. Methods : We conducted in-depth interviews with 6 occupational therapists working at hospital for 8 weeks. The data were analyzed using the Palangsae 2.0 software to derive meaning through the process of transcription and data entry, coding, and topic creation. Results : From the collected data, 14 sub-themes, 9 theme clusters, 3 categories were identified. The cause of burnout was confirmed as being due to therapy-related work and workplace conditions. The impacts of burnout on individual life were psychological in nature and also included being an adverse, influence on others. The coping strategies to deal with burnout were enrichment of personal life, self-awareness and philosophy, conscious approach, social support, and setting new goals for life. Conclusion : We suggest that spontaneous efforts are needed by occupational therapists in order to alleviate the conflicts of job identity. In addition, organizational regulations and programs and the establishment of horizontal organizational cultures are required to overcome and prevent burnout of occupational therapists.

• KIPS Transactions on Software and Data Engineering
• /
• v.9 no.11
• /
• pp.367-376
• /
• 2020

The fourth industrial revolution will greatly contribute to the entry of objects into an intelligent society through technologies such as big data and an artificial intelligence. Through the revolution, we were able to understand human behavior and awareness, and through the use of an artificial intelligence, we established ourselves as a key tool in various fields such as medicine and science. However, the fourth industrial revolution has a negative side with a positive future. In this study, an analysis was conducted using text mining techniques based on unstructured big data collected through social media. We wanted to look at keywords related to the fourth industrial revolution by year (2016, 2017 and 2018) and understand the meaning of each keyword. In addition, we understood how the keywords related to the Fourth Industrial Revolution changed with the change of the year and wanted to use R to conduct a Keyword Analysis to identify the recognition flow closely related to the Fourth Industrial Revolution through the keyword flow associated with the Fourth Industrial Revolution. Finally, people's perceptions of the fourth industrial revolution were identified by looking at the positive and negative feelings related to the fourth industrial revolution by year. The analysis showed that negative opinions were declining year after year, with more positive outlook and future.